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Enhancing probabilistic Wind Power Forecasting using ECMWF’s 100m EPS winds

Enhancing probabilistic Wind Power Forecasting using ECMWF’s 100m EPS winds Lueder von Bremen , Constantin Junk, Detlev Heinemann ForWind Center for Wind Energy Research University of Oldenburg, Germany. EWEA 2012, Copenhagen, 17 April 2012. Outline.

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Enhancing probabilistic Wind Power Forecasting using ECMWF’s 100m EPS winds

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  1. Enhancing probabilistic Wind Power Forecasting using ECMWF’s 100m EPS winds Lueder von Bremen, Constantin Junk, Detlev Heinemann ForWind Center for Wind Energy Research University of Oldenburg, Germany EWEA 2012, Copenhagen, 17 April 2012

  2. Outline • Meteorological Ensembles for Wind Power Forecasting • Spatial distribution of forecast uncertainty • Ensemble Prediction System with 100m winds • for improved Wind Power Forecasting • Summary

  3. Why weather forecasts can not be precise? ? x ? x dx / dt = a (y - x) dy / dt = x (b - z) - y dz / dt = xy - c z (E. Lorenz, 1963) • Lorenz Paradigm: Numerical Weather Forecasting is an initial state problem • Quantify the uncertainty in the forecast to be aware of forecast errors

  4. Ensemble Prediction System (EPS) at ECMWF • Lorenz Paradigm: Numerical Weather Forecasting is an initial state problem • Quantify the uncertainty in the forecast to be aware of forecast errors

  5. Powergrams: Communicating Ensemble Forecasts • Example: 1 March 2010 extreme event Storm Xynthia in • Eastern Germany (50Hertz, ~10.7 GW) +48h +96h measurement ensemble mean deterministic forecast simulated wind power • New: What is the distribution of uncertainty in the control zone

  6. Example: Storm Xynthia (50 Hertz)1 March 2010, 0UTC +96h +48h Ensemble Mean Forecast Uncertainty (RMS Ensemble Spread)

  7. Example: Storm Xynthia (50 Hertz)1 March 2010, 0UTC +96h +48h Forecast uncertainty (RMS Ensemble Spread) absolute forecast error

  8. Evaluation of storm Xynthia (50 Hertz)1 March 2010, 0UTC +96h forecast: crosses +48h forecast: bullets uncertainty [MW] • The level of uncertainty can be used as indicator of a (likely) forecast error

  9. Overcome the usage of 10m winds • Isn‘tthatold? • New Ensemble System: 100m windsinsteadof 10m winds • from 26 Jan 2010 at ECMWF • ECMWF analysisspeedsareweightedaccordingtospatial wind power distribution • „Regional Power Curve“ for 50Hertz (VET) • 10m winds hubheight • 100m winds  hubheight Feb 2010-Apr 2011

  10. Performance of the Ensemble System compared to a single forecast (here: Germany) Single forecast Ensemble Mean new system with 100m winds old system -30% • at D+2 ensemble mean starts to be better than single forecast • New system is improved up to 30%

  11. Can we trust the probabilistic forecast? • Evaluation ofreliabilityofthe Ensemble Prediction System • here: Event wind power>9GW in Germany • +72h forecast horizon new System with 100m winds old System: overconfident • New system has much better reliability

  12. Improvement of probabilistic forecast • Continuous Rank Probability Skill Score: CRPSS=1-CRPSnew/CRPSold Germany 50Hertz • Very strong (25%) improvement of probabilistic forecast skill

  13. Summary Thank you for your attention • Forecast uncertaintycanbederivedfrommeteorological Ensembles andisrelatedtotheoccurenceoffcerrors • Geographicaldisplayofforecastuncertainty • at Day+2 Ensemble meanstartstobebetterthansinglefc • Probabilisticforecast score isimprovedby 10-25% usingthenew Ensemble Predictionsystem Acknowledgements This work was supportedbythe Federal Ministry for Science and Culture ofLowerSaxonyandthe European Commission in theSafeWind Project (Grant No. 213740). Contact: lueder.von.bremen@forwind.de

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