SafeWind Advances in Short-Term Wind Power Forecasting with Focus on "Extreme" Situations

1. EWEA 2012,16-19 April 2012, Copenhagen, Denmark SafeWind Advances in Short-Term Wind Power Forecasting with Focus on "Extreme" Situations George Kariniotakis Head of Renewables & SmartGrids R&D Group MINES ParisTech - ARMINES georges.kariniotakis@mines-paristech.fr

2. Introduction • Ambitious targets for wind integration in EU 2002 2010 2020 20 GW 74.7 GW 230 GW Source: EWEA • Challenges for the power system management • Short-term forecastingof wind generation is recognized as a prerequisite for an efficient wind integration

3. Introduction • The actual wind power forecasting technology is quite mature • However, in some situations large forecast errors may have an important impact on power system operation Example in Germany: Path of low-pressure system was different than predicted, Maximum error: 5500 MW…

4. Introduction • The actual wind power forecasting technology is quite mature • However, in some situations large forecast errors may have an important impact on power system operation • The SafeWind projectwas developed to improve wind power predictability in challenging and extreme situations. • atvarious temporalscales (very short to longer term) • atvariousspatial scales (local/regional/continental)

5. Industry Research Meteorologists The SafeWind Project 2008-2012 9 countries 22 partners End-users Coordination ARMINES/ MINES ParisTech http://www.safewind.eu INTERNATIONAL, INDIA

6. Scientific & Technical Objectives (1) • New forecasting methods for wind generation focusing on uncertainty and challenging situations/extremes: • Probabilistic models • Event forecasting models (ramps, cut-offs) • Spatio-temporal correction models • Regime switching models • Prediction scenarios • … • Methodologies for communicating probabilistic forecasts & risks

7. Highlight:Ramps forecasting using ensembles • New approach for probabilistic prediction of ramp events: • Prediction of ramps occurrence and timing. • Based on ensembles. • Ramps detection through a derivative filtering based on edge detection theory. • Results: • Controllable tradeoff between ramp capture and forecast accuracy • Reliable probabilistic forecasts with sharpness • 5-15% performance increase with respect to climatology

8. Highlight: Scenarios and their verification Scenarios of wind power generation are a must-have for high-value decision-making, either • Using meteorological ensembles as input, • Or based on probabilistic forecasts and spatio-temporal statistical models Results : • A methodology for the comparative verification of scenarios • Methods to produce scenarios based on ECMWF ensemble forecasts or state-of-the-art probabilistic predictions • Readily operational and used in practice

9. Highlight: Spatio-temporal modelling for forecasting • New spatio-temporal forecasting approaches for the shorter term: • Wind farms are considered as measurement points • A spatio-temporal model is fitted on each wind farm using off site measurements • Improvement of wind power forecasts up to 18% (red when east, blue for west). Case: Denmark

10. Scientific & Technical Objectives (2) • Develop a "European vision"for wind power forecasting • Prepare the way for the coordinated management of 200+ GW wind generation at European Scale. Monitor the Wind Energy Weather over Europe in real-time with observation data from many different sources • More than 2000 weather stations • 120 single wind farms • 19 regions • 1 met mast SafeWind Data Management System Data from Synoptic Stations in Europe

11. Scientific & Technical Objectives (3) • Models for "alarming" for very short-term (0-6 h). • Monitor/assess the wind energy weather situation over Europe • Detect severe deviations in forecasts due to extreme events • Issuealertstousersthat a forecasterrorisoccurring • Produceimprovedupdatesoftheprediction in theshort-term • Alarming in Forecast Time Series (23/1/2009)for Eastern part of Germany (50 Hertz) Pressuregradientdeviation (forecast - AdHoc-Analysis)

12. Scientific & Technical Objectives (4) • Models for "warning" on the level of predictability in the medium-term (next day(s)). • Approaches based on ensembles and weather pattern identification

13. Scientific & Technical Objectives (5) • Develop research in meteorology oriented to wind power forecasting: • Deliver the meteorological component for skilful probabilistic wind power forecasts: • look at various products specifically suited for wind energy and extremes (i.e. new product - 100m winds) (next talk) • evaluate various ensemble forecasting configurations • wind energy oriented verification of ECMWF products • improving ensemble forecasts (wind & wind power) • Stimulate the interactionbetween meteorologists and wind power communities

14. Highlight:Recalibrated ECMWF wind ensembles Ensemble forecasts of wind speed and direction are of great value as input to wind power prediction • They need advanced post-processing (due to bias, lack of spread, etc.) • They can be recalibrated with adaptive statistical techniques Results: • ECMWF ensemble forecasts of (u,v)-winds recalibrated for the whole Europe over the period 2007-2009 • Substantial improvement of various skill scores and diagnostics

15. Scientific & Technical Objectives (6) • Evaluate the impact of short-term wind predictability in the resource assessment phase: • when wind farms participate in an electricity market. • study if a tradeoff between wind potential and predictability can be beneficial when selecting a site or expanding a portfolio Wind potential A B Predictability Flat Medium Complex Terrain complexity

16. Highlight: Forecast Error Maps • 1 & 2 day-ahead wind power forecast error for Central Europe • Computed from COSMO-EU Forecasts and Analysis (German Weather Service) • Normalization with estimated wind energy yield RMSE [MWh/MWh] • Spatial fc error smoothing considered (here: size of a DSO control zone) day-ahead day-ahead 2day-ahead 2day-ahead See Posters PO 51 & PO109

17. Highlight: Extreme Wind Atlases-from global data to site use • New approach to obtain turbine design winds • Downscale the global data to obtain extreme winds for turbine sites. • Use mesoscale modelling to simulate the yearly strongest storms over areas of different terrain complexity. • Use a post-processing to prepare the data for microscale modelling. • Results : • Extreme wind atlases for places of various extreme weather mechanisms and terrain complexities. • Reasonable agreement with measurements. • Output ready for microscale modelling for particular turbine sites. m/s Generalized extreme wind atlas for Navara region in Spain

18. Analyse how new measurement technologies like Lidars can be beneficial for better evaluation of external conditions, resource assessment and forecasting purposes. Scientific & Technical Objectives (7) Measurement campaigns at flat (DK) and complex (ES) terrains Høvsøre Large Wind Turbine Test Facility

19. Anemos.eXtreme: On-line demo of new approaches • On-line demonstration of SafeWind modules: • Integration of 8 new modules into the ANEMOS wind power prediction platform • Demonstration /evaluation for various TSOs • Test cases: • SONI (N. Ireland/U.K.) EirGrid (Ireland) • EDF (France) RTE (France) • PPC (Crete, Greece)

20. Conclusions • Successful scientific production: >75 papers so far • New approaches for forecasting in challenging situations • The work methodology designed to enable quick transfer of results for operational use by industrial stakeholders • Final project workshop:31 August 2012, Paris What comes next : • Improving predictability requires continuous collaborative R&D • Need to better integrate forecasts into power system management tools • R&D in forecasting is promoted as a priority (i.e. through TPWind).

21. Thank you for your attention 21