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Wind Integration: Regulation, Imbalance, and Reliability

Michael Milligan Consultant National Renewable Energy Laboratory WAPA/LAP Technical Information Meeting for Regulation and Frequency Response Service March 18, 2004. Wind Integration: Regulation, Imbalance, and Reliability. Brief Outline. Time Scales Regulation

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Wind Integration: Regulation, Imbalance, and Reliability

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  1. Michael Milligan Consultant National Renewable Energy Laboratory WAPA/LAP Technical Information Meeting for Regulation and Frequency Response Service March 18, 2004 Wind Integration: Regulation, Imbalance, and Reliability

  2. Brief Outline • Time Scales • Regulation • Characteristics of good tariff • Load-following/Imbalance • Characteristics of good tariff • Capacity Value and Reliability • Summary of Operations Studies

  3. Time-frames: Power System Operations System Load 0 6 12 18 Days Time (Hour of the Day) Cycles Transient stability & short-circuit Seconds to minutes Regulation Minutes to hours Load Following Daily scheduling/unit commitment

  4. Characteristics of a Good Regulation Tariff • Recognize the actual, measured physical impact on system regulation requirements • Independent of business structure of generators • Recognize the statistical nature of regulation • Recognize the obligation to balance the system (CPS-1 and CPS-2), not individuals • Distinguish between entities • With small or large regulation impacts • Consumer or supplier of regulation • Impacts may differ thru time

  5. Characteristics of a Good Regulation Tariff • Must recognize physical system aggregation • Must track actual cost to control area (cost-causality) • Should not over-collect or subsidize • For a constrained system it is even more important to correctly measure the regulation impacts • The method should be testable for all these properties • The ORNL vector allocation meets all of these tests • Verified in before/after examination by BPA

  6. Imbalance Impacts • Load-following time scale • Should be measured in terms of the physical impact on system imbalance • Independent of business structure of generators • Why don’t we run another resource to compensate for wind’s deviations from forecast? • Because of the statistical independence of wind forecast errors and load forecast errors • System must be balanced, individuals do not

  7. Example

  8. Case: Wind forecast error makes imbalance significantly worse than no-wind case

  9. Case: Wind forecast error makes imbalance somewhat worse than no-wind case

  10. Case: Wind forecast error makes imbalance somewhat better than no-wind case

  11. Wind forecast errors could conceivably improve system imbalance to zero (unlikely but possible)

  12. Load-Following/Imbalance Tariffs • Does method differentiate between previous cases? • Wind plants that have no aggregate impact on system imbalance • Wind plants that have moderate impact (positive or negative) on system imbalance • Wind plants that have significant impact (positive or negative) on system imbalance • Given the stochastic nature of imbalances, all of the above are likely to occur during parts of the year – does the method account for this?

  13. Characteristics of a Good Imbalance Tariff • Recognize the actual, measured physical impact on system imbalance requirements • Independent of business structure • Recognize the statistical nature of imbalance • Recognize the obligation to balance the system (CPS-1 and CPS-2), not individuals • Distinguish between entities • With small or large imbalance impacts • Consumer or supplier of imbalances • Impacts may differ thru time

  14. Characteristics of a Good Imbalance Tariff • Must recognize physical system aggregation • Must track actual cost to control area (cost-causality) • Should not over-collect or subsidize • For a constrained system it is even more important to correctly measure the imbalance impacts • The method should be testable for all these properties

  15. Imbalance/Load Following • Load following examples • Impact of geographically disperse wind • System studies that evaluate imbalance, regulation, and some other system impacts

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

  17. One-Hour Delta Hourly Output

  18. Iowa One-Hour Delta Hourly Output

  19. Key Results: Geographically Disperse Wind Development • Minnesota study examined system reliability only • Best LOLP/EUE was achieved with geographically disperse development • Iowa study examined economic benefit and reliability in separate optimizations • Best LOLP/EUE was achieved with geographically disperse development • Best economic benefit was achieved with geographically disperse development

  20. 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

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

  22. Variability of Load Following With/Without Wind

  23. Largest Single-hour Difference at 800 MW Penetration

  24. Largest Single-hour Difference at 1600 MW Penetration

  25. Load Following Allocated to Wind Difference due to geographic dispersion

  26. Imbalance Impact of Wind Increases with Penetration

  27. 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 and good load forecasting (lesser impacts for worse load forecasting) • Results will depend on wind regime, loads, and would be expected to differ in other situations

  28. Reliability Studies • Combined geographic benefits with reliability optimization (based on EUE/LOLP analysis) • Capacity value of wind = increase in load that can be supported holding EUE/LOLP constant

  29. 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

  30. Genetic Algorithm and Fuzzy Search Results: Economic Sites

  31. Tradeoff: Reliability vs. Economics

  32. Generic 100 MW Plant

  33. Capacity Credit Relative to Gas Reference Unit CA RPS Integration Study Results

  34. Example Integration Studies • Operational impact studies results • All use similar methods for evaluating regulation and load-following impacts • Load and wind treated stochastically • System is balanced

  35. Integration Study Results (UWIG)

  36. 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 • Recommend WAPA become engaged with UWIG

  37. Proposed Next Steps • Joint study of integration impacts of wind on the WAPA/LAP system • Utilize actual wind power output data and load data from WAPA’s system • DOE/NREL/ORNL analytic support • Other stakeholders

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