How sensitive are optimal fully renewable power systems to ...

HAL Id: hal-02180343https://hal.archives-ouvertes.fr/hal-02180343

Preprint submitted on 12 Jul 2019

HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.

How sensitive are optimal fully renewable power systemsto technology cost uncertainty?

Behrang Shirizadeh, Quentin Perrier, Philippe Qurion

To cite this version:Behrang Shirizadeh, Quentin Perrier, Philippe Qurion. How sensitive are optimal fully renewablepower systems to technology cost uncertainty?. 2019. �hal-02180343�

Behrang Shirizadeh

Quentin Perrier

Philippe Qurion

CIRED Working Paper

N° 2019-73 - Juillet 2019

How sensitive are optimal fully

renewable power systems

to technology cost uncertainty?

Contact : [email protected]

Jardin Tropical de Paris -- 45bis, avenue de la Belle Gabrielle 94736 Nogent-sur-Marne Cedex

Site web: www.centre-cired.fr Twitter: @cired8568

Centre international de recherche sur l’environnement et le développement

Unité mixte de recherche CNRS - ENPC - Cirad - EHESS - AgroParisTech

1

Abstract

Manystudieshavedemonstratedthefeasibilityof fullyrenewablepowersystems invariouscountriesandregions.Yetthefuturecostsofkeytechnologiesarehighlyuncertainandlittleisknownabouttherobustnessofarenewablepowersystemtotheseuncertainties.Toanalyze it,webuild315long-termcostscenariosonthebasisofrecentprospectivestudies,varyingthecostsofkeytechnologies,andwemodel the optimal renewable power system for France over 18meteorological years, simultaneouslyoptimizinginvestmentanddispatch.

Ourresultsshowthatthetotalcostofa100%systemisnotthatsensitivetothepowermixchosenin2050.Certainly,theoptimalenergymixishighlysensitivetocostassumptions:theinstalledcapacityinPV,onshorewindandpower-to-gasvariesbyafactorof5,batteriesandoffshorewindevenmore.Butthe total cost will not be higher than today, and choosing a non-optimal electrical mix does notsignificantlyincreasethistotalcost.Contrarytocurrentestimatesofintegrationcosts,thisindicatesthatrenewabletechnologieswillbecomebyandlargesubstitutable.

Keywords:Powersystemmodelling;Variablerenewables;Electricitystorage;Robustdecisionmaking.

2

TableofContents

1 Introduction....................................................................................................................4

2 Materialsandmethods...................................................................................................5

2.1 Modeldescription.................................................................................................................5

2.2 Modelequations...................................................................................................................8

ObjectiveFunction................................................................................................................................................8

Adequacyequation...............................................................................................................................................8

Renewablepowerproduction..............................................................................................................................8

Energystorage......................................................................................................................................................9

Secondaryreserverequirement...........................................................................................................................9

Power-production-relatedconstraints...............................................................................................................10

Storage-relatedconstraints................................................................................................................................11

2.3 Inputdata...........................................................................................................................12

Costdata.............................................................................................................................................................12

VREprofiles........................................................................................................................................................14

Electricitydemandprofile..................................................................................................................................14

Discountrate......................................................................................................................................................14

3 Results............................................................................................................................15

3.1 Theoptimalpowermixishighlysensitivetopowercostassumptions.................................15

3.2 However,settingacapacitymixinadvancehardlyincreasescosts......................................18

3.3 Sensitivitytoweatherdata..................................................................................................19

Testingweathersensitivity.................................................................................................................................19

3

Selectingarepresentativeyear..........................................................................................................................21

4 Discussion&Conclusion..................................................................................................23

4.1 Comparisonwithexistingstudies........................................................................................23

4.2 Modellimitations................................................................................................................24

Factorswhichcouldpushcostsup.....................................................................................................................24

Factorswhichcouldbringcostsdown................................................................................................................25

4.3 Conclusion...........................................................................................................................26

References......................................................................................................................................28

Appendix1.AdditionalinformationontheJRC2017study....................................................32

Appendix2.Windandsolarproductionprofiles....................................................................34

Appendix3.Weatheryearsensitivity....................................................................................35

Appendix4:theEOLESmodel................................................................................................38

Appendix5:Transportcostofcarbondioxideformethanation..............................................41

Appendix6:Costdecomposition............................................................................................42

Appendix7:Additionalinformation.......................................................................................43

4

1 Introduction1

AccordingtoArticle4.1oftheParisAgreement,thePartiesshallendeavortorapidlyreducegreenhousegasemissionsinordertoachieveabalancebetweenanthropogenicemissionsbysourcesandremovalsbysinksinthesecondhalfofthiscentury.Fromthispointofview,theelectricitysectorwillhaveakeyroletoplay,asdecarbonisation isconsideredtobeeasier inthissectorthan intransport,buildingsoragriculture.Renewableenergywillbethecornerstoneofdecarbonisation,making,withCO2captureandstorage,agreatercontributionthannuclearenergyandfossilfuels(Rogeljetal.,2018).

Following Joskow (2011) and Hirth (2015), many articles have focused on the optimal proportion ofrenewable energies in the electricity mix. This literature has highlighted the existence of systemicintegration costs related to the deployment of variable renewable energies. In particular, a “self-cannibalization”phenomenonwashighlighted,linkedtothefactthatallthesolarpanelsinagivenfarmproducetheirelectricityatthesametime,justlikewindturbines.Intheabsenceofaffordablestorage,these integration costs have two consequences: (i) deployment of renewable energies leads to asignificant additional cost, rapidly increasingwith the deployment rate; (ii) the right balancemust bestruckbetweenthedifferentproductiontechnologiestominimizethisadditionalcost.

Theseresultshavedirectpolicyimplicationsinthetrade-offbetweenvisibilityandflexibility.Ontheonehand, investorswant visibility for thedevelopmentof economic sectors, suchasquantified targets intermsofinstalledrenewablecapacity.Ontheotherhand,ahighsensitivityoftheoptimalpowermixtotechnologycostsarguesforaflexibleapproach,allowingtrajectoriestobereadjustedaccordingtotheevolutionoftechnologies.Currentresultsthustendtosupporttheflexibilityapproach–attheexpenseofvisibilityforinvestors.

However, these results of increasing costs and right balancemight not holdmuch longer, due to therapiddeclineinproductionandstoragecosts. Inthespaceofsevenyears,thecostofsolarpanelshasbeenreducedbyafactorofseven,whilebatteriesnowseemtobefollowingasimilarpattern(Henze,2019).Moreover,recentwindturbinesbenefitfromaflatterproductionprofilethanoldermodels(HirthandMüller,2016).Finally,methanation,whichoffersanalternativeforseasonalstorage,isalsomakingsignificantprogress.Thesedevelopmentswillprobablystillbesignificantby2050,thepoliticalhorizonusedtodayinthedesignofpublicpolicies.

Whilethefeasibilityofa100%renewablemixhasalreadybeenhighlightedbymanystudies(Brownetal, 2018, and references therein), the question is now that of cost sensitivity: do these reductions inproduction and storage costs call into question the previous conclusions about the announced highadditionalsystemiccostofrenewableenergy?

1WethankananonymousrefereefromtheFAEREWorkingpapersseriesforhisorherveryusefulcomments.

5

If this phenomenon of increasing costs does not hold anymore, itwouldmean that the relationshipbetweenrenewableenergysourcesischangingfrombeingcomplementstobeingsubstitutes.Itwouldbe thenpossible to identifyoneor several “robust”energymixes, in the sense that theiroverall costdoesnotvarymuch,evenifthecostofthedifferenttechnologiesfinallydiffersfromtheinitialforecasts.In such a case, the political conclusion would shift: proving flexibility to investors through fixedrenewabletargetswouldprevailoverflexibleapproaches.

Toshedlightonthesequestions,webuildanewopen-sourcemodelcalledEOLES(EnergyOptimizationfor LowEmission Systems) and apply it to continental France. EOLESminimizes the total system costwhile satisfying power demand at each hour for a period of up to 18 years. It includes six powergeneration technologies (offshoreandonshorewind, solar, two typesofhydroandbiogas)and threestoragetechnologies(batteries,pumpedhydroandpower-to-gas).

Usingthismodel,westudythesensitivityofthepowermixin2050,through315costscenariosfor2050,varyingallkeytechnologycosts:onshoreandoffshorewindby+/-25%;PV,batteriesandpower-to-gasby+/-50%.Thenweaimtoidentifywhetherarobustpowermixcanbefound.

Theremainderofthispaper isorganizedasfollows. InSection2wepresenttheEOLESmodel.ResultsarepresentedinSection3whileSection4providesadiscussionandconcludes.

2 Materialsandmethods

2.1 Modeldescription

EOLESisadispatchandinvestmentmodelthatcarriesoutlinearoptimizationwithrespecttototalcost.Itminimizestheannualizedpowergenerationandstoragecosts,includingthecostofconnectiontothegrid.

TheEOLESmodel includessixpowergenerationtechnologies:offshoreandonshorewindpower,solarphotovoltaics (PV), run-of-riverand lake-generatedhydro-electricity, andbiogas combinedwithopen-cycle gas turbines. It also includes three energy storage technologies: pump-hydro storage (PHS),batteries andmethanation combinedwith open-cycle gas turbines. These technologies are shown inFigure1.

ThemodelconsiderscontinentalFranceasasinglenode.PVandonshorewindaresimulatedforthe95departments(anadministrativeentitycorrespondingtotheEuropeanNUTS3level).Theproportionoftheinstalledcapacity ineachdepartmentremainsthesameinallsimulations,atthelevelobservedin

6

2017.ThemodeliswritteninGAMSandsolvedusingtheCPLEXsolver.ThecodeanddataareavailableonGithub.1

Figure2providesanillustrativeoutputofthemodel,i.e.theoptimaldispatchforaweekinwinterandforaweekinsummer,aswellasthecorrespondingpowerprice,foreachhouroftheweek.

The remainder of this section presents themain equations (2.2) and the input data (2.3). A detaileddescriptionofallsets,parametersandvariablesofthemodelisavailableinAppendix4.

Figure1GraphicaldescriptionoftheEOLESmodel

1https://github.com/BehrangShirizadeh/EOLES_elecRES.

7

Figure2Hourlypowergeneration,electricitydemand,storagechargeanddischargeprofilesandpowerpricesfor(a)thethird

weekofJanuary(Winter)and(b)thethirdweekofJuly(Summer)2006

8

2.2 Modelequations

ObjectiveFunction

InEOLES,dispatchandinvestmentaredeterminedsimultaneouslybylinearoptimization.CAPEX(capitalexpenditure)andOPEX(operationalexpenditure).

Theobjectivefunction,showninEquation(1), isthesumofallcostsoverthechosenperiod,includingfixed investment costs, fixed O&M costs (which are both annualized) and variable costs. For somestorageoptions,inadditiontotheCAPEXrelatedtochargingcapacityper!"#,anothertypeofCAPEXisintroduced:acapexrelatedtoenergycapacity,per!"ℎ#

%&'( = *+#, − .+#,#/ ×1223456+#,+#, + (:&;<=>?+@?+@ ×1223456?+@

#A ) + (*+#,×+#,

C&&=+#,) + ('?+@× E1FGHI5JEℎ + C&&=I5J

Eℎ )?+@ (K+#,,M×N&&=+#,)M+#, /1000 (1)

where *+#, represents the installed capacities of production, :&;<=>?+@ is the volume of energystorage in MWh,'?+@ is the capacity of storage in MW, 1223456 is the annualized investment cost,C&&= andN&&= respectively represents fixed and variable operation andmaintenance costs andK+#,,Misthehourlygenerationofeachtechnology.

Adequacyequation

Electricity demandmust bemet for each hour. If power production exceeds electricity demand, theexcesselectricitycanbeeithersenttostorageunitsorcurtailed(equation3).

K+#,,M+#, ≥ SGT12SM + '(&UVK>?+@,M?+@ (3)

Where K+#,,M is the power produced by technology tec at hour h and '(&UVK>?+@,M is the energyenteringthestoragetechnologystrathourh.

Renewablepowerproduction

For each variable renewable energy (VRE) technology, the hourly power production is given by thehourlycapacityfactorprofilemultipliedbytheinstalledcapacityavailableforeachhour(equation4).

KW@#,M = *W@#×ECW@#,M(4)

WhereKW@#,M istheelectricityproducedbyeachVREresourceathourh,*W@# isthe installedcapacityandECW@#,Misthehourlycapacityfactor.

9

Energystorage

Energy stored by storage option str at hour h+1 is equal to the energy stored at hour h plus thedifference between the energy entering and leaving the storage option at hour h, accounting forcharginganddischargingefficiencies(equation5):

'(&U>X?+@,MYZ = '(&U>X?+@,M + ('(&UVK>?+@,M×[?+@\A ) − (

]^_`,ab^_`cd_ ) (5)

Where'(&U>X?+@,Mistheenergyinstorageoptionstrathourh,while[?+@\A and[?+@ef+arethecharginganddischargingefficiencies.

Secondaryreserverequirement

ThreetypesofoperatingreservesaredefinedbyENTSO-E (2013),according to theiractivationspeed.The fastest reserves are Frequency Containment Reserves (FCRs), which must be able to be on-linewithin 30 seconds. The second group is made up of Frequency Restoration Reserves (FRRs), in turndividedintotwocategories:afastautomaticcomponent(aFRRs),alsocalled‘secondaryreserves’,withan activation time of no more than 7.5 min; and a slow manual component (mFRRs), or ‘tertiaryreserves’,withanactivationtimeofnomorethan15min.Finally,reserveswithastartup-timebeyond15minutesareclassifiedasReplacementReserves(RRs).

Eachcategorymeetsspecificsystemneeds.ThefastFCRsareusefulintheeventofasuddenbreak,likea line fall, to avoid system collapse. FRRs are useful for variations over several minutes, such as adecrease inwind or PV output. Finally, the slow RRs act as a back-up, slowly replacing FCRs or FRRswhenthesystemimbalancelastsmorethan15minutes.InthemodelweonlyconsiderFRRs,sincetheyarethemostimpactedbyVREintegration.FRRscanbedefinedeitherupwardsordownwards,butsincetheelectricityoutputofVREscanbecurtailed,weconsideronlyupwardreserves.

The quantity of FRRs required to meet ENTSO-E’s guidelines is given by equation (6). These FRRrequirements vary with the variation observed in the production of renewable energies. They alsodependontheobservedvariabilityindemandandonforecasterrors:

U':g@@,Mg@@ = (hW@#×*W@#)W@# + SGT12SM×(1 + iWj@\j+\eAkejl )×ifA,#@+j\A+m

kejl (6)

WhereU':g@@,Mistherequiredhourlyreservecapacityfromeachofthereserve-providingtechnologies(dispatchabletechnologies)indicatedbythesubscriptfrr;hW@# istheadditionalFRRrequirementforVREbecause of forecast errors, iWj@\j+\eA

kejl is the load variation factor and ifA,#@+j\A+mkejl is the uncertainty

factor inthe loadbecauseofhourlydemandforecasterrors.ThemethodforcalculatingthesevariouscoefficientsaccordingtoENSTO-EguidelinesisdetailedbyVanStiphout(2017).

10

Power-production-relatedconstraints

The relationship between hourly-generated electricity and installed capacity can be calculated usingequation (7). Since the chosen time slice for the optimization is one hour, the capacity enters theequationdirectlyinsteadofbeingmultipliedbythetimeslicevalue.

K+#,,M ≤ *+#, (7)

Theinstalledcapacityofallthedispatchabletechnologiesshouldbemorethantheelectricitygenerationrequired of those technologies to meet demand; it should also satisfy the secondary reserverequirements Installedcapacity fordispatchable technologiescan thereforebeexpressedbyequation(8).

*g@@ ≥ Kg@@,M + U':g@@,M (8)

Monthly available energy for the hydroelectricity generated by lakes and reservoirs is defined usingmonthlylakeinflows(equation9).Thismeansthatenergystoredcanbeusedwithinthemonthbutnotacrossmonths.This isaparsimoniouswayofrepresentingthenon-energyoperatingconstraintsfacedbydamoperators,asinPerrier(2018).

o1!Gp ≥ Kkjq#,Mge@M∈p (9)

Where Kkjq#,M is the hourly power production by lakes and reservoir, and o1!Gp is the maximumelectricity that can be produced from this energy resource during one month. This parameter iscalculated by summing hourly power production from this hydroelectric energy resource over eachmonthoftheyeartocapturethemeteorologicalvariationofhydroelectricity,usingtheonlineportalofRTE1(theFrenchtransmissionnetworkoperator).

The energy that can be produced by biogas is limited, since the main resources of this energy aremethanization (anaerobicdigestion)andpyro-gasificationofsolidbiomass.Bothprocessesare limitedby several constraintsandaccording to theADEME“visions2030-2050” report (2013)electricity frombiogasproducedbythesetwoprocessescanbeprojectedas15TWhperyearfrom2030on(Gs\etj?

pj/ ),whichispresentedinequation10.

Ks\etj?,MuvwxMyz ≤ Gs\etj?

pj/ (10)

Run-of-river power plants represent another source of hydro-electricity power. River flow is alsostrongly dependent on meteorological conditions and it can be considered as a variable renewable

1https://www.rte-france.com/fr/eco2mix/eco2mix-telechargement

11

energyresource.Hourlyrun-of-riverpowerproductiondatafromtheRTEonlineportalhasbeenusedtopreparethehourlycapacityfactorprofileofthisenergyresource,J4NGJMinequation(11);

K@\W#@,M = *@\W#@×J4NGJM (11)

AsshowninFigure1,tworenewablegastechnologiesareconsidered;biogasandmethanation.Bothofthemproducerenewablemethane,whichcanbeused ingaspowerplants. Inthemodel, the latter isconsideredtobeanopencyclegas turbine (OCGT)dueto itshighoperational flexibilityandequation(12)showstherelationshipofthepowerproductionfromthesetwomethaneresources;

Ktj?,M = K,eps,M,eps (12)

WhereK,eps,M is the power production from each renewable gas resource, andKtj?,M is the powerproductionfromtheOCGTpowerplantwhichusesthesetworesourcesasfuel.Itisworthmentioningthat theefficiencyof this combustionprocess is considered inboth the15("ℎ# of yearlyelectricityproductionfrombiogas,andthedischargeefficiencyofthemethanationprocessasdefinedinequation(5).

The maximum installed capacity of each technology depends on land-use-related constraints, socialacceptance, the maximum available natural resources and other technical constraints; therefore, atechnologicalconstraintonmaximuminstalledcapacity isdefined inequation (13)where.+#,pj/ is thiscapacity limit, taken from the development trajectories for the French electricity mix for the period2020-2060(ADEME,2018):

*+#, ≤ .+#,pj/ (13)

Storage-relatedconstraints

Topreventoptimization leading toaveryhighamountof storedenergy in the firsthour representedandalowoneinthelasthour,weaddaconstrainttoensurethereplacementoftheconsumedstoredelectricityineverystorageoption(equation14):

'(&U>X?+@,Myz ≤ '(&U>X?+@,Myuvwx (14)

Whileequations(5)and(14)definethestoragemechanismandconstraint intermsofpower,wealsolimittheavailablevolumeofenergythatcanbestoredbyeachstorageoption(equation15):

'(&U>X?+@,M ≤ :&;<=>?+@ (15)

Equation(16)limitstheenergyentrytothestorageunitstothechargingcapacityofeachstorageunit,whichmeansthatthechargingcapacitycannotexceedthedischargingcapacity.

12

'(&U>X?+@,M ≤ '?+@ ≤ *?+@ (16)

2.3 Inputdata

Themaininputdatacanbeplacedinthreemainclasses:costdata,VREprofilesandelectricitydemandprofiles.

Costdata

The economic parameters for the power production technologies are taken from the EuropeanCommission Joint Research Center (2017) study of scenario-based cost trajectories to 2050, whileenergytechnologyreferenceindicatorprojectionsfor2010-2050(JRC,2014,havebeenusedforOCGTgaspowerplants.Valuesattributedtotheeconomicparametersofpowerproductiontechnologiesfor2050aresummarized inTable1. It isworthmentioningthat thegridentrycostof€25.9/kWforeachpower plantmandated by RTE (2018) has been added to the capital expenditure values of each VREtechnology, and the annuities (annualized CAPEX) are the results of these calculations. MoreinformationaboutthecostscenariosandthecostestimationmethodologyusedintheJRC’s2017studycanbefoundinAppendix1.

Table1Economicparametersofpowerproductiontechnologies

Technology CAPEX(€/kWe)

Lifetime(years)

Annuity(€/kWe/year)

Fixed O&M(€/kWe/year)

VariableO&M(€/MWhe)

Source

Offshore wind

farm*2330 30 144.3677 47.0318 0 JRC(2017)

Onshore windfarm*

1130 25 77.6621 34.5477 0 JRC(2017)

SolarPV* 425 25 30.0052 9.2262 0 JRC(2017)

Hydroelectricity –

lakeandreservoir2275 60 110.2334 11.375 0 JRC(2017)

Hydroelectricity –

run-of-river2970 60 143.9091 14.85 0 JRC(2017)

Biogas

(Anaerobic

digestion)

2510 25 135.5066 83.9 3.1 JRC(2017)

OCGT 550 30 33.7653 16.5 0 JRC(2014)

*Foroffshorewindpoweronmonopilesat30kmto60kmfromtheshore, foronshorewindpower, turbineswithmediumspecific capacity(0.3kW/m2)andmediumhubheight(100m)andforsolarpower,anaverageofthecostsofutilityscale,commercialscaleandresidentialscalesystemswithout tracking are taken into account. In this cost allocation,we consider solar power as a simple average of ground-mounted,rooftopresidentialandrooftopcommercialtechnologies.Forlakeandreservoirhydrowetakethemeanvalueoflow-costandhigh-costpowerplants.

13

Forthestoragetechnologies,the“CommercializationofEnergyStorageinEurope”reportpreparedbyFCH-JU(2015)andaveryrecentarticlebySchmidt(2019)about long-termcostprojectionsofstoragetechnologieshavebeenusedrespectivelyforpumpedhydrostorageandLi-Ionbatterystorageoptions.“The potential of Power-to-Gas” study by ENEA consulting (2016) has been used for methanationstorage. Using these three studies the 2050 cost projection of storage technologies are presented inTable2.ThecostofmethanationismadeupofthecostofelectrolysisunitsandtheSabatierreaction1.

Table2Economicparametersofstoragetechnologies

Technology CAPEX(€/kWe)

CAPEX(€/kWhe)

Lifetime(years)

Annuity(€/kWe/y

ear)

FixedO&M

(€/kWe/year)

VariableO&M

(€/MWhe)

Storageannuity(€/kWhe/year)

Source

Pumpedhydrostorage(PHS)

500 5 55 24.6938 7.5 0 0.2261 FCH-JU(2015)

Batterystorage(Li-Ion)

140 100 12.5 14.8876 1.96 2 10.3247 Schmidt(2019)

Methanation 1150 0 20/25* 117.9262 75.75 3 0 ENEA(2016)

*Thelifetimeofelectrolysisunitsis20years,whilethelifetimeofmethanationunitsis25years.

Thecarbondioxiderequiredformethanationisassumedtocomefromcapturingandtransportingtheexcess carbondioxide resulting from themethanizationprocess (for theproductionofbiogas).About30%oftheproductofbio-methaneproductionfrommethanizationbyanaerobicdigestionisgasphasecarbon dioxide (Ericsson, 2017). According to ZEP (2011) on%&| transport, the cost of transportingcarbondioxidealonga200kmonshorepipelineis€4/5%&|.

Considering a 100km long onshore pipeline (considering maximum 100km of distance between themethanationunitsandthebiogasproductionunits),the%&|transportcostforthemethanationstorageis€1/MWh(Seeappendix5),tobeaddedtothegasstoragecostwhich is€2/MWh(accordingtoCRE(2018) - French energy regulation commission), the variable cost of the methanation storage is€3/="ℎ#.

1ThereactionthatproducesmethanefromhydrogenandcarbondioxideiscalledtheSabatierreaction.

14

VREprofiles

Variable renewable energies’ (offshore and onshore wind and solar PV) hourly capacity factors havebeenpreparedusingtherenewables.ninjawebsite1,whichprovidesthehourlycapacityfactorprofilesofsolarandwindpowerfrom2000to2017atthegeographicalscaleofFrenchcounties(départements),followingthemethodselaboratedbyPfenningerandStaffell(2016)andStaffellandPfenninger(2016).These renewables.ninja factors reconstructed from weather data provide a good approximation ofobserveddata:Moraesetal.(2018)findsacorrelationof0.98forwindand0.97forsolarpowerwiththein-situobservationsprovidedbytheFrenchtransmissionsystemoperator(RTE).

To prepare hourly capacity factor profiles for offshorewind power,we first identifiedall the existingoffshore projects around France using the “4C offshore” website2, and using their locations, weextracted the hourly capacity factor profiles of both floating and grounded offshorewind farms.Wethenaveragedthemostremarkableprojectsforeachoffshorewindfoundationtechnology(floatingandgrounded)foreachyearfrom2000to2017.TheSiemensSWT4.0130hasbeenchosenastheoffshorewind turbine technology because of recent increase in the market share of this model and its highperformance.Thehubheightofthisturbineissetto120meters.

Appendix 2 provides more information about the methodology used in the preparation of hourlycapacityfactorprofilesofwindandsolarpowerresources.

Electricitydemandprofile

HourlyelectricitydemandisADEME(2015)’scentraldemandscenariofor2050.Thisdemandprofilefallsinthemiddleofthefourproposeddemandscenariosfor2050inFrancebyArditietal.(2013)duringthenationaldebatesontheFrenchenergytransition(DNTE).Itamountsto422("ℎ#/year,12%lessthantheaveragepowerconsumptioninthelast10years.

Discountrate

WeuseadiscountrateofDR=4.5%i.e.thediscountraterecommendedbytheFrenchgovernmentforuseinpublicsocio-economicanalyses(Quinet,2014).Thisdiscountrateisusedtocalculatetheannuityintheobjectivefunction,usingthefollowingequation:

1223456+#, =ÄÅ×ÇÉÑÖÜ_áàZâ ZYÄÅ äã_ (2)

1https://www.renewables.ninja/

2https://www.4coffshore.com/

15

WhereDRisthediscountrate.

3 Results

3.1 Theoptimalpowermixishighlysensitivetopowercostassumptions

Totestthesensitivityoftheoptimalpowertothecostsofvarioustechnologies,weconsidertherangeofuncertaintyindicatedinTable3.Forpowergenerationtechnologies,uncertaintyappliestothefixedcosts,definedascapitalcostsandfixedoperationandmaintenancecosts.Forstoragetechnologies, itapplies to themain cost component of each of them; fixed costs formethanation (similar to powergenerationtechnologies)andenergy-relatedCAPEXforbatteries.Forwindtechnologies,thechoiceofa+/-25%uncertaintyrangeratherthan+/-50%comesfromtheexpertelicitationsurveybyWiseretal.(2016).No variation in the cost of hydro and biogas is accounted for, the former because it is a maturetechnology with low uncertainty and the latter because in the model the amount of biogas used isdeterminedbytheavailabilityconstraint,notbyitscost.

Table3Variationsinthecostsofkeytechnologiesaccountedforinthesensitivityanalysis

Technology SolarPV Offshorewind Onshorewind Batteries MethanationUncertaintyrange

-50%;-25%;0%;+25%;+50%

-25%;0%;+25% -25%;0%;+25% -50%;0%;+50% -50%;0%;+50%

AllthecombinationsofvariationspresentedinTable3wouldgive405differentcostscenarios(5Z×3å).Outofall theseoptions,weselect315scenarioswhichprovidehigher internal consistency. Indeed,afutureinwhichoffshorewindwouldbemoreexpensivethanexpectedandonshorewindcheaperthanexpected (or vice-versa) is not realistic, so we select only the scenarios in which the costs of thesetechnologies canonlydifferby25%atmost.This leads tosevendifferentoffshoreandonshorewindpower cost scenario combinations. Multiplying by five solar power cost scenarios and three costscenariosforeachstoragetechnology(7×5Z×3|),weobtain315futurecostscenarios.

Our results indicate that theoptimalenergymix ishighly sensitive to costuncertainty.Offshorewindoftenreacheseitherzeroinstalledcapacityorthemaximumallowedvalue,whiletherangeofonshorewind and PV capacities is approximately five-fold across the cost scenarios (Figure 3a). StoragetechnologiesalsodemonstratesuchhighsensitivitywiththeexceptionofPHSwhosecapacityisalwaysfixedbythemaximumallowedvalue.Batterycapacityrangesfrom7.6tomorethan279K"ℎ#,nearlyfourtimesthecapacity inthereferencecostscenario(Figure3d1),andmethanationrangesfrom7to33.5("ℎ,morethantwicethecapacityinthereferencecostscenario(Figure3d2).

This analysis also highlights some patterns of substitutability and complementarity betweentechnologies.Obviously,eachoption isparticularly influencedby itsowncost,butalsoby thecostofother technologies. In particular, a higher cost ofmethanation entailsmuchmore offshorewind and

16

vice-versa. Indeed, electricity fromoffshorewind suffers from a higher LCOE than other VREs but itsproduction is more stable, generating less need for storage. Conversely a higher cost of batteriesreducessolarcapacity:batteriesareespeciallyinterestingwhenenergymustbestoredforafewhours,sotheycomplementsolartechnology.

Finally, the system LCOE and the average power price are much more influenced by the cost ofgeneration technologies thanby thatof storage technologies1;Keeping the reference investmentcostscenarioforpowerproductiontechnologies,changingtheinvestmentcostofbatteryandmethanationstorageoptions from the lowest storage investment cost scenario (both -50%) to thehighest storageinvestmentcost scenario (both+50%)changes theoverall systemLCOE from€46/MWhto€51/MWh,whilechangingthe investmentcostof threeVREpowerproductiontechnologies fromthe lowestcostscenariotothehighestcostone(keepingthestorageoptionsatthereferencecostscenario),changestheoverallsystemLCOEfrom€37/MWhto58€/MWh.

1 Schlachtberger et al. (2018) find nearly no effect of storage cost variation on the final cost of the electricitysystem,whichisinaccordancewithourconclusions.

17

18

Figure3Optimizationresultsoverthe315futurecostprojectionscenarios.(a)powerproductionand(b)installedcapacityofeachVREresource;(c)loadcurtailmentandstoragelosses;(d)neededstoragevolumeinGWheforbatteriesandpumpedhydro

storage(d1)andinTWheformethanation(d2);(e)systemLCOEin€/MWhe;(f)averagepowerpricein€/MWhe.Thegreenpoint

showsthereferencecostscenario.Thecoloredlinesbesidewhiskerplotsshowtheimpactofvaryingseparatelythecostofone

technology,keepingallothertechnologiesattheirreferencecost.

3.2 However,settingacapacitymixinadvancehardlyincreasescosts

Globally,thepreviouscostsensitivityanalysisconfirmsthattheoptimalpowermixishighlysensitivetotechnologycosts.Thus,adecisionmakermightbetemptedtofavoraflexiblepolicyoveramorerigidone,attheexpenseofvisibilityforinvestors.However,ahighcostsensitivityoftheoptimalpowermixdoes not imply a high cost for choosing a non-optimal mix. In the case of highly substitutabletechnologies,asmallchangeincostwillleadtoastrongshiftintheoptimalmix,butchoosingonemixortheotherwouldnotchangetotalcostmuch.

The questionwe aim to answer in this subsection is the following: “Ifwe decide nowa trajectory ofrenewablecapacities for the futurebasedoncurrentcostestimates,could itentailahighover-cost ifour assumptions of technology costs are wrong”? To answer that question, we use the installedcapacities of generation and storage technologies optimized for the reference cost scenario, andwecalculatethesystemLCOEforthis“rigidcapacity”acrossour315costscenarios(Figure4).ThesystemLCOE is necessarily equal to or higher than that of the “flexible capacity”, the difference being the“regret”frombasingtheoptimizationonthewrongcostassumptions.

In most cases the regret is remarkably low given the wide range of cost scenarios considered: theaveragevalue is4% i.e.€2/="ℎ#, the thirdquartile is6%,and the regret isbelow9% in95%of thescenarios. A close examination of the 14 cost scenarios with the largest regret (more than 2 billion€/year, i.e. around 10%) shows that all but one concern scenarios in which the cost of onshore oroffshorewind, or both, is lower than expected. Hence the regret in this scenario stems from havinginstalled too little windpower. The only exception is a scenario in which PV and batteries are 50%cheaper than in the reference scenario, onshore at the reference cost, offshore 25% cheaper andmethanation50%moreexpensive.Inthiscaseonly,theregretstemsfromhavingnotinstalledenoughPVandbatteries.

TheaveragesystemLCOEfortherigidcapacity(blackvertical line inFigure4)equalsthesystemLCOEunder the reference cost scenario (red vertical dashed line). This result is due to the symmetricdistributionof technology cost shocks and the linear natureof themodel, and canbeunderstood asfollows:startingfromthesystemoptimizedoverthereferencecostscenario,atechnologycostshockby+25%entailsadecrease insystemLCOEby thesameamount (inabsolutevalue)asa technologycostshockby-25%,sotheaveragesystemLCOEisthesameastheonewithoutuncertainty.

19

Figure4DistributionofsystemLCOEacrosscostscenarios.Thereddistributionrepresentsoptimalenergymixes;thegreen

distributioniscomputedusingthecapacitiesofthereferencescenario.

3.3 Sensitivitytoweatherdata

Testingweathersensitivity

Totesthowtheoptimalmixofvariablerenewablesvariesfordifferentweather-years,weranthemodelforeachyearfrom2000to2017(henceforth“weather-years”).

Ourresultsshowthattheoptimalpowermixvariessignificantlyfromoneyeartoanother,bothintermsofelectricityproduction, installedcapacity, storagevolumeandstoragecapacity (Figures5and6andAppendix3).The largestvariationsbetweenminimumandmaximum installedcapacityareassociatedwith onshore and offshore wind power. In particular, offshore capacity ranges from zero to 20 GW,

20

which is themaximumvalue allowed1.High values foroffshorewindare reachedeither forweather-yearswithahighcapacityfactorforoffshorewind(asin2015)orforweather-yearswithalowcapacityfactor for onshorewind (as in 2016). In comparison, installed solar capacity ismore stable (between100.5GW and 122.2GW), due to a less volatile capacity factor (Figure 6c). Biogas always reaches themaximum allowed power generation and hydro the maximum allowed capacity. As far as storagecapacity is concerned, pumped hydro storage (PHS) also always reaches its maximum value whilebatteries andmethanation vary a lot acrossweather-years (Figures 6d1 and6d2). In comparison, thesystemLCOEandaveragepowerprice(thedualvariableoftheadequacyconstraint,i.e.equation3),aswellasthesumofVREcurtailmentandstoragelossesaremuchmorestable(Figures6eand6f).

Figure5VREgenerationmixforeachweather-yearinsingle-yearoptimizationandoverthewhole18-yearlongperiod

1MaximumvaluesarenotbindingforsolarPVandonshorewind.

21

These results show that if the aim is to find anoptimal energymix, running amodel on a randomly-chosenweather-yearcanbeverymisleading.Theoptimalmixof renewables ishighly sensitive to thechosenweather-year.ThisconclusionisconsistentwiththoseofCollinsetal.(2018)andZeyringeretal.(2018). As theweather of future years cannot be predicted, the best approachwould be to run themodeloverseveralweather-years,asinour18-yearsimulation.

However,thedrawback isamuchlongeroptimizationtime,whichpreventsusfromdoingthisforthe315 cost scenariosused inour sensitivity analysis.Hencewehave chosenanother approach: select arepresentativeyearthatgivestheresultsclosesttotheresultswhenoptimizingover18years.

Selectingarepresentativeyear

Theselectionofarepresentativeyearcouldbemadeusingseveralcriteria.Wechosetoselecttheyearwith a capacity factor closest to our 18-year optimal mix.We used the capacity factor because it isinvariablewith respect to technologycosts,onwhichweperformthesensitivityanalysis.Tomeasurethedistancetothe18-yearoptimalmix,wecomputethesumofabsolutedifference1ofthethreeVREs.Usingthisapproach,2006istheclosestyeartotheoverall18-yearlongperiod,withasumofabsoluteerrorvaluesof1.5%(TableA.4).Welaunchedthemodelwiththeoptimalinstalledcapacitiesfoundfor2006 over all otherweather-years to test the adequacy of this installed capacitywith respect to theother17weather-years,andwedidnotobserveanyoperationalinadequacy.

1Sumofnormalizedabsolutedifferences /éâ/∗é

/∗é

ê\yZ whereH\istheCFofeachtechnology4ineachyearandH∗\

istheCFofthattechnologyover18years.

22

Figure6.Optimizationresultsforeachweather-yearfrom2000to2017andforthewhole18-yearperiod.(a)powerproduction;

(b)installedcapacity;(c)averagecapacityfactorofeachVREandthegaspowerplantforbiogasproducedbyanaerobicdigestionandmethaneproducedbymethanationand(d)systemLCOEandaveragepowerpriceofelectricity.Thegreendot

showstheresultsoftheoptimizationoverthe18-yearperiodandthereddottheresultsforweather-year2006.Theboxplots

showthefirstandthirdquartilesandthemedianforeachscenario.

Figure 7 shows the energy mix of the chosen representative year (2006) and the whole 18-yearmodelling.Thereisaveryclosematchbetweenthepercentageofeachenergysourcefortheoverall18-year-longoptimizationandtherepresentativeyear.Onshorewindpowerisclearlydominantwithsolarpowerandoffshorewindpowerasthesecond-andthird-biggestsourcesofenergyrespectively.

Figure7:Energymixforthechosenrepresentativeweather-year(2006,left)andforthe18-yearoptimization(right)

23

4 Discussion&Conclusion4.1 Comparisonwithexistingstudies

Someauthorshavearguedthatthestoragefacilitiesrequiredforafullyrenewablepowersystemwouldmassivelyincreasethepowersystemcost(e.g.Sinn,2017,whoseconclusionshavebeenchallengedbyZerrahnetal.,2018).Inourreferencecostscenario,storage(batteries,PHSandmethanation)accountsforonly14.5%ofthesystemcost,vs.85.5%forelectricitygeneration(Appendix6).Moreover,wehaveseenthatthesystemLCOEismuchmorerobusttothecostofthestoragetechnologiesthantothatofPVandwind.Hencetheimportanceofthestoragecostshouldnotbeoveremphasized.

The system LCOE for power generation and storage ranges from €36 to €65/="ℎ#, depending ontechnology costs, with an expected value between €50 and €52/="ℎ#, depending on whether thepowersystemisoptimizedbeforeorafterthearrivalofinformationabouttechnologycosts.AccordingtothelatestquarterlyreportfromtheFrenchenergyregulator(CRE,2018),35%ofatypicalelectricitybill represents electricity production, hence from a bill varying between €160 and €170/="ℎ#, €56-€60/="ℎ# representsproduction.Hencethecostofa100%renewableelectricitysystemforFrancein2050wouldbelowerthanorsimilartothatofthecurrentpowersystem.

TheseresultscontrastwiththoseofKrakowskietal. (2016)whofindanannualizedcostofmorethan€60bn/yr. intheirscenario100RES2050(cf. theirFig.23)vs.€21bn/yr. inours.Theexplanationdoesnot stem from their investment cost assumptions, which are similar to ours (cf. their Table 1). Oneexplanationmight be that they take a higher discount rate, but theydonot disclose it sowe cannotverifythishypothesis.Partialexplanationsare(i)aslightlyhigherpowerdemand(cf.theirFig.7:about460("ℎ#/yr. vs. 422); (ii) a slightly lower capacity factor for onshorewind (28%) andoffshorewind(50%); (iii) the fact that they assume a perfect correlation between onshore and offshore windproduction,which artificially limits the complementarity between these technologies.Moreover, theybasetheirwindproductionprofilesonobservedpowergenerationin2012,whichneglectsthefactthatadvanced turbines generate electricity more constantly than those installed in the past (Hirth andMüller,2016).

Villavicencio (2017),who does not specify the time horizon considered, finds even higher annualizedcost:morethan€180bn/yr.for100%renewables,i.e.morethan8timesourresult.Severalfactorsmayexplainthishugedifference.First,hetakesarealdiscountrateof7%/yr.Thisismuchhigherthanours,whichcorrespondstotheraterecommendedforsocio-economicanalysisinFrance(4.5%).Second,hisinvestmentcost forPV ismuchhigherthanours:€3.6/"#,whilethecurrent investmentcostatutilityscaleisaround$1/"# (Lazard,2018).ThisexplainswhyPVdoesnotappearinhisreferencescenario(F1)with100%renewables.Third,totaldemandishigherthanours(512("ℎ# vs.422("ℎ#).

Tosumup,whileourresultspointtoamuchlowersystemcostthanthetwoabove-mentionedstudiesmodelinga100%renewablesystemforFrance,therearegoodreasonstoconcludethatthesystemcost

24

for 2050will be lower than that estimated by these studies. In the remainder of this subsection,weaddressseveralfactorsinturnwhichcouldpushourestimatesupordown.

4.2 Modellimitations

Factorswhichcouldpushcostsup

Costofthetransmissionanddistributionnetwork

OursystemLCOEincludesstorageandconnectionofpowergenerationtothegrid,butnotthecostofthetransmissionanddistributionnetwork.Currentlythiscostaccountsfor27%ofthetypicalelectricitybill,i.e.about€45/="ℎ#.Calculatingthiscostforthevariouspowersystemsconsideredinthepresentstudywouldexceed the scopeof thepresentarticle,but several recent studies indicate that thecostdifferentialacrossscenariosfeaturinggreaterorlesserpercentageofrenewableswouldbelimited.

• According to the RTE systems and network perspectives study (2018), for a 71% renewableelectricitymix(theso-calledWattscenariofor2035)inFrance,theextranetworkcostswouldbeintheorderof€1bn/yr., less than5%of the totalproductioncost.However, therelationship isnotlinearanditcannotbeeasilyextrapolatedforhigherproportionsofrenewables.

• AccordingtotwostudiesbyADEME(2015,2018),thecostofrenovatingtheFrenchnetwork,whichisplanned to takeplacebefore2030,willbeat leastoneorderofmagnitudemore than thecostrequiredtostrengthenthegridforafullyrenewablepowernetwork.

• AccordingtoEirGrid1(theIrishelectricitynetworkoperator),foranelectricitymixwithnearly90%ofrenewables,thereinforcementrequiredtointegrateVREswillcostnomorethan€1/="ℎ#.

Acceptabilityofwindpower

Ouroptimalscenariocorrespondingtothereferencetechnologycostsincludesabout80GWofonshorewind,12GWofoffshorewindand110GWofPV.TheavailabilityoflandforPVdoesnotappeartobeproblematical since the amount of suitable land is much higher than required (Cerema, 2017). Foroffshore wind, WindEurope’s “high” scenario for 2030 forecasts 11 GW, roughly equivalent to ouroptimal scenario corresponding to the reference technology costs. Here again, reaching this capacitydoesnotseemproblematical.

Foronshorewind,WindEurope’s(2017)“high”scenarioforecasts41GWin2030,vs.14in2018,i.e.anincreaseof2.2GW/yr.onaverage.Reaching80GWin2050meansanincreaseof2GW/yr.onaverage,from2018onwards,abitlessthanWindEurope’s“high”scenario,butalmosttwicethecurrentrateof

1http://www.eirgridgroup.com/newsroom/record-renewable-energy-o/index.xml

25

increase.Sustainingsuchahighrateofincreaserequiresahighdegreeofpoliticaldetermination,giventhecurrentoppositionfacedbymanywindprojectsinFrance.

Discountrate

Somestudiesusehigherdiscountratesthanours,e.g.7%inVillavicencio(2017),asmentionedabove.ThiswouldincreasetheannualizedLCOE,andespeciallythecostofcapital-intensivetechnologies.Whilehigher ratesmaywellbeusedbyprivatecompanies,4.5% isalreadymuchhigher thanboththerate-free real interest rate available on financial markets, and expected GDP growth over the next fewdecades. Using a higher rate in a socioeconomic analysis means than future generations would bepenalizedwhencomparedtocurrentones,whichcanhardlybedefendedonethicalgrounds.

Perfectweatherforecasts

Our optimization has been conducted on the assumption that the weather is known for the wholeperiod.With imperfect weather forecasts, the cost would be higher, but such an optimization for acountry-scale system would be computationally challenging. Gowrisankaran et al. (2016) haveperformedsuchanoptimization just forsolarenergy,ona limitedgeographicalscale,andhavefoundthat“intermittencyoverall isquantitativelymuchmore important thanunforecastable intermittency.”However,whetherthisconclusionwouldholdforacomplex,multi-energysystemisanopenquestion.

Factorswhichcouldbringcostsdown

Demand-sidemanagement

Our model does not feature price-elastic electricity demand or flexibility in the power consumptionprofile,becausethiswouldhaverequireddebatableassumptions.Moreover,thedemandprofile,takenfromADEME(2015),isalreadyflatterthanthecurrentone.Includingthesefeatureswouldreducetheneedforstorageandtherelatedenergylosses.

Interconnectionwithneighboringcountries

Manystudieshaveshownthatinterconnectionswithneighboringcountriescansignificantlyreducethecostofafullyrenewablesystem.Forinstance,Annan-PhanandRoques(2018)haveshownthatpowerpricevolatilitycanbereducedbycross-borderexchangeswithneighboringcountries.Indeed,thisleadsto benefits from the differences both in climatic and weather conditions between the countriesconcerned.

26

Spatialoptimizationofrenewableenergycapacities

Asmentioned above, we do not optimize the quantity of renewables at every location but only theaggregate capacity,which is thus scaledup compared to thevalueobserved in2017.A lower systemcostwouldbeobtainedbyoptimizingtheirlocation,whichwouldpresumablyleadtogreatercapacityinwindier or sunnier locations, although this effectwould bemitigated by the need to obtain a flatteraggregategenerationprofile.Yetthiswouldmakethemodelcomputationallyintractableandmightleadtounrealisticconcentrationsofonshorewindinsomelocations.

Neithervehicle-to-gridnorsecond-handbatteries

Wehavenotconsideredvehicle-to-gridi.e.thepossibilitythatelectricvehiclebatteriescouldbeusedtoprovideflexibilityintheelectricitysystem.Yetthestoragecapacityofelectricvehiclesmaybehugeby2050:TheFrenchTSORTE (2018)estimates itat900("ℎ#,about tentimesthebatterycapacities inourreferencecostscenario.MobilizingevenasmallpartofthiscapacityforpowerstoragewouldbringdownthesystemLCOE,butwehavepreferrednottoincludethisoptionbecausetheimpactonbatterylifetimeisstillbeingdebated.Anotherpossibilityistorecycleusedcarbatteriesasstationarybatteries,but again, we believe that modeling this option would require precise assumptions on batterydegradation.

4.3 Conclusion

Inthisarticle,wehavestudiedthesensitivityofoptimalfullyrenewablepowersystemstotechnologycost.Tothatend,wehavedevelopedEOLES,amodeloptimizinginvestmentanddispatchinthepowersector, and applied it to the study of fully renewable power systems in France. We built 315 costscenariosbycombiningassumptionsaboutthelong-termcostofthekeypowergenerationandstoragetechnologies.

Ourresultsindicatethateventhoughthetechnologiesinvolvedinafullyrenewablepowersystemarevery different, they are by and large substitutable. For instance, if batteries are 50%more expensivethanexpected,theoptimalenergymixincludesfewerbatteriesandlessPV,butthisiscompensatedforby additional wind power, with a very limited impact on the system LCOE. On the contrary, if windpoweris25%moreexpensivethanexpected,theoptimalmixobviouslyincludeslessofthistechnology,butthisiscompensatedforbymorePVandstorage.

Overall, the impact of storage cost should not be overestimated: even in a 100% renewable powersystem,storage(batteries,PHSandmethanation)accountsforonly14.5%ofthesystemcost,vs.85.5%forelectricitygeneration.Wereourmodeltoincludedemand-sidemanagement,interconnectionswith

27

neighboring countries, vehicle-to-grid or second-hand batteries, the share of storage in overall costwouldbeevenlower.

Across all cost scenarios, the system LCOE, including generation and storage, ranges from €36.5 to€65.5/="ℎ#, depending on the cost scenario, with an average value of €50/="ℎ#. This is cheaperthantoday’svalue.Andsettingacapacity target inadvance forevery technologywouldonly increasethesystemLCOEby€2/MWhaveragedoverthe315costscenarioscomparedtotheoptimummix,evenifcostsvaryby+/-25%forwindand+/-50%forsolarandstorage. Intermsofpolicy implications, thisresultcallsforprovidingvisibilitytoinvestors,evenifitentailsreducingflexibilitythepolicydesign.

Finally, our analysis shows that theoptimalpowermix is highly sensitive to the chosenweather-yearand to the cost assumptions. In the literature, many analyses of the powermix are still based on auniqueweather-year, chosen fordataavailability rather than representativeness.Our result thuscallsforcautionoversuchconclusionsontheoptimalpowermix,whentheyarebasedonalimitednumberofweather-yearsorcostscenarios.

This work could be extended in many directions, for example including the other power generationtechnologies that entail low direct CO2 emissions: CO2 capture and storage and nuclear power. TheircostandthepossibilityofstoringmassivequantitiesofCO2beingveryuncertainintheFrenchcontext,wedecidednottoincludetheminthepresentstudy,buttheycouldbeconsideredinfuturework.

28

References

ADEME(2013).L’exercicedeprospectivedel’ADEME“Vision2030-2050”-documenttechnique.

ADEME(2015).Versunmixélectrique100%renouvelable.https://www.ademe.fr/sites/default/files/assets/documents/mix-electrique-rapport-2015.pdf.

ADEME(2018).Trajectoiresd'évolutiondumixélectriqueàhorizon2020-2060.ISBN:979-10-297-1173-2

Annan-Phan,S.,&Roques,F.A.(2018).«MarketIntegrationandWindGeneration:AnEmpiricalAnalysisoftheImpactofWindGenerationonCross-BorderPowerPrices.”TheEnergyJournal39(3),1-25.

Arditi,M.,Durdilly,R.,Lavergne,R.,Trigano,É.,Colombier,M.,Criqui,P.(2013).Rapportdugroupedetravail2:Quelletrajectoirepouratteindrelemixénergétiqueen2025?Quelstypesdescénariospossiblesàhorizons2030et2050,danslerespectdesengagementsclimatiquesdelaFrance?Tech.rep.,RapportdugroupedetravailduconseilnationalsurlaTransitionEnergétique.

Brown,T.W.,Bischof-Niemz,T.,Blok,K.,Breyer,C.,Lund,H.,&Mathiesen,B.V.(2018).Responseto‘Burdenofproof:Acomprehensivereviewofthefeasibilityof100%renewable-electricitysystems’.Renewableandsustainableenergyreviews,92,834-847.

Cerema,2017.Photovoltaïqueausol.https://www.collins.fr/fr/actualites/photovoltaique-au-sol

Collins,S.,Deane,P.,Gallachóir,B.Ó.,Pfenninger,S.,&Staffell,I.(2018).“Impactsofinter-annualwindandsolarvariationsontheEuropeanpowersystem.”Joule2(10),2076-2090.

CRE(2018).Observatoiredesmarchésdedétaildel’électricitéetdugaznatureldu3etrimestre2018.https://www.cre.fr/content/download/20125/256999.

ENEAConsulting(2016).ThepotentialofPower-to-Gas.https://www.enea-consulting.com/sdm_downloads/the-potential-of-power-to-gas/

ENTSO-E(2013).NetworkCodeonLoad-FrequencyControlandReserves6,1–68.

Ericsson,K.(2017).“Biogeniccarbondioxideasfeedstockforproductionofchemicalsandfuels:Atechno-economicassessmentwithaEuropeanperspective.”EnvironmentalandEnergySystemStudies,LundUniversity:Miljö-ochenergisystem,LTH,Lundsuniversitet.

FCHJU(2015).CommercialisationofenergystorageinEurope:Finalreport.

29

Gowrisankaran,G.,Reynolds,S.S.,&Samano,M.(2016).Intermittencyandthevalueofrenewableenergy.JournalofPoliticalEconomy,124(4),1187-1234.

Henze,V.,2019.BatteryPower’sLatestPlungeinCostsThreatensCoal,Gas.https://about.bnef.com/blog/battery-powers-latest-plunge-costs-threatens-coal-gas/

Hirth,L.(2015):“TheOptimalShareofVariableRenewables”.TheEnergyJournal36(1),127-162.doi:10.5547/01956574.36.1.6.

Hirth,L.,&Müller,S.(2016).System-friendlywindpower:Howadvancedwindturbinedesigncanincreasetheeconomicvalueofelectricitygeneratedthroughwindpower.EnergyEconomics,56,51-63.

HuldT,GottschalgR,BeyerHG,TopičM.(2010).“MappingtheperformanceofPVmodules,effectsofmoduletypeanddataaveraging.”SolarEnergy2010;84(2):324–38.

Joskow,P.L.(2011).Comparingthecostsofintermittentanddispatchableelectricitygeneratingtechnologies.AmericanEconomicReview,101(3),238-41.

JRC(2014)EnergyTechnologyReferenceIndicatorProjectionsfor2010–2050.ECJointResearchCentreInstituteforEnergyandTransport,Petten.

JRC(2017)Costdevelopmentoflowcarbonenergytechnologies-Scenario-basedcosttrajectoriesto

2050,EUR29034EN,PublicationsOfficeoftheEuropeanUnion,Luxembourg,2018,ISBN978-92-79-77479-9,doi:10.2760/490059,JRC109894.

Jülch,V.,Telsnig,T.,Schulz,M.,Hartmann,N.,Thomsen,J.,Eltrop,L.,&Schlegl,T.(2015).Aholisticcomparativeanalysisofdifferentstoragesystemsusinglevelizedcostofstorageandlifecycleindicators.EnergyProcedia,73,18-28.

Krakowski,V.,Assoumou,E.,Mazauric,V.,&Maïzi,N.(2016).“Feasiblepathtoward40–100%renewableenergysharesforpowersupplyinFranceby2050:Aprospectiveanalysis.”Appliedenergy184,1529-1550.

LauretP,BolandJ,RidleyB.(2013).“Bayesianstatisticalanalysisappliedtosolarradiationmodelling.”RenewableEnergy2013;49:124–7.

Moraes,L.,Bussar,C.,Stoecker,P.,Jacqué,K.,Chang,M.,&Sauer,D.U.(2018).“Comparisonoflong-termwindandphotovoltaicpowercapacityfactordatasetswithopen-license.”AppliedEnergy225,209-220.

Perrier,Q.(2018).“ThesecondFrenchnuclearbet.”EnergyEconomics,74,858-877.

30

Pfenninger,S.,Staffell,I.(2016).“Long-termpatternsofEuropeanPVoutputusing30yearsofvalidatedhourlyreanalysisandsatellitedata.”Energy114,pp.1251-1265.doi:10.1016/j.energy.2016.08.060

PierrotM.(2018).Thewindpower.http://www.thewindpower.net

Quinet,E.(2014).L'évaluationsocioéconomiquedesinvestissementspublics(No.Halshs01059484).HAL.

RieneckerM.M.,SuarezM.J.,GelaroR.,TodlingR.,BacmeisterJ.,LiuE.,etal.(2011).“MERRA:NASA’smodern-eraretrospectiveanalysisforresearchandapplications.”JClimate2011;24(14):3624–48

RogeljJ,ShindellD,JiangK,FifitaS,ForsterP,GinzburgV,HandaC,KheshgiH,etal.(2018).Chapter2:Mitigationpathwayscompatiblewith1.5Cinthecontextofsustainabledevelopment.In:GlobalWarmingof1.5C-anIPCCspecialreportontheimpactsofglobalwarmingof1.5Cabovepre-industriallevelsandrelatedglobalgreenhousegasemissionpathways,inthecontextofstrengtheningtheglobalresponsetothethreatofclimatechange.IntergovernmentalPanelonClimateChange.

RTE,commissionperspectivesystèmeetréseau(2018).Pointd’étapesurlestravauxdubilanprévisionneletduschémadécennaldedéveloppementréseau.https://www.concerte.fr/system/files/concertation/2018%2009%2028%20CPSR_complet2.pdf

RTE(2018),Panoramadel’électricitérenouvelableau30Juin2018.

Schlachtberger,D.P.,Brown,T.,Schäfer,M.,Schramm,S.,Greiner,M.(2018).“CostoptimalscenariosofafuturehighlyrenewableEuropeanelectricitysystem:Exploringtheinfluenceofweatherdata,costparametersandpolicyconstraints.”arXivpreprintarXiv:1803.09711.

Schmidt,O.,Melchior,S.,Hawkes,A.,Staffell,I.(2019).“ProjectingtheFutureLevelizedCostofElectricityStorageTechnologies.”JouleISSN2542-4351https://doi.org/10.1016/j.joule.2018.12.008

Sinn,H.W.(2017).Bufferingvolatility:AstudyonthelimitsofGermany'senergyrevolution.EuropeanEconomicReview,99,130-150.

Staffell,I.,Pfenninger,S.(2016).“UsingBias-CorrectedReanalysistoSimulateCurrentandFutureWindPowerOutput.”Energy114,pp.1224-1239.doi:10.1016/j.energy.2016.08.068

VanStiphout,A.,DeVos,K.,&Deconinck,G.(2017).“Theimpactofoperatingreservesoninvestmentplanningofrenewablepowersystems.”IEEETransactionsonPowerSystems,32(1),378-388.

Villavicencio,M.(2017).“Acapacityexpansionmodeldealingwithbalancingrequirements,short-termoperationsandlong-rundynamics.”CEEMWorkingPapers(Vol.25).

31

WindEurope(2017).WindenergyinEurope,Scenariosfor2030.22September.https://windeurope.org/about-wind/reports/wind-energy-in-europe-scenarios-for-2030/

Wiser,R.,Jenni,K.,Seel,J.,Baker,E.,Hand,M.,Lantz,E.,&Smith,A.(2016).“Expertelicitationsurveyonfuturewindenergycosts.”NatureEnergy1(10),16135.

ZEP(2011).TheCostsofCO2Transport.Post-demonstrationCCSintheEU.ZeroEmissionsPlatform.http://www.zeroemissionsplatform.eu/downloads/813.html

Zerrahn,A.,Schill,W.P.,&Kemfert,C.(2018).Ontheeconomicsofelectricalstorageforvariablerenewableenergysources.EuropeanEconomicReview,108,259-279.

Zeyringer,M.,Price,J.,Fais,B.,Li,P.H.,&Sharp,E.(2018).“Designinglow-carbonpowersystemsforGreatBritainin2050thatarerobusttothespatiotemporalandinter-annualvariabilityofweather”.NatureEnergy3(5),395.

32

Appendix1.AdditionalinformationontheJRC2017study

InthisJRCreport,historic installedcapacityofeachtechnologyfor2015, learningraterelatedtoeachtechnologyandthecapitalinvestmentcostofeachtechnologyin2015hasbeentakenasinputvalues,andusingthreedifferentfutureinstalledcapacityscenarios,threedifferentfuturecosttrajectoriesareproposed. Equation (A1) shows themainmethodology used in the cost projection using the learningratemethod:

%ëI5+ = %ëI5z ∙ Ç_Çì

î (A1)

This log-linear relation relates the future cost (%ëI5+) of a technology to the existing cost (%ëI5z),existinginstalledcapacity(%z)andthefutureprojectedinstalledcapacity(%+)ofitusingtheexperienceparameteri.ThelearningrateLR isrelatedtotheexperienceparameterasit isdescribedinequation(A2);

;U = 1 −2î (A2)

TheJRCreportusesthreedifferentscenariostoprojectthefutureinstalledcapacityofeachtechnology,

andfinallytofindtheÇ_Çìratiofortheequation(16).ThesethreescenariosaredescribedinTableA-1;

TableA-1thechosenscenariosbyJRCforthe2050costprojectionsoflowcarbonpowerproductiontechnologies

Scenario Baseline This scenario is used to cover the lower end of RES-E deployment. It is based on the

"6DS" scenario of the Energy Technology Perspectives published by the InternationalEnergyAgencyin2016.Itrepresentsa"businessasusual"worldinwhichnoadditionaleffortsare takenonstabilizing theatmosphericconcentrationofgreenhousegases.By2050, primary energy consumption reaches about 940 EJ, renewable energy suppliesabout30%ofglobalelectricitydemandandemissionsclimbto55GtCO2.

Diversified The "Diversified" portfolio scenario is taken from the "B2DS" scenario of theInternational Energy Agency's 2017 Energy Technology Perspectives and is used asrepresentative for themid-range deployment of RES-E found in literature. To achieverapiddecarbonizationinlinewithinternationalpolicygoals,allknownsupply,efficiencyandmitigationoptionsareavailableandpushedtotheirpracticallimits.Fossilfuelsandnuclear energy participate in the technology mix, and CCS is a key option to realizeemission reduction goals. Primary energy consumption is comparable to 2015 levels(about580EJ),theshareofrenewableelectricityintheglobalsupplymixis74%whileemissionsdeclinetoabout4.7GtCO2by2050.

ProRES The"ProRES"scenario resultsare themostambitious in termsofcapacityadditionsofRES-E technologies. In this scenario the world moves towards decarbonization bysignificantlyreducingfossilfueluse,however,inparallelwithrapidphaseoutofnuclearpower.CCSdoesnotbecomecommercialandisnotanavailablemitigationoption.Deep

33

emissionreductionisachievedwithhighdeploymentofRES,electrificationoftransportandheat,andhighefficiencygains.Itisbasedonthe2015"EnergyRevolution"scenarioofGreenpeace.Primaryenergyconsumptionisabout430EJ,renewablessupply93%ofelectricitydemandandglobalCO2emissionsareabout4.5GtCO2in2050.

The used economical parameters for the power production technologies are taken from the 2050projectionsofthisstudyforthediversifiedscenarioasanaverageandmorerealisticscenario.

34

Appendix2.Windandsolarproductionprofiles

Thewindpowerhourlycapacityfactorprofilesexistingintherenewables.ninjawebsitearepreparedinfourstages:

a)Rawdataselection;usingNASA’sMERRA-2datareanalysiswithaspatialresolutionof60km×70kmprovidedbyRieneckeretal.(2011),b)Downscalingthewindspeedstothewindfarms;byinterpolatingthespecificgeographiccoordinatesofeachwindfarmusingLOESSregression,c)Calculationofhubheightwindspeed;byextrapolatingthewindspeedinavailablealtitudes(2,10and50meters)tothehubheightofthewindturbinesusinglogarithmprofilelaw,d)Powerconversion;usingtheprimarydatafromPierrot(2018),thepowercurvesarebuilt(withrespecttothechosenwindturbine),andsmoothedtorepresentafarmofseveralgeographicallydispersedturbinesusingGaussianfilter.

Thesolarpowerhourlycapacityfactorprofilesintherenewables.ninjawebsitearepreparedinthreestages:

a)Rawdatacalculationandtreatment;usingNASA’sMERRAdatawiththespatialresolutionof50km×50km.ThediffuseirradiancefractionestimatedwithBayesianstatisticalanalysisintroducedbyLauretetal.(2013)andtheglobalirradiationcalculatedininclinedplane.Thetemperatureisgivenat2maltitudebyMERRAdataset.b)Downscalingofsolarradiationtofarmlevel;valuesarelinearlyinterpolatedfromgridcellstothegivencoordinates.c)Powerconversionmodel;PoweroutputofapaneliscalculatedusingtherelativePVperformancemodelbyHuldetal.(2010)whichgivestemperaturedependentpanelefficiencycurves.

35

Appendix3.Weatheryearsensitivity

TheresultsforeachweatheryearcanbeseeninTablesA.1andA.2,A.3.

TableA.1installedcapacityofeachpowerproductiontechnologyinGWeandenergystoragecapacityofeachstorage

technologyduringeachoptimizationperiod

Year OffshoreWind

OnshoreWind

SolarPV

Run-of-river

Lake &reservoir

Biogas

Battery(GWh)

PHS(GWh)

Methanation(TWh)

2000 11.46 84.14 105.74 7.50 13.00 18.24 60.17 180 5.522001 0.38 104.62 101.16 7.50 13.00 28.61 41.91 180 8.452002 17.12 69.66 105.55 7.50 13.00 19.16 74.70 180 4.602003 10.21 90.15 106.83 7.50 13.00 25.70 62.78 180 5.522004 0.00 105.29 113.38 7.50 13.00 21.88 70.32 180 15.302005 0.00 105.89 110.38 7.50 13.00 25.22 60.27 180 9.372006 12.36 80.08 122.17 7.50 13.00 32.89 74.62 180 12.902007 0.00 98.40 118.33 7.50 13.00 27.61 65.73 180 12.052008 0.78 101.95 105.20 7.50 13.00 21.76 52.03 180 12.052009 11.61 89.32 107.79 7.50 13.00 18.83 51.47 180 6.922010 20.00 83.64 100.50 7.50 13.00 22.88 40.53 180 15.812011 20.00 65.81 114.17 7.50 13.00 28.32 101.33 180 8.542012 0.00 103.38 114.49 7.50 13.00 20.36 62.43 180 11.322013 10.32 92.30 100.82 7.50 13.00 21.54 37.06 180 10.592014 20.00 70.23 111.40 7.50 13.00 18.57 80.03 180 7.692015 20.00 64.77 103.78 7.50 13.00 34.09 63.19 180 8.222016 20.00 69.77 114.07 7.50 13.00 23.96 81.68 180 8.662017 5.29 100.72 111.62 7.50 13.00 19.30 50.05 180 11.77Mean 9.97 87.78 109.30 7.50 13.00 23.83 62.79 180 7.74All 11.77 83.30 112.21 7.50 13.00 33.25 66.71 180 16

TableA.2Yearlypowerproductionofeachproductiontechnology(inTWh)andcapacityfactorofVREresources

Year OffshoreWind

OnshoreWind

SolarPV

Run-of-river

Lake Biogas OffshoreWind

OnshoreWind

SolarPV

OCGTplant

2000 54.08 246.41 146.58 29.19 15.82 15 0.538 0.334 0.158 0.1392001 1.77 307.32 143.64 29.19 15.82 15 0.537 0.335 0.162 0.0892002 82.05 212.44 145.52 29.19 15.82 15 0.547 0.348 0.157 0.1272003 44.99 245.26 153.46 29.19 15.82 15 0.503 0.311 0.164 0.0882004 0.00 296.53 159.65 29.19 15.82 15 0.509 0.322 0.161 0.1302005 0.00 290.19 159.98 29.19 15.82 15 0.507 0.312 0.165 0.1022006 56.90 227.80 173.72 29.19 15.82 15 0.525 0.324 0.162 0.087

36

2007 0.00 294.71 170.24 29.19 15.82 15 0.532 0.341 0.164 0.1002008 3.67 296.22 145.50 29.19 15.82 15 0.536 0.331 0.158 0.1202009 51.41 246.86 153.65 29.19 15.82 15 0.504 0.315 0.162 0.1302010 88.51 226.65 140.74 29.19 15.82 15 0.505 0.308 0.160 0.1302011 91.47 179.83 165.84 29.19 15.82 15 0.522 0.311 0.165 0.0852012 0.00 294.01 164.07 29.19 15.82 15 0.523 0.326 0.163 0.1302013 48.17 259.67 138.87 29.19 15.82 15 0.533 0.320 0.157 0.1282014 89.18 193.92 153.49 29.19 15.82 15 0.509 0.314 0.157 0.1332015 96.26 190.85 148.57 29.19 15.82 15 0.549 0.335 0.163 0.0722016 88.09 187.04 160.28 29.19 15.82 15 0.502 0.302 0.160 0.1012017 23.35 272.47 160.58 29.19 15.82 15 0.504 0.309 0.164 0.135Mean 45.55 248.23 154.69 29.19 15.82 15 0.522 0.323 0.161 0.113All 53.79 235.53 158.75 29.19 15.82 15 0.522 0.323 0.161 0.079

TableA.3showsthetotalcost,marginalcostandthesystemLCOE1foreachyearlyoptimizationandforthewhole18-yearlongoptimization.

TableA.3Totalcost,averagemarginalcost(averagespotprice),levelizedcostofelectricity,loadcurtailmentandstorage

relatedlossesofeachyear

year TotalCost(b€)

SystemLCOE(€/MWh)

Marketprice(€/MWh)

LoadCurtailment

Storagelosses

Curtailment+loss

2000 20.23 47.89 53.83 11.64 5.06 16.702001 20.44 48.40 54.20 12.76 4.87 17.632002 19.77 46.82 54.60 10.90 4.62 15.122003 20.83 49.31 54.21 12.38 3.76 16.142004 21.33 50.51 56.91 11.75 6.43 18.182005 21.04 49.81 54.18 11.94 5.26 17.202006 21.82 51.65 56.46 11.99 6.53 18.522007 20.87 49.40 55.59 13.40 6.14 19.542008 20.19 47.81 55.23 11.27 5.16 16.432009 20.71 49.02 54.72 13.02 4.47 17.492010 21.91 51.87 57.29 11.83 6.30 18.132011 21.06 49.85 54.43 10.30 4.74 15.042012 20.87 49.41 54.81 12.67 5.80 18.472013 20.82 49.28 55.47 10.63 6.01 16.64

1 System LCOE (levelized cost of electricity) is an economic assessment of the average total cost to build andoperateanelectricitysystemoveritslifetimedividedbytotalelectricityconsumptionoverthatlifetime.

37

2014 20.68 48.95 56.90 10.10 4.84 14.942015 20.29 48.04 54.18 10.12 4.66 14.782016 21.00 49.72 56.46 10.07 4.67 14.742017 21.13 50.03 55.43 12.95 5.26 18.21Mean

20.83 49.32 55.27 11.65 5.25 16.90

All 21.33 50.50 56.01 11.52 5.34 16.86

TableA.4showstherankingofeachweather-yearincorrelationwithoverall18-yearperiod.

TableA.4Closestyearstotheoverall18-yearperiodregardingtothecapacityfactorofVREresources

Closestyear Secondclosestyear ThirdclosestyearOffshoreWind 2011 2012 2006OnshoreWind 2006 2004 2012SolarPV 2004 2006 2009Overallyear 2006 2012 2004Overallerror(absolute) 0.0150 0.0236 0.0280

38

Appendix4:theEOLESmodel

2.1.1.Setsandparameters

TableA.5presentsthesetsandindicesoftheEOLESmodel,TableA.6theparameters.Throughoutthepaper,everyenergyunit(e.g.MWh)orpowerunit(e.g.MW)isexpressedinelectricity-equivalent.Forinstance, someenergy is stored in the formofmethane, tobe transformed later intoelectricityusingopen-cyclenaturalgasplantswith45%efficiency.Inthiscase,whenweindicatethat45="ℎ# isstoredinthenaturalgasnetwork,itmeansthat100MWhofmethaneisstored,whichwillallow45="ℎ# ofelectricitytobegenerated.

TableA.5SetsandindicesoftheEOLESmodel

Index Set Description

ℎ ∈H Hours

T ∈M Months

5GE ∈TEC Electricitygenerationandenergystoragetechnologies

ñG2 ∈GEN⊆TEC Electricitygenerationtechnologies

NJG ∈VRE⊆TEC Variablerenewableelectricitygenerationtechnologies

I5J ∈STR⊆TEC Energystoragetechnologies

2EëTò ∈NCOMB⊆TEC Non-combustiblegenerationtechnologies

EëTò ∈COMB⊆TEC Combustiblegenerationtechnologies

CJJ ∈FRR⊆TEC Dispatchabletechnologiesforsecondaryreserves

TableA.6ParametersoftheEOLESmodel

Parameter Unit Value1 DescriptionTë25ℎℎ [-] Aparametertoshowwhichmontheachhourisin

1 For vectors and matrices, no value is displayed in the Table but the information is available athttps://github.com/BehrangShirizadeh/EOLES_elecRES.

39

ECNJG,ℎ [-] Hourly production profiles of variable renewableenergies

SGT12Sℎ [K"#] Hourlyelectricitydemandprofileo1!GT [K"ℎ#] MonthlyextractableenergyfromlakesJ4NGJℎ [-] Hourlyrun-of-rivercapacityfactorprofilehNJG [-] Additional frequency restoration requirement for

renewablesbecauseofforecasterrors.5GEGH [K"#] Existingcapacitybytechnology

12234565GE [M€/K"#/year] Annualizedcapitalcostofeachtechnology1223456I5J

G2 [M€/K"ℎ/year] Annualizedcapitalcostofenergyvolumeforstoragetechnologies

E1FGHI5JEℎ [M€/K"/year] Annualized capital cost of storage technology

chargingpowerC&&=I5J

Eℎ [M€/K"/year] Fixed operation and maintenance cost of storagetechnologychargingpower

C&&=5GE [M€/K"# /year] AnnualizedfixedoperationandmaintenancecostN&&=5GE [M€/K"ℎ#] Variable operation and maintenance cost of each

technology[I5J42 [-] Chargingefficiencyofstoragetechnologies[I5Jë35 [-] Dischargingefficiencyofstoragetechnologies

.ôfpô K"# 9.3 PumpingcapacityforPumpedhydrostorage

GÑöõpj/ K"ℎ# 180 Maximumenergy volume that canbe stored inPHS

reservoirsGs\etj?pj/ ("ℎ# 15 Maximumyearlyenergythatcanbegeneratedfrom

biogasifA,#@+j\A+mkejl [-] 0.01 Uncertaintycoefficientforhourlyelectricitydemand

iWj@\j+\eAkejl [-] 0.1 Loadvariationfactor

2.1.2.Variables

ThemainvariablesresultingfromtheoptimizationarepresentedinTableA.7.

40

TableA.7VariablesoftheEOLESmodel

variable Unit description

K+#,,M K"ℎ# Hourlyelectricitygenerationbytechnology

*+#, K"# Installedcapacitybytechnology

'(&UVK>?+@,M K"ℎ Hourlyelectricityenteringeachstoragetechnology

'(&U>X?+@,M K"ℎ# Hourlyenergystoredineachtechnology

'?+@ GW Installedchargingcapacitybystoragetechnology

:&;<=>?+@ K"ℎ EnergycapacitybystoragetechnologyU':g@@,M K"# Hourlyupwardfrequencyrestorationrequirement

%&'( b€ Overallfinalinvestmentcost,annualized

41

Appendix5:Transportcostofcarbondioxideformethanation

The cost of transporting carbon dioxide along a 200km onshore pipeline is€4/5%&|, for 100km lingpipeline, this transporting cost can be assumed around €2/5%&|. Given that each mole of carbondioxideweighs 44 grams, andwe can produce onemole ofmethane fromonemole of%&|with anefficiency of 80% and eachmole ofmethane can produce 802.3kJ of thermal energy, considering anOCGTcombustionefficiencyof45%(JRC2014):

Z+ÇúùZzzzzzztÇúù

×ååtÇúùZpekÇúù

×ZpekÇúùz.upekÇöü

×ZpekÇöüuz|.êq†

×Zq†+M

z.zzz|||vvvvuq°M+M×

Zq°M+M

z.åwq°M#k#,×Zzzzq°M#k#,

Z¢°M#k#,=

0.5486+Çúù

¢°M#k#,

Multiplying this transport cost by €2/5%&|, the %&| transport cost for methanation becomes€1.0972/="ℎ.

42

Appendix6:Costdecomposition

FigureA.1showstheshareofeachtechnologyinoverallcostofpowersystem(exceptdistributionandtransmissioncosts);

FigureA.1.Overalldecompositionofthesystemcostinpercentageforthereferencecostscenario

43

Appendix7:Additionalinformation

InTableA.8wesummarizetheyearlypowerproductionLCOE(thelevelizedcostofelectricityproducedfromeach power plantwithout considering any future load curtailment or other losses) and averagesellingprice foreachgeneration technologyandLCOS (levelizedcostof storage;cf. Jülchetal.,2015)andtheaveragesellingpriceofeachstoragetechnologyforweather-year2006.

TableA.8LCOE/LCOSandaveragepriceofelectricitysoldandboughtandunitprofitforeachtechnology,forweather-year2006

Prices(€/="ℎ#)

Offshore

Onshore PV Lake River Biogas Battery PHS Methanation

LCOE/LCOS 41.58 39.45 27.60 100.00 40.80 82.00 83.65 16.80 109.36

Average priceofenergysold

41.68 39.60 28.00 136.90 55.10 140.24* 98.00 89.10 140.24*

Averagepricepaidforenergy

- - - - - - 21.53 23.76 27.90

Unitprofit 0.10 0.15 0.40 36.90 14.30 58.24 -7.18 48.54 2.98

*Priceofgassold,convertedintoelectricity-equivalentbydividingthegaspricebytheenergyefficiencyofOCGTs.

For all power production technologies, the averagemarket selling price is higher than the LCOE, thedifference being very low for the VRE resources (offshore wind ~€0.10/="ℎ#, onshore wind~€0.15/="ℎ# and solar PV ~€0.40/="ℎ#) while for the other technologies this difference ismuchgreater, especially for biogas (€44.40/="ℎ#). The profitability of hydro is due to the capacityconstraint,while for biogas it is due to the production constraint, since these constraints generate ascarcityrent.

While theprofitabilityanalysis is straightforward forall thepowerproduction technologies, it ismorecomplicatedforthestoragetechnologiessincetheybuyelectricityfromthemarket,andtherearelossesrelated tocharginganddischarging inefficiencies.Equation (17) shows theprofitabilitycriteria for thestoragetechnologiesinthecalculationofunitprofit:

FJëC45?+@fA\+ = (K?+@,M×FM

pj@q#+)M − ('(&UVK>?+@,M×FMpj@q#+)M − (*?+@× %1FGH?+@ + C&&=?+@ +

:&;<=>?+@×%1FGH?+@#A + K?+@,M×N&&=?+@ )M / K?+@,MM (17)

WhereFMpj@q#+isthemarketpriceofelectricityathourℎandFJëC45?+@fA\+isthenetprofitoftheunitof

electricity bought by the storage units, charged and sold on the electricity market (accounting forstorage-related inefficiencies), which can be considered as the net present value of each storagetechnologyperunitofpowersold.

44

PHSishighlyprofitablebecauseitscapacityis limited,whichgeneratesascarcityrent.Conversely,theprofitabilityofbatteriesisnegativebecausetheFRRrequirementleadstoahigherbatterycapacity(byafactorofapproximatelytwo).