Wind Integration: What Have We Learned

1. Wind Integration: What Have We Learned? Michael Milligan Consultant National Renewable Energy Laboratory Planning for Wind Workshop NW Power & Conservation Council Portland, OR Dec 5, 2003

2. Utility Wind Interest Group Interest in UWIG has surged as more utilities have evaluated/adopted wind �Clearing house� for operational issues, solutions, etc. www.uwig.org

4. Brief Outline Planning horizon Geographic benefit/reliability Operational horizons Load following Reserve allocation based on reliability modeling Overview of CA Renewable Portfolio Standard Integration Study Capacity value Regulation Load following Summary of Operations Studies

5. Geographically Disperse Wind Development Two projects: Joint project with Minnesota Department of Public Service (Commerce) Joint project with Iowa Wind Energy Institute

8. Modeling Methods Minnesota: Dynamic fuzzy search to maximize system reliability Iowa: Dynamic fuzzy search to maximize two separate objective functions Economic benefit System reliability Corroboration of the economic benefit results with a genetic algorithm

9. Genetic Algorithm and Fuzzy Search Results: Economic Sites

10. Tradeoff: Reliability vs. Economics

11. Iowa Load Following Study 8 wind scenarios Wind capacity 800 MW 1,600 MW (22.7% of peak load) Scenario 1 1,300 MW at one site All other scenarios Geographic spread based on optimal locations

12. How Does Wind Affect 1-Hour Load Following?

13. Variability of Load Following With/Without Wind

14. Largest Single-hour Difference at 800 MW Penetration

15. Largest Single-hour Difference at 1600 MW Penetration

16. Load Following Allocated to Wind

17. Imbalance Impact of Wind Increases with Penetration

18. Iowa Load Following Conclusions Geographically disperse wind causes an increase in the standard deviation of load following requirements of about 2.5% of rated capacity at 22.7% penetration rate with a backward-looking analysis Geographically disperse wind causes an increase in the standard deviation of imbalances of about 4% of rated capacity with a simple wind forecast at 22.7% penetration rate Results will depend on wind regime, loads, and would be expected to differ in other situations

19. Reliability-based Reserve Allocation Examine how much of the fraction of operating reserve that should fall on a wind power plant Method should be based on reliability theory and practice, and take probability of various system failures into account Should provide market signals that encourage reliability and accurate wind forecasts Strbac/Kirschen (Electricity Journal, October 2000) model fulfills these goals, except doesn�t consider wind � Milligan (AWEA/EWEA 2001) adapts to wind I use 1-hour wind forecast errors as �outage� rates for system reliability calculations

21. Effect of Geographic Diversity

22. Implications Worse-case scenario analyzed shows the reserve allocation at about 5.5% of rated capacity of the wind plant Average is less than 1% of wind capacity Improvements in forecast will reduce wind�s risk Wind does contribute to EUE (risk) �but at a very low rate relative to rated capacity Geographic dispersion reduces composite forecast error and reserve allocation

23. California RPS Integration Study Project Team Primary investigators in Methods Group: David Hawkins, California ISO Brendan Kirby, ORNL Yuri Makarov, California ISO Michael Milligan, NREL California Wind Energy Collaborative Kevin Jackson Henry Shiu

25. Identify significant characteristics of California�s load and installed renewable and conventional generators. Define and implement methodologies for evaluating the capacity credit for renewables. Provide a comparison of the capacity credit between various renewable and conventional generators. Define and implement methodologies for evaluating integration costs. Provide a comparison of the magnitude of load following and regulation services for various renewable and conventional technologies. The final report documenting the one year analysis results of existing generation resources has been released for public comment.

26. Data Processing OASIS: Open Access Same-Time Information System CAISO Power Information (PI) system Error removal Data storage error Results from PI system data compression The standard deviation of data storage error is 160 MW or �0.6% of the average annual load.

27. CC12CC12

28. Regulation Cost Results Used ORNL method, CA regulation prices A negative price means there is a cost imposed on the system. A positive price means there is a benefit provided to the system. The baseline for comparison is a generator with constant output and a regulation price of zero. Caution: regulation is a capacity service; cost in $/MWh as a convenience

29. Load Following Analysis Deviations between the scheduled generation and the actual load requirements are compensated through purchases from the CAISO supplemental energy market. The system operator must compensate for aggregate scheduling error, individual errors must be viewed in the context of the full system. Market participants provide CAISO with bids for the hour ahead energy market and create the �stack� of available generators. The purpose of the load following analysis was to determine if the renewable generators affected the size or composition of the �stack� and therefore changed the cost for the load following service.

30. Scheduled Hour Ahead Load

32. California Preliminary Conclusions Capacity credit for wind is low but non-zero Phase II will examine higher penetration, newer technology, and different locations Regulation impact of wind is small Because of data storage error these results are not precise, but the regulation cost adders should be used until more accurate results can be obtained in Phase II Will examine this issue in Phase II Load following impact has negligible impact on supplemental energy stack at this penetration No cost adders for wind can be justified at this time

33. Other Results Penetration calculated as (wind rated capacity) / (system peak)Penetration calculated as (wind rated capacity) / (system peak)