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混合微电网滚动优化调度
Abstract：
Withtherapiddevelopmentofdistributedenergyresources(DERs)suchassolarphotovoltaics(PV)andwindfarms,theconceptofmicrogridshasemergedasapromisingsolutiontomitigatethechallengesfacingpowergrids.However,theintegrationofvariousenergysourcesandloadsinmicrogridsposesgreatchallengestoachieveefficientandreliablepowersupply.Inthispaper,weproposearollingoptimizationschedulingmethodforahybridmicrogridsystem,whichaimstoachievecost-effectiveandoptimalschedulingofenergysourcesandloadswhileensuringpowerbalanceandsystemstability.
Introduction：
Amicrogridisasmall-scalepowersystemthatcanoperateindependentlyorinterconnectedwiththemainpowergrid.Microgridscanintegratedifferenttypesofdistributedenergyresources(DERs)suchassolarPV,windturbines,andbatteries,andprovidemorereliable,efficient,andrenewablepowersupplytolocalcommunities.However,theintegrationofvariousenergysourcesandloadspresentsgreatchallengestoachieveoptimalandstableoperationofmicrogrids,particularlyforhybridmicrogridsthatincorporatemultipletypesofenergysources.
Toaddressthesechallenges,microgridschedulingbecomesacrucialproblemthatrequiresefficientandreliablesolutions.Rollingoptimizationscheduling,whichinvolvesforecastingenergydemandandoptimizingpowersupplyandloadallocationoverarollinghorizon,hasemergedasapromisingmethodtoachievecost-effectiveandoptimalschedulingofmicrogridsystems.Thispaperproposesarollingoptimizationschedulingmethodforahybridmicrogridsystem,aimingtoachieveoptimalandstableoperationofthemicrogridwhileconsideringvariousconstraintssuchasdemand,supply,capacity,andaccessibility.
Methods：
Theproposedrollingoptimizationschedulingmethodcombinesday-aheadandreal-timeschedulingtoachieveefficientandreliableoperationofthehybridmicrogrid.Themethodconsistsofthefollowingsteps:
1.Day-aheadscheduling:Thisstepinvolvesforecastingthefutureenergydemandandsupplyofthemicrogridoverthenext24hours.Theforecastingcanbeachievedthroughstatisticalmethods,machinelearningalgorithms,oracombinationofboth.Basedontheforecast,the