SMART GRID SYSTEM RELIABILITY
Introduction
The power grid is a system that delivers electrical energy from generators to users
through transmission and distribution lines. Its purpose is to deliver power to consumers in
accordance with acceptable demand, economic levels, reliability, and quality. Power systems
are highly susceptible to abnormalities such as control errors, protection or communication
failures, system disturbances, and human error factors. Thus, keeping the power system
stable and reliable is an important issue in future power system design. Conventional power
grids are not designed to be integrated with the latest technologies, are not economical, do
not consider the effects of carbon emissions, and do not consider energy conservation and
efficiency. Conventional power grids suffer from the following problems.
Electricity market deregulation cannot yet be applied to power grids that
present uncertain power flow scenarios.
The network does not support good data information flow.
The increasing penetration of renewable energy on the system has added to
the problem of system stability.
The digital technology-using society requires a power supply with high
quality and availability
The need to use renewable energy that has a good impact on the environment.
With the shortcomings of conventional grids, the concept of smart grids emerged,
which applies digital technologies and new components to the power sector to improve
reliability, reduce costs, and increase efficiency.
Smart grid is part of the energy smart initiative, which combines various technologies
and practices to operate the power system more efficiently. In addition, smart grids facilitate
the use of efficient energy in buildings and industries, and enable the use of new renewable
energy. On conventional networks the question often arises- 'how can there be an
improvement in system reliability electricity, while a smart grid system that utilizes
information technology and communication systems (ICT) can be a viable contributor in its
own right, in addition to improving operational efficiency, thereby reducing the need for
extensive infrastructure expansion.
Instrumentation such as sensors and metering and smart forms of communication
infrastructure are the backbone of ICT. ICT systems that support smart grids provide
enormous opportunities for improved diagnosis of power system failures and thus many
opportunities to improve system reliability. Some of the measures that can be implemented
include efficiency improvement through demand side management, demand response,
energy source integration, energy storage systems, and so on.
Theoretical Foundation
Smart grid technologies can contribute to the reduction of greenhouse gas emissions by
improving efficiency and conservation, facilitating the integration of renewable energy, and
enabling the use of electric cars (Plug in Hybrid Electric Vehicles - PHEV). In addition,
consumers can control their electricity usage. Smart grid technology consists of
communication networks, advanced sensors, and monitoring equipment, which underlie new
concepts of power generation and distribution. Smart grids allow for bi-directional power
flow, due to the large amount of distributed generation especially in load areas. This power
flow needs to be regulated in order to obtain efficient and optimal network performance. To
regulate the power flow, it is necessary to regulate the equipment or assets on the network,
therefore the smart grid is closely related to communication and information technology.
This arrangement can be done because the smart grid has a two-way data communication
system. Smart grids have many energy storage centers, large, small, mobile, or stationary,
scattered on the network. These energy storage centers are used to anticipate sudden load
changes or fluctuations in wind power generation and solar generation. Technology Quick-
response Battery Energy Storage System (BESS) and Voltage Source Converter (VSC) have
been tested and are suitable for energy storage systems. It is predicted that future electrical
loads will no longer be stationary loads. The development of technology battery technology
currently It is estimated that in the future there will be 10 million cars connected to the smart
grid every day, such as in parking lots, shopping centers, homes, and offices. The batteries in
these cars can be considered as a load when charging or as an energy source. The charging
and discharging of these batteries must be controlled to avoid power fluctuations."[5]
Control section
In smart grids, it is necessary to implement new control methods and algorithms that are
able to monitor components in the network, diagnose quickly, predict conditions, and have
appropriate responses to events.
Communication Section
Computer networks and data communications play an important role in smart grid
systems. The communication system used must have sufficient speed, have two-way
communication, and be fully integrated so as to make the smart grid so dynamic and
interactive for data and power exchange in real time.
Real-time data is a valuable source of information for automatic control to maintain
system stability. Based on research, the widespread power disconnection in the western
USA on August 10, 1996 could have been avoided if 0.4% of the load had been removed for
30 minutes. The disconnection in 1996 shows that information exchange over a large area in
real time is an important factor for the reliability and stability of power systems in the
future. With real-time, high-speed measurement, appropriate protection and control actions
can be taken to ensure system reliability in the event of a fault. Distribution and
acquisitionBroad, real-time, and fast information is the key to controlling and optimizing
power system operations over large areas. To support these data communication needs,
future communication technologies and architectures must be able to provide real-time data
information when the system needs it. In addition, future networks require protocols and
standards, making it easier to implement.
Application Section
Smart grids require real-time applications and tools that allow operators to make
decisions quickly. This decision support requires equipment that connects the grid to the
user. These applications and tools must be easy to use.
Results And Discussion
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
Characteristics of Smart Grid
The study entitled A New Method For Smart Grid Reliability, states that the
characteristics of the smart grid are:
Self healing: it will run steadily, self-assessment and take average prevention way of
control, timely detect, rapid diagnosis and eliminate hidden faults. When a fault occurs,
if there is no little human intervention or not, it can quickly isolate the fault, self healing
to avoid the occurrence of extensive power outages.
Interaction: The system operated is connected to the electricity market to achieve a
seamless interface. It will support the effective execution of electricity transactions, and
optimize resource allocation. At the same time, it will encourage better participation in
the electricity market from the main power grid security management through market
transactions to improve the safety level of system operation.
Robust: each smart grid element will be considered security requirements to ensure a
certain degree of integration and balance in the entire system.
Compatible: not only will it be compatible with large, centralized plants, it should be
compatible with the growth of distributed energy.
Quality: can provide different prices according to various power levels, reduce quality
problems caused by transmission and distribution components, monitoring of
components is necessary to achieve fast and good solutions, applying the latest research
results from superconductors, materials, energy storage, power electronics to improve
power quality[3].
Potential for Improved Reliability of Smart Grid Systems
A case study was conducted in Norway, by examining a smart grid (SG) system to
answer the hypothesis that by utilizing SG at the distribution level, it is expected to reduce
the frequency and duration of customer interruptions, and the associated interruption costs.
This paper presents a reliability perspective to derive the individualized impact of smart grid
technologies at the distribution level. A case study is conducted at Hafslund Nett, the largest
distribution network company in Norway. Data shows that in the 10-year period 2001-2010
installations from 1 kV to 22 kV contributed 75% of the energy not supplied (ENS) due to
disturbances, while networks from 33 kV to 420 kV contributed 25% of the total ENS.
One SG technology was used for testing by Hafslund, with the use of fault current sensors
connected to SCADA using remote terminal units (RTUs). Placement of sensors and RTUs
at various locations in the MV-grid was done to investigate their practical usefulness and
actual effect on supply reliability in different types of networks (underground cables vs.
overhead lines, and combinations thereof).
Based on the analysis conducted on this pilot study in Hafslund, it is estimated that
the percentage of ENS will drop by about 30%, and the permanent fault rate will drop by
about 5%. Sensor technology costs and revenue regulation schemes will plays a decisive role
to determine the net gain. However, for transient faults there is no significant reduction from
ENS, but improving the knowledge of where transient faults might occur would provide
more efficient value in the inspection field."[2]
Demand Side Management
A review of the possible impact of demand side management (DSM) in its function on
improving reliability performance and new procedures in the assessment of future power
grid reliability formulation. Two DSM scenarios are discussed and implemented in a
reliability analysis based on the load profile of the UK residential sector. The paper assumes
that only suitable "wet loads" affect system reliability. The paper considers that only suitable
"wet loads" affect system reliability. The wet loads consist of approximately 22%
dishwasher load, 31% washing machine load, 31% tumble dryer load and 16% combined
washing machine-dryer load, with the scheme:
The first scheme of DSM (DSM 1) is defined as shifting 40% of the wet load
from night hours (17:30-21:00) to night hours (21:30-1:00), this is a typical DSM
scheme to reduce peak loading conditions.
The second scheme of DSM (DSM 2) is defined in this paper as a new approach
in DSM, applied to improve the reliability index, i.e. to reduce the number of
interruptions of residential customers.
DSM 2 also assumes a 40% shift in wet load, but at a different time interval from
the peak night-time load.
As a first step, the traditional use of supply continuity metrics and assessment indices for
system reliability tests without considering DSM schemes was performed. The differences
in results with respect to reliability indices in system modeling tests with bulk loads and
system modeling with detailed configurations of supply networks connected to the loads are
quantified, emphasizing the errors that occur when parts of the system are neglected during
estimation of reliability performance. Afterwards, the effect of DSM on the same reliability
performance tests is analyzed, in order to assess the potential benefits of DSM to network
operators, particularly with regard to annual performance reports. Finally, possible changes
in the use of reliability metrics, such as smart grids that allow to replace the standard
equations represented by the load system, at higher voltage levels, with more accurate and
detailed information on the load side, and the load structure, including the estimated
contribution of the load system, are discussed the manageable portion of the load in terms of
load to total demand[4].
Smart grid generation pool
A study on the reliability of smart gridgeneration pool which consists of several types
of generating units, such as diesel generator sets, wind turbines, solar power plants and the
like. Depending on the power condition situation at any given time, it is expedient on the
part of the system operator to extract optimal generation from all units. The unit
commitment (UC) technique can be applied in such situations. Each unit has been associated
with variables such as continuity ratings, start-up and shut-down characteristics, reliability
indices, failure and repair rates. UC has been explored in depth for many years. The
application to smart grid is complex as the characteristics of each power plant are very
different in this smart grid. It is proposed to reduce the complexity by introducing redundant
state analysis. It generates redundant state by the combination of failure and repair in the
pool of machines. It is based on the concept of sequential machines in the context of digital
electronics. This paper proposes a novel method of redundant state analysis to assist a smart
grid generation pool in efficient completion of unit commitment. Redundant state is
identified by using the concept of state difference, which is taken from the finitestate of the
machine which is the cash characteristic of Mealy machine. Data state adjustment is required
as the first place choice of the unit data. This proposed efficiency method has been studied
in a seven-day schedule of seven unit systems[4].
Smart Grid Technology in Distribution and Transmission Systems
Using modeling methods, a study entitled Reliability Impact of Different Smart Grid
Techniques on a Power Distribution System. This paper, focuses on the application of smart
grid technology to a distribution system and its impact on the reliability of the system. Smart
grid techniques have been thoroughly researched in the form of a literature review. Some of
the techniques that are considered to have an impact on reliability are selected to be modeled
using Monte Carlo simulations performed with MATLAB. A Roy Billinton Test System
(RBTS) with 5 feeder buses was used for the testing system, where 1 & 2 were chosen as the
modeled techniques. The simulation was separated into 2 base cases and 5 smart grid
techniques were applied to these cases. Each technique was implemented separately with the
last case being a combination of all techniques. The techniques have shown an improvement
in the reliability index although the amount of improvement varies between the techniques.
one technique with another. The results of the study include:
The fault location scheme provides an improvement of between 7 - 10% in outage
duration.
Feeder automation reduced outage duration by 12.5%.
Load balancing increases the fault occurrence rate by 20 - 30%.
Utilization of all additive technologies resulted in an overall SAIDI reduction of 18%
and SAIFI reduction of 23%.
In comparison, automated individual feeder technology was shown to have provided
the most benefit to reduce outage duration while load balancing was the only
technology to significantly reduce interruption frequency."[10]
Another study, Implications of Smart Grid Technology on Transmission System
Reliability, explores the feasibility of improving system reliability by applying a monitoring
system to select circuit breakers and transformers. Smart grid technology lowers
maintenance costs by monitoring system conditions that provide information to operators
regarding possible failures that occur under certain conditions. Since the condition
monitoing system can increase operating costs, only critical transformers and circuit
breakers are selected in this system. This paper describes a method to identify transformers
and circuit breakers using the ranking contigency method.
This paper discusses a method to improve the reliability of power systems by providing
operators with more tools for failures in the system. The concept is to implement the smart
grid concept. Monitoring the condition of circuit breakers and introducing a transformer
component status monitor based on estimating the probability of component failure.
Simulation results indicate that sensor signals can be used to improve power system
reliability. If signals are received from monitoring the condition of circuit breakers and
transformers, allowing for an increased indication of component failure, operators are
advised to take corrective action to prevent future failures.
The contigency ranking method showed effective results in identifying the most severe
stresses of transformers and circuit breakers. Simulation results show that the contigency
ranking method is valuable for this application. One advantage of this approach is that it is
more sensitive to system stress points that have the potential for outages. Consequently,
operation is safer and the system is likely to be more reliable[7].
Smart Grid Technology Key
The key to smart grid technology is[3] :
Unified communication. The integration of communication technology is the
foundation of smart grid as smart grid depends on data collection, protection and
control.
Advanced power facilities. Apply the latest research results, such as
superconductivity, energy storage, power electronics and microelectronics technology
to achieve higher transmission capacity, better system stability and quality, improved
efficiency and realtime diagnosis system.
Advanced control methods. Analyze, diagnose and predict the situation of smart grid
devices and algorithms, make correct decisions and take appropriate actions to
eliminate, reduce or avoid power shortages and power quality problems.
Technology Support Decision. Using papanseamless, real-time information on power
equipment and tools to help managers make decisions quickly. Advanced visualization
will clearly show the status of the system to the operation department, through
advanced simulation and training process to improve the capacity of operators in
decision making.
Sensing and measurement. Apply digital sensors, magnetic sensors to overcome
traditional electromagnetic interference, carry out all-digital measurement.
Conclusion
From the discussion of the reliability of the smart grid system, several things can be
concluded as below:
Smart grid applications in distribution networks, with investments in sensors and
communication and IT networks, are profitable and feasible to implement.
The demand side management policy implemented in this study, which simulates
shifting wet load operations from peak load times to off-peak load times, provides
improved system reliability and also provides higher efficiency.
A study with monte carlo simulation using matlab and RBTS testing system found that
the implementation of smart grids in distribution networks provides a level of
reliability that is feasible to implement.
The smart grid application on the transmission network by installing sensors on circuit
breakers and transformers is able to provide increased reliability by knowing the
maintenance schedule in accordance with priorities.
