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多时空尺度的风力发电预测方法综述.docx

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多时空尺度的风力发电预测方法综述 Abstract Windenergyisbecominganincreasinglypopularsourceofrenewableenergyduetoitsbenefitsforreducinggreenhousegasemissionsandenvironmentalimpactscausedbytraditionalfossilfuel-basedenergysources.Windpoweroutputis,however,highlyvariableanddifficulttopredict.Inthispaper,wepresentacomprehensivereviewofvariousmethodsforpredictingwindpoweroutputformultiplespatialandtemporalscales. Introduction Astheworldmovestowardsasustainableandenvironmentallyfriendlyenergyfuture,renewableenergysourcesarebecomingmorepopular.Amongthese,windenergyhasemergedasoneofthemostpromisingformsofrenewableenergy.Windenergyhasanumberofbenefits,includingitsabundance,availability,andcost-effectiveness.However,thevariabilityofwindpoweroutputmakesitachallengetopredict. Windpoweroutputishighlyvariable,andthisvariabilityisaffectedbyanumberoffactors,includingwindspeed,winddirection,andatmosphericstability.Windpowerpredictionmodelsmusttakeintoaccountthesefactorsandtheirinteractionswitheachother.Moreover,windpredictionmodelsmustbedevelopedforavarietyofspatialandtemporalscales. SpatialScales Windpowerpredictionmodelsmustbedevelopedforvariousspatialscales,includingmicroscale,mesoscale,andmacroscale.Micro-scalereferstopredictingwindspeedsanddirectionsatthelevelofindividualturbines,mesoscalereferstowindspeedsanddirectionsatthelevelofwindfarms,andmacroscalereferstowindspeedsanddirectionsoverlargegeographicalareas. Microscalepredictionmodelsutilizecomputationalfluiddynamics(CFD)simulationstopredictthewindvelocitiesanddirectionsatthelevelofindividualturbines.ThesemodelsaregenerallybasedontheNavier-Stokesequationsandcanprovidedetailedpredictionsofwindconditionsaroundindividualturbines. Mesoscalepredictionmodelsusenumericalweatherprediction(NWP)modelstopredictwindspeedsanddirectionsoveralargerarea.Thesemodelsutilizeacombinationofatmosphericphysics,thermodynamics,andenergybalanceequationstopredictwindconditions.Mesoscalemodelscanberunoverlargeareasandcanprovidedetailedpredictionsofwindconditionsoverwin