Abstract
The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.
Highlights
The National Center for Atmospheric Research (NCAR), in collaboration with Xcel Energy addressing users’ needs and requirements, has developed a comprehensive wind power forecasting system
We have developed an empirical power conversion algorithm and combined it with an analog ensemble (AnEn) approach to quantify the uncertainty of these predictions
NCAR applies a Weather Research and Forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) system for wind power prediction, which is based on Newtonian relaxation [12] and adds forcing terms in the equations for momentum, temperature, and moisture
Summary
The National Center for Atmospheric Research (NCAR), in collaboration with Xcel Energy addressing users’ needs and requirements, has developed a comprehensive wind power forecasting system. Since no Energies 2019, 12, x FOR PEER REVIEW single weather forecasting methodology can perform optimally across all these temporal scales, The wind power forecasting provides(NWPs) essential that information for effective of at times we have combined numerical weathersystem predictions provides skillfulintegration predictions variable generation into the power grid and addresses requirements for both the effective beyond a maintenance few hours of with specialized methods based on observations that can improve the very reliable electric grids and energy trading. Of extreme events, Disparate sources of data, including power generation data, as well as local and regional weather such as ice storms, can greatly aid system operations and methods, and tools for generating warnings observations, are combined using artificial intelligence methods with the information about physics of potentialand impacts of these processes on wind power generation are developed. By extreme events, such as can greatly aid system operations and methods, toolsdeveloped for generating warnings of potential of these processes oniswind power generation are foreCast
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