文档名：基于集合经验模态分解和多目标遗传算法的火多储系统调频功率双层优化
摘要：针对分布于区域电网不同网络节点的多座储能电站参与电网调频功率调度问题,该文提出一种基于集合经验模态分解(EEMD)和多目标遗传算法(MOGA)的火-多储系统调频功率双层优化策略.该策略包含火-储调频功率优化层和多储能电站调频功率优化层:上层计及火-储调配资源各自优势及剩余调频能力,构建火-储调频功率优化分配模型,完成火-储调频功率的分配;下层引入关于调频成本和荷电状态(SOC)的自适应权重系数,以调频成本最低和SOC均衡为优化目标,完成调频功率在多储能电站之间的分配.仿真结果表明,所提策略可以提升区域电网调频效果并降低调频成本,均衡控制多个储能电站的调频成本和SOC,可以防止经济性较好的储能电站长期处于SOC越限边缘状态,提升储能电站参与调频的积极性和可持续性.
Abstract：Aimingatthepowerschedulingproblemoffrequencymodulationinvolvingmultipleenergystoragestationsdistributedindifferentnetworknodesofregionalpowergrid,atwo-layeroptimizationstrategyforfrequencymodulatedpowerofthermalgenerationandmulti-storagesystembasedonensembleempiricalmodedecomposition(EEMD)andmulti-objectivegeneticalgorithm(MOGA)isproposed.Thisstrategyincludesathermalpower-energystoragefrequencymodulationpoweroptimizationlayerandamultienergystoragepowerstationfrequencymodulationpoweroptimizationlayer:Theupperlayercountstherespectiveadvantagesofthermalpower-energystorageallocationresourcesaswellastheresidualfrequencymodulationcapacityofthermalpowerunitsandenergystorage,constructstheoptimizationdistributionmodelofthermalpower-energystoragefrequencymodulationpower,andcompletesthedistributionofthermalpower-energystoragefrequencymodulationpower.Thelowerlayerintroducestheadaptiveweightcoefficientaboutfrequencymodulationcostandstateofcharge(SOC)comprehensivestate,takesfrequencymodulationcostandSOCcomprehensivestateastheoptimizationobjective,andcompletesthedistributionoffrequencymodulationpoweramongmultipleenergystoragesystems.Inordertoverifytheeffectivenessoftheabovecontrolstrategies,thispapertakesanactualAGCcommandfromacertainlocationinChinaforsimulationanalysis.Thespecificconclusionsareasfollows:Firstly,thethermalpower-energystoragefrequencymodulationpowerallocationmodelconstructedinthisarticlecandecomposetheoriginalAGCinstructionsintotheAGCinstructionsthatthethermalpowerunitasawholeandtheenergystoragepowerstationasawholeneedtobear.Thedecomposedsignalhasagoodtrackingeffectontheoriginalsignal.Thefrequencymodulationsignalbornebythethermalpowerunitunderthisstrategyfollowstheoriginalfrequencymodulationsignal,whichis64.29%higherthanthetraditionalfilteringstrategy.Secondly,thedualleveloptimizationmodelforfrequencyregulationpowerproposedinthisarticlecanimprovetheoverallfrequencyregulationeconomyoftheregionalpowergrid.Comparedwiththecapacityproportionallocationstrategy,thefrequencyregulationcostofthermalpowerunitsisreducedby3.00%,andtheunitwearisalsoslightlyreduced;Comparedtotheproportionalallocationstrategy,theoverallunitenergyconsumptioncostofenergystorageplantshasbeenreducedby3.6%.Finally,thefrequencymodulationpowerallocationmodelproposedinthisarticlecancoordinatethefrequencymodulationcostandSOCrecoveryofeachenergystoragestation.Onthebasisofreducingtheoverallfrequencyregulationcost,theSOCmaintenanceeffectofeachenergystoragestationis34.78%betterthantheproportionalallocationstrategy;TheoverallSOCbalanceeffectofmultipleenergystoragepowerstationsis52.69%betterthantheproportionalallocationstrategy.
作者：李翠萍 司文博 李军徽 严干贵 贾晨 Author：LiCuiping SiWenbo LiJunhui YanGangui JiaChen
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国网辽宁省电力有限公司电力科学研究院沈阳110006
刊名：电工技术学报
Journal：TransactionsofChinaElectrotechnicalSociety
年，卷(期)：2024, 39(7)
分类号：TM73
关键词：多火电储能系统 二次调频 双层优化控制 多目标遗传算法(MOGA)自适应权重系数
Keywords：Multithermalpower-energystoragesystem secondaryfrequencymodulation two-layeroptimalcontrol multi-objectivegeneticalgorithm(MOGA) adaptiveweightcoefficient
机标分类号：TM732TM912TP301.6
在线出版日期：2024年4月12日
基金项目：国家电网有限公司总部科技项目基于集合经验模态分解和多目标遗传算法的火-多储系统调频功率双层优化[
期刊论文] 电工技术学报--2024, 39(7)李翠萍 司文博 李军徽 严干贵 贾晨针对分布于区域电网不同网络节点的多座储能电站参与电网调频功率调度问题,该文提出一种基于集合经验模态分解(EEMD)和多目标遗传算法(MOGA)的火-多储系统调频功率双层优化策略.该策略包含火-储调频功率优化层和多储能电...参考文献和引证文献
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基于集合经验模态分解和多目标遗传算法的火-多储系统调频功率双层优化.pdf
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