简介:Accuratepredictionofwindpowerissignificantforpowersystemdispatchingaswellassafeandstableoperation.BymeansofBPneuralnetwork,radialbasisfunctionneuralnetworkandsupportvectormachine,anewcombinedmethodofwindpowerpredictionbasedoncooperativegametheoryisproposed.Inthemethod,everysingleforecastingmodelisregardedasamemberofthecooperativegames,andthesumofsquareerrorofcombinationforecastingistakenastheresultofcooperation.TheresultisdividedamongthemembersaccordingtoShapleyvalues,andthenweightsofcombinationforecastingcanbeobtained.Applicationresultsinanactualwindfarmshowthattheproposedmethodcaneffectivelyimprovepredictionprecision.
简介:ThispaperintroducesaMonteCarloscenariogenerationmethodbasedoncopulatheoryforthestochasticoptimalpowerflow(STOPF)problemwithwindpower.Byusingcopulatheory,thescenariosaresimulatedfrommultivariablejointdistributionbutonlyfromtheirdependencymatrix.Hence,thescenariosgeneratedbyproposedmethodcancontainfullstatisticalinformationofmultivariate.Here,thedetailsofsimulatingscenariosformulti-wind-farmareexplainedwithfoursteps:determinemarginofonewindfarm,fitthecopulas,chooseoptimalcopulasandsimulatescenariosbyMoteCarlo.Moreover,theproducingprocessofscenariosisdemonstratedbytwoadjacentactualwindfarmsinChina.Withthescenarios,theSTOPFisconvertedintothesameamountdeterministicsubOPFmodelswhichcanbesolvedbyavailabletechnologyperfectly.Resultsusingcopulatheoryarecomparedagainstresultsfromhistorysamplesbasedontwodesigns:IEEE30-busandIEEE118-bussystems.Thecomparisonresultsprovetheaccuracyoftheproposedmethodology.
简介:2013年初藏中电网内的乃琼、夺底、曲哥站完成SVC装置投运.如何对三站的SVC进行控制,保障电网安全成为了调度部门研究的重点.本文结合藏中电网实际,提出不基于通信的联合控制策略,并应用于电网实际运行中,取得了良好效果.