PhotovoltaicandSolarForecasting:

StateoftheArt

ReportIEAPVPST1401:2013

ForecastPVpower ActualPVpowerForecastPVpower ActualPVpower

PhotocreditscoverpageUpperleftimage:EnvironmentCanada,DatacourtesyofNOAA(February27,2013)Upperrightimage:DaveTurcotte,CanmetENERGY,NaturalResourcesCanada

INTERNATIONALENERGYAGENCYPHOTOVOLTAICPOWERSYSTEMSPROGRAMME

PhotovoltaicandSolarForecasting:

StateoftheArt

IEAPVPSTask14,Subtask3.1ReportIEAPVPST1401:2013

October2013

ISBN9783906042138

Authors:

SophiePelland,spelland@nrcan.gc.caJanRemund,jan.remund@meteotest.ch

JanKleissl,jkleissl@eng.ucsd.eduTakashiOozeki,takashi.oozeki@aist.go.jp

KarelDeBrabandere,Karel.DeBrabandere@3e.eu

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Contents Contents...................................................................................................................................................1Foreword..................................................................................................................................................2Acknowledgements..................................................................................................................................3Executivesummary..................................................................................................................................31. Introduction.....................................................................................................................................42. Photovoltaicforecastingandlinktosolarforecasting....................................................................63. Forecastingmethodsfordifferentforecasthorizons.....................................................................8

3.1 SolarandPVforecasting0to6hoursahead(Intradayforecasts)........................................83.1.1 TotalSkyImagery.........................................................................................................103.1.2 SatelliteCloudMotionVectorApproach.....................................................................113.1.3 StochasticLearningTechniques...................................................................................12

3.2 SolarandPVforecasting6hourstodaysahead..................................................................123.2.1 Numericalweatherpredictionmodels........................................................................133.2.2 ImprovingforecaststhroughpostprocessingofNWPmodels...................................14

4. Pointforecastsandareaforecasts................................................................................................184.1 Upscaling...............................................................................................................................184.2 Errorreductionforareaforecasts........................................................................................19

5. Forecastaccuracy..........................................................................................................................225.1 Accuracymetricsandconfidenceintervals..........................................................................225.2 Benchmarkingofforecasts...................................................................................................235.3 Factorsthatinfluenceforecastaccuracy..............................................................................24

6. Solarforecastsurveyresults..........................................................................................................28Conclusion..............................................................................................................................................32References..............................................................................................................................................33

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ForewordTheInternationalEnergyAgency(IEA),foundedinNovember1974,isanautonomousbodywithintheframeworkoftheOrganizationforEconomicCooperationandDevelopment(OECD)thatcarriesoutacomprehensiveprogrammeofenergycooperationamongits23membercountries.TheEuropeanCommissionalsoparticipatesintheworkoftheAgency.

The IEA Photovoltaic Power Systems Programme (IEAPVPS) is one of the collaborative R & Dagreements established within the IEA and, since 1993, its participants have been conducting avarietyofjointprojectsintheapplicationsofphotovoltaicconversionofsolarenergyintoelectricity.

Themissionof the IEA PVPSprogram is: To enhance the international collaborative effortswhichfacilitatetheroleofphotovoltaicsolarenergyasacornerstoneinthetransitiontosustainableenergysystemsby:

1. ensuringsustainablePVdeployment,2. improvingPVperformanceandreliability,and3. assistingindesigningnewmarketstructuresandregulationswhichwillbesuitableforthe

widespreadadoptionofunsubsidisedPV.

The overall program is headed by an Executive Committee composed of one representative fromeach participating country, while the management of individual research projects (Tasks) is theresponsibilityofOperatingAgents.Bymid2012,fourteenTaskshadbeenestablishedwithinthePVPSprogram.

TheoverallgoalofTask14:HighPenetrationofPVSystemsinElectricityGridsistopromotetheuseof gridconnected PV as an important source in electric power systems at the higher penetrationlevelsthatmayrequireadditionaleffortstointegratedispersedgenerators.Theaimoftheseeffortsis to reduce the technical barriers to achieving high penetration levels of distributed renewablesystems.

The currentmembersof the IEAPVPSTask14are:Australia,Belgium,Canada,Switzerland,China,Germany,Denmark,Spain,Israel,Italy,Japan,Portugal,SwedenandtheUnitedStatesofAmerica.

This report describes the state of the art of solar and photovoltaic forecastingmodels used tofacilitate the integration of photovoltaics into electric systems operation, and reduce associateduncertainties. The report represents, as accurately as possible, the international consensus of theTask14expertsonthesubject.Further informationontheactivitiesandresultsoftheTaskcanbefoundat:http://www.ieapvps.org.

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AcknowledgementsThe Belgian contribution to this work has been funded by the three Belgian regions: BrusselsHoofdstedelijk Gewest/Rgion Bruxelles Capitale, Vlaams Gewest and RgionWallonne. FinancialsupportforUCSanDiego'sresearchinsolarforecastingthatformedthebasisforthecontributiontothisreportwasprovidedbytheCaliforniaPublicUtilitiesCommissionCaliforniaSolar Initiative,TheCaliforniaEnergyCommissionPIER,Panasonic,andtheUSDepartmentofEnergyHighPVPenetrationProgram.FinancialsupportforCanmetENERGY'scontributiontothisreportwasprovidedbyNaturalResourcesCanadathroughtheProgramonEnergyResearchandDevelopment.FinancialsupportforMeteotest's contribution to this report was provided by Swiss PVPS Pool. Financial support forJapanese Committee of PVPS TASK 14 includingAIST contribution to this reportwas provided byNEDO.ThemanagementofPVPSTask14andthecoordinationofthereportsbyAustriaarefundedbyBMVITandFFG.

ExecutivesummaryThefieldofsolarandphotovoltaic(PV)forecastingisrapidlyevolving.Thecurrentreportprovidesasnapshotofthestateoftheartofthisdynamicresearcharea,focusingonsolarandPVforecastsfortimehorizonsrangingfromafewminutesaheadtoseveraldaysahead.DiverseresourcesareusedtogeneratesolarandPVforecasts,rangingfrommeasuredweatherandPVsystemdatatosatelliteandsky imageryobservationsofclouds,tonumericalweatherprediction(NWP)modelswhichformthebasis ofmodernweather forecasting. The usefulness of these resources varies depending on theforecasthorizonconsidered:veryshorttermforecasts(0to6hoursahead)performbestwhentheymake use of measured data, while numerical weather prediction models become essential forforecasthorizonsbeyondapproximatelysixhours.ThebestapproachesmakeuseofbothdataandNWPmodels.ExamplesofthisstrategyincludetheuseofNWPmodeloutputsinstochasticlearningmodels, or the use of measured data for postprocessing NWP models to correct systematicdeviationsbetweenNWPmodeloutputsandmeasureddata.

BenchmarkingeffortshavebeenconductedtocomparetheaccuracyofvarioussolarandPVforecastmodelsagainstcommondatasets.Suchbenchmarkingiscriticaltoassessingforecastaccuracy,sincethis accuracy depends on numerous factors, such as local climate, forecast horizon andwhetherforecastsapplytoasinglepointorcoverawidegeographicarea.Inthelattercase,whichisoftenthemaininterestofelectricsystemoperators,higheraccuraciescanbeachievedsincerandomerrorsatdistantlocationstendtobelargelyuncorrelatedandtopartiallycancelout.

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1. IntroductionFollowingontheheelsofwindpower,photovoltaic(PV)electricitygenerationismakingrapidinroadsinelectricitygridsworldwide,withgrowth rates in installed capacity ranging from34% to82% forOECDcountriesoverthepastdecadeand installedcapacity inthesecountriesreaching63.6GWattheendof2011(IEAPVPS2012).InsomeEuropeancountries,PVproductionalreadyreaches30%ofoverallpowerproductionduringclearsummerdaysonaregularbasis(EPIA2012).TherapidgrowthingridpenetrationofPVandothervariablerenewableshaspromptedresearchandrelatedinitiatives,suchastheIEAPVPSTask14HighPenetrationofPVSystemsinElectricityGrids.

ThetwomainchallengestohighpenetrationratesofPVsystemsarevariabilityanduncertainty,i.e.thefactthatPVoutputexhibitsvariabilityatalltimescales(fromsecondstoyears)andthefactthatthisvariabilityitselfisdifficulttopredict.Thecurrentreportaddressesthesecondissue,uncertainty,andthemethodusedtoaddressit:photovoltaicforecasting.

Thisreport isstructuredas follows.Section2discussesthe linkbetweenweather forecastsandPVforecasts.Section3presentsareviewofforecastingmethods,firstforforecastinghorizonsof0to6hoursahead(Section3.1)andthenforlongerforecastinghorizons,from6hourstoafewdaysahead(Section3.2).Section4specificallyexaminesupscaling,whichisusedtoforecasttheoutputofalargenumber