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Integrating Wind into the Transmission Grid

Integrating Wind into the Transmission Grid. Michael C Brower, PhD AWS Truewind LLC Albany, New York mbrower@awstruewind.com. Providing integrated consulting services to the wind industry Responsible for the Irish Wind Atlas (with ESBI, initiated by SEI)

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Integrating Wind into the Transmission Grid

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  1. Integrating Wind into the Transmission Grid Michael C Brower, PhD AWS Truewind LLC Albany, New York mbrower@awstruewind.com

  2. Providing integrated consulting services to the wind industry Responsible for the Irish Wind Atlas (with ESBI, initiated by SEI) Forecasting for 2000+ MW of wind plant in US and Europe Conducted wind integration studies in US About AWS Truewind

  3. Regulation: seconds to minutes Load following: minutes to hours Unit commitment: hours to days Reliability: months to years Time Scales – Electric Power

  4. Time Scales – Wind

  5. +33% +10% Wind and Wind Plant VariabilityNot the Same 53 MW Capacity

  6. Propagation of Gusts Through a Wind Farm

  7. Mean Change in Power vs Number of Turbines at Flat Rock

  8. Spatial Diversity of Turbine Output Correlation coefficient of power change for different average times over the distance From Ernst et al, 1999

  9. Typical 4-Hr PIRP Forecast Performance San Gorgonio Pass, California - May 2003 Wind Forecasting

  10. Forecast Accuracy Vs Time

  11. Forecast Accuracy Vs Output3-Hour Ahead Forecasts

  12. Evaluating 3300 MW of wind on a 33,000 MW system Time scales from seconds to days AWS Truewind provided wind data GE PSEC performing grid analysis (from AGC to day-ahead scheduling) New York Integration Study

  13. How to simulate the behavior of 3300 MW of wind with little site data? Must capture spatial and temporal correlations Met stations often not in windy areas and exhibit wrong diurnal pattern Solution: Mesoscale modeling NY Study: The Challenge

  14. Selected 33 potential project sites with 50-300 MW capacity Used a mesoscale weather model to simulate hourly wind speed, direction, temperature for 5 continuous years Sampled 1-min and 1-sec data to synthesize sub-hourly fluctuations Created statistical model to synthesize plant forecasts – based on actual forecasts NY Study: Tasks

  15. Error Distribution Forecasting

  16. Validation of Dynamic Behavior

  17. “Extreme” Wind Events

  18. “Extreme” Event System Response

  19. Wind, turbine, and wind plant variability are not the same The more spatial diversity, the less temporal variability Mesoscale modeling provides a powerful tool for analyzing scenarios of large wind penetration Conclusions

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