计及分布式新能源不确定性的虚拟电厂调度边界概率分布刻画方法-豆柴文库

您所在位置：网站首页 / 计及分布式新能源不确定性的虚拟电厂调度边界概率分布刻画方法.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

计及分布式新能源不确定性的虚拟电厂调度边界概率分布刻画方法
Title:AProbabilityDistributionCharacterizationMethodforVirtualPowerPlantDispatchBoundaryconsideringUncertaintyofDistributedRenewableEnergy
Abstract:
Theincreasingpenetrationofdistributedrenewableenergysources,suchaswindandsolar,introducessignificantuncertaintyintothepowersystemoperation,posingchallengesforeffectivepowerdispatchinvirtualpowerplants.Inthispaper,weproposeaprobabilitydistributioncharacterizationmethodtomodelthedispatchboundaryofavirtualpowerplant,takingintoaccounttheuncertaintiesassociatedwithdistributedrenewableenergysources.Themethodaimstoprovidemorereliableandaccuratedispatchstrategies,ensuringefficientutilizationofrenewableresourcesandmaintainingpowersystemstability.
1.Introduction:
Therapidgrowthofdistributedrenewableenergysourcespresentsbothopportunitiesandchallengesinpowersystemoperation.Virtualpowerplants,whichaggregateandmanagemultipledistributedenergyresources,haveemergedasapromisingconcepttointegrateandoptimizetheuseoftheseresources.However,theuncertaintyinducedbyintermittentrenewablesourcesmakesthedispatchofvirtualpowerplantsmorecomplexanddelicate.Thispaperaddressesthischallengebyproposingamethodtocharacterizethedispatchboundaryofavirtualpowerplant,consideringtheuncertaintiesassociatedwithdistributedrenewableenergy.
2.LiteratureReview:
Theliteratureonvirtualpowerplantsprimarilyfocusesonoptimizationalgorithmsforschedulinganddispatchingpowerresources.However,mostexistingmethodsdonotaccountfortheuncertaintyofdistributedrenewableenergysources.Somestudiesincorporateprobabilisticforecastingmodelstopredicttheoutputvariationofrenewablesources,butfailtocapturetheimpactofuncertaintyonthedispatchboundary.
3.Methodology:
Theproposedmethodstartsbyconstructingaprobabilisticforecastmodeltoestimatetheuncertainoutputofdistributedrenewableenergysources.Thismodelutilizeshistoricaldata,meteorologicalconditions,andotherrelevantfactorstogenerateaprobabilitydistributionforeachrenewablesource.Theprobabilitydistributionreflectsthepossib