单亲遗传算法对于在梯级水力发系统中多级电站的经济分配方法的改进作用毕业论文外文翻译Word格式文档下载.docx

单亲遗传算法对于在梯级水力发系统中多级电站的经济分配方法的改进作用毕业论文外文翻译Word格式文档下载.docx

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单亲遗传算法对于在梯级水力发系统中多级电站的经济分配方法的改进作用毕业论文外文翻译@#@外文资料原文@#@Animprovedparthenogeneticalgorithmformulti-objectiveeconomicdispatchincascadedhydropowersystems@#@Abstract：

@#@Themulti-objectiveeconomicdispatch（MOED）problemincascadedhydropowersystemsisacomplicatednonlinearoptimizationproblemwithagroupofcomplexconstraints.Inthispaper,animprovedparthenogeneticalgorithm（IPGA）forresolvingtheMOEDprobleminhydropowerenergysystemsbasedonthenon-uniformmutationoperatorisproposed.Inthenewalgorithm,thecrossoveroperatorisremovedandonlymutationoperationismade,whichmakesitsimplerthanGAinthegeneticoperationsandnotgenerateinvalidoffspringduringevolution.Withthehelpofincorporatinggreedyselectionideaintothenon-uniformmutationoperator,IPGAsearchesthesolutionspaceuniformlyattheearlystageandverylocallyatthelaterstage,whichmakesitavoidtherandomblindjumpingandstayatthepromisingsolutionareas.Finally,theproposedalgorithmisappliedtoarealistichydropowerenergysystemwithtwogiantscalecascadedhydropowerplantsinChina.Comparedwithotheralgorithms,theresultsobtainedusingIPGAverifyitssuperiorityinbothefficienc.@#@Keywords:

@#@multi-objectiveoptimization;@#@Improvedparthenogeneticalgorithm;@#@Non-uniformmutationoperator.Cascadehydropowerstationgroupof.@#@@#@Introduction@#@Optimizationofmulti-objectiveeconomicdispatch（MOED）incascadedhydropowersystemsisoneofthemostcomplicatedissuesinwaterresourcesmanagementasittypicallyinvolvestrade-offs.Forexample,asinglemultipurposereservoir,whichnotonlyserveshydropowerbutalsonavigation,itsdispatchermaywishtomaximizebenefitsfromhydropowergeneration,whilereleasingsufficientwaterfornavigationtosatisfythedemands.However,ahigherprofitfromhydropowergenerationwouldconflictwiththenavigationreleases,thatistosay,anyimprovementofoneobjectivecanbeachievedonlyattheexpenseofanother.Thecurveorsurface（formorethan2objectives）,describingtheoptimaltrade-offsolutionsbetweentheobjectives,isknownastheParetofront.Inreallife,mostofreservoirsystemsservemultiplepurposesandtheyaremulti-objectiveinnature.Duetothedispatchrulesforajointoperationofcascadereservoirsenabletodevelopthecapacityofhydropowergeneration,theMOEDproblemincascadedhydropowersystemsbecomesanactiveresearchareainrecentyears.ThegoalofMOEDincascadedhydropowersystemsistodeterminethewaterdischargeprocessofallhydropowerstationsduringtheschedulingperiodinordertomaximizethetotalbenefitwhilefulfillingvariousactualwaterdemandsandothercomplicatedconstraintssimultaneously.Becauseofthecomplexpowerandhydraulicrelationsbetweencascadedhydropowersystems,themulti-objectiveoptimaloperationofcascadehydropowerstationsisalargescale,dynamic,andstrongcouplingnonlinearproblem,whichinvolvesmanyvariables,suchas,inflow,storage,discharge,waterlevel,waterhead,outputandgeneratedenergy.@#@Sofar,differentmethodstosolvetheMOEDproblemhavebeenproposedanddiscussedbymanyresearchers.Thetraditionalmethods,suchas,mixedintegerlinearprogramming（MILP）,Lagrangerelaxation（LR）,nonlinearprogramming（NLP）,dynamicprogramming（DP）andprogressiveoptimalityalgorithm（POA）,havebeenwidelyappliedinthepast.Andmanyachievementshavealsobeenobtained.Nevertheless,allofthemethodslistedaboveexistsomeshortcomingswhichmakethemlessefficientandevendifficultinsearchingfortheoptimalsolution.WhenMILPisemployedtosolvetheMOEDproblem,linearizationofthemulti-objectivefunctionsandconstraintscoulddeviatetheoriginalproblem,whichmakesthenon-inferiorsolutioninaccuracy.ForNLPmethodology,someapproximateapproachesareadoptedtodealwithdiscontinuous,non-differentiableandnon-convexmulti-objectivefunctions,andthesemethodsarecomputationalexpensive.LagrangemultipliersupdatingstrategyhasanunfavorableinfluenceontheefficiencyofLR,whichmakesthestabilityofsolutionpoor.ThoughDPmethodcanresolvetheoptimaloperationofasinglemultipurposereservoir,itsuffersfromthe‘‘curseofthedimensionality’’whichmakesthecomputationtimeincreasedramaticallywhenthedimensionofcascadedhydropowersystemsincreases.POAiswidelyusedinthemulti-objectiveoptimaloperationofcascadedhydropowerplants,butitissensitivewiththeinitialsolution,whichgenerallyshrinksthesearchingareaandtrapsitinthelocalParetooptimalfronteasily.@#@Recently,therehasbeenanincreasinginterestinadaptiveheuristicsearchalgorithmsmodeledfromthebiologicallymotivatedadaptivesystems,forsolvingtheMOEDproblem,becauseoftheirpowerfulglobalsearchingcapacity,suchasgeneticalgorithm（GA）,antcolonyoptimization（ACO）algorithm,particleswarmoptimization（PSO）algorithm,andartificialbeecolony（ABC）algorithm.However,beingsamewiththeclassicalmethods,alltheseheuristicsstochasticsearchalgorithmsmentionedabovehavetheirdrawbackstoo.Forexample,sometimestheymaysufferfromprematureconvergence,andthesearchingabilityofthesealgorithmsissensitivetoparametersettings.GAisoneofthemostpromisingtechniquesinnaturaladaptivesystemfieldofevolutionaryalgorithmparadigmandhasreceivedgreatattention,becauseofitsflexibilityandeffectivenessforoptimizingcomplexsystems.Butsometimes,thetraditionalGAmayproduceaviolatingoffspringinthecross-overoperation,whichhasanegativeeffectontheefficiencyofGAbyreasonofhavingtorestoretheviolatingoffspringoradoptapenaltyfunctionintheobjectivefunctionstodiscardtheviolatingoffspring.Inordertoovercomethisdisadvantage,parthenogeneticalgorithm（PGA）isproposedasanimprovedGA.InGA,thecrossoverisalwaysseenasthemajoroperatorandthemutationoperatorjustplaysanassistantrole.ButinPGA,onlythemutationoperationismadeandnoinvalidoffspringisgenerated.@#@Accordingtotheparthenogeneticschematheoremsproposedinliterature,wefindthathighrankschemasinthesubsequentgenerationdecreaseexponentiallythoughtheirfitnessvaluesaremoreoptimalthantheaverageoneinthepopulation,whichmaycausethehighrankschemasthatmatchtheglobaloptimalindividualtobedesertedfastbeforetheglobaloptimalindividualisfound.ItseemsthatPGArunsawayfromtheareawheretheglobaloptimalindividualliesthoughsearchnearsthearea,whichreducesthesearchingefficiencyofPGA.Inordertoovercomethisshortcomingandavoidtheundesirabletendency,thispaperpresentsanimprovedparthenogeneticalgorithm（IPGA）basedonthenon-uniformmutationoperatortosolvetheMOEDproblemincascadedhydropowersystems.Meanwhile,thegreedyideaandtheideaofmutatingasinglecomponentoftheindividualvectorratherthanmodifyingallthecomponentsareincorporatedintoIPGAinordertoavoidpossiblerandomjumpsandtoensurethealgorithmtostayatthepromisingareajustfoundbythesearchengineforthebetteroptimalindividual.@#@Therestofthepaperisorganizedasfollows.Section‘Numericalmodel’describesthemodeloftheMOEDproblemincascadedhydropowersystems.Section‘Improvedparthenogeneticalgorithm’presentstheIPGAandintroducesitsrealizationprocessindetail.Section‘ImplementationofIPGAforeconomicdispatchincascadehydropowersystems’evaluatestheproposedalgorithmandcomparesitwithdifferentalgorithmsusingthedatafromahydropowerenergysystemwithtwocascadereservoirsinChina.Finally,weconcludethispaperinSection‘Conclusions’.@#@Numericalmodel@#@Theeconomicdispatchofcascadedhydropowerplantsisatypicalproblemintheoptimalfieldsofhydropowerenergysystem.Usually,theoperationpurposeistomaximizethetotalpowergenerationbenefitsortotalelectricityproductionofcascadedhydropowerplantsduringtheschedulingperiodbydeterminingtheoptimalprocessofwaterdischargevalueofhydropowerstations,undertheconditionofsatisfyingallconstraints.However,withthedevelopmentofsocialeconomyinChina,changeofpowerconsumptionstructurerequiresmostlarge-capacityunitstobeinvolvedinpeakloadregulationofelectricitygrid.Inordertoguideoptimaloperationofcascadereservoirsandgivefullplaytocapacitybenefitsofcascadehydropowerstations,amathematicalmodelisestablishedbasedontheprinciplesof

（1）maximumtotalelectricityproductionand

（2）maximumtotalleastoutputofthecascadedhydropowerplantsinthispaper.Thespatialcouplingofahydropowerenergysystemwithtwocascadereservoirsisshownin,andtheobjectivefunctionsandconstraintsofaforementionedmathematicalmodelareexpressedasfollows:

@#@@#@Obviously,thisisamulti-objectiveoptimizationproblem.Weightedapproachandconstraintmethodareusuallyemployedbymanyresearcherstohandleitinwaterresourcesmanagement.Fortheconstraintmethod,allobjectivesexceptthemajoroneareconstrainedtospecificvaluestoyieldaParetooptimalsolution.Withincrementofthespecificvalues,themodelisrunagaintofindanotherParetooptimalsolutionuntilthetrade-offrelationshipbetweentheobjectivesissufficientlyrepresented.Inweightedmethod,allobjectivesareincorporatedintooneobjectivefunctionsimultaneouslybydifferentweights,andadifferentsetofweightsisadoptedineachrunoftheoptimizationmodelfortheoptimaltrade-offsolution.However,theconstraintmethodhasbeenusedmorefortheMOEDprobleminasinglemultipurposereservoirorcascadedhydropowersystemsbyvariousresearchers.Becausewhenthenon-inferior