盈利型虛擬電廠的市場(chǎng)策略（英文）

中國(guó)電機(jī)工程學(xué)金

901ANNVEsA

中國(guó)電機(jī)工程半金|IET

The5thCSEE-IETEnergy&PowerDevelopmentForum第五屆CSEE-IET能源電力發(fā)展論壇

盈利型虛擬電廠的市場(chǎng)策略

MarketStrategiesforProfitableVirtualPowerPlants

ProfessorJianguoZHU

CentreforFutureEnergyNetworks(CFEN)

FacultyofEngineering,UniversityofSydney,Australia

SchoolofElectricalandComputerEngineering.

Fmail:jtanguo.zhu@.au

October11,2024Bejing

Contents

·

Australia'sEnergyTransition

·VirtualPowerPlants(VPPs)forSimultaneousOptimalNetworkandMarketOperations

AssemblyandCompetitionStrategyforVPPswithMultipleESPsthroughA“Recruitment-Participation”Approach

AFlexibleVPPRecruitmentApproachtoCaptureUnconventionalArbitrageOpportunities(UAOs)towardsImprovedProfits

●AHolisticP2PTradingMarketinaVPPEnvironmentConsideringActiveandReactivePowerSimultaneouslyandItsRiskManagementStrategies

·OptimalCoordinationStrategyforanInternalMarketamongMultiple

Network-ConstrainedVPPsviaMulti-AgentDeepReinforcement

Learning

Australia'sEnergyTransition

·

AustraliaisundertakingthesinglegreatesttransformationofitsenergyindustrytomeetitsEommltment

tonetzeroCO?emissionsby2050.Coalfiredpowerplantsarebeingphasedoutandreplacedbycleanrenewablepowergenerations.TheAustralianEnergyMarketOperator(AEMO)projectsthatthetotalinvestmentrequiredtomeetthecleanenergytargetbetweennowand2050willbeAUD320bn.

·

Thecurrentarchitectureofourexistingcentralisedelectricitygeneration-transmission-distributionsystem

cannotdeliverthattransition.Thepillarsofthisenergytransitionwillseeamassiveincreaseincapacity

requiredinthestationaryenergysector(generationandstorage),thetransitionofthetransportsector

fromhydrocarbon-fuelledtoelectricvehicles(EVs),and

theintelligentintegrationofdistributedenergyresources(DERs)intothegrid.

·Thenewelectricityparadigmwillbede-centralised.

Microgrids,clustersoDERs,andvirtualpowerplants

(VPPs)willbecomethefundamentalunitsofenergy

activityinthenewenergylandscape.AEMOsuggests

30%oftotalinvestment(AUD92bn).

thatDERinvestmentcouldaccountforasmuchas

·NewdigitalelectricenergysystemsbasedonloT,

machinelearning,peer-to-peer(P2P)marketand

distributedmulti-agentrobustoptimizationare

beingdevelopedtoachievesimultaneousoptimal

networkandmarketoperationsforimproved

resilienceandeconomicbenefits.

ForecasNMapackyto2050.5opChangesceraro

CouneAM0202mg

AVPPconnects/aggregatesgeographicallydispersedDERsandmicrogrids,including

·VirtualPowerPlants(VPPs)

renewableandnon-renewableenergysources,batteries(alsoEVs),flexibleloads,byIoT

·networks.

Objectivesandconstraints:

TheVPPmanagementsystembasedonthedata-drivenapproachwithanalytictoolsin·cloudcomputercoordinatesthesimultaneousoptimalnetworkandmarketoperations.

highsystemreliability,efficiency,resilience,powerquality,andeconomicbenefits.

PowerSystem

PowerGridVPP

ControlCentre

動(dòng)

要

Elec

hcity

PowerLineWiredorwirelessloT

[1]DigitalEnergySystems:Challenges,opportunitiesandtechnologies,USYDCFENVPPWhitePaper,October2020,

.au/content/dam/corporate/documents/faculty-of-engineering-and-information-technologies/industry-and

-government/the-warren-centre/vpp-white-paper-the-warren-centre.pdf

Optim

AIPowered

Security

quE

Load

ElectricityMarketmanag

entrythresholdis1MWincapacitywitha

AI

techniques

settlementtimeof5min,whichpresents

ThecurrentAustralianwholesalemarket

hugeopportunitiestoDERsandVPPs,that

supportedbystrongcapabilitiestopredict

haveprofitablemarketoperationstrategies

themarketelectricitypriceaccuratelyand

managetheirresourceseffectivelytomeet

themarketrequirements.MarkettradingdataConsumersdata

Centralised

Distributed

RecentStudiesonMarketStrategiesforProfitableVPPs

·ThecurrentindustryrunVPPsarecentrallycontrolled.Asitsmembersdonothavetheright

Tosupportthischangebymeetingtherisk-profitrequirementsofdifferentDERparticipants,a

non-energy-service-provider(ESPJ-centricVPPconstructionapproachisproposed,bywhich

tocontroltheirPVsandbatteries,DERswouldhavemanyconcernswhenparticipatinginVPPs,·includinglowreturns,andthus,itlacksincentivesforDERstoparticipateinVPPs

multipleESPsofferdifferentVPPplansandrecruitDERstoimprovefinancialprofits[2].

·AflexibleandprofitableVPPrecruitmentapproachisproposedtomaximizeprofitswhenan

unconventionalarbitrageopportunity(UAO)occurs[3]byintegratingregularandcasual

algorithmwasproposedtooptimizetheincentivecoefficient,whichcanencourageDER

recruitmentmechanisms,cateringtoconservativeandambitiousDERparticipants.ACEPR-TD3

participantsandhelptheESPachievehighprofits.

·Ourrecentstudies[4]and[5]showthatadecentralizedVPPwithaninternalP2Pmarketand

andmoreeconomicalbenefitsthroughelectricityandancillarymarketoperations.

aninternalmarketamongmultipleVPPsinaclustercanyieldbettertechnicalperformance

[2]XinLu,JingQiu,CuoZhang,GangLei,JianguoZhu,"AssemblyandCompetitionforVirtualPowerPlantswithMultipleESPsthroughA'Recruitment-Participation'Approach,IEEETransactionsonPowerSystems,Vol.39,No.2,p.4382-4396,March2024,DOl:

10.1109/TPWRS.2023.3296738

[3]XinLu,JingQiu,CuoZhang,GangLei,JianguoZhu,"Seizingunconventionalarbitrageopportunitiesinvirtualpowerplants:Aprofitableandflexiblerecruitmentapproach",AppliedEnergy,Vol.358,15March2024,122628,doi:10.1016/j.apenergy.2024.122628

[4]YuanMeng,JingQiu,CuoZhang,GangLei,andJianguozhu,"AHolisticP2PTradingMarketinaVPPEnvironmentConsideringActiveandReactivePowerSimultaneously",AppliedEnergy,Vol.356,2024,122396,DOl:10.1016/j.apenergy.2023.122396.[5]XiaoLiu,SinanLu,JianLearning",IEEETransactionsonSmartGrid,,Vol.14,No.4,July2023,pp.3016-3031,DOl:10.1109/TSG.2022.3225814

AssemblyandCompetitionforVirtualPowerPlantswithMultipleESPs

Knowledgegaps:(1)PreviousstudiesignorehowtoincentivizeDERownerstoparticipatein

·throughA“Recruitment-Participation”Approach(1/4)

theVPP,anditremainsuncertainwhetherprovidingpayoffincentiveswouldyieldahigherparticipationrateamongDERparticipants.(2)ExistingVPPsareESP-centered,i.e.,ESPsaretheprofitallocationrulemakers.Thus,theprofitsofESPsandindividualDERscannotbe

fairlydistributed.(3)AsingleVPPcannotcatertothediverseneedsofDERowners.Itis

unknownwhethertheassemblyandcompetitionofmultipleVPPswillaffectindividualDERreturnsandoverallparticipationrates.

·Maincontributions:(1)Arecruitment-participationapproachisproposedtoreplacetheESP

centeredVPPconstructionmodel,whereESPsofferdifferentrisk-returnstrategiesand

recruitDERstobuildaVPPtogether.(2)Apayoffallocationmethodbasedonfairnessand

incentivesisintroduced.AfairprofitallocationmethodisdevelopedforindividualDERsandESPsbasedontheSharpleyValue(SV)method.TheincentiveapproachaimstoencourageDERstoparticipateinVPPsthroughrebates.(3)TomeetthediverseneedsofDERs,the

paperproposesamulti-ESPVPPmodelthatconsidersDERs'behaviorsandallowsforESP

selection.DRLisemployedtosolvetherebatecompetitionproblemamongESPs,achievingastablesolutionamongESPs.

[2]XinLu,JingQiu,CuoZhang,GangLei,JianguoZhu,"AssemblyandCompetitionforVirtualPowerPlantswithMultipleESPsthroughA“Recruitment-Participation”Approach”,IEEETransactionsonPowerSystems,Vol.39,No.2,pp.4382-4396,March2024,DOI:

10.1109/TPWRS.2023.3296738

AssemblyandCompetitionforVirtualPowerPlantswithMultipleESPs

throughA“Recruitment-Participation”Approach(2/4)

1間(g

VPPAssembly

ig1

ESP:MultipleESPsjoinasparticipants;eachESPprovidesaprospectustoattractDERsbeforeanOC

DER:PartialDERschoosedifferentESPstoassembleVPPsaccordingtothepublishedprospectusandtheirownpreferences.

DisassemblyandReassembly

ESP:Thehigh-riskandlow-profitESPswilleventuallydisassembletheVPP.

DER:ThedisassembledDERcanchooseotherESPs.

器踐

VPPCompetition

?

露

ESP:ESPsproviderebatestoDERparticipantstorewardthem

器

,

器

DER:TherebatespromoteESPreselectionfromPVandbatteryparticipants

Fig.IProposed'Recruitment-Participation'approachforVPP.

Prospectus

★中

■ESPsPublishProspectus

DERsSelectESPswpPAssembh

VPPOperation

PayofEatimation▲PayoffAllocation

0C3

D1-7ID8

0C1

ESPsattractDERsbypublishingaprospectus,whichincludesthefollowinginformation:

OC2

AsummaryoftheESP'sbackground,>BasicinformationontheVPP,including

D15-2110221

Predictedprofits;Nominalrisks;andRebatecoefficients;HistoricalinformationofVPP(ifavailable),including

Fig.2VPPAssemblyFlowforOperationCycles(OCs)

VPPprofitsandfluctuations;ReturnsforDERs

AssemblyandCompetitionforVirtualPowerPlantswithMultipleESPs

AlgorithmISimulationofESPSelectionfrDERParticipantsmarketattimetwith

1:ForDERParticipantj=1:J:predictedscenariowcanbecalculatedas:

4:prospectuspublishedbyESPmpYPP=P?Ypy-Hshpch+μ:chpdch

::tbiaie:oett,uL,,,Rs,DE.calefromφtm=plTppYPp

5:Generaterandomfactorθother

6:CalculatepotentialfforESPmsclectedbyDERj.μsh+μtch≤1

7:EndpPYpy,Pch,pdch≥0

0poe1g12wucicnEsrs1oDenj.

9:DERJdeteminestheselectedESP

10:End

Participationornon-participationModelForDER

SoCmin≤SoCSoCo=SoC

Risk-NeutralObjective:

ESPSelectionModelbyDERParticipants

「=5?θReturnL+ξ?θReturns+ξ?(1-θRisk)+54(1-θFluc)+ξ?θscale

+5?θother

DERParticipantType

yP

RiskProtile

Expectation

Professionaymbihot

ModerateConscTvadiv

autious

akesNecessaryRisks

HighlyRiskTaking

ComfortuableIevelsofRisk

RiskAvers

FxtremelyRiskAverse

MaxinumReturn

HighShort-TemmRetum

Good,SteadyRetum

RegularRetumMmimumRetum

AssemblyandCompetitionforVirtualPowerPlantswithMultipleESPs

throughA“Recruitment-Participation”Approach(4/4)

g20

4

Fg3Proantadoor35VPs

Fig.4PayoffforPVandbatteryindividualsforVPP1,4,and6

VPP3VPP4

Fig.5ChangeinthenumberofPVandbatteryparticipantsfor200Cs

Conclusion:

replacetheESP-centeredVPPconstructionmodel,

whereESPsofferdifferentrisk-returnstrategiesand

·Arecruitment-participationapproachisproposedto

recruitDERstobuildaVPPtogether

·Apayoffallocationmethodbasedonfairnessandincentivesisintroduced.

·DRLisemployedtosolvetherebatecompetitionproblemamongESPs,achievingastablesolution

amongESPs.

throughA“Recruitment-Participation”Approach(3/4)

AFlexibleVPPRecruitmentApproachtowardsImprovedProfits(1/3)

ProfitableandFlexibleApproach-Introduction:

·Intheelectricitymarket,unconventionalarbitrageopportunities(UAOs)often

fossilfuelpricesandweatherconditions.

appearirregularlyduetovariousnon-conventionalfactors,suchassuddenchangesin

recruitment-participationapproachincorporatingbothlong-termregularandshort-

·TocaptureUAOsformaximumprofits,weproposeaprofitableflexibleVPP

fairandbet-onmodes.Inthebet-onmode,asetofpre-determinedpayoffconditions

termcasualrecruitments.CasualrecruitmentcaterstoambitiousDERparticipantsin

areestablished.Thefulfillmentornon-fulfillmentofthepayoffconditionsconfersthe

participantsacontractualrighttogetcompensationfromtheESP.

·Toensurethesuccessoftheproposedrecruitmentapproach,

-Firstly,weintroduceanewindex,UAO,toevaluatefutureprofitsandproposeaconditionaltimeseriesgenerativeadversarialnetwork(CTSGAN)topredictUAOwithweatherconditions.

incentivestomotivatecasualDERparticipants.Theincentivecoefficientsare

optimizedusinganimprovedDRLalgorithm.

-Secondly,weintroduceapayoffallocationmethodthatcombinesfairnessand

[3]XinLu,JingQiu.CuoZhang.Ga

andflexiblerecruitr

nergy.2024.122628

gLei,Jianonalarbitrageopportunitiesinvirtualpowerplants:Aprofitable

helptheESPachievehighprofits.

profits.Iftheseexpectedprofitsarenotrealized,theconcernedDERparticipants

network(CTSGAN)-basedUAOpredictionmethodthataccountsforweather

theincentivecoefficienttoencourageDERsand

MF.Dyurs,etal[19]

2021SmartBuildngClusters

Ye

No

No

OptimalPostFow

Y.Xiaetal[20]

2023Commurities

Ye

No

No

vVC

DF.Botethoetal[21]

2MerogridClusters

Yes

Yes

No

P2PNetwerkConstrint

M.Shietal[22]

2023McrogidChsters

Ye

Yes

No

WVC

oposed

VPP

Yes

Yes

Yes

RectiePauep2pT

AFlexibleVPPRecruitmentApproachtowardsImprovedProfits(2/3)

ProfitableandFlexibleApproach-Framework:

·

Step1UAOPrediction

UAOPrediction

ESPpredictstheUAOtodeterminetheneedforcasual

recruitment.Thisstepoccursbefore18:00onthe

1800

recruitmentday(thedaybeforetheoperationday).

·

Step2CasualRecruitment

ESPcomparesthepredictedUAOvaluewiththeUAOthreshold(UAT).IftheUAOexceedstheUAT,theESP

willpublishtheincentivecoefficientsandexpected

payoffstoattractcasualparticipants.Then,thecasual

DERparticipantsarecombinedwiththelong-termregularrecruitmentonestoaggregatetheVPP.

·

Step3VPPOperation

Regula

Ontheoperationalday,theESPmanagesallrecruitedPVandbatteryparticipantsasaunifiedVPPto

participateintheelectricitymarkettoincreaseprofits.

·

Step4ProfitAllocation

Onthefollowingday(allocationday),theprofitsare

thenallocatedtotheparticipants,whichutilizesthe

Sharpleyvalue(SV)methodforregularrecruitment

participantsandafairandincentivizedallocation

OperationAllocation

methodforcasualrecruitmentparticipants.

Fig.25FrameworkoftheproposedDERparticipantrecruitmentapproach

AFlexibleVPPRecruitmentApproachtowardsImprovedProfits(3/3)

Maincontributions:

assessfutureprofitopportunities.Aconditionaltimeseriesgenerativeadversarial

(1)Anunconventionalarbitrageopportunity(UAO)isproposedtodescribeand

conditionsisproposed,whichcanhelpanESPanticipatefutureUAOscenarios.

(2)TomaximizeprofitswhenanUAOoccurs,aflexibleandprofitableVPP

mechanisms,cateringtoconservativeandambitiousDERparticipants.

recruitmentapproachisproposedthatintegratesregularandcasualrecruitment

(3)Theintroductionofabet-onagreementeliminatestheprerequisiteforDERs

aggregatingspecificDERparticipantswhohavereservationsaboutexpected

andtheESPtoreachaconsensusbeforeVPPformation.Thisallowsfor

areeligibletoreceivecompensationfromtheESP.

Acombinedexperiencepoolreplay(CEPR)-TD3algorithmisproposedtooptimize

(4)Differentincentiveapproachesareproposedfordifferentrecruitmentmodes.

Fig.2Multiple-recruitment-mode-based

approachforDER

Fig.3.Risk-returnforregularandcasualparticipants

w/

w/o

w/

w/o

w/

w/o

Regular

1.055

2.972

1025.23

882.66

Casual(fair

1.289

1.002

3.283

3.103

Casual(bet-on,TA

1.319

093

3.29

3.203

al(b-on,TN

1.320

0.947

3.297

3.037

Regular

1.327

4.537

1693.42

1003.72

Casual(fair)

1.440

1.091

6.776

3.973

Casual(bet-on,TA

1.4

1.183

6.973

4.841

Casual(bet-on,TN

1.442

1.130

6.983

4.502

AHolisticP2PTradingMarketinaVPPEnvironmentConsidering

ActiveandReactivePowerSimultaneously(1/2)

ComparisonAuthon

amongtheYea

existinglocalP2Pmarketsconsideringyoltagecontrolgroblemptimlcomebftofvohg

Thad-PartyEntitis[ssuP2Ptadingcontrolproblem?Voltagecontrolmethod

p040b1

NARIOANDSIMULATIONRESULTS

TABLEISCE

StraeB

DARosults

DResults

02

C食

AUD

5884.6

55533

380.8

5142.8

NetworkLos(MWh

1.35

135

0.18

0.18

TotalCos

(AUD

5955.1

5341.7

5155.8

4892.7

NetworkL05(MWh

1.01

1.01

0.15

0.1

TotalSaving(AUD

542.9

728.8

1

network

ElectricitymarketsrelatedtotheproposedVPPmodel.ForecasteddataofPVandload

farket

ar

WholesakeM

P

4]YuanMeng,JingQiu,GangLei,andJianguoZhu,"AHolisticP2PTradingMarketinaVPPEnvironmentConsideringActiveandReactivePower

Simulta

024.396

)

6/j.apenergy.2023.122396

AHolisticP2PTradingMarketinaVPPEnvironmentConsidering

ActiveandReactivePowerSimultaneously(2/2)

VPPcustomers'totalcostcomparison:cost

differencesbetweena)-b)w/wolocalP-P2PMarket;c)-d)w/wolocalQ-P2PMarket.

PV3,e)PV19,andf)PV31

·TheP2PmarketismonitoredbyaVPPcoordinatortorealize

showthattheproposedmodelcanprovideconsiderable

simultaneousP-P2PandQ-PzPtransactions.Theresults

opportunitiesforVPPcustomerstotradewitheachother,

reducingtheirelectricitycostssignificantly.Whenthetotal

numberofusersremainsunchanged,thismodelwillnot

Amoreaccuratemodelthatcanconsidermultiple

·seriouslyimpacttheinterestsoftheVPPcoordinator.

uncertaintiesisbeingdevelopedinfutureworktoachieve

a)P-P2Pandb)Q-P2PpricesinDAtimescale

Intra-daytotalP2Ptradedpowerina)P-optimalperformanceandimprovedstabilityforpractical

P2Pandb)Q-P2Pmarketsapplications.

RiskandManagementStrategiesforHolisticP2PMarkets

(1/7)

Concern1:DERsintermittencyandparticipants'forecastingcapabilities·Concern2:PotentialmaliciousanddefaultbehaviorsofP2Ptransaction

Becauseoftheinformationasymmetryandnon-transparencynaturesofP2Ptrading,distrustcanbeacommonissuespreadingamongtheP2Pmarketparticipants.Duetoseveralconcerns,thebuyersandsellersmayquestioneachother'stransactioncompletioncapabilitiesand,hence,themarketfairnessinP2Ptrading.

counterparties

Strategy1:FromtheperspectiveoftheVPPcoordinator,thispartproposesan

improvedreputationmanagementsystem(RMS)withatrustedcoordinatorforP2P

energytrading.ThepropositionofthisRMSisinspiredbysuccessfulexperienceofthefinancialsector.TheRMSisimprovedbasedonenergytransactions'characteristics.

Strategy2:FromtheP2Pparticipants'perspective,ariskmanagementstrategy

consideringanovel"self-reservation"conceptisproposed.ThismodelfocusesontheinternalriskmanagementstrategiesofP2Pparticipants,allowingthemtomanagerisksduringday-aheadandreal-timetransactiondecision-makingproactively.

RiskandManagementStrategiesforHolisticP2PMarkets

RMS-IntervenedP2PEnergyMarketFORMULATION2/7

>Calculateandupdate

VPPCoordinator:

>Calltbi

participants

fromP2P

participants'

reputationscores

SendRMS

participants

informationtoall

"buffers"toprovide

mismatchenergy

Actasreal-time

RiskandManagementStrategiesforHolisticP2PMarkets

RMS-Intervened

ProposedRMS

ImprovedRMS

P2P

Model

Model

EnergyMarketFORMULATION

一

BasedonanAdapted6Model

Updatedby3DForgettingFactor

Dimension1:TemporalEffects

>Dimension2:EventEffects

FormulationUsingtheShort-TermReputationChange

Dimension3:EventEffectsFormulationUsingthe

Long-TermReputationChange

Table4-1Simulatedparticipants'networkconnectionsandVPPinteractionsettings

RiskandManagement

VPP

Participants

Placement

ettn

ShareinfowithVPP?

ESS

PV

DSM

Sellers

18,22,26,29,33

V

V

V

X

Buyers

14,20,23,27,31

X

X

X

FixLoads

Other

X

X

X

V

StrategiesforHolisticP2P

Markets(4/7)

CaseStudyDesign

人Basecase:Reputation-freeP2Pmarkets

Table4-2ComparativeanalysisofdifferentcasesusingproposedKPlsinaP2Pelectricitymarket

Bayesianreputationwithoutforgettingfactors.

Case

.

Pg

Mean

Reputation

Score

Total

Successful

Transaction

Volumes

(MWh)

28days

Successful

ra0saction

Rate(%)

Dispute

Frequency

(%)

Participant

Retention

Rate(%)

Base

n/a

482.56

84.43%

17.31%

67.86%

1

0.79

522.32

90.67%

10.51%

78.21%

2

0.81

530.2

93.96%

10.27%

93.21%

3

0.86

532.36

94.16%

8.61%

92.86%

0.87

538.52

96.24%

5.34%

98.93%

>Case2:RMS-intervenedP2Pmarketsusingβ

▲Case1:RMS-intervenedP2Pmarketsusing

Crt3a:tRio-ittePtt2igmfrckttr.singβ

factorsformulation.

reputationwithconventionaltimeforgetting

adaptiveβreputationwiththenovelthree-

>Case4:RMS-intervenedP2Pmarketusing

dimensional

forgettingfactorformulation.

KeyFindings-MoreReliableP2P

TradingEnvironment

market,asreputationsystemsgenerally

encouragegoodbehavioranddiscouragebad

Improvethehealthandfunctionalityofthe

behavior.

RiskandManagement

Strategiesfor

HolisticP2PMarkets

(5/7)

No

decision

Reachconsensus7

Ives

Strategiesinthe

Real-TimeMarket

ConditionalVahue-at-Risk-

BasedSenarioOptimizati

TheProposedRisk

Management

8險(xiǎn)nkmsasetalegy

8)DPtii)aasgeberategy

→Energy

→Datainformation→Action

Conditional

Value-at-Risk-BasedScenarioOptimization

here-and-goeeisions

C-ADMMupdates

WaitforRTtransactions

Potentialartnerj

Eror

assesmen

Potentialarthert

Eror

assessment

7sassets

vailable

7sassets

hereand

Self

walP

P2P

ary

Sellers

ene

Buyers

resen

RiskandManagementStrategiesforHolisticP2PMarkets

MathematicalFormulation

Algorithm

MulhstageP2PDAnegotationandkTtransacton

consideningtheproposedrnskmanagementstrategy

1:Initializationiter=0."W.R2p(t)

WHILEprimalcriteriaanddualcriteriaarenotsatisfied

3:Stage1:

LocallysobetheadaptedC-ADMMlocalproblemSendthesolutionX[(1)totheStage2.

4:Stage2:

LocallysolveCVtimisationproblem

5;UpdateR2pt(.z"(t)basedonx|(t)

6:GloballyupdateperceivedP2Ppricesn"t(t)

7:ta=iha+

END

9:RTTransaction:Wat-and-seevarables,xl.(0)aredeteminedafieractualrealisationsisfixed

Stage

1

Decisionvariables:(6/7)

x()=[(),P2.(1),,(),E(0),'(

0]

LocalADMMproblems:

4:Stage2

Decisionvariables:

CVaRProblem

5:to8:

>ADMMUpdates

RiskandManagementStrategies

forHolisticP2PMarkets

(7/7)

hme

Fig.5-1PVGenerationwithUncertaintyIntervals.

CaseStudyDesign

crtalntflenl

cme

Fig.5-2LoadForecastingwithUncertaintyIntervals.

CaseStudyResults

SellersandBuyers.

Fig.5-3ContractedAmountsbetween

Fig.5-4AverageP2PPriceandPenalty.

P2PSellersandBuyers

KeyFindings:DifferentPerformanceon

Fig.5-6EnergyTransactionsandSelf-

ReservationsofBuyers.

ReservationsofSellers.

Fig.5-5EnergyTransactionsandSelf-

OptimalCoordinationforMultipleNetwork-ConstrainedVPPs

WeproposeaninternalmarketoperationtocoordinatetheADN-basedVPPagentswithmarketpowerandMG-basedVPP

operations.TheADN-basedVPPagentscansubmitabidintheexternalwholesalemarketasaprice-maker,andthemarket

internal/externalmarketcanimprovetheprofitsforallagentsinthishierarchicalframeworkandprotecttheprivacyand

willbeclearedbythemodifiedPQCmethod,andtheMGsserveasprice-takersintheinternalmarket.Theproposed

fairnessofallentitiesinanend-to-endfashion.

agentswithsmallcapacity,wheretheinternalmarketpricecontainstheinformationofboththenetworkandmarket

·viaMulti-AgentDeepReinforcementLearning(1/2)

basedVPPagentscanalsosupportthevoltageforthěupstream

improvetheoverallsystemcoordinationperformance.

canbetreatedasanindependentlearnerintheenvironment.Communicationandlow-dimensionalfingerprintsareleveraged

·Weconsiderthenetwork-constrainedmodel.Eachagentrunsthe

ACoptimalpowerflowmodelinsidetheVPP.TheMG-

electricitynetwork-basedagentwithaglobalrewardsignalto

trainingwithdecentralizedexecution(DTDEIframework.DTDEcanmaintainprivacyatthetrainingstagesuchthateachagent

·Weproposeacommunication-basedindependenttwindelayeddeepdeterministicpolicygradient(C-ITD3)algorithmtocoordinatemultipleVPPsunderthepartiallyobservableMarkovgame(POMG).Thetraíningschemeisdecentralized

4Tkes

toenhancethestationarityofanindependentlearnerandbettercoordinationperfo