微電網(wǎng)多目標優(yōu)化調(diào)度策略研究

微電網(wǎng)多目標優(yōu)化調(diào)度策略研究摘要：微電網(wǎng)是一種新型的分布式電力系統(tǒng)，具有環(huán)保、高效、可靠等優(yōu)良特性。在微電網(wǎng)中，電源與負載呈現(xiàn)多種多樣的組合，因此如何制定科學有效的調(diào)度策略對于微電網(wǎng)的穩(wěn)定運行非常重要。本文針對微電網(wǎng)的多目標優(yōu)化問題，提出了一種基于遺傳算法和模糊綜合評價的調(diào)度策略。首先，對微電網(wǎng)系統(tǒng)進行建模，分析了微電網(wǎng)的功率平衡和能量管理等問題。然后，通過定義多個調(diào)度目標，并確定相應的權(quán)重參數(shù)，將微電網(wǎng)調(diào)度問題轉(zhuǎn)化為多目標優(yōu)化問題。接著，運用遺傳算法優(yōu)化求解多目標優(yōu)化問題。在遺傳算法搜索過程中，引入模糊綜合評價方法，考慮調(diào)度目標之間的相互影響，提高了優(yōu)化求解的效率和準確性。最后，通過案例仿真驗證了所提出的調(diào)度策略的有效性和可行性。

關(guān)鍵詞：微電網(wǎng)，多目標優(yōu)化，遺傳算法，模糊綜合評價，能量管理。

Abstract:Microgridisanewtypeofdistributedpowersystemwithenvironmentalprotection,highefficiency,andreliability.Inmicrogrid,therearevariouscombinationsofpowersourcesandloads,sohowtodevelopeffectiveschedulingstrategiesisveryimportantforthestableoperationofmicrogrid.Inthispaper,aimingatthemulti-objectiveoptimizationproblemofmicrogrid,aschedulingstrategybasedongeneticalgorithmandfuzzycomprehensiveevaluationisproposed.Firstly,themicrogridsystemismodeled,andtheissuesofpowerbalanceandenergymanagementofmicrogridareanalyzed.Then,bydefiningmultipleschedulingobjectivesanddeterminingthecorrespondingweightparameters,theschedulingproblemofmicrogridistransformedintomulti-objectiveoptimizationproblem.Next,geneticalgorithmisusedtooptimizeandsolvethemulti-objectiveoptimizationproblem.Inthesearchprocessofgeneticalgorithm,thefuzzycomprehensiveevaluationmethodisintroducedtoconsidertheinfluenceofschedulingobjectivesoneachother,whichimprovestheefficiencyandaccuracyofoptimizationsolution.Finally,thevalidityandfeasibilityoftheproposedschedulingstrategyareverifiedbycasesimulation.

Keywords:Microgrid,multi-objectiveoptimization,geneticalgorithm,fuzzycomprehensiveevaluation,energymanagementNowadays,microgridshavebecomeapopularchoiceforenergymanagementduetotheirabilitytoprovidereliable,efficientandsustainablepowersupplytoconsumers.However,asthenumberofdistributedenergyresources(DERs)increases,themanagementandcontrolofmicrogridsbecomemorecomplex.Toovercomethisproblem,amulti-objectiveoptimizationstrategybasedongeneticalgorithmisproposedforenergymanagementinmicrogrids.

Inthisstrategy,theobjectivesincludingcostminimization,emissionreductionandloadbalanceareconsideredsimultaneouslytoobtaintheoptimalschedulingofDERs.Todealwiththeconflictsbetweentheseobjectives,afuzzycomprehensiveevaluationmethodisintroducedtoquantifythedegreeofsatisfactionforeachobjective.Bycombiningthefitnessvaluesofindividualsinthegeneticalgorithmwiththeircorrespondingcomprehensiveevaluationscores,anewfitnessfunctionisobtained,whichcanbetterreflecttheoverallperformanceofthemicrogridsystem.

Tovalidatetheeffectivenessoftheproposedschedulingstrategy,acasesimulationisimplementedbasedonarealmicrogridsystem.Thesimulationresultsshowthattheproposedstrategycanachievesignificantimprovementintermsofcostreduction,emissionreductionandloadbalance,comparedwithotherexistingmethods.Itdemonstratesthatthecombinationofgeneticalgorithmandfuzzycomprehensiveevaluationmethodcaneffectivelysolvethemulti-objectiveoptimizationproblemforenergymanagementinmicrogrids.

Inconclusion,theproposedmulti-objectiveoptimizationstrategybasedongeneticalgorithmandfuzzycomprehensiveevaluationmethodhasgreatpotentialtoimprovetheenergymanagementofmicrogrids.ItcanprovideareliableandefficientsolutionfortheoptimalschedulingofDERsinmicrogrids,leadingtobetterutilizationofrenewableenergysourcesandmoresustainabledevelopmentofthepowerindustryMoreover,theproposedapproachcanalsoenhancetheresilienceofmicrogridsbytakingintoaccountmultipleobjectives,suchasreducingenergycosts,minimizingcarbonemissions,andimprovingpowerquality.Byconsideringtheseobjectivessimultaneously,thesystemcanachieveabalancebetweeneconomic,environmental,andsocialfactors,leadingtoamoresustainableenergyfuture.

Additionally,themulti-objectiveoptimizationapproachcanalsoreducethecomplexityanduncertaintyofenergymanagementinmicrogrids.Theuseofgeneticalgorithmcanefficientlysearchfortheoptimalsolutioninalargesolutionspace,whilethefuzzycomprehensiveevaluationmethodcanhandletheuncertaintyandimprecisionofinputdataandsystemparameters.Thisway,theapproachcanprovidearobustandflexiblesolutionthatcanadapttodifferentoperatingconditionsandconstraints.

Furthermore,theproposedapproachcanalsofacilitatetheintegrationofnewrenewableenergysourcesandenergystoragetechnologiesintomicrogrids.Theflexibleandadaptivenatureoftheapproachallowsittoincorporatenewcomponentsandchangesystemconfigurations,enablingtheintegrationofemergingtechnologiesinthefuture.Inthisway,theapproachcancontributetothedevelopmentofamoresustainableandresilientpowersystemthatcanaccommodatetheincreasingdemandforrenewableenergy.

Insummary,themulti-objectiveoptimizationapproachbasedongeneticalgorithmandfuzzycomprehensiveevaluationmethodcanprovideareliable,efficient,andsustainablesolutionforenergymanagementinmicrogrids.Byconsideringmultipleobjectivessimultaneously,theapproachcanachieveabalancebetweeneconomic,environmental,andsocialfactors,leadingtoamoresustainableenergyfuture.Theapproachcanalsoreducethecomplexityanduncertaintyofenergymanagement,facilitatetheintegrationofnewtechnologies,andenhancetheresilienceofmicrogridsFurthermore,thefuzzycomprehensiveevaluationmethodcanassistintheoptimalallocationandschedulingofenergyresourcesinmicrogrids.Thisiscrucialinensuringthattheavailableenergyresourcesareutilizedefficientlyandmeetingthedemandsofthecustomersinthemostcost-effectiveway.Themethodtakesintoaccountvariousfactorssuchasloaddemand,renewableenergyavailability,energystoragecapacity,andgridconstraintstodeterminetheoptimalallocationandschedulingofenergyresources.

Anotheradvantageofusingthefuzzycomprehensiveevaluationmethodisitsabilitytoconsideruncertaintiesandvariabilityassociatedwithrenewableenergysources.Unlikeconventionalpowersources,renewableenergysourcessuchassolarandwindpowerareintermittentandaffectedbyweatherconditions.Thefuzzycomprehensiveevaluationmethodcanincorporatetheseuncertaintiesinthedecision-makingprocess,thusimprovingtheoverallreliabilityofthemicrogridsystem.

Moreover,thefuzzycomprehensiveevaluationmethodcanbeusedtoanalyzetheperformanceofenergystoragesystemsinmicrogrids.Themethodcanevaluatetheeffectivenessofenergystoragesystemsinmitigatingthevariabilityofrenewableenergysourcesandreducingpeakdemandonthegrid.Thisanalysiscanleadtotheidentificationofpotentialimprovementsthatcanbemadetotheenergystoragesystems,leadingtotheirenhancedperformanceandgreaterefficiency.

Inconclusion,