文档名：面向两个细则考核的风储场站日前日内两阶段功率优化上报策略
摘要：针对两个细则考核风功率上报准确率的问题,提出一种可有效降低风-储场站风险考核电量的日前-日内两阶段功率优化上报策略.首先,提出面向两个细则的风-储场站日前-日内功率协同上报框架;然后,以期望考核电量最小化为目标,建立计及储能调节的日前-日内两阶段风-储功率优化上报模型,模型中,日前、日内风功率场景集通过基于预测误差分箱的Cornish-Fisher级数与Cholesky分解法生成;最后,针对模型的多重非线性特征与不同时间尺度的求解效率要求,制定基于小步长线性化迭代的求解算法.算例仿真结果表明,相较其余上报策略与求解算法,该文提出的日前-日内协同上报策略及相应算法有效地提升了上报准确率及求解性能.
Abstract：Therationaloptimizationofthepowerreportedbywindfarmstothegridisacriticalroutinetoimprovethebenefitsofwindfarmsundertheassessmentofthe'twodetailedrules'.However,mostofthetraditionalwindpowerreportingstudiesreportthepredictedpowerdirectly,orreporttheexpectedwindpowerobtainedbysuperimposingtheforecasterrordistributionswiththepredictedpower.Inaddition,thesalesrevenue,thecompensationforancillaryservicesareemployedasthemainoptimizationgoalsinthesestudies.Hence,itisdifficulttoapplythetraditionalreportingstrategiesdirectlyunderthebackgroundofthe'twodetailedrules'assessment.Toaddresstheseissues,thispaperproposesatwo-stageday-aheadandintra-dayoptimizedpowerreportingstrategyofwind-storagestationsthatfitsthemechanismofthe'twodetailedrules'assessment.Firstly,theframeworkoftheproposedstrategyisconstructed,wheretheday-aheadreportingresultsareembeddedintheintra-dayreportingmodelasthekeytosynergy,inordertoachievetheoptimalpowerreportingonbothtwotimescalesforthe'twodetailedrules'assessment.Secondly,thespecificassessmentmechanismofthe'twodetailedrules'incentralChinaisusedasanexampletoformulatetheday-aheadandintra-dayoptimizedwindpowerreportingmodelrespectively.Particularly,theexpectedassessmentpowerofday-aheadwindpowerscenariosisregardedastheobjectivefunctionoftheday-aheadreportingmodel.Besides,thereportedwindpowerseriesobtainedbytheday-aheadmodelisutilizedintheintra-dayreportingmodel.Withtheenergystorageasthemedium,thetotalassessmentpowerofallintra-daywindpowerscenariosonthetwotimescalescanbeminimizedintheintra-daymodel.Asforthescenariogeneration,theday-aheadandintra-dayhistoricalerrorsarebinnedaccordingtometeorologicalconditionsanddifferentforecasttimestepsrespectively.TheCornish-FisherseriesandCholeskydecompositionareappliedtogenerateandreordertheerrorscenariosconsideringtemporalcorrelations.Bysuperimposingthereorderederrorscenarioswiththepredictedpower,theday-aheadandintra-daywindpowerscenarioscanbeobtainedtorefinethewindpowerreportingmodel.Owingtotherequirementsofdifferenttimescalesforthemodelsolving,atwo-stagealgorithmisproposed.Intheday-aheadstage,duetothesmallscaleofthemodel,theimprovedbaldeaglesearch(IBES)algorithmwithstrongglobalsearchabilityisadopted.Intheintra-daystage,inordertomeettherequirementsof15-minutesrollingreportingforsolvingefficiency,asmallsteplinearizediteration-basedsolvingalgorithmisdevelopedtotransformtheintra-daymodelwithmultiplenonlinearconstraintsintolinearmodels.SimulationresultsonanactualwindfarmincentralChinashowthat,thedifferencebetweenthecorrelationcoefficientsofreorderederrorscenariosandhistoricalerrorsamplesarenomorethan0.0228onaverage.Furthermore,comparedwithreportingtheforecastpowerandreportingtheoptimizedpowerbasedonindependentlysolvingtheday-aheadandintra-daymodel,thetwo-stagecollaborativepowerreportingstrategycanreducetheassessmentpowerbymorethan50%inbothtwotimescales.Inaddition,thesmallsteplinearizediteration-basedsolvingalgorithmcanefficientlysolvetheintra-daymodelonaverage30s,whichislessthanothertypesofintelligentalgorithms.Meanwhile,comparedwiththeregulationoftheenergystoragebasedonthepredictedpower,theproposedreportingstrategysavesabouthalfofthedischargeelectricity.Thefollowingconclusionscanbedrawnfromthesimulations:(1)Thereorderedwindpowerscenariosismoreinlinewiththecorrelationcharacteristicsofhistoricalsamples,whichlaysafoundationforthemodelingofreporting.(2)Comparedwithotherreportingstrategies,thetwo-stagecollaborativereportingstrategycanbetterfitthetrendofactualwindpowerandreducetheassessmentpower.(3)Thesmallsteplinearizediteration-basedsolvingalgorithmbalancestheoptimizationeffectandsolutionefficiency,whichisworthyofpracticalapplication.(4)Theproposedenergystorageoptimizationschemeismoreenergy-efficientthantheschemebasedonthepredictedpower,andithelpstoextendthelifeofenergystorage.
作者：吴浩天 柯德平 刘念璋 方珂 郑景文 Author：WuHaotian KeDeping LiuNianzhang FangKe ZhengJingwen
作者单位：武汉大学电气与自动化学院武汉430072国网江苏省电力有限公司南京供电公司南京210019国网湖北省电力有限公司电力科学研究院武汉430077
刊名：电工技术学报
Journal：TransactionsofChinaElectrotechnicalSociety
年，卷(期)：2024, 39(1)
分类号：TM76
关键词：两个细则 风功率上报 日前-日内协同优化 时间相关性 储能调节
Keywords：Twodetailedrules windpowerreporting day-aheadandintra-daycoordinativeoptimization temporalcorrelation regulationofenergystorage
机标分类号：F064.1TP393V448.25
在线出版日期：2024年1月18日
基金项目：国家电网公司总部科技项目面向两个细则考核的风-储场站日前-日内两阶段功率优化上报策略[
期刊论文] 电工技术学报--2024, 39(1)吴浩天 柯德平 刘念璋 方珂 郑景文针对两个细则考核风功率上报准确率的问题,提出一种可有效降低风-储场站风险考核电量的日前-日内两阶段功率优化上报策略.首先,提出面向两个细则的风-储场站日前-日内功率协同上报框架;然后,以期望考核电量最小化为目标...参考文献和引证文献
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面向两个细则考核的风-储场站日前-日内两阶段功率优化上报策略 Two-Stage Day-Ahead and Intra-Day Optimized Power Reporting Strategy of Wind-Storage Stations for the'Two Detailed Rules'Assessment
面向两个细则考核的风-储场站日前-日内两阶段功率优化上报策略.pdf
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