INTEGRATING WIND ENERGY WITH ELECTRIC SYSTEMS ERIC HIRST Consultant in Electric-Industry Restructuring Bellingham, WA December 2003

2 PURPOSE OF PRESENTATION Provide background and structure for workshop –Long-term issues Transmission planning and pricing Resource adequacy (capacity credit) –Operations (and spot markets) Day-ahead unit commitment Real-time dispatch Intrahour balancing –Load following –Regulation Summarize key findings from three projects –MN wind farm with PJM system –PNW wind farms with BPA system –Simulations of large wind farms with SPS system

3 SCHEMATIC OF WIND COSTS AND REVENUES

4 WIND HAS UNUSUAL ENERGY AND LOCATIONAL CHARACTERISTICS ENERGY –Limited control –Relatively unpredictable –Variable Affect balancing costs and ability to sell energy in forward markets TRANSMISSION –Access (recovery of fixed costs) –Congestion –Losses Remote locations increase transmission costs for low-capacity-factor wind

5 ELECTRICAL SYSTEMS HAS UNIQUE CHARACTERISTIC Because electricity cannot be economically stored, generation and load must be balanced in real time Balancing requirement (NERC’s CPS 1 and 2) interacts with unique characteristics of wind

6 LONG-TERM PLANNING

7 TRANSMISSION CAN BE EXPENSIVE FOR LOW-CAPACITY-FACTOR WIND Wind pays $4 to $9/MWh for firm transmission, with 33% capacity factor

8 OTHER TRANSMISSION CHARGES Losses –Higher if wind is remotely located and/or connected to system on low-voltage lines –Average vs. marginal losses Congestion –Locational marginal prices OR –Transmission loading relief

9 SHOULD WIND RECEIVE CAPACITY CREDIT? Threshold issue: Can energy markets alone provide sufficient capacity –Critical role of dynamic pricing Must qualifying capacity be –Dispatchable: (probably not, e.g., nuclear power) –Have high availability factor –Would higher accuracy of day-ahead wind forecast models increase amount of qualifying capacity Traditional capacity-expansion models value wind based on LOLP at about capacity factor (e.g., 35%) ?

10 SHORT-TERM OPERATIONS

11 SEVERAL COSTS AND REVENUES FROM DAY-AHEAD TO REAL-TIME

12 ENERGY PRICES CAN BE VOLATILE

13 SPOT PRICES AFFECT WIND PAYMENTS Day-ahead prices are much less volatile than real-time prices On average (e.g., monthly), day-ahead and real-time prices should converge Hourly regulation prices also quite volatile Accurate forecasts of wind output reduce volatility (risks) of energy payments

14 LAKE BENTON WIND FARM IN SW MINNESOTA IS TYPICAL 138 turbines, capacity = MW 35% capacity factor from 7/2000 through 6/2001 Monthly average output varied widely –18 MW in 7/2000 –48 MW in 4/2001 Hourly output completely uncorrelated with either NSP (Xcel Energy) or PJM load

15 LB MONTHLY OUTPUT VARIABLE

16 DAILY OUTPUT MORE VARIABLE

17 HOURLY OUTPUT MUCH MORE VARIABLE

18 PJM SUMMER LOAD AND PRICE HIGHLY CORRELATED, r = 0.88 Sun

19 PJM SYSTEM LOAD STABLE FROM DAY TO DAY 5 weekdays in August 2000

20 WIND OUTPUT VARIES FROM DAY TO DAY Same 5 weekdays in August 2000

21 GEOGRAPHICAL DISPERSION REDUCES WIND VARIABILITY AND FORECAST ERROR Five sites, ~200 miles end to end 30% lower

22 DA AND RT HOURLY PAYMENTS DECLINE WITH WIND CAPACITY

23 REGULATION COSTS $0.1 - $3/MWh, LOAD FOLLOWING ~ZERO

24 LESSONS LEARNED FROM 3 STUDIES Feasible and practical to calculate costs and revenues for wind farm Advocacy positions incorrect: –Because wind is small, it imposes no costs on system –Because wind is volatile, system must offset every MW movement Scheduling wind ahead of time increases revenues Analyze wind as part of total system –Incorrect to analyze wind in isolation Results depend strongly on hourly prices, perhaps more than on wind output Market design matters - avoid arbitrary penalties Much more work must be done to quantify and monetize the situation