储能未来研究-分布式太阳能和储能展望：方法与情景（英）

StorageFuturesStudy

DistributedSolarandStorageOutlook:MethodologyandScenarios

AshreetaPrasanna,KevinMcCabe,BenSigrin,andNateBlair

2

StorageFuturesStudy

DistributedSolarandStorageOutlook:

MethodologyandScenarios

AshreetaPrasanna,KevinMcCabe,BenSigrin,andNateBlair

SuggestedCitation:Prasanna,Ashreeta,KevinMcCabe,BenSigrin,andNateBlair.StorageFuturesStudy:DistributedSolar

andStorageOutlook:MethodologyandScenarios.Golden,CO:NationalRenewableEnergyLaboratory.NREL/TP-7A40-79790.

/docs/fy21osti/79790.pdf.

NOTICE

ThisworkwasauthoredbytheNationalRenewableEnergyLaboratory,operatedbyAllianceforSustainableEnergy,LLC,fortheU.S.DepartmentofEnergy(DOE)underContractNo.DE-AC36-08GO28308.FundingprovidedbyU.S.DepartmentofEnergyOfficeofEnergyEfficiencyandRenewableEnergySolarEnergyTechnologiesOffice,U.S.DepartmentofEnergyOfficeofEnergyEfficiencyandRenewableEnergyWindEnergyTechnologiesOffice,U.S.DepartmentofEnergyOfficeofEnergyEfficiencyandRenewableEnergyWaterPowerTechnologiesOfficeandU.S.DepartmentofEnergyOfficeofEnergyEfficiencyandRenewableEnergyOfficeofStrategicAnalysis.TheviewsexpressedhereindonotnecessarilyrepresenttheviewsoftheDOEortheU.S.Government.

ThisreportisavailableatnocostfromtheNational

RenewableEnergyLaboratory(NREL)at

/publications.

U.S.DepartmentofEnergy(DOE)reportsproduced

after1991andagrowingnumberofpre-1991

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freevia

www.OSTI.gov.

Frontandbackcoverphotos:iStock936999506,iStock1178922834,iStock1202603676,iStock1270012506.

NRELprintsonpaperthatcontainsrecycledcontent.

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Preface

ThisreportisoneinaseriesoftheNationalRenewableEnergyLaboratory’sStorageFuturesStudy(SFS)publications.TheSFSisamultiyearresearchprojectthatexplorestheroleandimpactofenergystorageintheevolutionandoperationoftheU.S.powersector.TheSFSisdesignedtoexaminethepotentialimpactofenergystoragetechnologyadvancementonthedeploymentofutility-scalestorageandtheadoptionofdistributedstorage,aswellasthe

implicationsforfuturepowersysteminfrastructureinvestmentandoperations.Theresearchfindingsandsupportingdatawillbepublishedasaseriesofreports,witheachreportbeingreleasedonitscompletion.ThefollowingtableliststhespecificresearchtopicsplannedforexaminationundertheSFSandtheassociatedpublicationformats.

Thisreport,thefourthintheSFSseries,providesasetofscenariosforcost-effectivenessand

customeradoptionforarangeofscenariosthatincludefuturetechnologycostsandvaluationofbackuppower.

TheSFSseriesprovidesdataandanalysisinsupportoftheU.S.DepartmentofEnergy’s

EnergyStorageGrandChallenge,

acomprehensiveprogramtoacceleratethedevelopment,commercialization,andutilizationofnext-generationenergystoragetechnologiesandsustainAmericangloballeadershipinenergystorage.TheEnergyStorageGrandChallengeemploysausecaseframeworktoensurestoragetechnologiescancost-effectivelymeetspecificneeds,

andincorporatesabroadrangeoftechnologiesinseveralcategories:electrochemical,

electromechanical,thermal,flexiblegeneration,flexiblebuildings,andpowerelectronics.

Moreinformation,anysupportingdataassociatedwiththisreport,linkstootherreportsinthe

series,andotherinformationaboutthebroaderstudyareavailableat

/analysis/storage-futures.html.

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Title

Description

RelationtoThisReport

TheFourPhasesof

Explorestherolesandopportunitiesfor

Providesbroadercontexton

StorageDeployment:

new,cost-competitivestationaryenergy

theimplicationsofthecost

AFrameworkforthe

storagewithaconceptualframework

andperformance

ExpandingRoleof

basedonfourphasesofcurrentand

characteristicsfortheU.S.

StorageintheU.S.

potentialfuturestoragedeployment,and

gridandprovidesagrid-scale

PowerSystem

presentsavaluepropositionforenergystoragethatcouldresultincost-effectivedeploymentsreachinghundredsof

gigawattsofinstalledcapacity.

backdroptothedistributedstorageconclusionsofthisreport.

StorageFuturesStudy:StorageTechnology

ModelingInputData

Report

Reviewsthecurrentcharacteristicsofa

broadrangeofmechanical,thermal,andelectrochemicalstoragetechnologieswithapplicationtothepowersector.Provides

currentandfutureprojectionsofcost,

performancecharacteristics,andlocationalavailabilityofspecificcommercial

technologiesalreadydeployed,includinglithium-ionbatterysystemsandpumpedstoragehydropower.

Providesstoragetechnologycostandperformance

assumptionsthatinform

storagedeploymentandgrid

evolutionscenariospresentedinthisreport.

StorageFuturesStudy:

Assessestheeconomicpotentialforutility-

Analyzesutility-scalestorage

EconomicPotentialof

scalediurnalstorageandtheeffectsthat

deploymentandgrid

DiurnalStorageinthe

storagecapacityadditionscouldhaveon

evolutionscenariosasa

U.S.PowerSector

powersystemevolutionandoperations.

complementtothisreport.

StorageFuturesStudy:

Assessesthecustomeradoptionof

Thisreport.

DistributedSolarand

distributeddiurnalstorageforseveral

StorageOutlook:

futurescenariosandtheimplicationsfor

Methodologyand

thedeploymentofdistributedgeneration

Scenarios

andpowersystemevolution.

GridOperational

Assessestheoperationandassociated

Considerstheoperational

Implicationsof

valuestreamsofenergystoragefor

implicationsofstorage

WidespreadStorage

severalpowersystemevolutionscenarios

deploymentandgrid

Deployment

andexplorestheimplicationsofseasonal

evolutionscenariostotestthe

(forthcoming)

storageongridoperations.

four-phaseframeworkandReEDSresults.

StorageFuturesStudy:

Synthesizesandsummarizesfindingsfrom

Includesadiscussionofall

ExecutiveSummaryand

theentireseriesandrelatedanalysesand

otheraspectsofthestudy

SynthesisofFindings

reports,andidentifiestopicsforfurther

andprovidescontextforthe

(forthcoming)

research.

resultsofthisstudy.

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Acknowledgments

WewouldliketoacknowledgethecontributionsoftheentireStorageFuturesStudyteam,aswellasourU.S.DepartmentofEnergy(DOE)OfficeofEnergyEfficiencyandRenewable

EnergyStrategicAnalysisTeamcolleagues,ascorecontributorstothisdocument.Those

contributorsincludePaulDenholm,WesleyCole,WillFrazier,NateBlair,andChadAugustinefromtheNationalRenewableEnergyLaboratory(NREL)andKaraPodkaminerfromDOE.WewouldliketothankDariceGuittetandBrianMirletzandthebroaderSystemAdvisorModel

teamfortheirhelpintegratingPySAMmoduleswithintheNRELDistributedGenerationMarketDemand(dGen)model,andspecificallySamKoebrichfortheuseofhiscodetogeneratesomeofthefigures.

WewouldalsoliketoacknowledgethefeedbackandcontributionsofotherNRELstaffandthe

TechnicalReviewCommittee,includingDougArent(NREL/Chair),PaulAlbertus,Ines

Azevedo,RyanWiser,SusanBabinec,AaronBloom,ChrisNamovicz,ArvindJaggi,Keith

Parks,KiranKumaraswamy,GrangerMorgan,CaraMarcy,VincentSprenkle,OliverSchmidt,

DavidRosner,JohnGavan,andHowardGruenspechtforprovidingreviewsanddetailedcomments.

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ListofAcronyms

BTM

behind-the-meter

DER

distributedenergyresource

dGen

DistributedGenerationMarketDemand(dGen)model

EIA

U.S.EnergyInformationAdministration

kW

kilowatt

kWh

kilowatt-hour

LBNL

LawrenceBerkeleyNationalLaboratory

MW

megawatt

MWh

megawatt-hour

NPV

netpresentvalue

NREL

NationalRenewableEnergyLaboratory

PV

photovoltaics

ReEDS

RegionalEnergyDeploymentSystem

SAIDI

systemaverageinterruptiondurationindex

SAIFI

systemaverageinterruptionfrequencyindex

SAM

SystemAdvisorModel

SFS

StorageFuturesStudy

USD

U.S.dollars

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ExecutiveSummary

Decliningbatterystoragecostsandthegrowingemphasisonresiliencyandgridserviceshaveledtoheightenedinterestinpairingbatterystoragewithdistributedsolartoprovidevalueto

customersandthedistributiongrid.Theincreasingdeploymentofdistributedenergyresources

(DERs),includingbatterystorage,isanimportantandemergingthemeinmodernpower

systems.DERscancontributetogridflexibility,reducegridpowerlosses,andsupportdemand-sidemanagement.Existingbehind-the-meterbatterycapacityisestimatedtobeapproximately0.8GW/1.6GWhintheUnitedStatesatyear-end2020(WoodMackenzieandU.S.Energy

StorageAssociation2020).Themarketforsmall-scalebatterysystemsisexpectedtoincreasedramatically,pushedbyadesireforbackuppowerandthedeploymentofdistributedsolar

photovoltaics(PV).TherecentlyapprovedFederalEnergyRegulatoryCommission(FERC)

Order2222(FERC2020)enablesDERstoparticipateinregionalwholesalecapacity,energy,

andancillaryservicemarketsalongsidetraditional(utility-scale)generation.Order2222andnewDERcompensationmechanismsliketheNewYorkStateValueofDistributedEnergyResources(VDER)(NYSERDA2020b)areanticipatedtounlocknewmarketopportunitiesforDERsand

thusleadtoadditionaldeploymentofDERcapacity.

Duetothenascentmarketstatusfordistributedbatterystoragesystems,therearerelativelyfewpublishedprojectionsofdistributedbatterystoragedeployment.Thisworkaddressesthatgapbycharacterizingthepotentialforbehind-the-meterbatterystorageandidentifyingkeydriversof

adoption.ThisreportdescribestheexpandedcapabilitiesoftheDistributedGenerationMarketDemand(dGen)modeltoanalyzetheeconomicsofdistributed(behind-the-meter)PVpairedwithbatterystoragesystems1andpresentsprojectionsofadoptionforthecontiguousUnitedStatesoutto2050underarangeofscenarios.Thesescenariosusetechnologycostand

performanceassumptionsconsistentwiththeNationalRenewableEnergyLaboratory’s2020

StandardScenariospairedwithupdatedbatterycostprojections(AugustineandBlair2021)andexistingpolicies.Additionalscenariosevaluatesensitivitiestothevalueofbackuppowerand

DERcompensationmechanisms,collectivelycharacterizingthefuturepotentialforbehind-the-meterstorageandidentifyingkeydriversofadoption.2

InordertocalculatebatterystoragesystemandPVadoption,thedGenmodelfirstdeterminesthetechnical,economic,andmarketpotential:

.Technicalpotential:ThemaximumamountoftechnicallyfeasiblecapacityofPV-onlyandPV+batterystoragesystems,withPVsystemsizelimitedbycustomer’srooftop

areaandenergyconsumption,andbatterycapacitycappedasafractionoftheoptimalPVcapacityataspecificsite.

.Economicpotential:Asubsetoftechnicalpotential,economicpotentialisestimatedasthetotalcapacitythathasapositivereturnoninvestmentorapositivenetpresentvalue(NPV).Economicpotentialcanalsobeinterpretedasthetotalcapacityofsystemsthatarecost-effectiveinaspecificyear.

1Stand-alonebatterystoragesystemsarenotconsideredinthisanalysis.

2Broaderpowersectorandeconomywidedecarbonizationtargetsarenotcapturedinthisanalysis,whichwouldlikelyaccelerateandincreasetheadoptionofbothdistributedPVandbatterystoragesystems.

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.Marketpotential:Thefractionofeconomicpotentialrepresentingthecustomer’swillingnesstoinvestinatechnologygivenaspecifiedpaybackperiod.

.Adoption:Adopted3capacityisthecapacityprojectedtobepurchasedbyresidential,

commercial,andindustrialbuildingownersandinstalledatthecustomerpremisesina

behind-the-meterconfiguration.AdoptionisbasedonapplyingaBassdiffusionfunctionwheretheupperlimitofadoptionissettothemarketpotential.

AdescriptionofeachlevelandthekeyassumptionsandcorrespondingpotentialcapacityfortheBaseCasescenarioin2050isdescribedinFigureES-1.

FigureES-1.Methodologytodetermineadoption/deploymentofdistributedstoragesystemsandPVandbatterypotential(GW)fortheBaseCasescenarioin2050

AdaptedfromLopezetal.(2012)

TableES-1summarizestheeconomicpotentialalongsidetheprojectedcumulativebatteryandPVcapacitydeployedoradoptedby2050forallscenariosevaluated.4

3Thetermsdeploymentandadoptionareusedinterchangeablyinthisreport.

4ThecumulativePVcapacitypresentedinTableES-1isthesumofPVcapacityfromPV-onlyandPV+batterystoragesystems.

TableES-1.DistributedPVandBatteryEconomicPotentialandAdoptionforallScenariosThrough2050

ScenarioName

BatteryPV

ScenarioDescription

EconomicPotentialGW/GWh

ProjectedCumulativeAdoptionGW/GWh

EconomicPotential(GW)

Projected CumulativeAdoption(GW)

BaseCase

ModeratecostprojectionsforbothPVandbatterystoragesystems;allotherinputsaredefault

values;thevalueofbackuppowerisconsidered

114/228

8/16

1,104

152

AdvancedCost

BatteriesScenario

Advanced(low)costprojectionsforbatteriespairedwithmoderatecostprojectionsforPV

147/294

11/22

1,114

160

AdvancedCostPVScenario

Advanced(low)costprojectionsforPVpairedwithmoderatecostprojectionsforbatteries

116/232

11/22

1,142

223

AdvancedCostPV+BatteriesScenario

Advanced(low)costprojectionsforPVpairedwithadvanced(low)costprojectionsforbatteries

147/294

16/32

1,143

234

NoBackupValueScenario

ModeratecostprojectionsforPVandbatteriesandnovalueofbackuppower

85/170

5/10

1,100

146

NoBackupValue+

Advanced(low)costprojectionsforbatteriesand

AdvancedCost

BatteriesScenario

novalueofbackuppower

116/232

7/14

1,110

150

2xBackupValue

ModeratecostprojectionsforPVandbatteries

Scenario

anddoublethevalueofbackuppoweracrossallstatesandsectors

138/276

11/22

1,060

139

2xBackupValue+

Advanced(low)costprojectionsforbatteriesand

AdvancedCost

doublethevalueofbackuppoweracrossall

245/490

17/34

1,085

151

BatteriesScenario

statesandsectors

NetMetering

ExtensionsScenario

Allstatesswitchtonetmeteringcompensationfrom2020through2050

111/222

8/16

1,080

209

NationalNetBillingScenario

Allstatesswitchtonetbilling

compensationin2020through2050

114/228

8/16

1,105

145

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Forallmodeledscenarios,wefindaneconomicpotentialforbatterystoragecapacityranging

from85–245GW/170–490GWhandcumulativeadoptedbatterystoragecapacityin2050

rangingfrom5–17GW/10–34GWh.Althoughthereissignificanteconomicpotentialfor

behind-the-meterbatterystorage(morethan300timestheexistinginstalledcapacity),onlya

smallfractionofthisisadoptedunderourmodeledscenarios.Selectedinsightsfromouranalysisfollow:

.ThereissignificanteconomicpotentialfordistributedPV+batterystoragesystemsunderallmodeledscenarios.TheBaseCaseeconomicpotentialfordistributedbatterystoragecoupledwithPVisapproximately114GW/228GWh,whichismorethan90

timesthe2020capacity.Inthescenariosinvestigated,theupperboundofeconomic

potentialfordistributedbatterystoragecoupledwithPVis245GW/490GWhunderthe2xBackupValue+AdvancedCostBatteriesScenario,andthelowerboundis85GW/

170GWhundertheNoBackupValueScenario.

.Despitethehigheconomicpotential,modestgrowthindistributedPV+battery

storageadoptionisprojectedunderourmodeledscenarios.UndertheBaseCase,theprojecteddeploymentofdistributedbatterystoragecapacityis8GW/16GWh,7%oftheeconomicpotential,witharangeacrossscenariosfrom5–17GW/10–34GWh.

.Thesubstantialdecreasefromeconomicpotentialtoadoptionreflectsalong

paybackperiod,andconsequentlyalowershareofcustomerswillingtoinvest.TheaveragepaybackperiodsofdistributedPV+batterystoragesystemsarefairlylong:11yearsfortheresidentialsector,12yearsforthecommercialsector,and8yearsforthe

industrialsectorin2030.

.Atthenationalscale,themostimportantdriversofdistributedco-adoptedbattery

storageareacombinationofadvanced(low)futurebatterycostandahighvalueforbackuppower.Thehighestadoptionestimateforbatterycapacityisunderthe2x

BackupValue+AdvancedCostBatteriesScenario(+121%comparedtotheBaseCase).

.CombinedcostreductionsinbothPVandbatterystoragetechnologiesdrive

additionaladoptioncomparedtocostreductionsinbatterytechnologyalone.TheAdvancedCostPV+BatteriesScenario,whichconsidersareductioninfuturecostsforbothPVandbatteries,hashigherbatterydeploymentcomparedtotheBaseCase,

increasingby106%.

.PV+batterysystemshavelargerPVcapacitycomparedtoPV-onlysystems.

AveragePVsystemsizeinPV+batterystoragesystemconfigurations(8kWfor

residentialsystems)islargerthaninPV-onlyconfigurations(4kWforresidential

systems).BatterystoragethusincreasesthePVcapacity.ThisislikelyduetotheabilityofthebatterytoincreasetheeconomicvalueofPV.

.Localconditionsdictateadoption.Differencesinlocation-specificparametersacrosstheUnitedStatesalsoresultinsignificantdifferencesintheamountandrateatwhichdistributedbatterystoragecapacityisadoptedinvariousstatesandcounties.

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.Storagedeploymentishighlysensitivetotheregionalvalueofbackuppower.The

valueofbackuppowerusedinthisanalysishashighregionalvariationacrosstheUnitedStates.Thesensitivityofstoragedeploymenttothevalueofbackuppowerishigherin

specificstatesandsectorswithhighervalueofbackuppower.

.Retailtariffsthatincludehighdemandcharges,time-of-usetariffs,andtieredtariffsencouragePV+batterystorageadoption.However,otherfactorssuchasclimate,loadprofile,electricityprice,andDERcompensationmechanism,combinedwithretailtariffs,canminimizetheirimpact.Intheresidentialsector,fixedstructurerates,themost

commonretailratestructure,donotincentivizebatterystorage.

WiththisfirstdemonstrationofthebatterycapabilitiesofthedGenmodel,theresultspresentedinthisreportareprimarilyusefulforscenariocomparisontounderstanddifferentdriversof

deployment,buttheyhavesomelimitationsandarenotintendedaspreciseforecasts.The

numericalprecisionreportedintheresultsisintendedtodifferentiateandallowcomparison

acrossscenarioswheredifferencesinvaluesaresmall.Asthemarketevolvesandadditionaldataareavailable,furthercalibrationshouldbeperformed.Inaddition,themodeldoesnotconsider

emergingsourcesofrevenueforPV+batterystoragesystemssuchasparticipationinwholesalemarkets,demandresponseprograms,orgridservices.AdditionalenhancementsofdGenwillbeneededtoexploresuchresearchquestions.Finally,deploymentofdistributedstoragemaybe

affectedbybulkpowersystemevolutionandfront-of-themeterstoragedeployment.However,

thisanalysisdoesnotconsiderthoseinteractions.PotentialareasoffurtherinterestareprojectingtheadoptionofcommunityDERsandstoragecapacityandtheirimpactonthedistributiongrid,explorationofthetrade-offsbetweendistributedandutility-scalestorage,andtheroleofDERsinsupportingthetransitiontoadecarbonizedeconomy.

Insummary,economicpotentialfordistributedbatterystorageissignificant.Theincreasing

customeradoptionofPV+batterystoragesystemscanbringaboutbothbenefitsandchallenges

forelectricutilities.AdoptionprojectionsofDERandbatterystorageathighspatialand

temporalresolution,aspresentedinthisreport,canenableinformedplanningoftechnical

infrastructurethatcanhelpplannerscapturethebenefitsandmitigatechallengestosupporttheongoingtrendtowarddistributedelectricitygeneration.

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TableofContents

1Introduction 1

2MethodsandData 3

2.1StudyParameters 3

2.2Costs 3

2.3LoadProfiles 5

2.4RetailElectricityRatesandIncentives 5

2.5WholesalePrices 6

2.6ValueofBackupPower/Resiliency 7

2.7HistoricalStorageAdoption 11

2.8PySAMDetailedBatteryModelIntegration 11

2.8.1SelectionofOptimalSystemConfigurations 12

2.8.2StorageDispatch 15

2.9ScenarioAnalysisFramework 17

3Results 19

3.1EconomicPotential 19

3.2PaybackPeriod 22

3.3PVandBatteryAdoptionEstimates 26

3.3.1TechnologyCostScenarios 27

3.3.2ValueofBackupPowerScenarios 28

3.3.3DERValuationScenarios 29

3.4State-andCounty-LevelResults 31

3.4.1ValueofBackupPowerScenarios 34

3.4.2DERValuationScenarios 35

3.5County-LevelResults 39

3.6Sector-LevelResults 41

3.7AverageSystemSizeandCo-Adoption 42

3.8ModelLimitationsandCaveats 45

4Discussion,Conclusions,andFutureWork 46

References 49

Appendix.BackupPowerCalculation 53

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ListofFigures

FigureES-1.Methodologytodetermineadoption/deploymentofdistributedstoragesystemsandPV

andbatterypotential(GW)fortheBaseCasescenarioin2050 ix

Figure1.Modelsandtoolstodetermineadoption/deploymentofPVandbatterystoragesystems 2

Figure2.CostofresidentialPVstand-alone,batterystoragestand-alone,andPV+batterystorage

systemsestimatedusingNRELbottom-upmodels 4

Figure3.Estimatedcostsofcommercialandindustrialstand-alonePV,batterystoragestand-alone

systems,andPV+batterystoragesystemsusingNRELbottom-upmodel 5

Figure4.Valueofbackuppower(USDperyear)bystateandsectorforthecontiguousUnitedStates 10

Figure5.Observedannualenergystoragedeployments(MW)intheresidentialandnonresidential

sectors 11

Figure6.PVandbatterysystemsizeswithevaluatedNPVforanofficebuildinginCalifornia 14

Figure7.HourlybatterypowertoloadforanofficebuildinginCalifornia 16

Figure8.HourlybatterypowertoloadforanofficebuildinginTexas 16

Figure9.Hourlybatterypowertoloadforasingle-familyhouseinCalifornia 16

Figure10.Economicpotentialforbatterystoragebyyear 20

Figure11.Impactofsensitivitycaseson2050economicpotential 21

Figure12.Relationbetweenmaximummarketshareandpaybackperiod 23

Figure13.AveragepaybackperiodsforPV+batterystoragesystemsforallsectorsundertheBase

Case 24

Figure14.AveragepaybackperiodsforPV+batterystoragesystemsforallsectorsunderthe

AdvancedCostPV+BatteriesScenario 25

Figure15.Cumulativebatterydeploymentbyyearforallscenarios 26

Figure16.CumulativePVdeploymentbyyearforallscenarios 27

Figure17.Cumulativebatterydeploymentbyyearforthetechnologycostscenarios 27

Figure18.Cumulativebatterydeploymentbyyearforthevalueofbackuppowerscenarios 28

Figure19.CumulativebatterydeploymentbyyearfortheDERvaluationscenarios 30

Figure20.CumulativePVdeploymentbyyearfortheDERvaluationscenarios 30

Figure21.CumulativebatterydeploymentbystatefortheBaseCasein2050 32

Figure22.Sensitivitytobackuppowerbystate:differencesinbatterycapacity 35

Figure23.SensitivitytoDERvaluationbystate:differencesinbatterycapacity 37

Figure24.AdoptiontrajectoriesforCaliforniaundertheBaseCase,NationalNetBillingScenario,

andNetMeteringExtensionsScenario 38

Figure25.CumulativebatterydeploymentbycountyfortheBaseCasein2050 39

Figure26.AverageNPVofPV+batterystoragesystemsforeachcountyin2030and2050 40

Figure27.Cumulativebatterydeploymentbyyearandsectoracrossallscenarios,withalineforthe

BaseCase 41

Figure28.NumberofPV+batterystorageadoptersbyyearandsectorintheBaseCase 42

Figure29.Impactofsensitivitiesbysectorandscenarioon2050batterycapacity 42

Figure30.PVsystemsizeforPV-onlysystemsandPV+batterystoragesystemsintheresidential

sectorfortheBaseCase 43

Figure31.BatterysystemsizeintheresidentialsectorfortheBaseCase 44

Figure32.Co-adoptionofbatterystoragesystemsunderselectedscenarios 44

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ListofTables

TableES-1.DistributedPVandBatteryEconomicPotentialandAdoptionforallScenariosThrough

2050 ix

Table1.IncentivesforBatteryStorage 6

Table2.DescriptionofAllModeledScenarios 18

Table3.EconomicPotential,MarketPotential,andAdoptedBatteryStorageCapacityby2050 22

Table4.CumulativeAdoptedPVandBatteryStorageCapacityby2050fortheTechnologyCost

Scenarios 28

Table5.CumulativeAdoptedPVandBatteryStorageCapacityby2050fortheValueofBackup

PowerScenarios 29

Table6.CumulativeAdoptedPVandBatteryStorageCapacityin2050fortheDERValuation

Scenarios 30

Table7.StatesinOrderofHighestProjectedBatter