Conservation Voltage Reduction Econometric
Impact Analysis
PresentedtoAESPSpringConference
PresentedbySanemSergici,TheBrattleGroup
(incollaborationwithPepcoMDteamledbySteveSunderhauf andBasilAllison)
May 11, 2016
AgendaBackgroundDataOverviewMethodology SelectingControlGroups ConservationAnalysis PeakAnalysis
Results ConservationAnalysis PeakAnalysis
Background: What is Conservation Voltage Reduction?
ConservationVoltageReduction(CVR)isareductioninfeedervoltagewhichresultsinareductioninenergyconsumption
Keyengineeringprincipal:VoltagecanbekeptonlowerendofAmericanNationalStandardInstitutestandardvoltagebandof114‐126volts
PepcoMaryland’simplementationofAdvancedMeteringInfrastructurehasenabledPepcotomonitorandvaryvoltagelevelswhileremainingwithinspecifiedstandards
Background and Objectives PepcoMDinitiatedtheCVRpilotprogramonAugust1,2013.Itencompasses7substations Approximately45,000residentialcustomers Approximately4,000non‐residentialcustomers
Thevoltagelevelswerereducedby1.5%atthosesubstationsparticipatinginthepilot
Theobjectiveofourstudywasto: QuantifytheconservationimpactoftheCVRprogramforresidentialandnon‐residentialcustomers
QuantifythepeakdemandimpactoftheCVRprogramforresidentialandnon‐residentialcustomers
BackgroundOverview of Previous Research‐ I
Moststudieshavebeenengineeringstudiesasopposedtoeconometricanalysis,andhavenotestimatedapeakdemandvsenergyconservationimpact,oraresidentialvsnon‐residentialimpact
SeveralstudieshavedemonstratedthattheimplementationofCVRleadstodecreasedconsumption,butthereisnoconsensusfora“CVRfactor”(energyreduction/voltagereduction) StudiesindicatearelativelywiderangeofCVRfactors,generallyrangingfrom.5to1
BackgroundOverview of Previous Research‐ II
Residentialandnon‐residentialloadmayresponddifferentlytheCVRasnon‐residentialloadgenerallyhasalargershareofmotorload,whichmaymitigatetheeffectofCVR
CVRasanideahasbeenaroundfordecades,buthasrecentlygainedmoreattentionasitisbecomingmorecost‐effectiveandalsoeasiertocontrol/monitorduetothedeploymentofAMI
BackgroundReview of Select Previous Studies‐ I
WestPennPowerCompany(2014) Studyreducedvoltageby1.5%doinga“onforaday,offforaday”approach
SimilartoPepcoMDstudyinthatitusesdifference‐in‐differencesmethodology
RangeofCVRfactorsbutaverageis0.86 IndianapolisPower&LightCompany(2013)
Studyturned“on”CVRforafewshortperiodsin2012and2013,andcompareddropinusageduringthoseperiodstopredictanimpact
StudyestimatedaCVRfactorof0.7‐0.8
BackgroundReview of Select Previous Studies‐ II
DominionVirginiaPower(2012) Studycomparedbaselinepre‐CVRperiodtoconsumptionduringperiodafterCVRwasimplemented
Impactcalculateusingaday‐pairingmethodinsteadofdifference‐in‐differences
Day‐pairingmethodmatchesdayfromthepre‐treatmentperiodtodaysinthepost‐treatmentperiodtocalculateCVRimpact
StudyfoundaCVRfactorof0.92
BackgroundReview of Select Previous Studies‐ III
PacificNorthwestNationalLaboratory(2010) EstimatedimpactofCVRon24modeledfeedersbyrunningaone‐yearsimulationofsystemandre‐runningwithreducedvoltagelevels
Resultswerevaried,butalmostallfeedersexperiencedsomereductioninbothpeakdemandandenergyconsumption
NorthwestEnergyEfficiencyAlliance(2007) StudymeasuredCVRimpactbycomparing24hoursonand24hoursoff,insteadofusingasetcontrolgroup
StudyfoundCVRfactorsforpeakdemandrangingfrom0.55‐1.12andforenergyrangingfrom0.3‐0.86
AgendaBackgroundDataOverviewMethodology ConservationAnalysis PeakAnalysis
Results ConservationAnalysis PeakAnalysis
Data OverviewThefollowingdatasetswereutilizedforthisanalysis
Billingdata Hourlyconsumption Weatherdata(dewpointanddrybulb temperatures) Advancedmeteringinfrastructure(AMI)activationdate ParticipationinDemandSideManagementprograms RecipientsofOpower HomeEnergyReports Netenergymetering(NEM)status
Data Overview Forthepeakanalysis,theprimarydatasetwashourlydatafromAMIforJune‐August2013and2014,hours‐ending15‐19
Fortheconservationanalysis,theprimarydatasetwasmonthlybillingdatafromSeptember2012throughAugust2014 MonthlydatausedbecausehourlydatawasonlyavailableforsummerbeforeCVRimplementationasAMIactivationstartedinearly2012butwasnotcompleteduntilmid‐2013
AgendaBackgroundDataOverviewMethodology SelectingControlGroups ConservationAnalysis PeakAnalysis
Results ConservationAnalysis PeakAnalysis
MethodologySelecting Control Groups
PepcoMD’sCVRprogramwasnotdesignedasarandomizedcontroltrial
PepcoMarylandengineeringandloadexpertsmatchedeachsubstationwhichreceivedCVRtreatmentwithacontrolsubstationwhichdidnotreceiveCVRtreatment
Tomatchtreatmentandcontrolsubstations,theexpertsconsideredcustomerandloadcharacteristicsandensuredthattreatmentandcontrolpairingsaregenerallyadjacent
Allpairingsareinasinglejurisdiction,manyfactorswhichaffectconsumption(e.g.,economicfactorsandweather)aresimilarbetweenpairings
MethodologySubstation Pairings
TreatmentSubstations ControlSubstations
KensingtonSub.193 LindenSub.156
LongwoodSub.192 WoodAcresSub.154
MontgomeryVillageSub.56 GaithersburgSub.31
BranchvilleSub.69 GreenbeltToaping CastleSub.173
RiverdaleSub.4 BladensburgSub.175
CampSpringsSub.72 St.BarnabasRd.Sub.59
Wildercroft Sub.178 LanhamSub.149
MethodologyValidating Control Group Wecarried‐outafter‐the‐factcomparisonofPepcoMaryland’scontrol‐treatmentpairingstovalidatethecontrolgroup
BelowarecomparisonsofcontrolandtreatmentconsumptionusinghourlyAMIdataforthepeakanalysis
MethodologyValidating Control Group We find that the residential customer load profiles are verysimilar to each other in terms of their shape and level for thetreatment and control groups This implies that the residential control group customersrepresent the but‐for usage of the residential treatmentcustomers fairly well
For the non‐residential customer load profiles, we find thatthey are very similar to each other in terms of their shapebut they differ in terms of the level of usage between thetreatment and control groups Treatment customers are slightly larger than the control groupcustomers, on average. This difference will be accounted for bythe fixed effects estimation routine
Methodology: Difference‐in‐Differences through Panel Data AnalysisWe carried out a Difference‐in Differences (DID) analysis through apanel data regression analysis to estimate the CVR impact Regression model compares the usage of the treatment and control
group customers before and after the CVR treatment, while accountingfor other factors that could potentially confound the estimated impactsuch as weather conditions, DSM program participation, AMI activation,and calendar dummies
The Fixed Effects (FE) estimation routine was used to ensure that theestimated coefficients from the resulting model are unbiased. FEestimation assumes that the unobservable factor in the error term isrelated to one or more of the model’s independent variables. Therefore,it removes the unobserved effect from the error term prior to modelestimation using a data transformation process
MethodologyCVR Impacts estimated in this Study
Impact Dataset AnalysisVariable
Pre‐treatmentPeriod(*)
Post‐treatmentPeriod
Peak Hourly AMIDataset HourlyUsage
June–August2013
June–August 2014
Conservation
MonthlyBillingData
Average DailyUsage
Sept.2012–August2013
Sept.2013–May2014
(*) The CVR program has begun on August 1st, however the CVR activation for the last treatment substation was on August 12, which is the effective start date of the CVR program for our analysis. For that reason, August 2013 is partially a pre-treatment month
MethodologyConservation Model SpecificationConservationmodelmeasuresaverageenergysavingsfromCVR
∗ ∗ ∗ ∗
∗ ∗ ∗ ∗
Where:Averagehourlyconsumptionforhouseholdi indayt.FlagindicatingthatthestartofthetreatmentperiodFlagindicatingthatthecustomerhasreceivedtheCVRtreatmentImpactofTemperatureHumidityIndexonusageFlagindicatingthatacustomer’sAMImeterhasbeenactivatedMonthspecificimpactcommontoallhouseholds
∗ MonthspecificimpactoftheTemperatureHumidityIndexIndicatorthatacustomerisparticipatinginDSMprogramCustomerfixedeffectiid errorterm,clusteredbyhousehold
Methodology Peak Impact Model PeakimpactmodelmeasurespeakdemandsavingsfromCVR Asthepeakimpactanalysisisfocusedonquantifyingthesavingsduringsystempeakconditions,weundertakeouranalysisusingdataonthehottestdaysoftheyear Wedefinepeakashoursending15‐19(usingPJM’scapacitymarketpeakdefinitionforsummer)
WedefinehottestdaysasthosewithaveragepeakTHIsgreaterthan77,whichequatestoroughly85°F)
Werunthepeakimpactmodelforweekdays,weekendsandalldaystogaugewhetherthepeakimpactvariesduetodifferentpeakloadcharacteristicsduringthesedays
MethodologyPeak Impact Model Specification
∗ ∗ ∗
∗ ∗ ∗
∗ ∗ Where:
Averagehourlyconsumptionforhouseholdi indaytFlagindicatingthestartofthetreatmentperiodFlagindicatingthatthecustomerhasreceivedtheCVRtreatmentImpactofTemperatureHumidityIndexonusageMonthspecificimpactcommontoallhouseholds
∗ MonthspecificimpactoftheTemperatureHumidityIndexIndicatorthatacustomerisparticipatinginDSMprogramgroupkCustomerfixedeffectiid errorterm,clusteredbyhousehold
AgendaBackgroundDataOverviewMethodology SelectingControlGroups ConservationAnalysis PeakAnalysis
Results ConservationAnalysis PeakAnalysis
ResultsConservation ImpactResidentialCustomersA1.5%reductioninvoltageisestimatedtoresultina1.4%reductioninconsumption Significantatthe1%level ImpliedCVRfactorof.93whichiswithinrangesuggestedbypreviousstudies
ResultsConservation ImpactNon‐ResidentialCustomers1.5%reductioninvoltageisestimatedtoresultina0.9%reductioninconsumption Notstatisticallysignificant,thoughstillanunbiasedestimateofthemeanimpact
ImpliedCVRfactorof0.6whichiswithinrangesuggestedbypreviousstudies
Insignificantresultlikelydrivenbysmallersamplesizeandalsoheterogeneityofcustomers
ResultsPeak ImpactResidentialCustomersA1.5%reductioninvoltageisestimatedtoresultina1.1%reductioninpeakconsumption Significantatthe1%level ImpliedCVRfactorof.73whichiswithinrangesuggestedbypreviousstudies
ResultsPeak ImpactNon‐ResidentialCustomers1.5%reductioninvoltageisestimatedtoresultina2.5%reductioninpeakconsumption Significantatthe1%level ImpliedCVRfactorgreaterthan1isbeyondexpectedrangeforCVRimpact
Highimpactimpliesthatthereareotherunobservableeffectswhichwewerenotabletocaptureinthisanalysis,likelyduetoheterogeneityofnon‐residentialcustomers
ResultsPeak ImpactResidentialpeakresultsarerobustacrossdaysandhours
Hour Ending All DaysWeekends & Holidays Only
Weekdays
(% Impact) (% Impact) (% Impact)
Hour 15 ‐1.13% ‐1.67% ‐0.90%Hour 16 ‐1.02% ‐1.23% ‐0.90%Hour 17 ‐1.02% ‐1.08% ‐1.00%Hour 18 ‐1.21% ‐1.16% ‐1.20%Hour 19 ‐1.17% ‐1.10% ‐1.20%
15‐19 Pooled ‐1.11% ‐1.28% ‐1.08%
ResultsConclusion
Residentialimpactisrobust PepcoMaryland’sCVRpilotprogramhasbeensuccessfulinleadingtoadecreaseinresidentialconsumptionduringpeakhoursandalsoyear‐round
Theresultsarestableacrossmultipleeconometricmodels
Non‐Residentialimpactismoredifficulttoquantifyusingeconometricmethodsduetoheterogeneityandsamplesizeissues Inthefuture,largerdatasetswithlargersamplesizemayresultinstatisticallysignificantresults
