A Wind Power Forecasting System to Optimize Grid Integration

Abstract

Wind power forecasting can enhance the value of wind energy by improving the reliability of integrating this variable resource and improving the economic feasibility. The National Center for Atmospheric Research (NCAR) has collaborated with Xcel Energy to develop a multifaceted wind power prediction system. Both the day-ahead forecast that is used in trading and the short-term forecast are critical to economic decision making. This wind power forecasting system includes high resolution and ensemble modeling capabilities, data assimilation, now-casting, and statistical postprocessing technologies. The system utilizes publicly available model data and observations as well as wind forecasts produced from an NCAR-developed deterministic mesoscale wind forecast model with real-time four-dimensional data assimilation and a 30-member model ensemble system, which is calibrated using an Analogue Ensemble Kalman Filter and Quantile Regression. The model forecast data are combined using NCAR's Dynamic Integrated Forecast System (DICast). This system has substantially improved Xcel's overall ability to incorporate wind energy into their power mix.