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基于改进的模拟退火-粒子群算法的区域综合能源系统经济性运行.docx

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基于改进的模拟退火-粒子群算法的区域综合能源系统经济性运行 Title:EconomicOperationofRegionalIntegratedEnergySystemsBasedonImprovedSimulatedAnnealing-ParticleSwarmOptimizationAlgorithm Abstract: Withtheincreasingconcernsaboutenvironmentalsustainabilityandfossilfueldepletion,theintegrationandoptimizationofmultipleenergyresourceshavebecomeessentialfortheefficientandeconomicoperationofregionalenergysystems.Thispaperproposesanintegratedenergymanagementapproachbasedontheimprovedsimulatedannealing-particleswarmoptimization(SA-PSO)algorithmfortheeconomicoperationofregionalintegratedenergysystems.Theobjectiveistominimizethetotalcostofenergysupplywhilemeetingthedemandsofvariousenergysources. 1.Introduction Theintegrationofvariousenergysources,suchaselectricity,naturalgas,heating,andcooling,intoaregionalenergysystemoffersseveralbenefits,includingreducedrelianceonfossilfuels,improvedenergyefficiency,andlowergreenhousegasemissions.However,theoptimaloperationofsuchsystemsischallengingduetothediversecharacteristicsandconstraintsofdifferentenergysources.ThispaperpresentsaneconomicoperationapproachbasedontheSA-PSOalgorithmtoaddressthesechallengeseffectively. 2.Methodology Theproposedeconomicoperationapproachconsistsoftwostages:thesimulatedannealing(SA)stageandtheparticleswarmoptimization(PSO)stage.IntheSAstage,thermalunitsofintegratedenergysystemsareoptimizedtominimizethetotaloperatingcostundervariousconstraints.TheimprovedSAalgorithmacceleratestheconvergencespeedandenhancesthesearchabilitybyusingadynamicmutationstrategyandanewstoppingcriterion.InthePSOstage,theoptimaldispatchingofvariousenergyresources,includingelectricity,naturalgas,andheat,isachievedbyminimizingthetotalenergycostusingtheparticleswarmoptimizationalgorithm.TheinertiaweightandtheaccelerationcoefficientsareadaptivelyadjustedduringthePSOprocesstoimprovetheglobalsearchability. 3.CaseStudy Tovalidatetheeffectivenessoftheproposedapproach,acasestudyisconductedonaregionalintegratedenergysystem.Thesystemincludesdifferentenergysources,suchaswindpower,ph