Forecasting Uncertainty Related to Ramps of Wind Power Production

1. Forecasting Uncertainty Related to Ramps of Wind Power Production European Wind Energy Conference, Warsaw, 20-23 April 2010 Arthur Bossavy, Robin Girard,Georges Kariniotakis Center for Energy and Processes MINES-ParisTech/ARMINES

2. Introduction • Need to improvewind power forecastingwith focus on extremesituations • Various temporal/spacescales • Focus on uncertainty and weatherpredictability • Distribution tailevents • To contribute to • An increased and more securewindintegration to power grid • Lowercosts (i.e: reducedimbalances) • …

3. Introduction A problem with usual wind power forecasts Centeredpredictionintervals of coverage: predictions observations

4. Objectives of the work 1. Improve the reliability of usual confidence intervals w.r.trampevents 2. Forecast confidence intervals to estimate the uncertainty of ramps timing

5. Outline • A methodology for rampsdetection • A probabilistic model usingramps information • Forecast of ramps timing using ensembles

6. Detection of ramps FILTERING THRESHOLDING Ramp detected intensity Threshold timing

7. Detection of ramps • Evolution of the rampintensitythroughDenmark

8. Outline • A methodology for rampsdetection • A probabilistic model usingramps information • Forecast of ramps timing using ensembles

9. A probabilistic model using ramps information Objective: Produce more reliableprobabilisticforecasts by using information on forthcomingramps

10. Production of spot forecasts Ramps detection 3-stage forecasting process using ramps information Probabilistic processing Spot forecasting model SCADA NWP INTENSITY SCADA TIMING NWP spot Filtering • Ramps • TIMING • INTENSITY forecasts Thresholding Probabilistic forecasting model Ramps Information

11. Case-studies • 1 windfarm in Ireland, 1 in Denmark • 18 months of data (02/01-08/02 and 01/03-07/04) • Hourly power measures • Hourlywind speed/direction NWP forecasts (10m height). • Probabilistic model based on the Quantile RegressionForestsprocedure

12. Evaluation measures Reliability: Sharpness:

13. Results Wind farm in Denmark Wind farm in Ireland • Forecastsunderestimate quantiles • Reliabilityimproved for highest quantile forecasts • Sharpnessremainsunchanged

14. Results • Estimation of the uncertaintymaybeimprovedatramps • Need of more tests: other quantile estimation methods

15. Outline • A methodology for rampsdetection • A probabilistic model usingramps information • Forecast of ramps timing using ensembles

16. Forecast of ramps timing using ensembles Objective: Aggregateramps information provided by members of a wind power forecasts ensemble

17. Forecast of ramps timing using ensembles Filteringmembers of a forecasts ensemble More than 35 over 51 memberspredictingthisramp h1 h2 h3

18. Forecastramps timing using ensembles Proposal for a probabilisticforecast of ramps timing • Mean value for the ramp timing: • Confidence intervals:

19. Case-studies and evaluationresults • Case-studies: 3 wind farms in France • Wind speed forecasts ensemble (51 members from the EPS system of ECMWF) • Random Forest procedure • Evaluation of forecast probabilities: Brier Score: Brier Skill Score w.r.tClimatology:

20. Visualization of confidence intervals 70% 57% 39% 65% • 39 memberspredicting the • increasingramp • 15 memberspredicting the • decreasingramp 43% 28%

21. Conclusions • The probabilistic model usingramps information maybevaluablewhenestimating the highest quantiles • The approachbased on ensembles provide confidence intervals to forecastramp occurrence. Reliabilityw.r.tClimatologyisimproved Need of more experiments

22. Acknowledgments • Project SAFEWIND: « Multi-scale data assimilation, advanced wind modelling and forecasting with emphasis to extreme weather situations for a safe large-scale wind power integration » • Industrial partners of the project for providing data

23. Thank you for your attention! 29