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SafeWind Wind power forecasting for extreme situations

EWEC 2009 – Marseille, France. SafeWind Wind power forecasting for extreme situations. George Kariniotakis Ph.D, Head of Renewable Energies Group MINES-ParisTech/ARMINES georges.kariniotakis@mines-paristech.fr. Context – Research in Wind Power Forecasting. Latest European Projects.

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SafeWind Wind power forecasting for extreme situations

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  1. EWEC 2009 – Marseille, France SafeWind Wind power forecasting for extreme situations George Kariniotakis Ph.D, Head of Renewable Energies Group MINES-ParisTech/ARMINES georges.kariniotakis@mines-paristech.fr

  2. Context – Research in Wind Power Forecasting • Latest European Projects • ANEMOS : FP5, 2002-2006

  3. Context – Research in Wind Power Forecasting • Latest European Projects • ANEMOS : FP5, 2002-2006 • ANEMOS.plus : FP6, 2008-2011 ANEMOS.plus 3

  4. Context – Research in Wind Power Forecasting • Latest European Projects • ANEMOS : FP5, 2002-2006 • SAFEWIND : FP6, 2008-2012 • ANEMOS.plus : FP6, 2008-2011 SafeWind 4

  5. The SafeWind Consortium 2008-2012 9 countries, 22 partners End-users (8) SMEs (2) Research (5) Universities (6) Meteorologists(2) Coordination ARMINES/ Mines ParisTech

  6. SafeWind objectives First step : Definition & identification of extremes : Extreme meteorological events High wind speeds (cut-off events) Thunderstorms Consider regional effects Extreme forecasting errors Extreme small scale events (Remote Sensing) Errors with an extremely high impact in the grid management or market participation. Costs (Balancing, intraday markets) Grid congestion Connector capacity Coincidence with load, ramping capabilites ...

  7. 12000 10000 8000 Power [MW] 6000 4000 measurement prediction 2000 0 00:00 01:15 02:30 03:45 05:00 06:15 07:30 08:45 10:00 11:15 12:30 13:45 15:00 16:15 17:30 18:45 20:00 21:15 22:30 23:45 time Example: low pressure took path further to the South prediction L observation Predictions for Germany : Path of low-pressure system was different than predicted, maximum error: 5500 MW could have been avoided by extreme event correction. Source : Emsys

  8. Example: Phase error in ramp events Movement of low or fronts faster/slower Observation Prediction Prediction L P Measurement L h Source : Emsys

  9. SafeWind objectives Improvewindpredictabilitywithfocus onextremes : • atvarious temporalscales • Veryshort-term (orderof 5 min) • Short term (hours to days) • Longer term (beyondfewdaysahead) • atvariousspatial scales : • local scale: Extreme gusts or shears. • regional scale: Extreme events (like thunderstorms) can cause the loss of significant amounts of wind energy with potential impact on the grid management. • continental (European) scale: Extreme weather situations (like fronts) can propagate causing impacts in different member states.

  10. SafeWind objectives • Models for "alarming": very short-term (0-6 h). • Develop methods to adequately monitor and assess the wind energy weather situation over Europe in order to detect severe deviations in the wind power forecast due to extreme events. • React on such deviations by issuing suitable alerts to users that a forecast error is occurring. • Produce improved updates of the prediction in the short-term

  11. SafeWind objectives • Models for "warning": providing information for the level of predictability in the medium-term (next day(s)). • Such tools, based on ensemble weather forecasts and weather pattern identification, can be used to moderate risks in decision making procedures related to market participation, reserves estimation etc.

  12. SafeWind objectives • Develop a "European vision" for wind power forecasting • Preparetheway for thecoordinated management of 100+ GW windgenerationatEuropeanScale. • Creationof a Data Information System for centralising information useful for • large scaleforecastingand • continously monitor "energyweather" over Europe i.e. Data from Synoptic Stations in Europe

  13. SafeWind objectives • Develop research in meteorology oriented to wind forecasting. • Improve ensemble forecasts (wind & wind power) (i.e. ECMWF’s Ensemble Prediction System (EPS)) • Evaluate various EPS configurations • Produce optimally combined forecast products

  14. SafeWind objectives • Link resource assessment to wind predictability. • Analyse how new measurement technologies like Lidars can be beneficial for better evaluation of external conditions, resource assessment and forecasting purposes.

  15. Planned measurement campaigns at flat (DK) and complex (ES) terrains SafeWind objectives Høvsøre Large Wind Turbine Test Facility

  16. SafeWind objectives • Develop research in meteorology orientated to wind forecasting. • Link resource assessment to wind predictability. • Analyse how new measurement technologies like Lidars can be beneficial for better evaluation of external conditions, resource assessment and forecasting purposes. •  Demonstrate the operational benefits from new models.

  17. Conclusions • The SafeWind project develops synergies among different disciplines and actors to improve actual wind power forecasting technology; • The work methodology is designed to enable quick transfer of results for operational use by industrial stakeholders. • Expected impact : • Economy : • Increased competitiveness of wind energy in markets • Reduced project risk due to better site selection • Technology : • New or improved software tools • Better "operational" decision making for wind energy management • Maintain excellence of European R&D in the field www.safewind.eu

  18. F.R.E. 2861 Thank you for your attention 18

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