Wind Speed Ramp Rate Predictions Using Wind Farm SCADA Data Assimilation and a WRF Ensemble

Abstract

Abstract This study presents a novel method for improving wind power ramp events forecasts up to six hours ahead by utilizing data assimilation of SCADA measurements with an ensemble of Weather Research and Forecasting (WRF) models estimates. Leveraging data from nine wind farms in France and Belgium, the approach aims to improve WRF model predictions for wind speed and ramp event timing. The methodology employs grid and observational nudging techniques, enhancing model accuracy by incorporating real-time observational data. Key findings demonstrate that nudging significantly reduces Mean Absolute Error (MAE), decreases the Time Distortion Index (TDI), and increases the Probability of Detection (POD) of ramp events. Nudged ensemble members outperform non-nudged counterparts, exhibiting better accuracy in identifying true ramp events and reducing false alarms. MAE, TDI and POD improvements are as high as 3.7%, 8.5% and 37%, respectively. The study also explores the benefits of an ensemble approach, highlighting improved accuracy in predicting ramp rate magnitudes and providing valuable insights for grid stability management. This research contributes to wind power forecasting, showcasing the importance of integrating SCADA data into predictive models.