Improving Short-Term Wind Ramp Forecasting in the Tehachapi Wind Resource Area through Targeted Measurements and Next-Generation Prediction Techniques

The management of wind power variability is a key factor in minimizing the cost of integrating wind-based power generation into electric grid systems while maintaining the required very high level of reliability. Short-term forecasting of wind power is one of the most cost-effective and easily implemented tools available to system operators for this purpose. However, many system operators in North America have indicated that they need more accurate intra-day prediction of events characterized by large changes in wind-based power production over short periods (i.e. “wind ramps”). The California Independent System Operator (CAISO) has indicated that the prediction of such events in the Tehachapi Wind Resource Area (TWRA) of California would have great value to their grid operations due to the large amount of wind generation capacity that is concentrated in the TWRA. The California Energy Commission (CEC) has provided support to a diverse team lead by the University of California at Davis for a 2-year project to improve the accuracy of wind ramp forecasts in the TWRA through improved meteorological measurements and refinements to physics-based and statistical atmospheric prediction models. The major components of the project are: (1) the deployment of a targeted regional sensor network of remote sensing systems such as wind profiling radars, ceilometers, sodars, and microwave radiometers; (2) a forecast sensitivity analysis to determine which components of the forecast system have the greatest impact on forecast performance; (3) improvements to the physics-based atmospheric boundary layer and earth's surface sub-models to account for processes typically not well-modeled in existing sub-models; (4) improvements to the data assimilation schemes to account for flow dependent variations in the optimal representation of the spatial influence of measurement data in the initialization of physics-based models; (5) custom configuration of an ensemble of advanced machine learning methods for application to the very short-term (0-3 hours ahead) prediction of wind ramp events from time series data from the forecast sites and the targeted sensor network; (6) a 1-year evaluation of the impact of the targeted sensor data and modeling improvements on 0-15 hour wind ramp forecast performance relative to a state-of-the-art baseline prediction system. The project commenced during the first half of 2015 and will be completed on the middle of 2017. The presentation will provide an overview of (1) the TWRA and the impact of wind variability in this region on CAISO operations, (2) the climatology of wind power variability in the TWRA and examples of wind ramp events that cause operational issues for CAISO, (3) the project objectives and the design of each project component, (4) quantitative results from the early phases of the project, and (5) the anticipated benefits that the project will provide to system operations.