case study on an alternative approach to distributed … · 19 optimization: costs &...
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C A S E S T U D Y O N A N A L T E R N A T I V E A P P R O A C H T O
D I S T R I B U T E D E N E R G Y D E M A N D D I S P A T C H
CHASELANSDALE,ProductManager
ESIG2019SpringTechnicalWorkshopMarch20-22,Albuquerque,NM
3/20/2019ESIGSpringWorkshop–ChaseLansdale
www.epeconsulting.com
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E L E C T R I C P O W E R E N G I N E E R S
Serving:InvestorownedutilitiesMunicipalitiesElectriccooperativesIndependentSystemOperatorsEnergyaggregatorsGovernmententitiesIndependentpowerproducersRenewableEnergyownersanddevelopersEnergystorageownersanddevelopers
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B A C K G R O U N D
ElectricPowerEngineers,Inc.(EPE):aleadingglobalpowersystemengineeringandconsultingfirmheadquarteredinAustin,Texas,withservicescoveringtheentirespectrumofGENERATION,TRANSMISSIONandDISTRIBUTION.
OURVISIONBetheleaderandinnovatorintheapplicationofaholisticapproachtostudy,design,andimplementofaninfrastructurethatenablesanintegratedgridofthefutureacrossG,T&D
GridAnalytics&PlanningPLATFORM
4yearsof
R&D
CONSULTING+SOFTWAREIntegratedPlanning
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O V E R V I E W
• Whydotheanalysis?
• WhatisDemandDispatch?
• Howisitmodeled?
• OptimizationandConstraints
• Results&Conclusions
• What’sNext
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I D E N T I F Y I N G S T U D I E S
DataRich InformationPoor
UTILITIES OurGoal:• Identifylow-hangingfruitsforinformative
analysis• TransformerLoading• LineLoss• PhaseBalance• Etc.
• Findandexplorenewfrontierswiththeavailabledataforplanning
• Testinformationtheoryinreal-worldapplications
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I D E N T I F Y I N G S T U D I E S
OurGoal:• Identifylow-hangingfruitsforinformative
analysis• TransformerLoading• LineLoss• PhaseBalance• Etc.
• Findandexplorenewfrontierswiththeavailabledata
• Testinformationtheoryinreal-worldapplications
3/20/2019ESIGSpringWorkshop–ChaseLansdale
DataRich InformationPoor
UTILITIES
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A L T E R N A T I V E A P P R O A C H T O D E M A N D
Demand Dispatch is an emerging science for controlling flexible loads to provide grid services. With the proper design, a distributed control approach can improve the utilization of the power grid while satisfying consumer preferences. This study explains how a Demand Dispatch analysis is conducted and provides results from simulations based on a physical system.
3/20/2019ESIGSpringWorkshop–ChaseLansdale
LOCALIZED DEMAND RESPONSE Customers set the comfort level
Utility shapes demand to arrive at that comfort level • Demand dispatch is built around the idea that many types of loads can be flexible in their
power consumption while delivering the same quality of service to the consumer • For example, consumers care about their water temperature, not instantaneous power
consumption • Maintaining water temperature within a desired range can be accomplished using many
different power consumption trajectories
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C O L L A B O R A T O R S
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A P P L I E D T H E O R Y : C A I S O M O D E L
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M O D E L I N G : P A R T I C I P A N T S E L E C T I O N
ParticipantSelection[CIS+GIS+AMR]
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M O D E L I N G : A P P L I A N C E S
RESIDENTIALAPPLIANCES
SpaceHeatersFast&SlowWaterHeaters
RefrigeratorsPoolPumps
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M O D E L I N G : L O A D D A T A MemberUsageData[AMR/AMI]
AggregateSystemData[SCADA]
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O P T I M I Z A T I O N : C O S T S & C O N S T R A I N T S
Constrained optimization computes net load with demand dispatch • Costs are placed on:
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O P T I M I Z A T I O N : C O S T S & C O N S T R A I N T S
Constrained optimization computes net load with demand dispatch • Costs are placed on:
§ Generation ($ / Traditional >> $ / Demand Response)
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O P T I M I Z A T I O N : C O S T S & C O N S T R A I N T S
Constrained optimization computes net load with demand dispatch • Costs are placed on:
§ Generation ($ / Traditional >> $ / Demand Response) § Ramping
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O P T I M I Z A T I O N : C O S T S & C O N S T R A I N T S
Constrained optimization computes net load with demand dispatch • Costs are placed on:
§ Generation ($ / Traditional >> $ / Demand Response) § Ramping § Deviation from nominal energy demand (change in energy
behavior)
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O P T I M I Z A T I O N : C O S T S & C O N S T R A I N T S
Constrained optimization computes net load with demand dispatch • Costs are placed on:
§ Generation ($ / Traditional >> $ / Demand Response) § Ramping § Deviation from nominal energy demand (change in energy
behavior) § Deviation from nominal power demand (Utility Control
Signal)
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O P T I M I Z A T I O N : C O S T S & C O N S T R A I N T S
Constrained optimization computes net load with demand dispatch • Costs are placed on:
§ Generation ($ / Traditional >> $ / Demand Response) § Ramping § Deviation from nominal energy demand (change in energy
behavior) § Deviation from nominal power demand (Utility Control
Signal)
• Constraints guarantee: § Quality of service to the consumer (temp, etc.)
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O P T I M I Z A T I O N : C O S T S & C O N S T R A I N T S
Constrained optimization computes net load with demand dispatch • Costs are placed on:
§ Generation ($ / Traditional >> $ / Demand Response) § Ramping § Deviation from nominal energy demand (change in energy
behavior) § Deviation from nominal power demand (Utility Control
Signal)
• Constraints guarantee: § Quality of service to the consumer (temp, etc.) § Same net daily energy consumption
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O P T I M I Z A T I O N : S T O C H A S T I C M O D E L I N G
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Stochastic, distributed control techniques enable tracking of the desired power deviation • Power deviation is computed centrally, but decisions are made locally
§ Local control reduces computation and communication requirements § Local control maintains privacy and guarantees QoS
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O P T I M I Z A T I O N : S I M U L A T E D B E H A V I O R
Preheating
Supporting
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R E S U L T S : L O A D S H A P I N G
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123MWFullSystem8MWPeakReduction6.7%ofPeakLoad
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R E S U L T S : L O A D S H A P I N G
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R E S U L T S : L O A D S H A P I N G
10-yearEconomicAnalysis
Peakchargesavings $448,540
Linelosssavings $82,641
CapExDeferralsavings N/A
CostofimplementingDD $104,850
Netsavings$426,331
• 8%reductioninpeakkW• Significantlylowerramprates• Significantlysmoothernetloadtrajectory
1Feederwith1,400participatingappliances
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R E S U L T S : R E L I A B I L I T Y
BASECASE DEMANDDISPATCH
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R E S U L T S : R E L I A B I L I T Y
BASECASE DEMANDDISPATCH
VoltageViolations
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R E S U L T S : R E L I A B I L I T Y
BASECASE DEMANDDISPATCH
VoltageViolations
DeferredCapitalExpendituresReliableServicetoMembers
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W H A T ’ S N E X T
LoadDisaggregation
• Determinewhatappliancesarebeingusedandtheir
traditionalbehavior
LocalizedControlModeling
• Modeleachload’scontrollerindividuallytobetter
simulatereal-worldapplication
FieldTesting+PilotProject
• Implementcontrollersonreallifesystems
• Testdifferentcontrolsignalsandanalyzeload
responseinrelationtotraditionalbehavior
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T H A N K Y O U
POWERED BY
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L A T E E V E N I N G R E A D I N G M A T E R I A L !
1. MathiasJ,BušićA,MeynS."DemandDispatchwithHeterogeneousIntelligentLoads."Proceedingsofthe50thHawaiiInternationalConferenceonSystemSciences.2017.
2. ChenY,HashmiMU,MathiasJ,BušićA,MeynS.“Distributedcontroldesignforbalancingthegridusingflexibleloads.”EnergyMarketsandResponsiveGrids,2018(pp.383-411).Springer,NewYork,NY.
3. HaoH,SanandajiBM,PoollaK,VincentTL."Aggregateflexibilityofthermostaticallycontrolledloads."IEEETransactionsonPowerSystems30.1(2015):189-198.
4. CammardellaN,MoyeR,ChenY,MeynS.“AnEnergyStorageCostComparison:Li-ionBatteriesvsDistributedLoadControl.”2018ClemsonUniversityPowerSystemsConference.2018
5. CammardellaN,MathiasJ,KienerM,BušićA,MeynS.“BalancingCalifornia'sGridWithoutBatteries.”57thIEEEConferenceonDecisionandControl(CDC2018).2018Dec.
6. MathieuJ,DysonM,CallawayD,RosenfeldA."Usingresidentialelectricloadsforfastdemandresponse:Thepotentialresourceandrevenues,thecosts,andpolicyrecommendations."ACEEESummerStudyonEnergyEfficiencyinBuildings.2012.
7. Huber,L.,Bachmeier,R.,2018.Whatnetflixandamazonpricingtellusaboutratedesignsfuture.PublicUtil.Fortnightly60
8. HelenLoa,SethBlumsack,PaulHines,SeanMeyn,2019.Electricityratesforthezeromarginalcostgrid..https://www.sciencedirect.com/science/article/pii/S1040619019300594?via%3Dihub
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