CASE STUDY ON AN ALTERNATIVE APPROACH TO DISTRIBUTED … · 19 OPTIMIZATION: COSTS & CONSTRAINTS Constrained optimization computes net load with demand dispatch • Costs are placed

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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

[email protected]

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


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.


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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§  Generation ($ / Traditional >> $ / Demand Response)


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§  Generation ($ / Traditional >> $ / Demand Response) §  Ramping


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§  Generation ($ / Traditional >> $ / Demand Response) §  Ramping §  Deviation from nominal energy demand (change in energy

behavior)


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behavior) §  Deviation from nominal power demand (Utility Control

Signal)


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Signal)

•  Constraints guarantee: §  Quality of service to the consumer (temp, etc.)


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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


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


123MWFullSystem8MWPeakReduction6.7%ofPeakLoad

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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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VoltageViolations


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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


www.epeconsulting.com

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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


Documents

CASE STUDY ON AN ALTERNATIVE APPROACH TO DISTRIBUTED … · 19 OPTIMIZATION: COSTS & CONSTRAINTS Constrained optimization computes net load with demand dispatch • Costs are placed