文档名：基于区域间场景等值的配电网随机潮流加速算法
摘要：针对配电网随机潮流算法中需要对节点功率注入进行大量模拟所引起的计算效率低的问题,提出一种基于区域间场景等值的配电网随机潮流加速算法.首先,从配电网的拓扑连接规律出发,将各区域内节点功率注入对其他区域潮流的影响转换为对边界量的影响,并由此建立了基于边界量的多区域潮流模型;其次,提出一种区域间场景等值方法,快速等值引起其他区域相同潮流变化的功率注入场景;再次,设计了一种随机潮流的分布式求解算法,在求解过程中各区域通过交换边界等值场景集并结合局部功率注入信息重新构造潮流方程,分布式地求解各区域内的随机潮流;最后,通过IEEE118节点测试系统进行算例分析,验证了所提方法的准确性,且该方法能够显著提高计算效率.
Abstract：Inthestochasticpowerflowalgorithmofdistributionnetwork,thepowerinjectionneedsalotofsimulation,whichwillleadtotheproblemoflowcomputationalefficiency.Inrecentyears,scholarshavemadesomeachievementsintheresearchofscenereduction,butthedesignedmethodsmaystillfacetheproblemoflowcomputationalefficiencyafterbeingimplementedinsystemswithtoomanynodes.Therefore,thispaperproposesastochasticpowerflowaccelerationalgorithmfordistributionnetworksbasedoninter-regionalscenarioequivalence.Basedontheexistingmainstreamscenarioreductionmethod,thestochasticpowerflowcalculationisfurtheracceleratedbyaddingscenariosthatleadtotheinjectionofthesameboundaryquantityinotherregions.Firstly,basedonthetopologyconnectionlawofdistributionnetwork,theinfluenceofpowerinjectionscenariosineachregiononpowerflowinotherregionsistransformedintotheinfluenceonitsboundaryquantities,andthemulti-regionalpowerflowmodelthataffectseachotherthroughboundaryquantitiesisestablished.Secondly,aninter-regionalscenarioequivalencemethodisproposed,whichcanquicklyequalizescenariosthatcausethesamepowerflowchangeinotherregions.Then,adistributedsolutionalgorithmforstochasticpowerflowisdesigned.Eachregioncompletesthedistributedsolutionofstochasticpowerflowbyexchangingboundaryequivalentscenariosandcombininglocalpowerinjectioninformation.Accordingtothismethod,theIEEE118nodetestsystemissolvedwithpartitionandstochasticpowerflow.Simulationresultsshowthattheproposeddistributedalgorithmconvergesinallregions.Intermsofcalculationaccuracy,themaximumerrorsofvoltageandactivepowerbetweenthesceneequivalentimitationmethodproposedinthemainregionandtheMonteCarlosimulationmethodare0.069%and0.98%respectively,whilethemaximumerrorsofvoltageandactivepowerbetweenthesceneequivalentimitationmethodproposedinthesub-regionandtheMonteCarlosimulationmethodare0.058%and0.003%respectively.Itcanbeconsideredthatthecalculationresultsareconsistent.Intermsofcomputationalefficiency,thenumberofscenariosrequiredbyMonteCarlosimulationanddistributedsolutionalgorithmsis531441and43497,respectively.ComparedwithMonteCarlosimulation,theaccelerationratioofdistributedsolutionmethodis11.32,whicheffectivelyreducesthesimulationscenariosrequiredbystochasticpowerflow.Throughtheexampleanalysis,thefollowingconclusionscanbedrawn:(1)Bymergingthepowerinjectionscenariosthatcausethesameboundaryquantityinotherregions,theredundantinformationinthetransmissionprocesscanbeeffectivelyreduced.(2)Theproposedstochasticpowerflowdistributedalgorithmdoesnotrequirecentralizedprocessingandunifiedcalculationofthestochasticpowerflowinjectionsamplesofeachnode,butonlyneedstosolvethestochasticpowerflowresultsthroughthepowerflowsamplesofeachregionandexchangetheequivalentscenarioinformation.(3)Comparedwiththeexistingcentralizedstochasticpowerflowalgorithm,thedistributedstochasticpowerflowalgorithmcancalculatemoresamplesunderthesametimeconstraint.Inthenextstep,thepointestimationmethodcanbecombinedwiththeproposedinter-zonescenarioequivalencemethodtoimproveitscalculationaccuracyandenrichthedistributionnetworkregiondivisionmethod.
作者：李军徽 赵寒杰 朱星旭 郭琦 李翠萍 Author：LiJunhui ZhaoHanjie ZhuXingxu GuoQi LiCuiping
作者单位：现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学)吉林132012内蒙古电力(集团)有限责任公司电力调度控制分公司呼和浩特010020
刊名：电工技术学报
Journal：TransactionsofChinaElectrotechnicalSociety
年，卷(期)：2024, 39(7)
分类号：TM711
关键词：配电网 场景等值 分布式光伏 随机潮流
Keywords：Distributionnetwork sceneequivalent distributedphotovoltaic distributed stochasticpowerflow
机标分类号：TM744.1TH12TP391.1
在线出版日期：2024年4月12日
基金项目：内蒙古自治区科学技术厅揭榜挂帅项目，东北电力大学博士科研启动基金项目基于区域间场景等值的配电网随机潮流加速算法[
期刊论文] 电工技术学报--2024, 39(7)李军徽 赵寒杰 朱星旭 郭琦 李翠萍针对配电网随机潮流算法中需要对节点功率注入进行大量模拟所引起的计算效率低的问题,提出一种基于区域间场景等值的配电网随机潮流加速算法.首先,从配电网的拓扑连接规律出发,将各区域内节点功率注入对其他区域潮流的影...参考文献和引证文献
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基于区域间场景等值的配电网随机潮流加速算法 Stochastic Power Flow Acceleration Algorithm of Distribution Network Based on Scene Equivalence between Regions
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