香港城市大学：陶悦川-AI在灵活负荷管理和电网协调中的应用

CollegeofBusinessWorldwideR202445thWorldwideGlobalRankingofAcademicSubjects2023WorldwidePerformanceRankingofScientifcPapersforWorldUniversitiesNationalTaiwanUniversityRanking202329thWorldwideU.S.NewsBestGlobalUniversitiesRankings2024ResearchExperienceandInterestsTheUniversityofSydneyVerifiedemailat.auPowersystemplanningElectricitymarketsSmartGridData-Analytics201Carbon-orientedoperationalplanningincoupledelectricityand201YWang,JQiu,YTao,JZhaoIEEETransactionsonPowerSystems35(4),3145-31572020Integratedelectricityandhydrogenenergysharingincoupledenergy2020YTao,JQiu,SLai,JZhaoIEEETransactionsonSmartGrid12(2),1149-1162386665h-index386665h-indexi10-indexAll3851385165900ResearchInterests:Specializedin:Combiningmulti-disciplineknowledgeofelectricalengineering,datascience,andeconomicsData-drivenlow-carbonenergysystemplanning(e.g.,optimalallocationofPVhydrogeninfrastructure,etcArtificialintelligence(AI)applicationsinenergymanagementandenergytrading(e.g.,data-drivenbehavioralmodelling,deepreinforcementlearningoptimization,data-drivendemandresponsemanageResearchResearchExperience:systemplanningandelecmarketmodeling,focusingonlow-carbonenergytransitioneconomicperspectives.optimization,machinelearningsmartgridswithrenewableenresourcepenetration.Oneofthefirstmoverstoutilizedata-drivenenergymanagementproblemsinResearchSummary √Earlyretirementofcoal-firedgeneratorsNetworkEnergystorageandUncertaintymodelingBehaviorsanalysisofend-usersDataEnergy√Demandresponse√MarketparticipationasVPPs·Improvedynamicnetworkoptimie-BusinessEqupmemAppiancesGenerationThemostaternet/e-BusinessWideAreaSystemFieldDeviceSource:NISTSmartGridInteroperabilityFrameworkAlfacilitatesAlfacilitatesDERintegrationinsmartgridstosupporenewablegeneration,EVdownstreamoptimization.basedonAlandlearningstructure.scenariostoaugmentdataset.forrobustdecisionsMainContributionsMachinelearningtechnologiesareappliedtomodeltheareutilizedtomodeltheuncertaintiesmoreaccurately.Ahybridlearningandoptimizationframeworkispuandfuturenetwor..Data-driData-DrivenlBatteCollaborativNetworkanH三AData-driveFast-ChargCarbon-CmrasneTnowArmzumuzK阳mmMethodforPowerSystemPlanningYuctumTa0,niugQnMcmb,E,andSmuyigLalerSt或ohiolioeypSdfmecaonforteDiMwSnpleprMW.业MatimamgyhngeafESS三年 t五时AAmirNossc1.Drmaper三WmEscreucoxm金2m2-7mproblem.Thelearning-basedalgorithmsbringnewperspectivesinaddressingthecomplexsystemproblemseffectivelyandefficiently.TransmissionTransmissionindividuals●RenewablePriceHistoricalYEndGeneratorNetworkPowerflowconstraintsOtherconstraintsSecurity&StabilityAssessment@PSSSystemPlanning@MATLABOperationperformanceevaluationUpdateNInvestmentevaluationSystemStateOutput:FinalplanningstrategyFutureFutureEVleaveActivepowerActivepowererror0Branches@tActivepowerflowerrorcomparisonAccuracy:91.04%Accuracy:91.27%-1-0.500.50.4-0.2-0.4-1Accuracy:90.23%Accuracy:99.67%0.4-0.6-0.8Accuracy:97.22%00.40.4ComparisonofactualandpredictedVESSstoragestatesLinearizedACPFSupervised-learning20000.000.010.020.03Errorofvoltagemagnitude(p.u.)0ThedistributionoftheEVfast-chargingdemandretire.TherenewablebeutilizedtocopewiththeCase3:TheCFPPswillretireaccordingtotheminimumcost.Thecarbonemissionwillpenaltytermtothecost(treatedasacarbontax),whichistheCase4:TheCFPPswillretireaveragecostofcarbonemissionreduction(Proposed)0860.81)8(0.063)(0.0013)(0.0013)IntensityAfter(0.0013X0.83)(0.0099)P2GStationCarbonIntensityofthesystem5.442*10^80/5.806*10^87.278*10^62.852*10^98.471*10^72.088*10^97.328*10^72.130*10^97.969*10^7DistribChargingDistribCharging三三dednpth三andSenVehiEHCTi生二三DemanEnvelopAData-drivenAgent-basedPlanningStrategyofFast-ChargingStationsforElectricVehiclesVYinerhknrlivhkesfosslfuc¹AoasholdbenoedouttecntonmisionmedastioefietofEciruitlrssandwolagvinlatianl11.HencetheutilayfacesbefonnulawediocupewiththeeverinoreaslsgchargingdemandncecpeclypnemhastelngmumnoEV.Tisasingtionnyholdathecarledweopineneof151LForeample.ref[6lmoposedadatrikeioallyobestwiththeincreaneinEYrglngdemunds181.Honeyer.therie8ducboThepenetrationofdistributedgeneratorsandtheadvancementoffacilitatethetransformationofthetraditionalpassivenetworkrequirementofthecomplexsystem,hybridlearningandisregardedasatooltosubstitutepartofoptimizationworks,suchenhancement.TogetherwiththeadvancedoptiSystems(IF:7.326),IEEETransactionsonSustainableEne※Machinelearningalgorithmscanquicklyidentifyanomaliesorgrid.Advancedoptimizationstrategiescanhelpindevisingcontingencyplans,makingthegridmoreresilienttofailuresandattacksTransmissionTransmissionnetworkDistributionnetworkBehind-meterCyber-physicalNetworkIndustrialResidentialCommunicationlinksBESSTCLsEVsBipartitegraphConsumersetConsumersetW;WMax-weight60-20%30%0.70.6--0-N=50.70.60Standalone45%70-40ProviderepresentativesignalsProviderepresentativesignalsDay-aheadschedulingDay-aheadpredictionofthenetworkvariablesReal-timeDRwithoperatingenvelopebasedonReal-timesensitivityfunctionproducedbytheReal-timedispatchofeachcontrollableresourcesReal-timeDR—Case1—Case2—Case3—Case4---UpwardLimit——Case1—Case2—Case3——Case4---DownwardLimit0.970.970.9650.9550.9450.935Currentprofileofdifferentcases.AAHybridClDemandResLearAHuman-NMethodforPoF蓦Customer- DataVehicle-to-Ved2.emmRwmsurammleesEawckeraegaonrakeocuea加AConsideringInformationAsymmetryuechuanTao,Jngolu,Member,IEEE,anaShuyingLadusertancenycanheiradedinoted⁸]LaThkeareappliedtomodeltheend-userbehaviorsinthefutureweathersensitivityaremarketparticipantsformulatedecision-makingstrategies26228230332ParameterOperatorActionDataprocessingformulizationActorMainNetDatasensingBufferActorDemandVoumeCriticTargetNetReward2000202002000Episode0005010015023DQNDDPGHuman-MachineDDPGDQNDDPG6.3s6.5s6.6sHuman-MachineDDPG6.6s6.8s7.1sDQNDDPGHuman-MachineDDPGResearcharea(3):Artificialintelligence(AI-basedenergymarketanalysisower(kW)ower(kW)0:003:006:009:004.06:009:00Power(kWPower(kW)TimeTimeDisaggregationresultsofTCLsconsideringmulti-typesoffeatures.0.090.090.080.070.060.050.030.0206:009:0012:0015:0018:0021:00一NILM-Integrated0Retailer'senergypurchasecostAlex-netdAEGRUS2PProposedRetailerRetailer'srevenueAlex-netdAEGRUS2PProposed0.04Alex-netdAEGRUS238000Alex-netdAEGRUS2PProposedAlex-netdAEGRUS2PProposedSystemtotalenergyprFig.12.ImpactofdifferentNILMapproachesonDRresultsProblemsofexistingAssumptionacquisitionequilibriumaggregated1.Assumptionof1.AssumptionofCompleteDataAcquiprivacy.ThismeansthatmodelsbasedontacquisitiondonotaccuratelyreflecttheuncertaintiesandpartialTherealsituationischaracterizedbyboundedrationalityandirrationality.Thissuggeststhatdecision-makersdonotalwaysmakeperfectlyrationalchoicesduetocognitivelimitations,lackofinformation,oremotionalfactors.Modelsassumingcompleterationalityandusingaspecificutilityfunctionmayfailtopredictactualbehaviors.3.Multi-agentinteraction:Analyticmodelsoftenpartiallyignoreotheragents'decisionsorinteractions,whichcanleadtoinaccuraciesbecause,inreality,thereissignificantinteractionbetweenmultipleagents(multi-agentinteraction)thataffectsoutcomes.Thisapproachfailstoaccountforthecomplexityofindividualbehaviorsandinteractionsinthesystem.Inpractice,itishardtomodelindividualsduetothediversityofbehaviorsandtheinfluenceindividualshaveoneachBasedonhistoricaldatainModelgenerationEstimatebehaviourscurrentmarketMixedbehaviourdistributionofhumanandcomp