Review on Energy Resilience

5444 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 15, NO. 10, OCTOBER 2019

A Resilience-Based Architecture for Joint Distributed Energy Resources Allocation and

Hourly Network Reconfiguration Ehsan Kianmehr, Saman Nikkhah, Vahid Vahidinasab , Senior Member, IEEE,

Damian Giaouris, and Philip C. Taylor, Senior Member, IEEE

Abstract—As a result of the recent innovations in the de- ployment of plug-in electric vehicles (PEVs), this technology can play an important role as a distributed energy resource in supplying the system demand of the power systems of the future. This paper introduces a methodology for op- timal coordinated allocation of wind farms (WFs), energy storage systems, and PEV’s parking lots considering de- mand response programs and hourly distribution network reconfiguration in normal and severe contingency condi- tions. In the proposed methodology, the participation of dif- ferent types of loads is also examined. The objective func- tion is to minimize the total costs of purchased power from the upstream network and WFs, along with the costs of commercial/industrial loads flexibility and residential loads curtailment. To validate the performance of the proposed methodology, it is implemented on the well-known IEEE 33- bus distribution test system. The simulation results validate the feasibility and effectiveness of the proposed approach.

Index Terms—Allocation of distributed energy resources (DERs), demand response programs (DRPs), network re- configuration, parking lots, plug-in electric vehicle (PEV), resilience, storage systems, wind farms (WFs).

NOMENCLATURE

Indices ij Branch between the buses ith and jth. i, j Index of buses.

Manuscript received June 6, 2018; revised November 1, 2018 and Jan- uary 11, 2019; accepted February 10, 2019. Date of publication February 25, 2019; date of current version October 3, 2019. This work is carried out as part of the inteGRIDy project. The inteGRIDy project is financed by the European Commission under Grant Agreement 731268. Paper no. TII-18-1455. (Corresponding author: Vahid Vahidinasab.)

E. Kianmehr is with the Department of Electrical Engineering, Doroud Branch, Islamic Azad University, Doroud 1477893855, Iran (e-mail:, [email protected]).

S. Nikkhah is with the Department of Electrical Engineering, University of Zanajn, Zanajn 45371-38791, Iran (e-mail:, [email protected]).

V. Vahidinasab is with the Department of Electrical Engineer- ing, Abbaspour School of Engineering, Shahid Beheshti University, Tehran 19839 69411, Iran, and also with the School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K. (e-mail:, [email protected]).

D. Giaouris and P. C. Taylor are with the School of Engineer- ingNewcastle University, Newcastle upon Tyne NE1 7RU, U.K. (e-mail:, [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org

Digital Object Identifier 10.1109/TII.2019.2901538

t Time interval [hour].

Sets Γb/l All system buses/lines. Γr /c/d Residential/commercial/industrial load buses. Γsub Substation connecting the network to main

grid. Γt Time intervals. ΓW F/ESS/PEV Wind farms/energy storage systems/PEV-PLs.

Parameters C

sub/W F p Cost of active power procurement from main

grid/WFs ($/MW·h). C

cu r /flex p Cost of load curtailment/flexibility

($/MW·h). CF W Fi,t Coefficient for expected power output from

WF installed at bus i on hour t. Gij /Bij Conductance and susceptance of element ijth

of the YBUS matrix. N

W F/ESS/PEV m ax Maximum number of WFs/ESS/PEV-PLs.

N M PEVm ax Maximum number of PEVs. P D

r /c/d i,t Active power demand of residen-

tial/commercial/ industrial loads (MW). P

cm a xE S S i,t /P

dm a xE S S i,t Maximum charging/discharging power of

ESS. P

W F ,m ax/ m in i Maximum/minimum wind power generation

(MW). (P/Q)m ax / m insu b Maximum/minimum active/reactive power

of substation (MW/MVar). P

cm a xP E V i /P

dm a xP E V i Maximum charging/discharging power of

PEV. QD

r /c/d i,t Reactive power demand of residen-

tial/commercial/ industrial loads (MVar).

η (c/d)E S S / P E V i,t Charging/discharging efficiencies of

ESS/PEV’s battery at bus i on time t. |Γ(l/t)| Number of lines/time intervals. γ

c/i m ax Maximum flexibility of commer-

cial/industrial loads.

Variables CapPEV m a xi,t Maximum capacity of ith PL at time t. EPEVi,t Energy stores at PEV (MW·h). IR /M i j Real/imaginary part of current flow.

1551-3203 © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.

KIANMEHR et al.: RESILIENCE-BASED ARCHITECTURE FOR JOINT DERS ALLOCATION AND HOURLY NETWORK RECONFIGURATION 5445

L W F/ESS/PEV i Binary variable of the location of

WFs/ESSs/PEV-PLs (1 = installed, 0 = otherwise).

N PEVi,t Integer variable for modeling the number of PEVs of ith PL at time t.

P Subi,t /Q Sub i,t Active/reactive power of substation

(MW/MVar). P Di,t /QDi,t Active/reactive power demand (MW/MVar). P curi,t /Q

cur i,t Curtailed active/reactive demand

(MW/MVar). P C E S Si,t /P

D E S S i,t ESS charging/discharging power (MW).

P D cf i,t /P D

df i,t Flexible active power of commer-

cial/industrial loads (MW). P W Fi,t /Q

W F i,t Injected active/reactive power of WFs

(MW/MVar). P C P E Vi,t /P

D P E V i,t PEV charging/discharging power (MW).

QD cf i,t /QD

df i,t Flexible reactive power of commer-

cial/industrial loads (MVar). SOCESSi,t State of charge of ESS (MW·h). Vi,t Voltage magnitude of bus i at time t. γflexi,t Load flexibility variable. θij,t Voltage angle difference between nodes i and

j at time t. ϑij,t Binary variable for the line between buses i

and j (1 = connected, 0 = disconnected).

Functions πsub/W F Cost of purchasing power from main grid/WFs. πLsh/flex Cost of load curtailment/flexibility.

Abbreviations DER Distributed energy resource. DG Distributed generation. DRP Demand response program. DFIG Doubly fed induction generator. DNR Distribution network reconfiguration. DV Dependent variables. ESS Energy storage system. GAMS General Algebraic Modeling System. HNR Hourly network reconfiguration. HDNR Hourly DNR. IDV Independent decision variables. MINLP Mixed-integer nonlinear programming. PV Photovoltaic. PEV Plug-in electric vehicle. PEV-PL PEV parking lot. SOC State of charge. SOH State of health. V2G Vehicle to grid. V4G Vehicle for grid. WF Wind farm. WT Wind turbine.

I. INTRODUCTION

T HANKS to the recent innovations in the modernizationof power system, distributed energy resources (DERs) are now crucial in supplying the system demand in different condi-

tions. In this regard, in addition to conventional DERs such as wind turbines (WTs), photovoltaic (PV) systems, diesel gener- ators, and energy storage systems (ESSs), an alternative option is introduced as the DER for improved operation of smart grid technologies. This alternative option is transportation electrifi- cation with the concept of plug-in electric vehicles (PEVs).

Due to the potential of PEV’s parking lots (PEV-PLs) to exchange energy with the electric power system, they can be considered as the DER. Therefore, in the near feature, PEVs can play a significant role in supplying system loads. Although, the PEVs consume electric power and act as a consumer, de- ployment of vehicle to grid (V2G) technologies allows the PEVs to exchange energy with the power grid. In such circumstances, PEV’s owners can play their own role in power system and gain from participation in the V2G services and in parallel through involvement in the provision of grid services, play an important role as a grid facility which can be called vehicle for grid (V4G). All of the above-mentioned technologies are important cross- functional solutions that accelerate the integration of DERs and help the network operator in optimizing grid operation.

In addition to the DERs, there are more attractive and afford- able alternatives which make today’s power systems smarter than traditional networks. One of these alternatives is the distri- bution network reconfiguration (DNR). Although the concept of DNR was introduced several years ago, this methodology is now taken into consideration as a flexibility solution in the modern- ization of the power systems [1]. The DNR is defined as the pro- cess of changing the status of normally open/closed switches of the distribution network to reach a configuration that optimizes desired objectives while satisfying all operational planning con- straints of the network without isolating any network node(s)[2].

In addition to the role of these technologies in normal network operation, they provide more flexibility for power utility in the severe contingency conditions in which power lines are dam- aged, or connection with the upstream network is disrupted. This problem has forced network operators to make a pervasive plan for the resilient operation of the system in severe contingency conditions such as technical problems, natural disasters, and man-made problems which cause irrecoverable losses. There- fore, the occurrence of severe contingency condition is really a prominent problem and consequently, the development of an ap- propriate strategy to decrease the negative impacts of this issue on the network have become necessary.

Up to now, various research works have been published in the context of the smart grid operation in both normal and contin- gent conditions. In this study, the literature has been classified according to the types of the components as a) PEV-PLs, b) distributed generation (DG) and ESS, c) DNR, and d) demand response program (DRP).

A. PEV’s Parking Lots

In spite of their challenges, PEVs have remarkable economic, social, and operational advantages for the networks. Conse- quently, many research works have been published to investi- gate the different aspects of PEVs in the power grid. A multi- objective optimization model is proposed by El-Zonkoly et al. in

5446 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 15, NO. 10, OCTOBER 2019

[3] for optimal allocation of PLs within the distribution network using artificial bee colony optimization algorithm. In [4], the renewable energy sources and PEV-PLs are simultaneously al- located in the network using a two-level optimization approach in which the mutual behavior of PEV-PLs and renewable-based DGs is investigated. A robust management model has been pro- posed in [5] for optimal scheduling of the electric vehicles active and reactive power, considering different uncertainties. Since the robust optimization is a bi-level technique, authors have used the Benders decomposition to reduce the processing time. A remarkable techno-economic planning model was proposed in [6] for long-term planning of PEV-PLs from parking lot owners’ perspective. The work presented considers a coordinated charg- ing scheme which shifts the energy consumption from on-peak to off-peak periods. Effect of different DRPs (e.g., real-time pricing, time-of-use) on the real-time operation of PEVs has been investigated in [7]. The PEVs have been considered as the end-user and their participation in the incentive/price-based DRPs have been investigated. Bidirectional PEVs, which have numerous advantages compared to unidirectional, have been used in [8] for management of a smart distribution network and compensation of the harmonics raised by nonlinear loads. In [9], a methodology is proposed that uses the fuel of PEVs as a source of power for the residential loads when the link of such loads with the upstream grid is disrupted.

B. DG and ESS

Due to the key role of DGs and ESSs in supplying system loads in nowadays’ power system, several studies have been published for allocating such DERs in the network, in differ- ent conditions. Nick et al. [10] proposes a seasonal planning procedure for optimal siting and sizing of the storage units and optimal network reconfiguration. Also, in [11], a planning schedule is proposed for ESSs with the incorporation of DGs and DNR. In this study, the authors focused on coordination of power electronic devices such as smart inverters with ESSs to decrease the investment costs of ESSs. In [12], a voltage stabil- ity constrained model is proposed for optimal wind farm (WF) allocation in a long-term planning horizon. Awad et al. [13] proposed a long-term model for planning of ESSs with the aim of profit maximization of distributed ESSs. The authors consid- ered the load curtailment to prevent complete blackout during the planning horizon in a contingency condition. A probabilis- tic approach for optimal allocation of DGs is introduced in [14] considering the uncertainty of system demand. An economical model is proposed in [15] for allocation of ESS in the presence of volatile wind power generation. The authors used the five- point estimate method to model the uncertainty of wind energy which has considerable drawbacks in modeling uncertainty in comparison with information gap decision theory [16].

C. Distribution Network Reconfiguration

Recently, numerous research works have been published for the problem of DNR. Generally, authors of these papers have employed different methodologies and techniques to solve the problem of DNR. Arasteh et al. [17] proposed a planning model

that coordinates the DNR and active distribution network ex- pansion planning which considers DRPs as the virtual dis- tributed resources. In [18], a fast, nondominated sorting genetic algorithm has been proposed to the problem of DNR. The re- sults obtained in [19] demonstrate the robustness of proposed adaptive particle swarm optimization in comparison with other techniques like genetic algorithm which is employed by Asraei et al. [20] for the reconfiguration of the network in the presence of DGs. The concept of DNR is coordinated with microgrid formation in [21], to restore the system loads after the natural disaster. Lin et al. [22] combined hardening and operational measures as a main aspect of power system resilience using a tri-level defender-attacker-defender model. The authors per- formed the reconfiguration and microgrid islanding schemes as a third level, which is defender plan, to measure the operation of the grid from the system operator perspective.

D. Demand Response Program

In smart grids, customers can play their own role to im- prove the characteristics of networks [23]. A planning model is proposed in [24] for expansion of distribution systems in the presence of DRPs and DERs. In [25], the participation of cus- tomers in increasing the resilience of microgrid is investigated. To do so, four security indices have been introduced by authors to measure the resilience of power system after weather events. An emergency DRP is proposed in [26] so as to investigated the role of end-users in contingency condition in case of genera- tion failure. Taxonomy of the aforementioned research works is given in Table I. According to this table, some important points have been ignored in previous research works. For instance, al- though DGs are considered in [3], they have not been optimally allocated within the network. Besides, while DNR is employed in [5], the hourly changes of the network switches have not been taken into consideration.

Although a careful planning model is necessary for optimal operation of network in severe contingency conditions, to the best of the authors’ knowledge, there are no research works which have simultaneously considered the important compo- nents of smart grid such as DGs, ESSs, PEV-PLs, demand re- sponse (DR), and optimal switching of network to increase the resilience of grid in such a situation. Moreover, in the papers which have focused on the optimal allocation of DERs, there is not an appropriate model for selecting all system buses as candidate nodes for DERs installation. Likewise, there are no publications which have focused on coordination of the role of PEVs and hourly network reconfiguration in decreasing load curtailment. In this regard, this paper proposes a methodology to decrease the load shedding in normal and contingency con- ditions. The introduced model is a comprehensive methodology that considers the PEV-PLs, WFs, and ESSs as a DERs, and obtains the optimal hourly configuration for the network, and additionally, DRP is taken into consideration using the concept of load shedding for the residential and load flexibility for the commercial and industrial loads. The objective function of the problem is minimizing the costs of load curtailment and flexi- bility and costs of purchasing power from substation and WFs.

KIANMEHR et al.: RESILIENCE-BASED ARCHITECTURE FOR JOINT DERS ALLOCATION AND HOURLY NETWORK RECONFIGURATION 5447

TABLE I TAXONOMY OF RESEARCH WORKS ON OPTIMAL ALLOCATION OF DERS AND DNR

The literature review has highlighted several important defi- ciencies which are as follows.

1) A wide majority of papers, which have focused on net- work reconfiguration, have not solved the problem on an hourly basis.

2) The proposed allocation methodologies in the previous literature have considered a small fraction of system buses for finding optimal location and the size of DERs and have not examined the important factors in planning and operation sectors.

3) The role of customers and PEVs have not been investi- gated in the resilient operation of the power systems.

4) The interconnection between DNR and co-operation of different DERs in normal/contingent condition has not been effectively demonstrated.

In brief, to the best of the authors’ knowledge, this paper contributes to the state of the art with the following key contri- butions.

1) A new model has been proposed for DNR in which the ON/OFF status of the network switches is optimized on an hourly basis.

2) A resilience-oriented allocation scheme is proposed for PEV-PLs, ensuring resilient operation of the network af- ter/before the occurrence of any faults in the system.

3) A comprehensive co-operation model is proposed that simultaneously defines the optimal location and size of WFs, PEV-PLs, ESSs, and optimal hourly configuration of the network.

4) The key role of different load types is demonstrated in the model to show the importance of customers’ participation in contingency conditions.

5) All system buses are considered as the candidate buses for installing different DERs with regard to both the physical and operational constraints of the system.

The rest of this paper is organized as follows. In Section II, the problem under study is described. The mathematical formu- lation is presented and discussed in Section III. The case study is provided in Section IV. Section V presents the numerical result. Finally, Section VI concludes the paper.

II. FRAMEWORK DESCRIPTION

Due to the techno-economic problems of the expansion of existing distribution systems, DERs could be an effective solu- tion for delivery of power to customers with minimum active power losses and load curtailment. Even though the distributed systems have a mesh structure, they operate in a radial configu- ration, owing to the considerable benefits of the radial operation (e.g., easy protection and short circuit current limiting). Regard- ing this, DSOs try to find an optimal radial configuration for the network that the loads of the system are supplied through ex- isting energy resources, and various operational, economic, and security constraints are satisfied. However, conventional DNR models fail to adapt to the constraints and opportunities pre- sented by new network technologies. Consequently, an hourly DNR (HDNR) is an absolutely necessary consideration for to- day’s systems.

In view of the above, adaptation of a comprehensive co- operation model in which, an optimal operation model for DERs along with HDNR, which is more likely to result in re- silient operation of the network, is vital. The aim of this study is to simultaneously define the optimal location and size of

5448 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 15, NO. 10, OCTOBER 2019

Fig. 1. (a) Proposed framework of the proposed co-operation problem. (b) Stages of optimal DERs allocation and HDNR in both normal and contingency condition.

PEV-PLs, ESSs, and WFs considering the optimal HDNR for radial distribution systems to achieve several benefits, especially resilient operation of the network in different conditions. It is to be noted that the co-operation of ESSs and PEV-PLs allows the DSO to benefit from penetration of WFs, and prevent possible operational problems due to the high penetration of wind en- ergy. Besides, it investigates the role of customers in providing the resilient operation in such modernized radial grid, using the concepts of load curtailment and flexibility. The main goal is to decrease the load shedding of residential loads, especially in severe contingency conditions with consideration of differ- ent operational costs. In this methodology, the operation of a distribution system for normal and contingency conditions is examined. To do so, at first, some of the distribution lines in- cluding the power line which connects the distribution system to the upstream network are selected as the candidate lines which are disrupted in a specific time because of weather events or man-made attacks. It is worth mentioning that the selected lines experience the connected and disconnected status which cause normal and contingency conditions in the system, during the operation horizon. Then, optimal location and size of PEV-PLs, ESSs, and WFs, simultaneously obtained with optimal hourly ON/OFF status of distribution system switches, amount of the load that should be curtailed or degree of flexibility of commer- cial and industrial loads of the network, to decrease the different cost components of the system which include cost of load cur- tailment. The proposed framework and the explained stages are depicted in Fig. 1.

III. PROBLEM FORMULATION

This section presents the general formulation addressed in this paper. At the following subsections, the different components of the model are expressed.

A. Objective Function

As it was mentioned, this paper aims to minimize an objec- tive function which consists of the costs of purchased power from upstream substation and WFs, load curtailment, as well as the cost that should be paid to the commercial and industrial customers for decreasing their consumption, as follows:

Minimize πsub + πW F + πLsh + πLflex (1)

πsub = ∑

i∈Γs u b

∑

t∈Γt P subi,t × Csubp (2)

πW F = ∑

i∈ΓW F

∑

t∈Γt P W Fi,t × CW Fp (3)

πLsh = ∑

i∈Γr s

∑

t∈Γt P curi,t × Ccurp (4)

πLflex = ∑

i∈Γc / i

∑

t∈Γt

( P Dci,t + P D

d i,t

) × Cflexp × γflexi,t . (5)

This objective function is subjected to the different equality and inequality constraints expressed in the following sections.

B. Active and Reactive Power Balance

To find an optimal schedule of DERs, operation and switch- ing states of the network, amount of curtailed load, and load participation in flexibility provision, the load flow equations must be considered. The following load flow constraints in- cluding the injected power from substation and WFs, ESSs and PEVs charging and discharging, net power of the loads from residential, commercial and industrial customers, and the net- work’s flows considered at each of the distributed system buses

KIANMEHR et al.: RESILIENCE-BASED ARCHITECTURE FOR JOINT DERS ALLOCATION AND HOURLY NETWORK RECONFIGURATION 5449

(∀i, j ∈ Γb , t ∈ Γt , ϑij ∈ {0, 1}):

P subi,t + P W F i,t +

( P C E S Si,t − P D E S Si,t

) + N PEVi,t

( P C P E Vi,t − P D P E Vi,t

)

− P Di,t + P curi,t = ∑

j

(ϑij,t × Pij,t ) (6)

Qsubi,t + Q W F i,t − QDi,t + Qcuri,t =

∑

j

(ϑij,t × Qij,t ) (7)

Pij,t = V 2 i,t Gij − Vi,t Vj,t (Gij cosθij,t + Bij sinθij,t ) (8)

Qij,t = −V 2i,t Gij − Vi,t Vj,t (Gij sinθij,t − Bij cosθij,t ) (9) P Di,t = P D

r i,t + P D

cf i,t + P D

df i,t (10)

QDi,t = QD r i,t + QD

cf i,t + QD

df i,t . (11)

Moreover, P subi,t and P sub i,t are nonzero variables stands for

the substation bus

{ P m insub ≤ P subi,t ≤ P m axsub ; ∀i ∈ Γsub 0; otherwise

(12)

{ Qm insub ≤ Qsubi,t ≤ Qm axsub ; ∀i ∈ Γsub 0; otherwise

. (13)

C. Radiality Constraints

In distribution networks, in order to have a radial configura- tion, the network should have a tree-like topology in where each load is linked through a unique path to the substation/DERs [1]. The next circumstance is satisfied via the load flow equations in which all of the system loads are fed by the substation/DERs and there is no mesh (loop) in the network. For the first condi- tion satisfaction, in each hour, the configuration of the network will be radial when the total number of the closed switches be equal to the number of the nodes minus one. Also, the status of ϑij,t and ϑj i,t should be the same. It is worth mentioning that in this study, the aforementioned radiality conditions should be satisfied each hour. The mentioned radiality constraints are modeled as

∑

i,j∈Γb ϑij,t = 2 (|Γ�| − 1) (14)

ϑij,t = ϑj i,t , ∀i, j ∈ Γb , t ∈ Γt (15)

where ϑij,t identifies the status of the branch between ith and jth buses. For the mentioned decision variable, ϑij,t = 1 shows the closed status of the switch and ϑij,t = 0 illustrates the opened status of the switch.

D. Operational Limits

The following represent the operational constraints of the network including the voltage profile, capacity of distribution lines, and injected power from the upstream network, which should be limited within permissible values as follows

(∀i, j ∈ Γb , t ∈ Γt , ϑij ∈ {0, 1}): V m ini ≤ Vi,t ≤ V m axi (16) ϑij,t (I

2 R i j , t

+ I 2M i j , t ) ≤ I 2M AX i j (17) IR i j , t = Gij (Vi,t cosθi,t − Vj,t cosθj,t )

− Bij (Vj,t sinθi,t − Vj,t sinθj,t ) (18) IM i j , t = Gij (Vi,t sinθi,t − Vj,t sinθj,t )

+ Bij (Vj,t cosθi,t − Vj,t cosθj,t ). (19)

E. Demand Response Programs

In the contingency condition, the participation of costumers in the management of the system is a decisive factor which can prevent the system from the complete blackout. This participa- tion which is known as DR is divided into different categories. The DR which is called here as DRPs, including direct load con- trol, or interruptible services can be used as additional reserves during contingency conditions. In this study, two different DRPs are considered taking into account the load curtailment with an associated cost for the residential customers, as well as allo- cating special payments for the flexibility of commercial and industrial customers. In this method, the residential customers curtail their loads with a significant high cost; commercial and industrial customers decrease their consumption to a specific level which have an incentive payment for them, as follows (∀t ∈ Γt ):

P curi,t ≤ P Dri,t ∀i ∈ Γr (20) Qcuri,t ≤ QDri,t ∀i ∈ Γr (21) P D

cf i,t = (1 − γflexi,t )P Dci,t ∀i ∈ Γc (22)

QD cf i,t = (1 − γflexi,t )QDci,t ∀i ∈ Γc (23)

P D df i,t = (1 − γflexi,t )P Ddi,t ∀i ∈ Γd (24)

QD df i,t = (1 − γflexi,t )QDdi,t ∀i ∈ Γd (25)

0 ≤ γflexi,t ≤ γc/im ax (26) where (20) and (21) are the amount of load curtailment in each bus at each hour which should be lower than that of the res- idential load. Also, (22)–(25) state the demand decrement of commercial and industrial loads. Equation (26) states the max- imum amount of flexibility provision by the flexible loads.

F. Limits on the Capacity and Number of WFs

The following is proposed to limit capacity and number of WFs (∀i ∈ ΓW F ).

0 ≤ P W Fi,t ≤ LW Fi × P W F,m axi × CF W Fi,t (27) − tg(ϕlead ) × P W Fi,t × LW Fi ≤ QW Fi,t

≤ LW Fi × tg(ϕlag ) × P W Fi,t (28) ∑

i∈Γb LW Fi ≤ N W Fm ax (29)

5450 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 15, NO. 10, OCTOBER 2019

where (27) and (28) show the active and reactive power limit of WFs, respectively. In order to optimally allocate the WFs within a network, a binary variable LW Fi is defined which indicates either WF is installed at bus i (i.e., LW Fi = 1), or not (i.e., LW Fi = 0). Also, the number of WFs that could be installed in the network should be limited. Consequently, (29) is proposed which specifies the number of WFs.

G. Limits on the Capacity and Number of ESSs

The ESS constraints include the state of charge (SOC) at each hour, limits on SOC and charging/discharging power, constraint to prevent simultaneous charging/discharging, plus limits on the number of ESSs that could be installed, as follows (∀i ∈ ΓE S S , ∀t ∈ Γt ):

SOCESSi,t = SOC ESS i,t + Δt ·

( pC E S Si,t η

cE S S i,t − pD E S Si,t /ηdE S Si,t

)

(30)

SOCm ini × LESSi ≤ SOCESSi,t ≤ SOCm axi × LESSi (31) 0 ≤ P C E S Si,t ≤ P

cm a xE S S i,t × LESSi ∀i ∈ ΓESS (32)

0 ≤ P D E S Si,t ≤ P dm a xE S S i,t × LESSi ∀i ∈ ΓESS (33)

P C E S Si,t × P D E S Si,t = 0 (34) ∑

t∈Γt

∑

i∈ΓE S S P C E S Si,t ≥

∑

t∈Γt

∑

i∈ΓE S S P D E S Si,t (35)

∑

i∈Γb LESSi ≤ N ESSm ax (36)

where SOC of ESSs is represented by (30) and limited by (31). Constraints (32) and (33) correspond to the upper and lower limit of charge/discharge power of ESSs, whereas (34) limits the simultaneous charge and discharge of ESSs. A binary vari- able LESSi states the location of ESSs installed in the system, i.e., LESSi = 1 for the ESS located at bus i and i.e.,L

ESS i = 0,

otherwise. Also, constraint (35) insures that the charging level of the ESSs should not be smaller than the discharging level. To limit the number of ESSs that could be installed in the system, (36) is introduced.

H. PEV-PLs’ Constraints

In this study, a model is proposed to limit the number of PEV-PLs, capacity of electric vehicles and number of electric vehicles of each PL. Also, to fully include the participation of PEVs in the contingency condition, a formulation is proposed to capture the participation of all available PEVs in the resilient operation of the grid. These constraints are expressed as follows (∀i ∈ ΓPEV ∀t ∈ Γt ):

EPEVi,t = E PEV i,t + Δt ·

( pC P E Vi,t η

cP E V i,t − pD P E Vi,t /ηdP E Vi,t

) (37)

Em ini × LPEVi ≤ EPEVi,t ≤ Em axi × LPEVi (38) 0 ≤ P C P E Vi,t ≤ P

cm a xP E V i × LPEVi ∀i ∈ ΓPEV (39)

0 ≤ P D P E Vi,t ≤ P dm a xP E V i,t × LPEVi ∀i ∈ ΓPEV (40)

P C P E Vi,t × P D P E Vi,t = 0 (41) ∑

t∈Γt

∑

i∈ΓP E V P C P E Vi,t ≥

∑

t∈Γt

∑

i∈ΓP E V P D P E Vi,t (42)

∑

i∈Γb LPEVi = N

PEV m ax (43)

∑

t∈Γt N PEVi,t = N M

PEV m ax × LPEVi (44)

N PEVi,t = Cap PEV m a x i,t × LPEVi (45)

where constraints (37)–(41) are the limits in the capacity of PEVs’ battery which include the energy stored at PEV (37), limit of energy that could be stored at PEV (38), maximum charging/discharging capacity of each PEV (39) and (40), and constraint on simultaneous charge/discharge of PEV (41). Based on the operation schedule of PEVs’ battery, their charging ca- pacity should be greater than their discharging capacity, as math- ematically expressed in (42). Due to the noticeable advantages of PEVs and increasing trends in increasing the penetration of PEVs into the grid, deployment of PLs is vital. In this regard, (43) is introduced to insure the charging support for all available electric vehicles in the network. In this study, an integer variable N PEVi,t is defined which indicates the number of electric vehicles in ith PL at hour t. In the contingency condition, load supply has become an important issue and all of the energy sources should participate in the demand supply. To capture the participation of all PEVs, all available electric vehicles should enter the PL during operation horizon. To do so, (44) is proposed. Also, (45) limits the number of electric vehicles in each hour due to the capacity of PLs. Please note that although the PEVs’ behavior needs a practical model that considers different mechanical and electrical factors, this paper is associated with the role of PEVs in normal and contingency condition, therefore, the proposed model deals with the PEVs battery along with optimal location and capacity of PEV-parking lots.

I. Decision Variables and Solution Strategy

The optimal values obtained from the solutions of the pro- posed optimization problem contain a number of decision vari- ables that demonstrate the interconnection between the model variables. The independent decision variables of the proposed resilience-oriented co-operation model include injected power from upstream network, active/reactive power of WFs, SOC of ESSs, the status of PEV’s battery, the participation of differ- ent load types, the number of PEVs, and the hourly status of network switches. Besides, the flow of the current through the transmission lines, voltage magnitude and angle of load buses, injected reactive of the substation, active charge/discharge of ESS/PV battery, for instance, are called dependent variables as such their values are determined by solving the optimization problem. Sets of the decision and independent variables are

KIANMEHR et al.: RESILIENCE-BASED ARCHITECTURE FOR JOINT DERS ALLOCATION AND HOURLY NETWORK RECONFIGURATION 5451

Fig. 2. Distribution system under study.

given in (46) and (47), respectively.

IDV =

⎧ ⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

P subi,t ∀i ∈ Γsub , ∀t ∈ Γt (P/Q)W Fi,t ∀i ∈ ΓW F , ∀t ∈ Γt SOCESSi,t ∀i ∈ ΓESS , ∀t ∈ Γt EPEVi,t ∀i ∈ ΓPV , ∀t ∈ Γt γflexi,t ∀i ∈ Γb , ∀t ∈ Γt N PEVi,t ∀i ∈ ΓPV , ∀t ∈ Γt ϑij,t ∀i, j ∈ Γb , ∀t ∈ Γt

⎫ ⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎬

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎭

(46)

DV =

⎧ ⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎩

Qsubi,t ∀i ∈ Γsub , ∀t ∈ Γt P

(C /D )E S S i,t ∀i ∈ ΓESS , ∀t ∈ Γt

P (C /D )P E V i,t ∀i ∈ ΓPV , ∀t ∈ Γt

Vi,t ∀i ∈ Γb , ∀t ∈ Γt I(R /M )i j , t ∀i, j ∈ Γb , ∀t ∈ Γt

⎫ ⎪⎪⎪⎪⎪⎪⎪⎬

⎪⎪⎪⎪⎪⎪⎪⎭

. (47)

The introduced model is implemented in the general alge- braic modeling system (GAMS) software [27]. The proposed framework is a mixed-integer nonlinear programming (MINLP) model which is solved by the SBB solver. The SBB is a GAMS solver for solving the MINLP models. It is based on a combi- nation of the well-known standard branch and bound technique from mixed integer linear programming domain and some of the supported standard nonlinear programming solvers by GAMS [27].

IV. CASE STUDY

The IEEE 33-bus distribution test system [1], as shown in Fig. 2, is used to implement the proposed model. The system consists of 37 lines, 5 tie lines as well as 32 sectionalizing switches. Although, the used IEEE 33-bus test system is a mod- erately large case study for distribution level studies, however, the proposed comprehensive framework of this study can be adapted to other large-scale networks with additional computa- tion cost. It is to be noted that the selected optimization solver is a powerful one for solving the relatively large systems and there is no limitation in this part. Nonetheless, for extremely large scale networks, it may be necessary to either apply the mathe- matical decomposition techniques to break down the problem

TABLE II CHARACTERISTICS OF EACH ESSS

into some small sub-problems or linearize the nonlinear parts of the problem.

The proposed model is executed on an Intel Core i7-3.00-GHz personal computer with 8 GB of RAM.

The paper presents results for a maximum of three PEV-PLs and maximum capacity of 40 PEVs in each hour. The focus of this study is on the collaborative operation of PEV along with other components of the smart grid so as to reduce the amount of load curtailment in different conditions. Hence, 1500 PEVs are assumed to be in the network which should participate in the supply of system loads to decrease the load curtailment in con- tingency condition. This number of PEVs in the grid is adequate for the dimension of the given distribution network under study. It is worth mentioning that the same travel pattern is assumed for the drivers and focus is on the role of PEVs in severe con- tingency condition. Nonetheless, this research work will also be conducted to incorporate the behavior of PEV owners as an uncertain parameter. The maximum and minimum SOC values of the battery of PEVs are assumed to be 90% and 10%, re- spectively, while the nominal capacity of batteries considered to be 16 KW·h. Also, rated charge and discharge capacity of the PEVs considered to be 2.3 KW.

Type of each load (i.e., residential, commercial, and indus- trial) to the load buses are specified in Fig. 2, while the variation of demand is shown in [28]. In addition, the maximum value of load flexibility index is set to 0.1. It is assumed that at the most three WFs with the rated capacity of 800 KW, and two ESSs, are allowed to be dispatched in the system. Characteris- tics of ESSs are given in Table II. The daily variation of WFs’ capacity factor is given in [28]. The contract price of purchasing power from WFs and main grid, loss of load value and price of load flexibility is assumed to be 0.04, 1, and 0.5 $/KW·h, respectively.

To capture the contingency condition in the operation horizon of the simulation, it is assumed that the substation and two power lines, connecting buses 5–6, and 27–28 are failed from hour 7 to 18, as a result of a natural disaster. The location of failures is given in Fig. 2. It is to be noted that the following assumptions are considered: first, all the WTs have DFIG technologies and as a result, they can contribute in the black start process; second, the DSO has been authorized for load curtailment in contingency conditions to maintain the frequency of the system.

V. NUMERICAL RESULTS

This section summarizes the findings of the paper. To demon- strate the DNR importance, the results obtained for two cases including Case I: with DNR and PEV-PLs, and Case II: without DNR and PEV-PLs. The following sections describe the results obtained for each case.

5452 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 15, NO. 10, OCTOBER 2019

Fig. 3. ON/OFF status of line switches.

A. Case I: Joint DNR and DER Allocation Model

In this case, the proposed model is solved while the ON/OFF status of distribution line switches changed during a day. The value of the objective function in this case is $3281.637. Fig. 3 shows the ON/OFF status of line switches for the study horizon.

This figure implies the importance of hourly network recon- figuration. As can be seen from this figure, the status of some line switches does not change during the operation horizon. However, the status of some of the important power lines such as L18−33 and L6−26 , which have a key role in the system, is changed several times during a day. Also, the line switch L1−2 is opened in hours 7–18, because the main grid is at fault in these hours; therefore, in these hours, the system experiences the islanded mode and the DERs supply the system, and as a result, the complete blackout does not occur.

The location of DERs in the initial configuration of the sys- tem is shown in Fig. 4. It can be observed that the PEV-PLs installed at the commercial and industrial load buses to charge in the off-peak periods and normal condition of the system and inject it to the system in the on-peak periods and contingency

Fig. 4. Optimal location of DERs in the system in Case I.

TABLE III NUMBER OF PEVS IN EACH PEV-PL

Fig. 5. Optimal active power of WFs in Case I.

Fig. 6. SOC of the ESSs in Case I.

KIANMEHR et al.: RESILIENCE-BASED ARCHITECTURE FOR JOINT DERS ALLOCATION AND HOURLY NETWORK RECONFIGURATION 5453

Fig. 7. Optimal power dispatch of different components of system for cases I and II.

condition. The number of electric vehicles that should be in each PL during the operation horizon is summarized in Table III. Ac- cording to this table, the total capacity of PLs located at buses 2, 11, and 33 is equal to 683, 616, and 201 electric vehicles, respectively. Comparison of this table and Fig. 3 imply the co- ordinated role of PEVs and DNR. For instance, the line which connects the PL installed at bus 2 closed during operation hori- zon.

The active power dispatch of WFs is depicted in Fig. 5. It can be observed that more active power is dispatched via WFs in hours that the network experiences the contingency condition. Also, SOC of ESSs is shown in Fig. 6. As can be seen, in contingency time intervals, the charge of ESSs is dropped in such a way that in peak hours the SOC of ESSs is in their minimum level.

B. Case II: Without DNR and PEVs

As previously mentioned, PEVs and DNR have a considerable effect on the smart grid economic and operational perspectives. In this regard, the proposed model is solved without the network reconfiguration and penetration of PEVs.

The figure for objective function in this case is $3746.937. This is because there will be several loads which are discon- nected from the distribution system and the capacity of DERs is not enough to support all system loads in contingency condition.

It is worth noting that for the sake of results comparison, the obtained results of this case are simultaneously illustrated with that of Case I. Consequently, Fig. 7 depicts the optimal power dispatch of the proposed method for both cases. It can

TABLE IV COMPUTATIONAL INFORMATION OF THE CASE STUDIES

be seen that the participation of customers is increased in Case II. Also, in Case I, some of the active power injected from WFs and substation is consumed by PEVs. On the other hand, PEVs discharged in the on-peak periods and act as storage systems. Furthermore, injected power from substation and WFs are de- creased in Case I in the result of PEVs discharge. Meanwhile, in Case I, islanded loads supplied through coordination of DERs and DNR which result in lower load curtailment.

Table IV gives information on the computation size of the proposed model in both cases, and for two different computation systems.

VI. CONCLUSION AND FUTURE WORKS

This study proposed a methodology for simultaneous allo- cation of DERs and hourly network reconfiguration in normal and contingency condition. The introduced model provides a resilience-based architecture in which, some distribution line

5454 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, VOL. 15, NO. 10, OCTOBER 2019

switches are opened for a specific time interval and optimal lo- cation and size of WFs, PEV-parking lots as well as ESSs are op- timally defined along with ON/OFF status of line switches for the operation horizon. Nonetheless, the participation of customers in system management is investigated through the concept of load curtailment of residential consumers and flexibility of com- mercial and industrial consumers so as to preserve the system from the complete blackout, during contingency condition. The obtained results substantiate the importance of simultaneous allocation of DERs and reconfiguration of the distribution net- work, as well as DR, in normal and contingency condition on the operation of the system.

In general, the numerical simulations allow that the following conclusions be drawn.

1) Network reconfiguration problems should be solved on an hourly basis so as to improve the characteristics of the network.

2) Coordination of DERs planning and hourly network re- configuration are important factors for increasing the re- silience of the distribution system.

3) Optimal location and capacity of DERs can be affected by contingency conditions.

4) End-users can play a crucial role in improving the system management in contingency condition.

Further research studies need to explore the effect of long- term planning of DERs on the resilience of power systems. In this regard, the additional constraints including the state of health of energy storage and battery of the PEVs can be included in the model. Besides, although the paper focused on the steady- state operation of the network, the consideration of dynamic behavior and transient stability of the system in the contingency condition is of the utmost importance. Consequently, in future works, the interconnection between the hourly network recon- figuration and different aspects of the system, e.g., transient and steady-state stability, will also be analyzed.

REFERENCES

[1] H. Arasteh, V. Vahidinasab, M. S. Sepasian, and J. G. Aghaei, “Stochastic system of systems architecture for adaptive expansion of smart distribution grids,” IEEE Trans. Ind. Informat., vol. 15, no. 1, pp. 377–389, Jan. 2019.

[2] N. G. Paterakis et al., “Multi-objective reconfiguration of radial distribu- tion systems using reliability indices,” IEEE Trans. Power Syst., vol. 31, no. 2, pp. 1048–1062, Mar. 2016.

[3] A. El-Zonkoly and L. dos Santos Coelho, “Optimal allocation, sizing of PHEV parking lots in distribution system,” Elect. Power Energy Syst., vol. 67, pp. 472–477, May 2015.

[4] M. Shafie-Khah, P. Siano, D. Z. Fitiwi, N. Mahmoudi, and J. P. Catalão, “An innovative two-level model for electric vehicle parking lots in distri- bution systems with renewable energy,” IEEE Trans. Smart Grid, vol. 9, no. 2, pp. 1506–1520, Jun. 2018.

[5] S. Pirouzi, J. Aghaei, V. Vahidinasab, T. Niknam, and A. Khodaei, “Robust linear architecture for active/reactive power scheduling of EV integrated smart distribution networks,” Elect. Power Syst. Res., vol. 155, pp. 8–20, Feb. 2018.

[6] A. S. Awad, M. F. Shaaban, T. H. El-Fouly, E. F. El-Saadany, and M. M. Salama, “Optimal resource allocation and charging prices for benefit maximization in smart PEV-parking lots,” IEEE Trans. Sustain. Energy, vol. 8, no. 3, pp. 906–915, Jul. 2017.

[7] M. Shafie-khah et al., “Optimal behavior of electric vehicle parking lots as demand response aggregation agents,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2654–2665, Nov. 2016.

[8] S. Pirouzi, J. Aghaei, T. Niknam, M. Shafie-khah, V. Vahidinasab, and J. P. Catalão, “Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks,” Energy, vol. 141, pp. 635–651, Dec. 2017.

[9] A. Gholami, T. Shekari, F. Aminifar, and M. Shahidehpour, “Microgrid scheduling with uncertainty: The quest for resilience,” IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2849–2858, Nov. 2016.

[10] M. Nick, R. Cherkaoui, and M. Paolone, “Optimal planning of distributed energy storage systems in active distribution networks embedding grid reconfiguration,” IEEE Trans. Power Syst., vol. 33, no. 2, pp. 1577–1590, Mar. 2018.

[11] L. Bai, T. Jiang, F. Li, H. Chen, and X. Li, “Distributed energy storage planning in soft open point based active distribution networks incorpo- rating network reconfiguration and DG reactive power capability,” Appl. Energy, vol. 210, pp. 1082–1091, Jan. 2018.

[12] S. Nikkhah and A. Rabiee, “Voltage stability constrained multi- objective optimisation model for long-term expansion planning of large- scale wind farms,” IET Gen. Transm. Distrib., vol. 12, pp. 548–555, Sep. 2017.

[13] A. S. Awad, T. H. El-Fouly, and M. M. Salama, “Optimal ESS allocation for benefit maximization in distribution networks,” IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1668–1678, Jul. 2017.

[14] V. Vahidinasab, “Optimal distributed energy resources planning in a competitive electricity market: Multiobjective optimization and probabilistic design,” Renew. Energy, vol. 66, pp. 354–363, Jun. 2014.

[15] S. Wen, H. Lan, Q. Fu, C. Y. David, and L. Zhang, “Economic allocation for energy storage system considering wind power dis- tribution,” IEEE Trans. Power Syst., vol. 30, no. 2, pp. 644–652, Mar. 2015.

[16] A. Rabiee, S. Nikkhah, and A. Soroudi, “Information gap decision theory to deal with long-term wind energy planning considering voltage stability,” Energy, vol. 147, pp. 451–463, Mar. 2018.

[17] H. Arasteh, M. S. Sepasian, and V. Vahidinasab, “An aggregated model for coordinated planning and reconfiguration of electric distribution net- works,” Energy, vol. 94, pp. 786–798, Jan. 2016.

[18] A. M. Eldurssi, and R. M. O’Connell, “A fast nondominated sorting guided genetic algorithm for multi-objective power distribution system reconfiguration problem,” IEEE Trans. Power Syst., vol. 30, no. 2, pp. 593– 601, Mar. 2015.

[19] A. R. Malekpour, T. Niknam, A. Pahwa, and A. K. Fard, “Multi- objective stochastic distribution feeder reconfiguration in systems with wind power generators and fuel cells using the point estimate method,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1483–1492, May 2013.

[20] Z. Ghofrani-Jahromi, M. Kazemi, and M. Ehsan, “Distribution switches upgrade for loss reduction and reliability improvement,” IEEE Trans. Power Del., vol. 30, no. 2, pp. 684–692, Apr. 2015.

[21] T. Ding, Y. Lin, Z. Bie, and C. Chen, “A resilient microgrid formation strategy for load restoration considering master-slave distributed genera- tors and topology reconfiguration,” Appl. Energy, vol. 199, pp. 205–216, Aug. 2017.

[22] Y. Lin, and Z. Bie, “Tri-level optimal hardening plan for a resilient dis- tribution system considering reconfiguration and DG islanding,” Appl. Energy, vol. 210, pp. 1266–1279, Jan. 2018.

[23] R. Sharifi, S. Fathi, and V. Vahidinasab, “A review on demand-side tools in electricity market,” Renew. Sustain. Energy Rev., vol. 72, pp. 565–572, May 2017.

[24] H. Arasteh, M. Sepasian, and V. Vahidinasab, P. Siano, “SOS-based mul- tiobjective distribution system expansion planning,” Elect. Power Syst. Res., vol. 141, pp. 392–406, Dec. 2016.

[25] X. Liu, M. Shahidehpour, Z. Li, X. Liu, Y. Cao, and Z. Bie, “Mi- crogrids for enhancing the power grid resilience in extreme condi- tions,” IEEE Trans. Smart Grid, vol. 8, no. 2, pp. 589–597, Mar. 2017.

[26] J. Aghaei, M. I. Alizadeh, P. Siano, and A. Heidari, “Contribution of emergency demand response programs in power system reliability,” En- ergy, vol. 103, pp. 688–696, May 2016.

[27] GAMS Development Corporation, General Algebric Modeling Systems (GAMS). Washington, DC, USA, Available at: http://www.gams.com

[28] S. Nikkhah and A. Rabiee, “A joint energy storage systems and wind farms long-term planning model considering voltage stability,” in Operation, Planning, and Analysis of Energy Storage Systems in Smart Energy Hubs. New York, NY, USA: Springer, 2018, pp. 337–363.

KIANMEHR et al.: RESILIENCE-BASED ARCHITECTURE FOR JOINT DERS ALLOCATION AND HOURLY NETWORK RECONFIGURATION 5455

Ehsan Kianmehr received the M.Sc. degree in electrical engineering from the Islamic Azad Uni- versity, Doroud, Iran, in 2014.

He has been a Lecturer with the Depart- ment of Electrical and Computer Engineering, Islamic Azad University of Doroud Branch, Iran, since 2015. He has a solid background in power systems, optimization, and power system eco- nomics as well as power distribution and trans- mission networks. His research interests mainly focus on power system operation and security,

security constrained unit commitment analysis, energy storage, demand response and electricity markets.

Saman Nikkhah received the B.Sc. degree in electrical engineering from Urmia University, Urmia, Iran, in 2013, and the M.Sc. degree in electrical power engineering from the University of Zanjan, Zanjan, Iran, in 2016. His research in- terests include power system economics, power system planning, power system operation, smart grids, power system resiliency, and security.

Vahid Vahidinasab (M’10–SM’17) received the B.Sc. degree from K. N. Toosi University of Tech- nology, Tehran, Iran, in 2004, and the M.Sc. and Ph.D. degrees from Iran University of Science and Technology, Tehran, Iran, in 2006 and 2010, respectively, all in electrical engineering.

Since 2010, he has been with the Depart- ment of Electrical Engineering, Shahid Beheshti University (SBU), Tehran, Iran. He has also ini- tiated and managed SOHA Smart Energy Sys- tems Laboratory, SBU. He has demonstrated a

consistent track record of attracting external fund and he has managed several industrial projects and closely worked with several large complex national/international projects. Since 2018, he is a Research Associate with Newcastle University and collaborates on the inteGRIDy as an EU Horizon 2020 project.

His research interest is oriented to different research and technology aspects of energy systems integration, smart grids/microgrids/nanogrids design, operation and economics, and application of artificial intelligence and optimization methods in energy system studies (modeling, forecast- ing, and optimization).

Dr. Vahidinasab is a member of the IEEE Power and Energy Society as well as IEEE Smart Grid Society.

Damian Giaouris received the B.Eng. degree in automation engineering from the Technolog- ical Educational Institute of Thessaloniki, Thes- saloniki, Greece, in 2000, the B.Sc. degree and Postgraduate Certificate in mathematics from Open University, Milton Keynes, U.K., in 2009 and 2011, respectively, and the M.Sc. and Ph.D. degrees in the area of control of electrical systems from Newcastle University, Newcastle upon Tyne, U.K., in 2001 and 2004, respectively.

He was a Lecturer in control systems with Newcastle University, since 2004, before moving to the Centre for Re- search and Technology Hellas (Greece), in 2011. Since September 2015, he has been a Senior Lecturer in control of electrical systems with New- castle University. His research interests include control of power con- verters, power systems, smart grids, electric vehicles, and nonlinear dynamics of electrical systems.

Dr. Giaouris has been an Associate Editor for IET Power Electronics and a Guest Associate Editor for the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS. He is currently an Associ- ated Editor for IEEE CAS II.

Philip C. Taylor (SM’12) received the Engineer- ing Doctorate in the field of intelligent demand side management techniques from the Univer- sity of Manchester Institute of Science and Tech- nology (UMIST), Manchester, U.K., in 2001.

He joined Newcastle University, Newcastle upon-Tyne, U.K., in April 2013, where he is the Head of the School of Engineering and holds the Siemens Chair of Energy Systems. He is a Visiting Professor with Nanyang Technological University in Singapore and he previously held

the DONG Energy Chair in Renewable Energy and was a Director of the Durham Energy Institute.