The California Energy Commission (CEC) has provided support to a diverse team lead by the University of California, Davis for a 2-year (2015-2017) project to improve the accuracy of TWRA wind ramp forecasts 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, 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 atmospheric boundary layer and earth’s surface sub-models of Numerical Weather Prediction (NWP) models to account for processes typically not well-modeled in existing sub-models; (4) improvements to the NWP data assimilation schemes to account for flow dependent variations in the optimal representation of the spatial influence of measurement data in the forecast model initialization; (5) custom configuration of an ensemble of advanced machine learning methods for application to the very short-term (0-3 hours) prediction of ramp events from time series data from forecast sites and the targeted sensor network; (6) a 1-year evaluation of the impact of these improvements on 0-15 hour wind ramp forecast performance relative to a state-of-the-art baseline prediction system.
This presentation will be a follow-up to the early stage overview of the project given at last year’s conference. It will provide an overview of (1) the TWRA and the impact of wind variability in this region on CAISO operations, (2) a description of the specific forecast system improvements implemented in this project, (3) results from an analysis of the impact of each improvement on wind forecast performance and a comparison to the performance of reference forecasts based only on output from operational prediction models from the US National Weather Service, and (4) a preliminary estimate of the anticipated benefits that the forecast improvements will provide to system operations based on feedback from CAISO.
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