energies Article A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction Branko Kosovic 1,* , Sue Ellen Haupt 1 , Daniel Adriaansen 1, Stefano Alessandrini 1, Gerry Wiener 1, Luca Delle Monache 2, Yubao Liu 1, Seth Linden 1, Tara Jensen 1, William Cheng 1, Marcia Politovich 1 and Paul Prestopnik 1 1 National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA; [email protected] (S.E.H.); [email protected] (D.A.); [email protected] (S.A.); [email protected] (G.W.); [email protected] (Y.L.); [email protected] (S.L.); [email protected] (T.J.); [email protected] (W.C.); [email protected] (M.P.); [email protected] (P.P.) 2 Scripps Institution of Oceanography, University of California at San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA; [email protected] * Correspondence: [email protected]; Tel.: +1-303-497-2717 Received: 5 December 2019; Accepted: 15 March 2020; Published: 16 March 2020 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. Keywords: grid integration; machine learning; renewable energy; turbine icing; wind power forecasting; wind energy 1. Introduction 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. The original forecasting system was designed for day-ahead forecasting to support power trading. The new augmented and enhanced forecasting system provides capabilities for short-term forecasting, including wind ramp detection, prediction of extreme events such as icing conditions that can significantly impact wind power production when wind resource is abundant, empirical wind-to-power conversion techniques, and uncertainty quantification in power forecasting. This system employs artificial intelligence methods [1–3] to integrate disparate data sources with publicly available numerical weather prediction model outputs. The development of the new comprehensive forecasting Energies 2020, 13, 1372; doi:10.3390/en13061372 www.mdpi.com/journal/energies Energies 2020, 13, 1372 2 of 16 system is motivated by risk reduction of wind power integration into a power grid and reduction of the levelized cost of wind power. The wind power forecasting system provides essential information for effective integration of variable generation into the power grid and addresses requirements for both the effective maintenance of reliable electric grids and energy trading. The grid operators and energy traders require accurate wind powerEnergies forecasts 2019, 12, xat FOR time PEER scalesREVIEW spanning a range from minutes to several days ahead.2 of 17 Since no single weather forecasting methodology can perform optimally across all these temporal scales, we have combinedThe w numericalind power forecasting weather system predictions provides (NWPs) essential thatinformation provides for effective skillful integration predictions of at times variable generation into the power grid and addresses requirements for both the effective beyond a few hours with specialized methods based on observations that can improve the very maintenance of reliable electric grids and energy trading. The grid operators and energy traders short-rangerequire forecasts. accurate wind power forecasts at time scales spanning a range from minutes to several days To developahead. aSince decision no single support weather system forecasting for the methodology effective integrationcan perform ofoptimally variable across generations, all these we have leveragedtemporal proven scales, forecasting we have methodologies combined numerical for w eacheather temporal, predictions as (NWP wells) asthat spatial, provides scale. skillful Disparate predictions at times beyond a few hours with specialized methods based on observations that can sources of data, including power generation data, as well as local and regional weather observations, improve the very short-range forecasts. are combined usingTo develop artificial a decision intelligence support system methods for the with effective the information integration ofabout variable physics generation ands, we dynamics of the atmospherehave leveraged to predict proven power forecasting output methodologies [4–7]. In addition, for each advancetemporal, knowledgeas well as spatial of extreme, scale. events, such as iceDisparate storms, sources can greatly of data aid, including system power operations generation and data methods,, as well as and local tools and forregional generating weather warnings observations, are combined using artificial intelligence methods with the information about physics of potentialand impacts dynamics of of these the atmosphere processes to on predict wind power power output generation [4–7]. In addition, are developed. advance knowledge of The schematicextreme events, diagram such as of ice the storms, comprehensive can greatly aid wind system power operation forecastings and methods system, and [1 –tools3] developed for by NCAR is showngenerating in Figurewarning1.s Theof potential central impacts component of these of theprocesses system on is wind the Dynamicpower generation Integrated are foreCast System (DICastdeveloped.®). DICast is an advanced machine-learning module that has been under development The schematic diagram of the comprehensive wind power forecasting system [1–3] developed at NCARby for NCAR over is twenty shown in years. Figure It1. The blends central publicly component available of the system model is the output Dynamic and Integrated high-resolution NWP modelsforeC configuredast System (DICast® for Xcel ). DICast Energy’s is anregions advanced with machine weather-learning observations module that has form been wind under farms and routine meteorologicaldevelopment at surfacesNCAR for andover uppertwenty airyears. observations. It blends publicly The available wind farmmodel data output include and high wind- speed measurementsresolution from NWP Nacelle models mounted configured anemometers.for Xcel Energy’s region For ans with improved weather observations short-term form forecast wind of wind farms and routine meteorological surfaces and upper air observations. The wind farm data include ramps, wewind have speed also integratedmeasurements the from variational Nacelle mounted doppler anemometers. radar analysis For systeman improved (VDRAS) short-term together with an observation-basedforecast of wind expert ramps system., we have An also alternative integrated forthe short-termvariational d forecastingoppler radar isanalysis the regime-switching system approach [(VDRAS)8,9]. While together the with regime-switching an observation-based approach expert system. has proven An alternative effective, for short our- focusterm forecasting was onproviding more accurateis thephysical regime-switching models approach for short-term [8,9]. While forecasting the regime-switching in the summertime approach has whenproven convectiveeffective, storm our focus was on providing more accurate physical models for short-term forecasting in the outflows aresummertime responsible when convective for numerous storm outflows wind rampsare responsible throughout for numerous the areaswind ramps served throughout by Xcel Energy. By assimilatingthe areas radar served observations, by Xcel Energy. VDRAS By assimilating is able to radar provide observations accurate, VDRAS short-term is able forecasts to provide not only of frontal passagesaccurate when short-term wind forecasts regimes not switchonly of frontal but also passages storm when outflows. wind regimes switch but also storm outflows. Figure 1. FlowchartFigure 1. Flowchart of the Nationalof the National Center Center for for Atmospheric Atmospheric Research Research’s’s (NCAR’s (NCAR’s)) Xcel Energy Xcel p Energyower power predictionp system.rediction NCEP:system. U.S.NCEP: National U.S. National Centers Centers for Environmental for Environmental Prediction, Prediction NAM:, NAM: North North American American model, GFS: global forecasting system, RUC: Rapid Update Cycle, GEM Global model, GFS: global forecasting system, RUC: Rapid Update Cycle, GEM Global Environmental Multiscale model, WRF RTFDDA: Weather Research and Forecasting-based real-time four-dimensional data assimilation, and VDRAS: variational doppler radar