Optimizing the Generation Capacity Expansion. Cost in the German Electricity Market

Transcription

1 Optimizing the Generation Capacity Expansion Cost in the German Electricity Market Hamid Aghaie Research Scientist, AIT Austrian Institute of Technology Novemebr

2 Motivation Energy-only Market Generators receive revenue for their electricity sale in the market They are paid just for MWh generation, and not for MW capacity Resource Adequacy Problem Insufficient investment in new non-renewable generation capacity Generators have difficulty to recover their investment cost 3 main reasons 1. Political or regulatory price interventions Bid cap or price cap suppresses scarcity (high) prices 2. Increasing investment risks Uncertainty in future market regulation and design 2

3 Price ( /MWh) Renewables Price ( /MWh) Renewables Solar + Wind Motivation 3. Integration of large share of variable renewables lead to: 3.1. Lower prices (Fig.1) 3.2. Less utilization of conventional generators (Fig. 2) 3.3. More need for back up capacity to mitigate volatility of generation profile (Fig. 3 & 4) Demand Demand Peak Demand Oil Oil Nuclear Coal Gas Nuclear Coal Gas Quantity (MW) Quantity (MW) Fig. 1. Merit order effect of renewables 3

4 Capacity (MW) Motivation 3.2. Less utilization of conventional generators Load Residual Load Dispatch of gas PPs Dispatch of coal PPs Reduced dispatch of nuclear PPs Reduced dispatch of lignite PPs Reduced dispatch of coal PPs Reduced dispatch of gas PPs Dispatch of lignite PPs 0 Dispatch of nuclear PPs Dispatch time (hours) Fig. 2. Reduced dispatch of conventional generation due to variable RES Definition: Residual load= load - RES generation 4

5 Motivation 3.3. More need for back up capacity to mitigate volatility of generation profile Generation volatility 10 Probability (%) 5 mean Hourly variation of renewable generation (MWh) 25 Probability (% ) mean 0 0% 17% 50% 100% 150% 200% 250% Hourly varation ratio of renewable generation(% of hourly generation) Fig. 3. Volatility of variable RES generation in one hour in Germany in 2012 (share of variable RES is 20%) 5

6 Motivation 3.3. More need for back up capacity to mitigate volatility of generation profile Generation forecast error Probability (%) Probability (%) Renewable Generation Forecast Error (MWh) % -10% -5% 0 5% 10% 15% Renewable Generation Forecast Error (% of hourly load) Fig. 4. Day-ahead forecast error of variable RES generation in Germany in 2012 (share of variable RES is 20%) 6

7 Motivation Research Question: What is the economically optimal condition to ensure longterm generation resource adequacy in the German energyonly market? Economically optimal reserve margin? Policy and economic implications? Required reserve margins to meet standard resource adequacy targets based on LOLP, LOLH and EUE? Are they consistent with the economically optimal reserve margin? 7

8 Model Generation expansion model Probabilistic Model Uncertainty from variable RES, DR, Resource inadequacy events are infrequent Approach Statistical analysis of price, Modeling generation and load uncertainty Proposed stochastic dynamic optimization framework Optimal new generation capacity in risk-neutral and risk-averse decision making Applicable to any energy-only market 8

9 Model Case Study: German electricity market analyzed for 30-years period from 2012 to 2042 New investment in renewables (RES) and gas-fired plants (CCGT) Variable RES: exogenous input to model, share of variable RES in generation profile rises from 20% in 2012 to 50% in 2042 CCGT: less capital-intensive and fast ramp-up Fig. 5. Typical supply curve 9

10 Model Simulation: Fig. 6. Simulation algorithm flowchart 10

11 Model Fig. 7. Simulation flowchart for one year 11

12 Model Contributions: Probabilistic model Capacity credit of variable RES LOLP is calculated by estimating the PDF of supply and demand Stochastic optimization framework to estimate optimal investment Conduct both reliability and economic analysis Limitations: Cross-border electricity trade 12

13 Model Generation Uncertainty Capacity credit of variable RES Generation by variable RES during peak load Peak load in Germany occurs in cold winter evenings Capacity credit of PV is almost zero Fig. 8. Capacity credit ratio of variable RES versus RES penetration 13

14 Model Forced outage of conventional generators Load Uncertainty Demand growth rate r n ~ Triangular Dist. (min: 0%, max: 2%, mode: r n 1 ) Load forecast error ~ N m D, σ f Weather-related load uncertainty ~N 0, σ w Monte Carlo approach 14

15 Model Market Clearing: Generation and load time series Probability of k-th realization of generation Probability of j-th realization of load Market Price Demand Supply 15

16 Model Optimal Investment Social welfare optimization problem Probability for each generation and load realization Constraints: Utility Function Cost Function KKT Conditions: 16

17 Model KKT Conditions result in: E P P c r = c r + P: Price c r : varaible cost of generation type r, f r Prob (P c r ) f r : fixed cost of generation type r Equilibrium is characterized by the assumption that in a free-entry and freeexit market, the expected profitability of new investment is zero. Price Duration Curve C : Price Cap Contribution Margin price ( /MWh) C 0 0 Hours

18 Model Resource Adequacy (reliability) metrics: LOLP (Loss of Load Probability) LOLE (Loss of Load Expectation) LOLH (Loss of Load Hours) EUE (Expected Unserved Energy) Standard reliability targets: LOLE = 0.1 event/year (PJM, MISO, NYISO, ) LOLH = 2.4 hours/year (France, Netherlands, ) EUE = 0.001% (Australian NEM, ) EUE is more robust metric which considers the size of the market 18

19 Model Reserve Margin Calculation: Reserve margin (RM) is calculated by considering the effective capacity (ECAP) of each generator RM = (ECAP peak load) peak load ECAP is equal to the capacity credit of each generation technology ECAP for thermal generators is between 85% to 100% of their installed capacity (by considering the forced outage) ECAP for Renewables is much lower (around 4% in average) 19

20 Reserve margin to meet 0.1 LOLE target: 9.2% of peak load Lost load: 468 MWh in 1.5 hours Results 20

21 Reserve margin to meet 2.4 LOLH target: 7.8% of peak load Lost load: 2440 MWh Results 21

22 Results Reserve margin to meet 0.001% EUE target: 7% of peak load Lost load: 4681 MWh in 5 hours (Total annual load in Germany is 470 TWh) 22

23 Results Economically optimal reserve margin: 6.5% of peak load The total generation cost is minimum at this point Fig Total generation expansion costs versus reserve margin 23

24 Results Total Generation Cost 24

25 Results Sensitivity Analysis of Economically optimal reserve margin CONE: (Base case: 60 /KW.yr, high: 90 /KW.yr, low 30 /KW.yr) VOLL: (Base case: 8.5 /KWh, high: 12 /KWh, low: 6 /KWh) Fig. 19. Total costs with varying VOLL and CONE 25

26 Conclusion and Policy Implications Capacity credit of variable RES in Germany is very low (4% in 2012 and 2.3% in 2042) Additional investment in thermal capacity is required to offset low capacity credit of RES -> huge cost for electricity system Risk-neutral economically optimal reserve margin: 6.5% of peak load Risk-averse reserve margin: Increasing the reserve margin from 6.5% to 8% results in 26 Million EUR per year (7% of annual generation expansion cost) = Cost of implementing a capacity mechanism 26

27 Risk-averse Investment in Energy-only Markets Thank You! 27