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Poster TitlePoster Title continued
ResearchersrsquoPresentersrsquo NamesInstitutionOrganizationCompany
Methods
Methods
Figure3 ProjectEnergyYieldforallthreecasestudies
WhyResourceMatters ImpactsofPreconstructionResourceDataonLongͲTermProductionEstimatesandProjectFinance
PaulThienpontndash MeteorologistMarieSchnitzerndash VPConsultingServicesRebeccaTilbrookndash SolarServicesTeamLead
bull Determinehowvarioussourcesofsolarandmeteorologicaldataimpactproductionestimates
bull Determinehowuncertaintyofdatasetsinhibittheabilitytoleveragetheproject
bull Determinehowplantoverproductioncanleadtolostrevenue
bull Determinehowplantoverproductioncanresultinlowerrateofreturnsfordebttaxequityandcash equityfinancepartners
Accurateresourceandenergyproductionassessmentsareneededtoestablishprojectrevenueswith confidencetherebysizingdebtappropriatelyHoweverwhenplantperformanceexceedsthepreͲ constructionenergyestimatewhatarethefinancialimplications
Thispresentationwillreviewtheimpactsofoverproductionontaxequityanddebtfinancingmodels whileevaluatingthemajorvariablesthatcontributetoplantsoutperformingthepreͲconstruction energyestimatessuchassolarresourceinputdataplantlossassumptionsanduncertainty
SomedevelopersarestillrelyingonhigheruncertaintyornonͲsitespecificsolarresourcedataforinputs intoenergysimulationmodelsThesedatasetstypicallyhavealargeruncertaintybandandareless accurateeitherunderͲestimatingoroverͲestimatingtheresourceattheprojectlocationWhenusedin anenergysimulationtherearedirectimpactsontheforecastedenergyproductionAdditionally accurateplantlossconsiderationsarecrucialtobestrepresentthelongͲtermproductionofthesolar plantWhenpoorplantlossassumptionsaremadeinconjunctionwithinaccurateandhighuncertainty solarresourcedatasetsactualplantproductionmayvaryconsiderablyfromthepreͲconstruction estimate
Plantproductionandtheassociatedrevenuearekeyinputsintofinancialmodelsusedtosizedebtortax equitycontributionsThereforerelyingonapoorqualityresourceandenergyestimatecanleadtoless thanoptimaldebtsizingwhichcanresultinlowerreturnsoninvestmentforequitypartners
Abstract
Objectives
Methodology Resource Inputs TotesttheimpactsofpotentialoverͲproductionAWSTevaluatedthreegeographicallydiversesitesacrossthe
UnitedStatesstudyingtheimpactsofhowvariousresourceinputspredictlongͲtermresourceversusahigh qualitygroundreferenceThesourcesofdataincludedTypicalMeteorologicalYear(TMY)from bull Satellitederiveddataset bull NationalSolarRadiationDatabase(NSRDB)TMY3[1] bull GroundmeasureddatafromtheUnitedStatesClimateReferenceNetwork(USCRN)[2]
ThegroundmeasurementsfromtheUSCRNsiteswereusedasthebaselineforallresultsandassumedtobe representativeoftheactualirradianceonsite
Thethreeprojectsitesselectedwerein bull MercedCalifornia bull TusconArizona bull MillbrookNewYork
UncertaintyforeachdatasetwereassessedutilizingAWSTrsquosstandardapproach Energy Analysis EnergywassimulatedforeachofthestudiedresourcefilesusingthePVSyst Software AllthreesitesweresimulatedusingAWSTruepower standardlossassumptions Basicplantdesignsareasfollows
bull 125MWDC 100MWAC (DCACRatio125) bull Generic300Wpolycrystallinemodule bull Generic500kWinverter bull RowtiltoptimizedforeachprojectlocationusingPVsyst bull Modeledwithoutnearshading bull Plantlossassumptionswereappliedconsistentlyforeachprojectlocationandresourceanalysis
Uncertainty Analysis Uncertaintyaroundresourcedataasapercentageoftheresourcedataishighlyvariableanddependenton
thesourceofdatatypeofcampaignandaccuracyofthesensorsutilized bull Measurements1Ͳ 5dependingonthemeasurementcampaign
Investor Interests
bull CashEquityInvestorsLongͲTermenergyproductionestimates(ReliantonP50analysis) bull TaxEquityInvestorsInterestsaregreatestinthebeginningoftheprojectlifeͲcycle(ReliantonP50
analysis) bull DebtLendersInterestedinonlytheminimumabilitytopaybackloan(ReliantonP90P99analysis)
Financial Model Key Performance Metrics bull DebtRatioRatioofacquireddebttocashequity
bull TaxEquityContributionAmountfinancedthroughtaxequityinvestor bull ProjectedInternalRateofReturn(IRR)EstimatedIRRforthecashequityinvestorwhenfinancing
witheachresourcedataset bull ActualIRRRealizedIRRforthecashequityinvestorwhenrunningtheactualgroundmeasured
resourcedatafilethroughthefinancialmodelfortheSatelliteTMYandNSRDBTMY3
Figure3presentsthegrossenergyyieldandlongͲtermnetenergyyieldfortheNSRDBTMY3and SatellitemodeledTMYforeachcasestudylocation
bull FromthisgraphthemostnotabletrendisthattheNSRDBTMY3underͲpredictstheenergy yieldwhencomparedtothegroundmeasureddataatallthreelocations
bull TheSatelliteModeledTMYismorevariableacrosseachregion
Figure4presentstheprojectedrevenuefortheSatelliteModelandtheNSRDBTMY3atthe MercedCAlocationwherebothestimatesunderͲpredictedtheresourceandenergy
bull Inbothcasestheprojectedrevenueislowerthanwhattheplantwouldactuallyproduce bull FortheNSRDBTMY3theestimateislowenoughthatoverthelongͲtermthePPA
overproductionthresholdwouldbemetresultinginlostrevenueofapproximately05
Figure 4 ProjectRevenuefortheMercedCAcasestudy
bull SatelliteModeled5Ͳ 10dependingontheresolutionofthemodelandtheprojectlocation
bull NSRDBTMY310orgreaterdependingontheproximityofthedatasettotheprojectlocation
Projected Revenue PowerPurchaseAgreement(PPA)pricingforeachprojectlocationwasdevelopedusingLocationalMarginal
Pricing (LMP)[3] bull TheBasePricewasassumedtobe15xtheaverageLMP bull TimeofDayandSeasonal(TOD)multipliersweredevelopedfromtherawLMPprices
PotentialprojectrevenuewasestimatedfromthehourlynetenergyandtheTODPPAprice
ProductionthresholdswereassumedwithinthePPAstructure bull GuaranteedEnergywasassumedtobetheannualP50estimate
bull Overproductionlimitationsbeganat110oftheGuaranteedEnergyandpaid75oftheTODPPAprice bull Defaultwasassumedtooccurat70oftheannualP50
Financial Model TraditionalDebtfinancingandTaxEquityFinancingstructureswereevaluated
bull CAPEX$22WAC InstalledCapacity bull OPEX$25kWACyearInstalledCapacity
DebtSizingsizedusingaDSCRof10andP99productionestimates TaxEquityInvestmentsizedassumingan80IRR
Results
Figure5 Exampleoflowuncertainty(left)andhighuncertainty(right)
Figure5illustrateshowenergyestimateswithlargeruncertaintybandscanimpactthe P90P99
bull Whenfinancingwithdebtuseofhigheruncertaintyresourcedatasetswilllimittherisk lendersarewillingtotakeon
Figure6presentstheDebtRatioProjectedIRRandActualIRRforatraditionaldebtfinancing structure
bull TheresultsoffinancingwiththeSatelliteModeledTMYfortheMercedCAcasestudy showsthatthecashequityinvestorwouldhaveyieldedanActualIRRof05lowerthanifa higherqualityresourcedatasetwasusedforfinancingIfweassumea$10Mcashequity investmentovera25Ͳyearprojectlifetimethe05IRRdifferentialwouldhaveacash equivalentofroughly$13Mofunrealizedrevenuepotential
bull TheresultsfromtheNSRDBTMY3analysisyieldactualIRRlossesofnearlydoublethoseof thesatellitemodelwhichisduetothelargeruncertaintybandaroundtheresourcedata ThispointIllustratestheimportanceofhighqualityresourceinputdatatoreducethe spreadbetweenP50toP90P99estimates
Figure7 presentstheTaxEquityContributionProjectedIRRandActualIRRforataxequity financingstructure
bull TheresultsoffinancingwiththeSatelliteModeledTMYfortheMercedCAcasestudy showsthatthecashequityinvestorwouldhaveyieldedanActualIRRof05 lowerthanif ahigherqualityresourcedatasetwasusedforfinancingAsinthescenarioabovefora $10Mcashequityinvestmentovera25Ͳyearprojectlifetimethecashequivalentisroughly $13Mleftonthetable
Conclusions bull Choosinganaccurateandreliablesourceofsolarresourcedataiscriticalforprojectfinancing
bull UnderͲestimatingsolarresourcecanleadtoplantoverproductionandlostrevenuetoPPAcaps
bull PlantoverͲproductioncanresultinlowerreturnsforlenderstaxequityandcashequityfinance partners
Solar Resource
bull GHI DNI DHI bull POA
Energy bull Losses bull Uncertainty
Revenue bull PPA bull OverUnder
Financial Model
bull CAPEX bull OPEX bull Debt
Sizing
Figure1FlowChartofCaseStudyMethodology
Annual Degradation (05 ndash 1 )
Transposition To Plane of Array
(05 ndash 2)
Energy Simulation Plant Losses
(3 ndash 5 )
Solar Resource Uncertainty (5 ndash 17)
Figure2SourcesofEnergyUncertainty
Figure6DebtFinancingStructureProjectYields
Figure7 TaxEquityDebtStructureProjectYields
References [1]NationalSolarRadiationDataBaseldquo1991ndash 2010Updaterdquo httprredcnrelgovsolarold_datansrdb1991Ͳ2010 [2]UnitedStatesClimateReferenceNetworkldquoHourlyDatardquo httpwww1ncdcnoaagovpubdatauscrnproductshourly02 [3]CaliforniaISOldquoOpenAccessSameͲtimeInformationSystemrdquohttpoasiscaisocom [4]PaulThienpontAWSTruepower ldquoIsSolarOverproductionCostingYourdquo18November2014 WebinarhttpswwwawstruepowercomknowledgeͲcenterwebinars
AWSTruepowerLLC|463NewKarner Road|AlbanyNewYork12205|+1Ͳ518Ͳ213Ͳ0044|awstruepowercom
PaulThienpontMeteorologistndash AWSTruepowerLLCpthienpontawstruepowercom
copyCopyright2015