Economics -energy market exam
Rannnn
slides_renewables_updated.ppt
Renewable Energy
Main Types of Renewable Energy
- Dispatchable Renewables
- Hydroelectric Power
- Geothermal
- Variable Energy Resources
- Wind Turbines
- On-shore
- Off-shore
- Solar
- Hot water heaters
- Concentrating solar power (also called, solar thermal)
- Solar Photovoltaic (PV)
Concentrating Solar Power
Solana station 280 MW parabolic trough solar plant, 70 miles SW of Phoenix
Ivanpah, CA Solar Tower Plant (377MW, $2.2 billion)
Solar PV
- A PV cell consists of two or more thin layers of semi-conducting material, most commonly silicon. When the silicon is exposed to light, electrical charges are generated and this can be conducted away by metal contacts as direct current (DC). The electrical output from a single cell is small, so multiple cells are connected together and encapsulated (usually behind glass) to form a module (sometimes referred to as a "panel"). The PV module is the principle building block of a PV system and any number of modules can be connected together to give the desired electrical output.
- Modules are connected to inverters, which convert DC power into AC power.
Crystalline silicon PV panels
Solar PV
- Types
- Crystalline silicon
- Thin film
- Concentrating PV
- Set Up
- Fixed tilt (land vs. rooftop); facing direction
- Single axis tracking
- Double axis tracking
- Scale
- Utility scale > 1 MW
- Distributed generation (rooftops, building sites) 1 kW – 1 MW
Room for solar?
- Consider the following facts
- US electricity consumption = 4 billion MWh/yr
- Solar PV capacity factor (AZ) = 20 %
- Solar PV space requirement = 3 acres/MW
- 640 acres land per square mile
- How much land would be required to supply all US electricity consumption from solar PV?
Growth in Renewables
- I’ll focus on wind and solar –
- Fastest growing renewables in electricity
- Wind turbine capacity growing ~ 20%/yr in US
- Solar PV capacity growing ~ 30%/yr in US
- Other renewables
- Hydro, geo-thermal, bio-fuels
- Why so much growth in wind & solar?
- Falling prices
- Favorable policies – subsidies, tax credits, RPS, …
- Concerns about environmental impacts of fossil fuel use
Solar Generation as % of Total Generation, 2018
Experience Curve (Learning by Doing)
Economic comparisons of electricity generation technologies
- Common metric in energy industry is the levelized cost of energy (LCOE)
- LCOE is equal to the constant price per unit energy that would equate NPV of revenues to NPV of costs; alternatively; a measure of the real average total cost of generation over the lifetime of a generation plant
EIA estimates of LCOE*
* Advanced coal has total system LCOE = 139
Classifying generation technologies
- Dispatchable generators
- Coal, gas combined-cycle, nuclear,…
- Can be controlled by a system operator; can be turned on and off based on economic conditions
- Can provide electricity generation as well as reliability services – such as spinning reserves and frequency regulation
- Intermittent generators
- Wind, solar PV, solar thermal
- Production depends on weather conditions
- Can’t be controlled by system operator (unless coupled with energy storage)
Public (or Social) vs Private Economics of Renewable Energy
- Public
- Focus on overall economic costs and benefits, taking into account things like environmental benefits, time-varying benefits and costs, electricity system reliability, …
Public (or Social) vs Private Economics of Renewable Energy
- Public
- Focus on overall economic costs and benefits, taking into account things like environmental benefits, time-varying benefits and costs, electricity system reliability, …
- Private
- Look at costs and benefits from viewpoint of an individual decision-maker:
- household considering installing solar panels (EXCEL)
- Electric utility considering investing in wind turbines
- These costs and benefits would be evaluated after the effects of subsidies and/or tax breaks
Economic value of intermittent renewable generation
- Timing of generation
- Does generation occur when electricity is valuable?
- Timing of (on-shore) wind, vs solar PV
- Solar PV vs. solar thermal
- Environmental benefits
- What fossil fuel generators are displaced?
- How much of each type of emission is reduced; how do you value emissions reductions?
- Grid integration costs
- At higher penetration of renewables, intermittency leads to more supply variability and could reduce system reliability.
- Does this require more spinning reserves, more backup generation capacity?
Short run value of intermittent renewable generation*
- Notation
- y = electricity load (quantity demanded) per period (random)
- C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)
- EM(x) = emissions associated with producing qty x; ϕ =EM’(x)
- τ = $ damages per ton of emissions
- K = renewable generation capacity
- s = renewable generation output per unit of capacity (random)
* See Baker, et al, “Economics of Solar Electricity” Annual Review of Resource Economics, 2013.
Short run value of intermittent renewable generation
- Notation
- y = electricity load (quantity demanded) per period (random)
- C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)
- EM(x) = emissions associated with producing qty x; ϕ =EM’(x)
- τ = $ damages per ton of emissions
- K = renewable generation capacity
- s = renewable generation output per unit of capacity (random)
- Value of K
- V(K)=E[[C(y) – C(y-sK) +τ EM(y) – τ EM(y-sK)]
Short run value of intermittent renewable generation
- Notation
- y = electricity load (quantity demanded) per period (random)
- C(x) = fossil fuel generation cost of producing qty x; λ = C’(x)
- EM(x) = emissions associated with producing qty x; ϕ =EM’(x)
- τ = $ damages per ton of emissions
- K = renewable generation capacity
- s = renewable generation output per unit of capacity (random)
- Value of K
- V(K)=E[C(y) – C(y-sK) +τ EM(y) – τ EM(y-sK)]
- Marginal value of K
- V’(K)=E[λ]E[s] + Cov[λ,s] + τ E[ϕ]E[s] + τ Cov[ϕ,s]
Marginal Value of Renewable Capacity
- Marginal value depends on:
- E[s] = Capacity factor of renewable
- E[λ] = Average of marginal cost (MC) of displaced fossil fuel gen
- Cov[λ,s] = Covariance of renewable generation and MC
- τ = $ damages per ton of emissions
- E[ϕ] = Average of marginal emission rates
- Cov[ϕ,s] = Covariance of renewable generation and ϕ
- Variation in marginal value:
- Different types of renewable energy – e.g., wind vs. solar – will differ across many of these variables, and so their marginal value may be quite different
- Even for a given type – e.g., solar – marginal value will vary based on local weather conditions and grid conditions
Net Metering for Solar PV
- A typical residential PV array will sometimes yield more energy than the household is using. Net Metering is a policy in which the distributor (eg, utility) buys back or credits the household for energy it puts into the grid.
- 2005 federal law - all utilities are required to offer net metering to their customers.
- ACC recently approved change from 1-for-1 net metering credits, to credits for excess generation based on (lower) avg wholesale elec rate
Grid Parity for renewables?
- Grid parity for a renewable technology is sometimes described as the point at which its LCOE matches that of fossil fuel generation.
- Basic idea is that once grid parity is achieved, that renewable technology wouldn’t need subsidies or special incentives to compete with fossil fuel technologies.
- OK, but here are a few other considerations
- Is it dispatchable; can it be used for reliability svcs?
- Timing of generation and value?
- Magnitude of environmental benefits?
- Are there major grid integration costs?
Jacobson, et al Proceedings of National Academy of Science 2015
The large-scale conversion to 100% wind, water, and solar (WWS) power for all purposes (electricity, transportation, heating/cooling, and industry) is currently inhibited by a fear of grid instability and high cost due to the variability and uncertainty of wind and solar. This paper couples numerical simulation of time- and space-dependent weather with simulation of time-dependent power demand, storage, and demand response to provide low-cost solutions to the grid reliability problem with 100% penetration of WWS across all energy sectors in the continental United States between 2050 and 2055.
Solutions are obtained without higher-cost stationary battery storage by prioritizing storage of heat in soil and water; cold in water and ice; and electricity in phase-change materials, pumped hydro, hydropower, and hydrogen.
PNAS 2017 – An assessment of 100% renewables claim
Previous analyses have found that the most feasible route to a low-carbon energy future is one that adopts a diverse portfolio of technologies. In contrast, Jacobson et al. (2015) consider whether the future primary energy sources for the United States could be narrowed to almost exclusively wind, solar, and hydroelectric power and suggest that this can be done at “low-cost” in a way that supplies all power with a probability of loss of load “that exceeds electric-utility industry standards for reliability”.
We find that their analysis involves errors, inappropriate methods, and implausible assumptions. Their study does not provide credible evidence for rejecting the conclusions of previous analyses that point to the benefits of considering a broad portfolio of energy system options. A policy prescription that overpromises on the benefits of relying on a narrower portfolio of technologies options could be counterproductive, seriously impeding the move to a cost effective decarbonized energy system.
Renewable Subsidies
- There is a vast array of federal, state, local and utility subsidy programs for renewables –
- dsireusa.org is a good info source
- Major federal subsidies
- 30% investment tax credit
- Electricity production tax credit
- State subsidy examples
- Renewable Portfolio Standard (RPS) – eg California, Arizona
- Sales tax exemptions - AZ
- Interest rate subsidies for loans - Texas
- City solar PV subsidies - Texs
Grid integration and reliability challenges for renewables
- Intermittency
- Timing and correlation with electricity demand
- Renewable cost and capacity factor
- Dispatchable vs non-dispatchable generators
- See Paul Joskow, American Econ Rev (2012)
Load and solar output at 4 sites; 3 days in August 2011
California Duck Curve
The duck sinks – negative mid-day wholesale prices in CAISO
Why were generators willing to sell at negative prices??
- The production tax credit.
- Some renewables owners (mainly wind) are eligible for a production tax credit, which essentially pays them for every MWh they produce. So, not producing means foregoing this credit. In theory, producers will pay to sell into the wholesale market as long as they’re paying less than the tax credit.
Why were generators willing to sell at negative prices??
- The Renewable Portfolio Standard.
- Under California’s Renewable Portfolio Standard (RPS), utilities are on the hook to provide 60% of their electricity from renewable sources by 2030 and 100% by 2045. The utilities sign contacts with renewable providers in order to try to meet their RPS targets; utilities pay a penalty if not met.
- So utilities want to encourage the renewable providers to produce. For example, under a very simple power purchase agreement, the utility would pay the renewable provider a pre-specified price per MWh irrespective of the wholesale market price, leaving the renewable provider no incentive to shut down when prices are negative.
Why were generators willing to sell at negative prices??
- Operating constraints.
- For some power plants, varying the output level entails high costs, particularly starting and stopping the plant. I think of those as analogous to the extra fuel, plus wear and tear, planes expend taking off. So, if it costs a lot to restart a nuclear plant or a coal plant, for example, you’re willing to pay not to have to turn it off to avoid incurring those costs.
Renewable Intermittency & Grid Reliability
- Electricity system operators need to balance demand and supply of electricity in real time to maintain reliability
- Managed via operating reserves and back-up generators
- Intermittency of renewables poses risks to reliability at high penetration
- Role for large-scale energy storage
- Absent more storage, system operators must to carry more operating reserves and back-up generators
- Role for improved use of demand-response
Research Approaches
- Structural model #1
- G. Gowrisankaran, S. Reynolds, M. Samano “Intermittency and the value of renewable energy” Jour Political Economy (2016)
- “This paper develops a method to quantify the social costs and reductions in carbon emissions from large-scale renewable energy generation. We estimate social costs by solving for the decisions that maximize total surplus under different levels of renewable energy capacity. Social costs depend crucially on (1) the variability of the source including the extent to which the variability correlates with demand; (2) the extent to which output from the source is forecastable; and (3) the costs of building backup generation required to maintain system reliability.”
“Intermittency and the value of renewable energy”
- Data from TEP, Tucson solar sites, EPA, NOAA, EIA
- Model of optimal elec utility operations –
- includes generator dispatch, operating reserves, investment, demand response, forecasting
- Model parameters either estimated by GRS or drawn from estimates in other studies.
- Results for 10 – 20% solar generation mandates
- Intermittency adds 2 – 3.5 ¢/kWh to solar PV costs
- Solar mandates estimated to be very costly at time of study, based on solar PV cost of $4.40/W; current solar PV costs much lower
- 20% solar mandate is ‘welfare-neutral’ at PV cost of $1.50/W if SCC = $40/ton CO2
*
Today in Energy March 6, 2017
U.S. wind generating capacity surpasses hydro capacity at the end of 2016
Source: U.S. Energy Information Administration, Preliminary Monthly Electric Generator Inventory Note: Data include facilities with a nameplate capacity of one megawatt and above. Installed wind electric generating capacity in the United States surpassed conventional hydroelectric generating capacity, long the nation’s largest source of renewable electricity, after 8,727 megawatts (MW) of new wind capacity came online in 2016. However, given the hydro fleet’s higher average capacity factors and the above-normal precipitation on the West Coast so far this year, hydro generation will likely once again exceed wind generation in 2017.
Source: U.S. Energy Information Administration, Electricity Data Browser Note: Data include facilities with a nameplate capacity of one megawatt and above. Wind and hydro generation both follow strong seasonal patterns. Hydro generation typically reaches its seasonal peak in the spring and early summer, especially in the Pacific Northwest and California where about half of U.S. hydropower is produced. Across most of the
Today in Energy
March 6, 2017
U.S. wind generating capacity surpasses hydro capacity at the end of 2016
Source: U.S. Energy Information Administration, Preliminary Monthly Electric Generator Inventory
Note: Data include facilities with a nameplate capacity of one megawatt and above.
Installed wind electric generating capacity in the United States surpassed conventional hydroelectric generating capacity, long the
nation’s largest source of renewable electricity, after 8,727 megawatts (MW) of new wind capacity came online in 2016. However, given
the hydro fleet’s higher average capacity factors and the above-normal precipitation on the West Coast so far this year, hydro
generation will likely once again exceed wind generation in 2017.
Source: U.S. Energy Information Administration, Electricity Data Browser
Note: Data include facilities with a nameplate capacity of one megawatt and above.
Wind and hydro generation both follow strong seasonal patterns. Hydro generation typically reaches its seasonal peak in the spring and
early summer, especially in the Pacific Northwest and California where about half of U.S. hydropower is produced. Across most of the
NREL | 26NREL | 26
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4.7% 4.2%
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Solar Generation as a Percentage of Total Generation, 2018
• The role of utility versus distributed solar varies by state, with northeastern states and Hawaii relying more on DPV.
Note: EIA monthly data for 2018 are not final. Additionally, smaller utilities report information to EIA on a yearly basis, and therefore, a certain amount of solar data has not yet been reported. “Net Generation” includes DPV generation. Net generation does not take into account imports and exports to and from each state and therefore the percentage of solar consumed in each state may vary from its percentage of net generation. Source: U.S. Energy Information Administration, “Electricity Data Browser.” Accessed April 3, 2019.
NREL | 62NREL | 62
PV Experience Curve • This experience curve displays the relationship, in logarithmic form, between the average selling price of a PV module and the cumulative global shipments of PV modules. As shown, for every doubling of cumulative PV shipments, there is on average a corresponding ~22% reduction in PV module price. – In 2010, the experience rate was 20%
• Since 2012, module ASP has been below the historical experience curve.
• Analysts project that by 2022 ASP will be approximately $0.2/W and globally we will have shipped a terawatt.
Source: 1976-2018: Paula Mints. "Photovoltaic Manufacturer Capacity, Shipments, Price & Revenues 2018/2019." SPV Market Research. Report SPV-Supply6. April 2019.
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U.S. Energy Information Administration | AEO2016 Levelized Costs 6
Table 1a. Estimated LCOE (weighted average of regional values based on projected capacity additions) for
new generation resources, plants entering service in 2022
Plant Type
Capacity
Factor
(%)
U.S. Capacity-Weighted1 Average LCOE (2015 $/MWh) for Plants Entering Service in 2022
Levelized
Capital
Cost
Fixed
O&M
Variable
O&M
(including
fuel)
Transmission
Investment
Total
System
LCOE
Levelized
Tax Credit
Total LCOE
including
Tax Credit2
Dispatchable Technologies
Advanced Coal with CCS3 N/B Natural Gas-fired
Conventional Combined Cycle 87 12.8 1.4 41.2 1.0 56.4 N/A 56.4 Advanced Combined Cycle 87 15.4 1.3 38.1 1.1 55.8 N/A 55.8 Advanced CC with CCS N/B Conventional Combustion Turbine
30 37.1 6.5 58.9 2.9 105.4 N/A 105.4
Advanced Combustion Turbine 30 25.9 2.5 61.9 3.3 93.6 N/A 93.6
Advanced Nuclear 90 75.0 12.4 11.3 1.0 99.7 N/A 99.7
Geothermal 91 27.8 13.1 0.0 1.4 42.3 -2.8 39.5
Biomass N/B
Non-Dispatchable Technologies Wind 42 43.3 12.5 0.0 2.7 58.5 -7.6 50.9 Wind – Offshore N/B
Solar PV4 26 61.2 9.5 0.0 3.5 74.2 -15.9 58.2
Solar Thermal N/B
Hydroelectric5 60 54.1 3.1 5.0 1.5 63.7 N/A 63.7 1The capacity-weighted average is the average levelized cost per technology, weighted by the new capacity coming online in each region. The capacity additions for each region were based on additions in 2018 -2022. Technologies for which capacity additions are not expected do not have a capacity-weighted average, and are marked as “N/B.” 2The tax credit component is based on targeted federal tax credits such as the production or investment tax credit available for some technologies. It only reflects tax credits available for plants entering service in 2022. EIA models renewable tax credits as follows: new solar thermal and PV plants are eligible to receive a 30% investment tax credit on capital expenditures if under construction before the end of 2019, and then tax credits taper off to 26% in 2020, 22% in 2021, and 10% thereafter. New wind, geothermal, and biomass plants receive a $23.0/MWh ($12.0/MWh for technologies other than wind, geothermal and closed-loop biomass) inflation-adjusted production tax credit over the plant’s first ten years of service if they are under construction before the end of 2016, with the tax credit for wind declining by 20% in 2017, 40% in 2018, 60% in 2019, and expiring completely in 2020. Up to 6 GW of new nuclear plants are eligible to receive an $18/MWh production tax credit if in service by 2020. Not all technologies have tax credits, and are indicated as “N/A.” The results are based on a regional model and state or local incentives are not included in LCOE calculations. 3Due to new regulations (CAA 111b), conventional coal plants cannot be built without CCS because they are required to meet specific CO2 emission standards. The coal with CCS technology modeled is assumed to remove 30% of the plant’s CO2 emissions. Coal plants have a 3 percentage-point adder to their cost-of-capital. 4Costs are expressed in terms of net AC power available to the grid for the installed capacity. 5As modeled, hydroelectric is assumed to have seasonal storage so that it can be dispatched within a season, but overall operation is limited by resources available by site and season. Source: U.S. Energy Information Administration, Annual Energy Outlook 2016, April 2016, DOE/EIA-0383(2016).
U.S. Energy Information Administration | AEO2016 Levelized Costs 6
Table 1a. Estimated LCOE (weighted average of regional values based on projected capacity additions) for
new generation resources, plants entering service in 2022
Plant Type
Capacity
Factor
(%)
U.S. Capacity-Weighted
1
Average LCOE (2015 $/MWh) for Plants Entering Service in 2022
Levelized
Capital
Cost
Fixed
O&M
Variable
O&M
(including
fuel)
Transmission
Investment
Total
System
LCOE
Levelized
Tax Credit
Total LCOE
including
Tax Credit
2
Dispatchable Technologies
Advanced Coal with CCS
3
N/B
Natural Gas-fired
Conventional Combined Cycle 87 12.8 1.4 41.2 1.0 56.4 N/A 56.4
Advanced Combined Cycle 87 15.4 1.3 38.1 1.1 55.8 N/A 55.8
Advanced CC with CCS N/B
Conventional Combustion
Turbine
30 37.1 6.5 58.9 2.9 105.4 N/A 105.4
Advanced Combustion Turbine 30 25.9 2.5 61.9 3.3 93.6 N/A 93.6
Advanced Nuclear
90 75.0 12.4 11.3 1.0 99.7 N/A 99.7
Geothermal
91 27.8 13.1 0.0 1.4 42.3 -2.8 39.5
Biomass
N/B
Non-Dispatchable Technologies
Wind
42 43.3 12.5 0.0 2.7 58.5 -7.6 50.9
Wind – Offshore
N/B
Solar PV
4
26 61.2 9.5 0.0 3.5 74.2 -15.9 58.2
Solar Thermal
N/B
Hydroelectric
5
60 54.1 3.1 5.0 1.5 63.7 N/A 63.7
1
The capacity-weighted average is the average levelized cost per technology, weighted by the new capacity coming online in each region. The
capacity additions for each region were based on additions in 2018 -2022. Technologies for which capacity additions are not expected do not have
a capacity-weighted average, and are marked as “N/B.”
2
The tax credit component is based on targeted federal tax credits such as the production or investment tax credit available for some technologies.
It only reflects tax credits available for plants entering service in 2022. EIA models renewable tax credits as follows: new solar thermal and PV
plants are eligible to receive a 30% investment tax credit on capital expenditures if under construction before the end of 2019, and then tax credits
taper off to 26% in 2020, 22% in 2021, and 10% thereafter. New wind, geothermal, and biomass plants receive a $23.0/MWh ($12.0/MWh for
technologies other than wind, geothermal and closed-loop biomass) inflation-adjusted production tax credit over the plant’s first ten years of
service if they are under construction before the end of 2016, with the tax credit for wind declining by 20% in 2017, 40% in 2018, 60% in 2019, and
expiring completely in 2020. Up to 6 GW of new nuclear plants are eligible to receive an $18/MWh production tax credit if in service by 2020. Not
all technologies have tax credits, and are indicated as “N/A.” The results are based on a regional model and state or local incentives are not
included in LCOE calculations.
3
Due to new regulations (CAA 111b), conventional coal plants cannot be built without CCS because they are required to meet specific CO
2
emission
standards. The coal with CCS technology modeled is assumed to remove 30% of the plant’s CO2 emissions. Coal plants have a 3 percentage-point
adder to their cost-of-capital.
4
Costs are expressed in terms of net AC power available to the grid for the installed capacity.
5
As modeled, hydroelectric is assumed to have seasonal storage so that it can be dispatched within a season, but overall operation is limited by
resources available by site and season.
Source: U.S. Energy Information Administration, Annual Energy Outlook 2016, April 2016, DOE/EIA-0383(2016).
In addition, Arizona’s Renewable Portfolio Standard mandates that 30% of total renew-
able energy consist of distributed generation, e.g. solar PV on customers’ rooftops. Our
model allows distributed solar to have di↵erent capital costs from non-distributed solar and
to reduce electricity transmission costs.
Figure 2: Load and solar output for di↵erent U.S. sites, Aug. 14-16, 2011
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Legend Berthoud, CO Rated 9.88 kW
New York, NY Rated 5.7 kW
San Diego, CA Rated 5.7 kW
Tucson, AZ Rated 9.2 kW (One Site Used in Analysis)
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Xcel Energy Inc. (Berthoud, CO)
New York ISO (New York, NY)
San Diego Gas & Electric (San Diego, CA)
Tucson Electric Power (Tucson, AZ)
Note: The Berthoud, New York, and San Diego solar generation data are from SMA Solar Technology AG’s Sunny Portal (https://www.sunnyportal.com/). The Tucson data displayed here are from one of 58 sites used in our main analysis and are from the University of Arizona Photovoltaics Lab. The site shown here was chosen because it is the near the center of the city. The load data are from Federal Energy Regulation Commission Form 714. Load is measured hourly while solar output is measured at the 15-minute level.
To illustrate the issues of intermittency, Figure 2 shows load and solar PV output for
four sites across the U.S., for three summer days during our sample period, Aug. 14-16, 2011.
We chose three sites from the Western U.S. with high solar potential (Figure 1), as well as
New York. During these three days, the solar installation in New York produces far below
its rated capacity at all times. The other installations all reach a peak output of 75% of
9
The first ramp of 8,000 MW in the upward direction (duck’s tail) occurs in the morning starting around 4:00 a.m. as people get up and go about their daily routine. The second, in the downward direction, occurs after the sun comes up around 7:00 a.m. when on-line conventional generation is replaced by supply from solar generation resources (producing the belly of the duck). As the sun sets starting around 4:00 p.m., and solar generation ends, the ISO must dispatch resources that can meet the third and most significant daily ramp (the arch of the duck’s neck). Immediately following this steep 11,000 MW ramp up, as demand on the system deceases into the evening hours, the ISO must reduce or shut down that generation to meet the final downward ramp.
Flexible resources needed To ensure reliability under changing grid conditions, the ISO needs resources with ramping flexibility and the ability to start and stop multiple times per day. To ensure supply and demand match at all times, controllable resources will need the flexibility to change output levels and start and stop as dictated by real-time grid conditions. Grid ramping conditions will vary through the year. The net load curve or duck chart in Figure 2 illustrates the steepening ramps expected during the spring. The duck chart shows the system requirement to supply an additional 13,000 MW, all within approximately three hours, to replace the electricity lost by solar power as the sun sets.
Oversupply mitigation Oversupply is when all anticipated generation, including renewables, exceeds the real-time demand. The potential for this increases as more renewable energy is added to the grid but demand for electricity does not increase. This is a concern because if the market cannot automatically manage oversupply it can lead to overgeneration, which requires manual intervention of the market to maintain reliability. During oversupply times, wholesale prices can be very low and even go negative in which generators have to pay utilities to take the energy. But the market often remedies the oversupply situation and automatically works to restore the balance between supply and demand. In almost all cases, oversupply is a manageable condition but it is not a sustainable condition over time — and this drives the need for proactive policies and actions to avoid the situation. The duck curve in Figure 2 shows that oversupply is expected to occur during the middle of the day as well.
Because the ISO must continuously balance supply and demand, steps must be taken to mitigate
Figure 2: The duck curve shows steep ramping needs and overgeneration risk
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