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Energies2020,13,568;doi:10.3390/en13030568www.mdpi.com/journal/energies
Article
LifetimeExpectancyofLi‐IonBatteriesusedfor
ResidentialSolarStorage
HectorBeltran*,PabloAyusoandEmilioPérez
DepartmentofIndustrialSystemsEngineeringandDesign,JaumeIUniversity,SosBaynatAvenue,
CastellódelaPlana,12071Castelló,Spain
*Correspondence:hbeltran@uji.es;Tel.:+34‐964‐72‐81‐78(H.B.)
Received:21November2019;Accepted:21January2020;Published:24January2020
Abstract:ThispaperanalysesthedegradationthatisexperiencedbydifferenttypesofLi‐ion
batterieswhenusedashomesolarstoragesystemscontrolledtominimizetheelectricitybillofthe
correspondinghousehold.Simulatingtheannualoperationofphotovoltaic(PV)residential
systemswithbatteriesatdifferentlocationswasundertakentoperformthestudyanditusesactual
consumptionvaluesandrealPVproductionprofiles,aswellasvalidatedsemi‐empiricalageing
modelsofthebatteries.Therefore,theworkprovidesarealisticprognosisaroundthelifetime
expectanciesforthedifferentLi‐ionchemistries.
Keywords:Li‐Ionbatteries;residentialPVstorage;calendarageing;cycleageing.
1.Introduction
PVtechnologyhasbecomethemostimportantpowergenerationsourceworldwideintermsof
addedcapacityperyearsince2016,overtakingwindpower,whichwastheleadingtechnologyupto
then[1].Inall,newPVinstallationsrepresentedapproximately100GWin2018,achievingan
accumulatedcapacitythatgoesbeyond500GW[2].Thisevolutionhasbeenpossiblethankstothe
hugepricedecreaseexperiencedbythisindustryduetotheeconomiesofscaleinthelastdecade.In
accordance,thecostforresidentialgrid‐connectedPVsystemshasworldwide‐averageddropped
downtoaround1.28€/W,beingapproximately20%higherthaninEurope,where1.13€/Wcanbe
generallyfound,and30%higherthaninAustralia,wherepricesplummetedto0.95€/W[3].Thanks
toit,theresidentialmarkethasalsoexperiencedabigdeploymentofPVinstallationsmainly
envisagedforself‐generation,whichachievesveryhighsharesoftheenergy
production‐consumptionincertainlocallowvoltagegrids(Figure1).
However,largeamountsofbehind‐the‐metersolarinstallationsthatproducestochastically
intermittentenergycanimplystabilityproblemsatthelowvoltagegridlevel[4].Hence,new
technicalandregulatorysolutionshavetobeimplementedtoavoidrunningintotheproblemof
systematicallyhavingtocurtailpartoftheirproduction.Theintroductionofenergystorage(ES)
systems,usuallybatteries,withinthehouseholds[5–7].Batteriesforenergystorageinbuildings
havebeenaroundforalongtimeinbothstand‐alone(off‐grid)andcommercialback‐uppower
systems(UPS).However,evenwiththestillrelativelyhighcostofbatteries,theircombinedusewith
solarsystemshasalsostartedtogainmomentumoverthelastfewyears.Thisisbecausesuch
systemsnotonlyallowforreducingtheintermittencyofthelocalproduction,stabilizingthelow
voltagegrid,butalsomakeitpossibletooptimizetheenergybillfortheinstallationownersand
evenprovideextendedsupplysecurity,whichcannotbetakenforgrantedeverywhere.Inthisway,
somespecializedconsultantsindicatethatoutofthe4GWofbatterieswithan8GWhcapacitytobe
deployedgloballyin2019(withcalculationstoachieve15GWwitha44GWhcapacityin2024and
between180and420GWhin2030),morethanhalfcouldbedistributed[8],i.e.,behindthemeterPV
residentialsystemswithbatteries,alsoknownashomesolarstoragesystems.Amongthem,these
Energies2020,13,5682of18
areclearlysettospreadacrossEurope.Italyseems,inthissense,tobeconsolidatingitspositionas
thesecond‐largestmarketafterGermanyandSpainiswellpositionedtodevelopitsownsizable
market,althoughthisisalwaysverydependentonthechangingpoliticalframeworkofthecountry.
FranceandtheUnitedKingdomkeepbeingpromisingmarkets,althoughtheyhavenotclearly
emergedyet[9].BeyondEurope,theUSAisaconsolidatedmarket,mainlyinstates,suchas
California,andalsoAustraliaisstartingtotakeoff.Notehow,inthelatter,thereisgreatincentiveto
storesolarenergyasthesolarfeed‐intariffhasbeenlatelyreducedtoaslittleas5¢perkWh
(¢—cent),whilethecosttopurchaseelectricityiscloserto30¢perkWh.Thishasbecomeadriving
forceandgreatincentivetostoresolarenergy,ratherthansendittothepowergridforlittlereturn.
Figure1.CommonwealthGamesAthleteʹsVillageinGlasgow(Scotland),bySolarTradeAssociation
(CCBY‐SA2.0).
Ascanbeunderstoodinsuchacontext,agreatbunchofbehindthemeterPVresidential
systemswithbatteriesarecommerciallyavailable[10]andtheyhavebeenlargelydiscussedand
analyzedintheliterature.However,mostofthepreviousacademicworksarefocusedon
optimizationsthatarerelatedtothesizingofthebatterysystem[11–13]ortomaximizingthe
economicincomeofthePVinstallations[14–17].However,fewamongthepreviousworkshave
alreadytakenthedegradationofthebatteriesduringthelifespanofthesystemintoaccount.
Degradationevolutionisveryimportantfortheeconomicanalysisofthesesystems,whicharestill
costly,asalreadyindicated,andwhoseprofitabilityisstillunderdiscussion[18].Infact,degradation
isalreadyakeyparameterintheprofitabilityanalysesofbatteriesbeingproposedinapplications,
suchasrampratecontroloflargerenewablepowergenerationplants[19,20],ancillaryservices
[21,22],energyarbitrage,andpeakshavingprovidedbylargeenergystoragesystems[23,24],and
evenforelectricvehicleintegration[25].Amongthosepublishedfortheresidentialsolarstorage
sector,somecanbehighlighted.Forinstance,theauthorsin[26]analyzetheimpactoftheinternal
batterypackcircuitdesignonthesystemoperationanditscorrespondingdegradation.Another
approachisproposedin[27]andin[28],inwhichforecastingmethodologiesareincludedto
optimizetheoperationofthesystemwiththeaimtoenhancethebatterylifetime.Finally,an
economicoptimizationofthebatteriessizingtakingintoaccounttheageingofthebatteriesis
performedin[29].However,theyuseeithersimplifiedbatterydegradationmodels[27,28]orgeneric
onesthattheyadapttodifferenttypesofbatteriesbeingvalid,evenforleadacidmodels[29].
Thisworkassumesthatthesizingandcontrolofthesystemisamaturedomainnowadaysand
itfocusesontheanalysisofdegradationthatthedifferentLi‐ionbatteriesusedinsuchan
applicationwouldexperienceunderawiderangeofoperationalconditions.Theanalysisisbasedon
well‐knownaccepteddetaileddegradationmodelsforthespecificchemistriesunderdiscussion,
whichhavealsobeenadaptedheretoactualstate‐of‐the‐artcommercialbatterypacks,whichallows
forprovidingaccurateandrealisticlifetimeexpectanciesforthoseparticularchemistriesincludedin
Energies2020,13,5683of18
currentcommercialproducts.Therefore,theresultsofthisworkareavaluablereferencefor
analyzingtheprofitabilityofthesestoragesolutions.
Thestructureofthispaperisasfollows.Section2isdevotedtothedefinitionofLi‐ion
chemistriestobeusedinresidentialPVstorage,totheintroductionoftheircorresponding
degradationmodels,andtodescribethemodeofoperationofthebatteriesthataredesignedto
minimizetheoperationalcostofthesystem.Subsequently,Section3summarizesthedegradation
resultsexperiencedbythebatteriesaccordingtooneyearlongsimulationsthatwereperformed
whileusingMATLAB®(Natick,MA,USA)andthediscussionontheexpectedlifetimeestimations.
Finally,someconcludingremarksareintroduced.
2.Methods
ThecombineduseofLi‐ionbatterieswithsolarPVsystemsisgainingmomentum,becausesuch
systemsnotonlyallowreducingtheintermittencyofthelocalproduction,butalsoprovideaboost
inthehandlingoflocalsolargeneration 𝑃 , i.e.,increasingtheamountofself‐productionthat
becomesself‐consumed[30].Moreover,solarstoragesystemsmakeitpossibletooptimizethe
energybillfortheinstallationownersbypeakshaving[5],[24]orbyprofitingatime‐of‐use
electricityratestructureforenergyarbitrage[31,32].Thisis,ascoupledwithsolar,batterysystems
canstoretheexcessgenerationandallowthecustomertodispatchthestoredenergyduringpeak
loadhourswhenelectricityismoreexpensive.Inthecomingfuture(alreadyarealityintheso‐called
VirtualPowerPlants,VPP)additionalrevenuescanbepassedontothebatteryuserswhenthe
systemiscentrallycontrolledbytheVPPoperatorandtheenergythatisstoredinthebatteriesis
accordinglydispatchedtoprovidegridsupportforhomeownersthatareinterestedinsharingthe
resourcewiththeutility.
Nonetheless,amongthedifferentpotentialapplicationsthataredefinedforhomestorage
systems,thesearemainlyinstallednowadaysforenergyarbitrageandpeakshaving(alsoback‐up
serviceincertainlocationssuchasCalifornia).Therefore,thisworkanalyzesthelifetimeexpectancy
thatisassociatedwithtwodifferentLi‐ionchemistrieswhenusedundersuchcontrolstrategy.Todo
so,thedifferentLi‐ionchemistriesarereviewedinthefollowingandsomearehighlighted.The
variousdegradationmodelsusedintheanalysisarethenintroduced.Finally,theoptimization
introducedcontrollingtheoperationofthehomesolarstoragesystemispresented.Theoperationof
differenthomestoragesystems,intermsofpowerandenergyratings,willbethensimulatedinan
annualcontextatdifferentlocations(radiationpatterns)andwithindifferenthouseholds(load
patterns).
2.1.Li‐ionBatteriesandtheirDegradationModels
Nowadays,thegenericlabelLi‐ionbatteriescoveruptosixdifferentbatterychemistrieswhile
usingsometypeoflithiumalloyintheirelectrodes.Thesecorrespondto:LithiumCobaltOxide
(LiCoO2),LithiumManganeseOxide(LiMn2O4),LithiumIronPhosphate(LiFePO4),LithiumNickel
CobaltAluminumOxide(LiNiCoAlO2),LithiumNickelManganeseCobaltOxide(LiNiMn‐CoO2),
andLithiumTitanate(Li4Ti5O12).ThevariousLi‐ionfamiliespresentdifferentpropertiesand
characteristicsasafunctionoftheirinternalstructureandcomposition[33],whichinvolvevarying
capabilitiesintermsofspecificpower(W/kg),specificenergy(Wh/kg),thermalstability,cyclability,
andperformance.Theyalsodifferincostandlifetimeexpectancy.
Dependingontheapplicationandtheassociatedbatteryrequirements,adifferentchemistry
willbeofchoice.Forhomestorageapplications,wherebatterieshavebeendefinedtobemainly
usedtomaximizethePVproductionexploitationwhilereducingtheeconomiccostoftheelectricity
consumptioninthehouseholdviaenergyarbitrageorpeakshaving,safety,andspacelimitations
aregenerallyassumedtobethemostdefiningorlimitingparameters.Inthiscontext,LiFePO4(LFP)
andLiNiMn‐CoO2(NMC)cellsconstitutethebatteriesbestfittingtheseconstraints,whichcanbe
confirmedbyanalyzingthedifferentmodelscommerciallyavailablenowadays,asinFigure2.
Energies2020,13,5684of18
HomeStorageSystemLi‐ionCells
(1) LGChemRESUNMC
(2) TeslaPowerwall2NMC
(3) VartaPulse6NMC
(4) SENECHomeLi10NMC
(5) AmpereEnergySquareNMC
(6) SonnenECOLFP
(7) BYDB‐BOXProLFP
(8) SimpliphiPHILFP
(9) PylonUS2000LFP
(10) PowerPlusEnergyL.Pr.LFP
Figure2.ListofmainhomestoragesystemsavailableinthemarketandtypeLi‐ioncells
implemented.
Amongthem,althoughLFPbatterieshavebeentraditionallymoreexpensivethantheNMC
ones,nowpriceshavematched.Therefore,vendorsarelookingfavorablyatthemduetotheirlackof
Cobaltasfiresafetyregulationsbecomestricter,eventhoughenergydensityisadrawbackforLFP.
Attendingtothedifferentconsiderationsintroduced,thedegradationandthelifetimeexpectancy
forthesetwochemistriesareanalyzedinthiswork.Inthissense,althoughdifferenttypesofmodels
forpredictingthelifetimeexpectanciesareavailableintheliterature[34–37],semi‐empiricalmodels
(equationsbasedonreallabmeasurements)areconsideredtobethebestapproachintermsof
complexityandreliabilitytradeoffinthiswork,asitisusuallyassumedinstudiesanalyzingthe
degradationoftheLi‐ionbatteriessubjecttorealoperationscenarios[38].Mostofthesemodelstake
differenttypesofstressfactors[34,39,40]thatdegradethecellsviathesocalledcalendarcycle
(associatedtotemperatureandstateofchargeduringstand‐byoperation)orcycleageing(also
associatedtotemperature,andtothenumber,depthofdischarge,andaveragestateofchargeforthe
cycles,aswellastheC‐rate)intoaccount[39,41,42].Amongthedifferentproposalsofsemi‐empirical
degradationmodels,twodifferentmodelsthatdisregardtheC‐ratestressfactorandfocusonthe
othershavebeenusedasreferencemodels.Thisisduetothefactthatmosthomesolarstorage
systemsusuallylimitviasoftwaretheirpowerexchangecapabilitytovaluesbelow1Cthatwould
beacertainlimitbeyondwhichC‐ratebecomesasignificantstressfactor[43,44].Thesemodels,
togetherwiththecorrespondingimprovedmodelsthatareupdatedtostate‐of‐the‐artcellsforeach
ofthechemistries,areintroducedinthefollowing.
2.2.ReferenceDegradationModelforLiFePO
4
Cells
Thesemi‐empiricaldegradationanalysisproposalusedasreferenceorbasemodelinthiswork
toestimatethelifetimeofLFPcellsisthatsuggestedbyStroeetal.[45].Thispaper,whichwas
publishedin2014,proposesamodelthatwasdevelopedforcylindricalbatterycellsasthosefrom
SAFTBatteries,modelVL41M[46].Themodelanalyzesthelossofbatterycapacityduetoboththe
useofthebattery(cycleageing)andthepassofthetimeitself(calendarageing).Thevalueofthe
capacityreduction(C
fade
)thatisassociatedtothelatterisdefinedby:
𝐶
_
𝑇,𝑡 𝛼
_
𝑒
𝑡.(1)
where𝛼_and𝛽aretwoparameterswhosevaluesdependontheprecisemodelofcell
beinganalysed,Tisthetemperature(inKelvin),andtisthetime(inmonths).Moreover,the
capacityreductionthatisassociatedtotheoperation(cycling),respondsto:
𝐶
_
𝑇,𝑁𝐶 𝛼
_
𝑒
𝑁𝐶.(2)
where𝛼_and𝛽areagainothertwoparameterswhosevaluesdependonthemodelofcell
beinganalysed,andNCrepresentsthenumberofequivalentfullcycles,whicharecalculatedfrom
theannualevolutionofthestateofcharge(SOC)ofthebatteryregisteredwhenoperatedinahome
Energies2020,13,5685of18
storageapplication.ThisSOCevolutionisintroducedtotheRainflowCountingAlgorithm(RFC)
[47],whichiscapableofgroupingthosecycleswithanequaldepthofdischarge(DoD)andaverage
SOC.Afterwards,bymeansofthePalmgren–Minerruleandthecapacityevolutioncurvesofthe
batteriesprovidedbymanufacturers,theNCvaluecanbeobtained,assummarizedinFigure3.
Figure3.Definitionoftheequivalentnumberoffullcycles(NC)fromapartiallycycledstateof
charge(SOC)evolution.
OncetheNCaredefined,itsvalueisintroducedintoEquation(1)and(2)togetherwiththeT
valuesandthetimerangeanalysed.TheresultingCfadevaluesarecombinedtofinallyprovidethe
lifetimeestimationprognosis,inyears,bymeansofthefollowingequation:
EOL% 𝐶
𝐶
year,𝑇 𝐶𝑁𝐶,𝑇𝑦𝑒𝑎𝑟𝑠
(3)
Notehowthisequationtakesintoaccountthatbatterymanufacturersusuallydefinethe
end‐of‐life(EOL)oftheLFPbatteriesasthemomentwhenthecapacityretainedbythecellisequal
toagivenpercentageofitsinitialcapacity(C0)that,stilldependingonthecellmodel,canrangefrom
60%to80%(70%forthecellunderanalysis).Thesolution,inyears,tothisequationistheestimated
lifetimeexpectancyforthebattery.
2.3.ReferenceDegradationModelforLi(NiMnCo)O2Cells
Thesemi‐empiricaldegradationmodelused,asreference,analyzingthelifetimeofNMCtype
cellsisthatproposedbySchmalstiegetal.[48].Notehow,alsointhiscase,theworkpublishedinthe
sameyear2014focusesonthebehaviorofcylindricalcells.Moreprecisely,itisdevelopedforthe
SanyoUR18650Ecell[49].Again,asfortheLFPmodel,thedegradationisanalyzedasthelossof
batterycapacityassociatedtobothcalendarandcycling.Thefirstofthemisdescribedaccordingto:
𝐶
_
𝑉,𝑇,𝑡𝛼
_
𝑉3.15𝑒
𝑡.(4)
where𝛼_isaparameterthatdependsonthemodelofcellunderconsideration,Visthe
averagedailyvoltage(inVolts),Tisthetemperature(inKelvin),andtisthetime(indays).
Conversely,thedegradationthatisassociatedtotheuseofthebatteryiscalculatedby:
𝐶Q, Ø𝑉,∆𝐷𝑂𝐷𝛼
_
1.8 Ø𝑉3.667∆𝐷𝑜𝐷0.1862Q.(5)
Itcanbestatedagainthat𝛼_isaparameterthatdependsonthemodelofcellunder
consideration,Qstandsforthechargethroughput(inampere‐hour),
∅
Vistheaveragevoltagefor
eachcycle(inVolts),andΔDoDisthedepthofdischargeofthecycle(inrangeof0–1).Subsequently,
𝑁𝐶 𝐷%∙𝑁𝐶
𝑀𝑎𝑥
100%
𝐷% 𝑁𝑐𝑦𝑐𝐷𝑜𝐷
𝑁𝑚𝑎𝑥𝐷𝑜𝐷
𝐷𝑜𝐷100
𝐷𝑜𝐷1
RFC
Palmgren
Miner
𝑁𝐶 𝐷%∙𝑁𝐶
𝑀𝑎𝑥
100%
𝐷%
𝑁𝑐𝑦𝑐𝐷𝑜𝐷
𝑁𝑚𝑎𝑥𝐷𝑜𝐷
𝐷𝑜𝐷100
𝐷𝑜𝐷1
Energies2020,13,5686of18
theresultingCfadevaluescorrespondinglyassociatedtocalendarandcycledegradationarecombined
tofinallyprovidethelifetimeestimationprognosis(𝑦𝑒𝑎𝑟,inyears),bymeansof:
EOL% 𝐶
𝐶
year,𝑇,𝑉 𝐶𝑄,∅𝑉,∆𝐷𝑜𝐷𝑦𝑒𝑎𝑟𝑠(6)
ThisfinalequationtakesonceagainintoaccounttheEOLofthebatteriesasapercentageofits
C0,alsobeingestimatedatthe70%forthiscell,accordingtomanufacturer’sinformation.
2.4.DegradationModelsforState‐of‐the‐ArtCells
Asindicated,previousdegradationmodelswerepublishedseveralyearsagoandthey
correspondtoLFPandNMCcylindricalcellscommercializedinthefirsthalfofthisdecade.Li‐ion
cellshaverapidlyevolvedlatelyandcurrentcommercialmodelspresentextendedcapabilitiesthat
providemuchimprovedperformances,mainlyintermsoflifespanandwarranty.Thisisduetothe
improvementinboththecurrentinternaldesignofthecells(mostofthemevolvingtowardspouch
cellsfortheseapplications)andintheirarrangementwithinthebatterypacks.Inthisway,new
batterypackspresentbetterthermalmanagementandreducedinternalresistances,whichimply
lowerinternaloperationtemperaturesandreduceddegradationduetocurrentexchanges.
Amongthedifferentcommercialmodelsthatareavailableinthemarket,thisworkfocuseson
thesolutionsbelongingtotwoofthemainglobalenergystorageplayersnowadays:theBYDB‐BOX
ProasLFPsystemandtheLGChemRESUasNMCsystem.Forthecaseofthefirstone,BYDcomes
withoneofthebestwarrantiesonthemarket,withanestimated60%retainedcapacityafter10years
anduptoaround5100fullcycles,ifoperatedinarangeoftemperaturesbetween−10°Cand50°C.
Thiscorrespondstoanoverall23.9MWhenergythroughputcapabilityfortheB‐BOXPro7.5[50].
LGChem,ontheotherhand,implementsNMCpouchcellswithanestimatedminimum60%
retainedcapacityafter10yearsifbeingoperatedwithin−10°Cand45°Candforanoverall20MWh
energythroughputcapabilityforitsRESU6.5batterypack[51],whichimplies4000fullcycles.
Therefore,apartfromthepreviouslyintroducedreferencedegradationmodels,thiswork
considersothertwomodelsthatadaptthetrendingcurvesdefinedthroughEquations(1)–(6)tothe
warrantiesthataredefinedbyBYDandLGfortheircurrentcommercialproducts,B‐BOXand
RESU,respectively.Inthisway,theweightedeffectofthedifferentstressfactorsovertheglobal
ageingofthecellsdemonstratedbypreviousstudiesiskept,butitisupdatedtothecurrent
performancesofthecellsbeinginstalledincommercialhomestoragesystemsnowadays.Theseare
labelledasState‐of‐the‐art(SoA)modelsandtheyhavebeenvalidatedwhileusingon‐the‐field‐data
anddataprovidedbysponsorsofthestudyandprotectedbyaNon‐DisclosureAgreement(NDA).
ThecorrespondingvaluesforthecoefficientsofthedifferentmodelsareintroducedinTable1.
Table1.CalendarandCyclingAgingModels’CoefficientsforthedifferentBatteryTypesanalyzed.
BatteryModel𝜶𝒄𝒂𝒍
_
𝑳𝑭𝑷𝜶𝒄𝒚𝒄
_
𝑳𝑭𝑷𝜷𝒄𝒂𝒍𝜷𝒄𝒚𝒄𝜶𝒄𝒂𝒍
_
𝑵𝑴𝑪𝜶𝒄𝒚𝒄
_
𝑵𝑴𝑪
Ref.LFP
(SaftVL41M)3.087×10−76.87×10−50.05176 0.02715 ‐ ‐
SoALFP
(BYDB‐BOX)1.985×10−7 4.42×10−50.0510 0.02676 ‐ ‐
Ref.NMC
(SanyoUR18650E)‐ ‐ ‐ ‐ 7.54×1064.081×10−3
SoANMC
(LGChemRESU6.5)‐ ‐ ‐ ‐ 3.02×1061.632×10−3
Theresultingglobaldegradationevolutions,asrepresentedascapacityfadepercentage,
togetherwiththepartialdegradationproceduresthatareassociatedtobothcalendarandcycling,
areshowninFigure4forthetwocellswithstate‐of‐the‐artwarranties(thoseoftheRESU6.5and
thoseoftheB‐BOX7.5batterypacks).Notehowbothachievetheirwarrantypoints(retainatleast
60%ofinitialcapacity)aftera10‐yearoperationperiodsubjectto45°C.However,despitealso
agreeingwiththewarranty,theNMCpresentsanoverallenergythroughputof19.9MWhinthat
Energies2020,13,5687of18
period(whatwouldrepresent1.2fullcyclesperday),whiletheLFPcellsfromtheBYDbatterypack
goupuntil23.9MWh(whichwouldrepresent1.4fullcyclesperday).
Figure4.Degradationevolutionthroughout10yearsunderwarrantyconditionsforthetwo
commercialLi‐ionbatterypacksanalyzedinthiswork.
2.5.OperationofPVSystemswithBatteries
Aspreviouslyintroduced,althoughhomesolarstoragesystemswillbepotentiallyusedfora
widevarietyofgridservicesthatwillhelpinenhancingtheprofitabilityoftheirinstallation,these
aremainlyinstallednowadaysforenergyarbitrageandpeakshaving(alsoback‐upincertain
locations).However,andregardlessoftheoperationgoalofthesystem,thecontrolalgorithmin
chargeofsolarstoragesystem’soperationmust,ateverygivenmoment,analyzethedifference
betweentwouncontrollablevariables(𝑃andtheconsumptionoftheloads, 𝑃)anddecidewhat
todoifthereisanexcessorashortageinpowerasafunctionofthemainoperationalgoal.Inthe
firstcase,itcaneitherchargethebattery(𝑃 0)orsellenergytothegrid(𝑃 0).Inthesecond,
itcansupplementthepowerthatisproducedbythePVpanelswithenergystored(ifavailable)or
bypurchasingfromthegrid.Inanelectricitymarketwithatime‐of‐useratesstructure(on‐peakand
off‐peakhours)thecontrolalgorithmmusttakethisdecisionbyconsideringnotonlythecurrent
momentintime,butalsotheexpectedevolutionof𝑃and𝑃.Otherwise,theimplemented
strategycouldavoidpurchasingenergyduringoff‐peakhours,onlytohavetobuyitinthefuture,
duringon‐peakperiods.SuchaproblemcanbeeasilyformulatedintheframeworkofModel
PredictiveControl(MPC)[52].Thisisacontrollerdesigntechniquethatisbasedonanoptimization
strategy,inwhichthefutureoutputsforagivenhorizonN,calledthepredictionhorizon,are
predictedateachinstantwhileusingappropriatemodels.Thesepredictedoutputsdependonfuture
decisionvariables,whicharecalculatedbyoptimizingadeterminedcriterionwhilefulfillingasetof
constraints.Althoughacompletesequenceoffuturedecisionvariablesiscomputed,onlythefirst
oneiseffectivelysenttothesystem,because,atthenextsamplinginstant,newinformationwillbe
available.Thisisknownasrecedinghorizon.
Fortheproblemathand,whichfocusedontheenergyarbitrage,theeconomicbalanceofthe
energyexchangedwiththegridmainlydeterminestheoptimizationcriterion.Regardingthe
constraints,thepowerbalance,includingpowersfromthePVsystem,thebattery,thegrid,andthe
loads,mustbemet;also,thestateofcharge(SOC)ofthebatterymustbekeptwithinitslimits;and,
finally,thepowerexchangedbothwiththegridandwiththebatterymustalsostaywithincertain
limits,eitherbecauseofpowerconverterslimitationsorpeakshavingstrategies.Notethat,even
though𝑃doesnotappearintheoptimizationcost,itisstilladecisionvariableanditaffectsthe
resultsbecauseoftheconstraints.
Energies2020,13,5688of18
Takingalloftheseconsiderationsintoaccount,andfollowinganapproachsimilartothatin
[53],theoptimizationproblemtobesolvedintheMPCframeworkcanbeformulatedas:
min 𝐽𝑇𝑐
𝑡𝑘𝑃
𝑡𝑘
(7)
subjectfor𝑘0…𝑁 to:
𝑃
𝑡𝑘𝑃
𝑡𝑘𝑃
𝑡𝑘𝑃
𝑡𝑘
𝐸𝑡𝑘1𝐸
𝑡𝑘𝑇𝑃
𝑡𝑘
𝐸, 𝐸
𝑡𝑘𝐸
,
𝑃, 𝑃
𝑡𝑘𝑃
,
𝑃, 𝑃
𝑡𝑘𝑃
,
where:
Tisthesamplingperiod.
𝑃
𝑡𝑘and𝑃
𝑡𝑘arethepredictionsfor𝑃𝑡𝑘and𝑃𝑡𝑘,respectively.
𝑃𝑡𝑘isthepowerexchangedwiththegridatinstant𝑡𝑘,with𝑃𝑡𝑘0when
energyispurchased.
𝑃𝑡𝑘isthepowerexchangedbythebatteryatinstant𝑡𝑘,with𝑃𝑡𝑘0when
discharging.
𝐸𝑡𝑘istheenergyavailableinthebatteryatinstant𝑡𝑘.
𝑃,and𝑃,arethelowerandupperboundsforthepowerexchangedwiththeESS.
Theseconstraintsareduetothelimitationsonthepowerconvertersand,therefore:𝑃,
𝑃,.
𝑃,and𝑃,arethelowerandupperboundsforthepowerexchangedwiththegrid
(𝑃, 𝑃
,).Thesecanalsorepresentlimitationsonaconverterorcanbeusedin
ordertoimplementpeakshaving.
𝐸,and𝐸,arethelimitsinbetweenwhichthebatterySOCmustbekept.
𝑐
𝑐ℎ 𝑓𝑜𝑟 𝑃 0
𝑐 𝑓𝑜𝑟 𝑃 0aretheelectricityprices.𝑐changesitsvalueswiththehour
ofthedayℎdependingontherateperiod(on‐peakoroff‐peak),while𝑐isconsidered
constantand𝑐ℎ𝑐
foranygivenℎ.
Inthiswork,thedescribedoptimizationisperformedhourly,updatingtheproblemwiththe
newmeasurementsof𝐸𝑡ateachstep,andalso 𝑃
𝑡𝑘whennewinformationisavailable.
Althoughmostoftheequationsintheaboveproblemsarelinear,suchaprobleminitscurrent
formulationisstilldifficulttosolvebecauseofthepiecewisenatureof𝑐.Thiskindoffunctions
canbedealtwithbyintroducingbinaryvariables,whichleadtoamixedintegerlinearprogram
(MILP),whichiscomputationallyprohibitivefortheproblemsize.Therefore,adifferent
formulationisproposed.Theideaistoreplace𝑃bytwonewvariables,𝑃and𝑃,forthe
caseswhenitispositiveornegative,whichyieldsthefollowingproblem:
min
𝐽
𝑇𝑐
𝑡𝑘𝑃𝑡𝑘𝑐𝑡𝑘𝑃𝑡𝑘
(8)
subjectfor𝑘0…𝑁 to:
𝑃
𝑡𝑘𝑃
𝑡𝑘𝑃
𝑡𝑘𝑃
𝑡𝑘𝑃
𝑡𝑘
𝐸𝑡𝑘1𝐸
𝑡𝑘𝑇𝑃
𝑡𝑘
Energies2020,13,5689of18
𝐸, 𝐸
𝑡𝑘𝐸
,
0𝑃
𝑡𝑘𝑃
,
0𝑃
𝑡𝑘𝑃
,
𝑃, 𝑃
𝑡𝑘𝑃
,
𝑃𝑡𝑘𝑃
𝑡𝑘0,
wherethelastquadraticconstraintistheonlyonenotlinear,beingintroducedtoavoidthe
suboptimalsolutioninwhichenergyissimultaneouslyboughtandsoldfromthegrid.
Then,letusconsideranewoptimizationproblembydroppingthislastconstraintthatinvolves
𝑃and𝑃.Becauseofthestructureofthisnewproblem,itsoptimalsolutionissuchthateither
𝑃
0or𝑃
0.Indeed,if𝑃
0and𝑃
0, as c c 0,therewouldexista
newfeasiblesolution𝑃′
𝑃
𝑃
andP′
0 withalowervalueofthecostfunction,
rendering𝑃
and𝑃
asbeingsuboptimal.Furthermore,thenewproblem,withboththecost
functionandconstraintsdefinedaslinearfunctions,isalinearprogram(LP),whichisaconvex
optimizationproblem.Convexityimpliesthattheoptimizationalgorithmwillfind𝑃′
and
𝑃′
insteadofthesuboptimalsolutions.Therefore,thisnewlinearprogram(LP)canreplacethe
originaloptimizationproblem,whichachievesthesameoptimalsolutionsandiseasilysolvedwith
standardoptimizationtools.
Finally,itisimportanttoremindthat,fortheapplicationoftheMPCstrategy,prediction
models𝑃
and𝑃
areneeded.
Regarding𝑃
,itispredictedinthisworkfromthePVsystemratedpowerandefficiencyin
combinationwithirradianceforecastedvaluesdownloadedfromthemodelsavailableonlineatthe
EuropeanCentreforMedium‐RangeWeatherForecasts(ECMWF)[54].ECMWFproducesan
ensembleofpredictionsthatindicatethelikelihoodofarangeoffutureweatherscenarios.Forecasts
aremadewhileusingNWPmethods,whichhavegoodperformancesforday‐aheadforecastsand
beyond.Theforecastspredictthenext10dayswithone‐hourtimesteps,andupdatedforecastsare
availableevery12hours,at12a.m.and12p.m.Thespatialresolutionoftheforecastsis0.1°forboth
latitudeandlongitude.
Ontheotherhand,giventheSouthernEuropeanlocationofthehouseholdsunderanalysis,
𝑃
isobtainedasoneofthereferenceloadprofilesdefinedbytheSpanishMinistryofIndustryfor
theresidentialsector[55].EnergyretailersintheIberianMarketusetheseprofilestobillthose
consumersnotupdatedyetwithconsumptiontelemetry,andarethereforesupposedtorepresent
theaveragetrends.Amongthevariousprofilesthataredefinedin[55],thisworkusesthat
designatedforuserswithtime‐of‐useelectricityrates,whicharethosethatarepronetobeeligibleby
consumerswithhomesolarstoragesystems.ThebluecontinuouslineinFigure4representsthe
annuallyaverageddailydistributionofthatloadprofile.
3.ResultsandDiscussion
TheanalysisofthelifetimeexpectancythatisofferedbythedifferenttypesofLi‐ionbatteries
hasbeenperformedbymeansofsimulationswhileusingMATLAB®.Accordingtotheintroduced
controlalgorithm,theoperationofthehomestoragesystemproducessuccessivechangesinthe
batterySOCthatbasicallydependonthepowerbalancetobegrantedhourafterhour.Therefore,the
continuousPVproductionandthehouseholdloadvaluesstronglycharacterizetheSOCevolution.
Inthissense,withtheaimtotrytogeneralizetheresults,theoperationofthehomestorage
systemsatthreedifferentlocationsinSouthernEuropeduringuptothreeyearswasexplored(2016,
2017,2018).Thus,uptonineannualirradiancepatterns,presentingallofthemaround1500peak
sunhours(PSH)peryearonthehorizontalplane,wereavailableandevaluated.Thethreeplaces
usedforanalysiswere:alocationintheeasternpartofSpain(ontheMediterraneancoastatsea
level,labelledasA),alocationinthecentralplateau(800mabovesealevel,labelledasB),anda
Energies2020,13,56810of18
locationinthenorthernpartofthecountry(420mabovesealevel,labelledasC),respectively.With
thesamegoal,threetypesofresidentialloadpatterns,thosewhoseannuallyaveragedhourly
distributionsareintroducedinFigure5,wereanalyzed.Notehowoneofthemispractically
coincidentwiththereferenceloaddistributionmodelthatisprovidedin[55],thatofhousehold3,
whiletheothertwoloadpatternsrepresentuserswithsignificantconsumptionduringthe
mornings,household1,orduringtheevenings,household2.Alsonotethat,althoughbeing
differentlydistributedthroughouttheday,theactualstochasticconsumptionofeachofthe
householdsdayafterdayaddsuptoanoverallannualconsumptionofaround5000kWh/year.
Moreover,anactualtime‐off‐usetariffforresidentialconsumerspresentingdailyoff‐peak
(from10pmtillnoon)andon‐peak(fromnoontill10pm)hours,regardlessofthedifferentseasons
oftheyearwasimplemented.ThiscomprehendsthecostsorpricesthataresummarizedinTable2
forthepurchaseofelectricityfromthegridorfortheinjectionofpowerintoit.Notehowthe
acquisitioncostisalwayshigherthanthegenerationone.
Table2.Costs,inc€/kWh,oftheelectricityfortheresidentialelectricitytariffconsidered.
PeriodOn‐peakOff‐peak
Purchaseprice(c€/kWh)2211
Saleprice(c€/kWh)55
Figure5.Loaddistributionprofileforthethreehouseholdsanalyzed,andreferenceloadcurveused
asmodel.
Afterwards,simulationshavebeenrunforeachofthecombinationsexplainedabove.Inall,if
thethreelocations,thedifferentyearsavailable,andthevariousloadprofilesareanalyzedwitheach
ofthealternativehomestoragetechnologicalsolutions,i.e.,considering2kWor4kWratedPV
installationsateachhousehold,andcombiningeachofthesePVsystemswiththevariousbattery
typesanalyzed(LFP,NMC…),which,inturn,areintroducedwiththreedifferentenergycapacities
(3.3kWh,6.5kWh,and10kWh)andaroundtripefficiencyof95%,thesesumup648combinations.
NotethatthevaluesofratedpowerforthePVplantssimulatedandthethreedifferentenergy
capacityratingsaccountedforthebatteriestryingtoreflectthemostpopularPVcasesforresidential
installationsinEuropeandtheusualbatterystoragecommercialmodelsbeinginstallednowadays.
3.1.AnnualEvolutionoftheGlobalDegradation
Foreachofthecases,anannualhourlysimulationofthehomesolarstoragesystemoperation
hasbeenperformed,providingtheSOCevolutionofthecorrespondingbatterypackasmainresult.
AsexplainedinSection2.1,thedifferentSOCevolutionsthatweregeneratedforthevariousbattery
Energies2020,13,56811of18
capacitiesconsideredatagivenhouseholdinoneofthelocationsunderstudywere,inturn,
introducedintotheRFCalgorithm.Thisalgorithmreturnedthehistogramofpartial
charge‐dischargecyclesthatwereannuallyregisteredbythedifferentbatteriesandtheaverageSOC
levelforeachcycle.Figure6representssuchhistogramsgenerated,inthatcase,forhousehold1in
locationAwhena4kWrated‐PVisassumedandforeachofthethreebatterycapacitiesconsidered
(3.3kWh,6.5kWh,and10kWh).Itisimportanttonoticeinthosedepictedcyclingpatternshowthe
maximumdepthofthecharge‐dischargecyclesis95%,limitedinthiswaybythecontrolsystemin
agreementwithmanufacturer’sindicationtoavoidexcessivedegradationofthebatterythatis
associatedwithverydeepcycles.Apartfromthismaincycle,nosignificantpartialcyclesintermsof
DoDareexperiencedbythehomestoragesystems.Mostofthepartialcyclesregisteredcorrespond
to25%andbelowDoDmaneuvers.Addingthemall,thesedonotevenachieveinnumberthe95%
DoDcycles.Therefore,homestorageapplicationscanbeconcludedtobeverymonotonousand
sortableintermsofDoDpatterns.Additionally,notehow,asthebatterycapacitygetslarger,the
numberoffullcycles(thoseof95%DoD)getssmalleranditisredistributedalongalltherangeof
partialDoDs.Thisdirectlyimpactsthecycledegradationofthedifferentbatteries.Fromthese
histograms,theamountofQ(chargethroughput)thatisexperiencedbythebatterycanbealso
estimated.Inthesameway,the
∅
VandVvaluesrequiredforthecalculationsinequations(4)and(5)
canbederivedfromtheresultingaverageSOCprovidedbytheRFCalgorithmandfromtheSOC
evolutionitself,respectively.Thisisdonebymeansofanexperimentalequationobtainedforthe
batterypacksmeasuredinourlabthatcorrelatesthevoltageofthebatterypackwiththe
correspondingSOC.Therefore,accordingtoEquations(2)and(5),suchamethodologyisusefulin
thedegradationanalysisassociatedwiththecycling.
Figure6.Histogramforthenumberofcyclesforeachdepthofdischarge(DoD)forthreedifferent
batterycapacities.
Conversely,notbeingtheonlystressfactor,temperatureisconsideredtobethemainonethatis
associatedtocalendarageingthough.Forthecasesthatweresimulatedinthiswork,thebatteries
havebeensupposedtobestressedtocelloperationtemperaturesalwayswithinthewarrantylimits
andrangingfrom35°Cto45°Calongtheyear.Thesearedefinedforthecellsasthetemperature
thatwouldbeachievedwithinthebatterypackduringregularoperationwheninstalledindoorsat
locationswithairtemperaturesrangingfrom20°Cto32°C.Therefore,thecalendarageingofthe
batterypacksalongtheyearcanbeequallyestimatedwhileusingEquations(1)and(4).
Calendarandcycleageingundertheseoperationconditionsarebothrepresentedforthefirst
yearofoperationinFigure7forthecommercialbatterypacksfromBYDandLGChem,respectively.
Energies2020,13,56812of18
(a)
(b)
Figure7.Cycling,calendar,andtotalcapacityreductioninoneyearfora3.3kWhLiFePO4(LFP)(a)
andLiNiMn‐CoO2(NMC)(b)batteryoperatedatlocationAwithinhousehold1.
InagreementwiththewarrantyevolutioncurvesthatarerepresentedinFigure4,notehowthe
maindifferenceamongthebatterydegradationmodelsisdefinedbytheirresponseintermsof
calendarageing.Itcanbeclearlyobservedhow,forthesameoperationconditionsand,accordingly,
thecorrespondingsimilarcycleageing,theglobaldegradationoftheLFPbatteryismuchmore
importantthanthatofitsNMCcounterpartduringthefirstyearofoperationduetotheaccelerated
calendarageing.However,thisbehaviorbecomesmorebalancedastimegoesby,becauseofthe
relationwithtimeofbothcalendarmodels(t0.75forNMCVs.t0.5forLFP).Thiscanbeclearlydeduced
fromFigure3anditisalsoreflectedinthelifetimeestimationvaluesthatareprovidedforthe
differentchemistriesinthedifferentcasesanalyzedandsummarizedinthefollowingsubsection.
3.2.ComparativeDiscussionamongTechnologies.
Outofthe648combinationsanalyzed,justsomeofthemareintroducedfordiscussion.The
selectedcasescoverthemaindifferentoperatingscenariosandallowforextractingthemain
conclusionsfromthisstudy.Tostartwith,Table3introducesthelifetimeexpectancyresultsthat
wereobtainedinagivenhousehold(specificloadcurve)andagivenlocation(irradianceprofile)for
thefourbatterymodels:thoseintroducedinSection2.1and2.2asreferenceforLFPandNMCcells,
respectively;and,thoseintroducedinSection2.3asstate‐of‐the‐art(SoA)models.Itisimportantto
noticethatthelifetimeexpectancyhasbeencalculatedassumingtheEOLisachievedwhenthe
batteryretains70%(asdefinedforthereferencecellsintheircatalogues)or60%(asdefinedby
manufacturersfortheSoAbatterypacks).Itcanbeobservedataglancehowallowingthecellsto
operateuntila40%capacityfadeoffersacertainlyincreasedlifetimeexpectancy.Therefore,
althoughthewarrantiesfrommanufacturersdefinedifferentEOLhorizonsforthevariouscells,for
thesakeofcomparison,bothhorizonshavebeenanalyzedforallofthem.
Energies2020,13,56813of18
Table3.Lifetimeexpectancyresultsforthedifferentbatteriesathousehold1inlocationA.
Modelof
Battery
PV
Power
(kW)
Battery
Capacity
(kWh)
Calendar
Ageing
EOL70%
Cycling
Ageing
EOL70%
Lifetime
Expect.
(years)
Calendar
Ageing
EOL60%
Cycling
Ageing
EOL60%
Lifetime
Expect.
(years)
LFPRef23.322.077.943.5629.4310.576.30
LFPRef26.522.827.193.7630.419.596.63
LFPRef21024.405.614.3832.577.447.59
LFPRef43.322.097.923.5729.4710.536.32
LFPRef46.522.397.623.6529.8910.126.45
LFPRef41023.406.604.0131.228.787.02
LFPSoA23.319.1210.8810.1625.5314.4717,80
LFPSoA26,520.0010.0111.0626.6913.3219.48
LFPSoA21022.027.9813.3829.3810.6223.53
LFPSoA43.319.1510.8510.2025.5614.4517.85
LFPSoA46.519.5210.4810.5326.0413.9618.55
LFPSoA41020.739.2711.8127.6712.3320.88
NMCRef23.315.0115.002.0521.6518.373.34
NMCRef26.517.5512.452.8724.8615.154.57
NMCRef21020.269.744.1928.4011.616.47
NMCRef43.315.6014.402.0722.3717.643.33
NMCRef46.517.4712.542.5724.6315.384.18
NMCRef41019.7610.253.0827.7312.284.65
NMCSoA23.317.7812.228.4925.2014.8013.48
NMCSoA26.519.9810.0211.5827.9312.0718.18
NMCSoA21022.437.5716.0230.989.0224.58
NMCSoA43.318.2711.738.4125.8014.2013.31
NMCSoA46.519.8010.2010.3727.7112.2916.19
NMCSoA41021.858.1511.5730.239.7717.75
ItcanbeconcludedfromresultsinTable3thattheSoAbatterymodelssignificantlyoutperform
theirreferencecounterparts.ThelifetimesfortheSoALFPbatteryareexpectedtobearoundthree
timesthoseofthereferencemodel,whileafourfoldrelationisregisteredbetweentheNMCmodels.
Theimprovedpropertiesofthenewbatteriesintermsofexpectedlifetimeactuallyenablethemasa
formalsolutionforresidentialstorage,whichwasunderdiscussionuntilrecentlyduetothetight
marginbetweentheirexpectedlifetimesandtheirpaybackperiods(around10years,withhighly
dependenceonthemarket).AccordingtotheresultsinTable3,newbatterydevelopmentswould
grantlifetimeexpectanciesbeyond10yearsformostofthecasessimulated,evenifonlya30%
capacityfadeisallowedtothebatterypriortosubstitution.
Whencomparingthechemistries,itcanbeobservedhowthecycleageingimpactsmoreNMC
cellsthanLFPones;thismakestheLFPbatteriesoutfitNMCmodelswhensmallbatterypacksare
implementedinthehomestoragesolution.Thisisbecausebatterieswillexperiencedeep
charge‐dischargecycleseverydayduetotheirlimitedcapacity.However,lifetimeexpectancies
convergeasbatterycapacitygetslarger.
Beyondthat,notehowincreasingthePVratedpowerfrom2kWto4kWdonotsignificantly
varytheexpectedlifetimewhensmallsizebatteriesareimplemented(thoseof3.3kWh).Itcan
introducea15%variationinthelifetimeifthebatterypresents10kWhthough.Thisisbecause3.3
kWhbatteriesgetregularlysaturated(profitalltheirDoD)everydayduringthenormaloperation
withanyPVinstallationbeyond2kW.Therefore,thesecannotdegradefasterduetocycling.Onthe
contrary,10kWhbatteriesdonotexperiencefulldischargedailycycleswiththe2kWsolarplant
(reducingtheircycleageing),whiletheyaremoreextensivelyprofited,intermsofcycling,withthe4
kWplant.Finally,notethat,asexpected,thelargerthebatterysizeisthelongerthelifetime
expectancyresults.ThisisduetothesamereasonjustexposedandalsoexplainedwithFigure6,as
thebatterypresentsmorecapacity,cyclesareshorter,anditsufferslessfromcycleageing.
InordertoanalyzetheimpactofthelocalPVproduction(irradiancecharacteristicsatthe
location),simulationsbothfordifferentyearsatthesamelocation(Table4)andforthethree
differentlocations(Table5)havebeencarriedoutThefirsttableintroducesthelifetimeexpectancy
thatwasobtainedfortheLFPbatterypackinstalled,inthatcase,athousehold3.Thisisanalyzed
Energies2020,13,56814of18
withannualirradiancedatafromthreesuccessiveyearsthatwereregisteredinthelocationlabelled
asA.NoteinTable4howusingagivenyearofannualirradiancedatadonotsignificantlyimpact
theprognosisintermsofexpectedlifetime.Thedeviationsareminimal.Ontheotherhand,itcanbe
observedinTable5howthedifferentirradiancepatternsregisteredatthevariouslocations,
althoughallofthemwithsimilarPSH,certainlyinfluencethelifetimeexpectancyofthebatteries.In
thiscase,variabilitycanrangefrom5%to20%,dependingonthelocationand,also,stronglyonthe
sizeofthebatteryunderanalysis.However,inanyofthecases,thebatterypackskeeppresenting
lifetimesbeyondthe10‐yearwarrantytimes.
Table4.LifetimeexpectancyresultsfortheLFPstate‐of‐the‐artbatterypackinstalledathousehold3
analyzedwithirradiancedatafromthreesuccessiveyearsinlocationA.
Modelof
Battery
PVPower
(kW)
Battery
Capacity
(kWh)
LifetimeExp.
Irradiance’16
(Years)
LifetimeExp.
Irradiance’17
(Years)
LifetimeExp.
Irradiance’18
(Years)
LFPSoA23.318.9618.9918.64
LFPSoA26.521.9121.8021.49
LFPSoA21025.9025.8225.52
LFPSoA43.318.1518.2118.07
LFPSoA46.519.2619.1419.01
LFPSoA41022.4822.5722.42
Table5.LifetimeexpectancyresultsfortheNMCstate‐of‐the‐artbatterypackinstalledathousehold
2andanalyzedatthethreedifferentlocations.
Modelof
Battery
PV
Power
(kW)
Battery
Capacity
(kWh)
LifetimeExp.
LocationA
(years)
LifetimeExp.
LocationB
(Years)
LifetimeExp.
LocationC
(Years)
NMCSoA23.313.5013.1212.93
NMCSoA26.518.5816.5915.71
NMCSoA21025.4321.4519.81
NMCSoA43.313.1512.4712.33
NMCSoA46.516.0914.9814.35
NMCSoA41018.2316.6715.68
Finally,thelifetimeanalysisoftheseLi‐ionbatteriesistheinfluenceoftheloaddistributionor
itsvariabilityistheotherfactortotakeintoconsideration.Theresultsinthissenseareintroducedin
Table6.Thistablesummarizeslifetimeexpectanciesthatwereobtainedforboththestate‐of‐the‐art
LFPandNMCbatterypackswheninstalled,forthiscase,atlocationBandforeachofthethree
householdsanalyzedwiththeircorrespondingdifferentlyhourly‐distributedloads,accordingto
Figure4.NotealsoagainthattheloadprofilesthatarerepresentedinFigure4arerealand,
therefore,alsopresentdifferentvariabilitiesamongthedaysthroughouttheyear.ObserveinTable5
howthedifferentloadprofiles(batteryoperationwithinthedifferenthouseholds)implylifetime
deviationsthatarebelow10%amongthecases,andforeachofthebatteries.Therefore,theeffectof
theloadcanbeconcludedtobelowerthanthatoftheradiationonthelifetimeexpectancyanalysis.
Table6.LifetimeexpectancyresultsfortheLFPandNMCstate‐of‐the‐artbatterypacksinstalledat
locationBinanyofthehouseholdsanalyzed.
Modelof
Battery
PV
Power
(kW)
Battery
Capacity
(kWh)
LifetimeExp.
Household1
(years)
LifetimeExp.
Household2
(years)
LifetimeExp.
Household3
(years)
LFPSoA23.317.6417.6817.95
LFPSoA26.518.6718.7920.13
LFPSoA21021.9922.4523.96
LFPSoA43.317.6217.6117.66
LFPSoA46.518.2818.2918.65
LFPSoA41020.7221.1622.27
NMCSoA23.313.1113.1213.66
Energies2020,13,56815of18
NMCSoA26.516.4416.5918.60
NMCSoA21020.9221.4524.28
NMCSoA43.312.5512.4712.77
NMCSoA46.514.9814.9815.62
NMCSoA41016.5516.6717.38
4.Conclusions
ThispaperintroducesthelifetimeexpectancyforfourdifferenttypesofLi‐ionbatteries
implementedashomesolarstoragesystems.Thechemistriesanalyzedarethosethatarebeing
proposednowadaysforthiskindofapplicationbymostofthemanufacturers,i.e.,LFPandNMC
typebatteries.Foreachofthem,twotypesofcellshavebeenconsidered:cellsthatwere
commercializedinthefirsthalfofthisdecadeandstate‐of‐the‐artcellsbeingusedincommercial
currentsolutions.Thefirstpresentsdegradationmodelsintheliteraturethatareusedasreference
foreachofthechemistries,whilethelatterpresentsextendedandimprovedoperationwarranties
thataretakenintoaccounttoupdateandcalibratenewdegradationmodels.Eachofthefourbattery
typeshasbeenanalyzedinvariousscenariosofloadpattern(threecasesofdistributionofthe
consumptionalongtheday),atthreedifferentlocationswiththeircorrespondingvariationsinthe
PVproductionprofile.ThisiscombinedwithtwosizesofPVinstallation(2kWand4kW)andwith
uptothreelevelsofenergystoragecapacity(3.3kWh,6.5kWh,and10kWh).Simulatingtheannual
operationofthePVresidentialsystemswithbatteriesateachofthescenariosintroducedandtaking
intoaccountbatteryoperationtemperaturesrangingfrom35°Cto45°C,dependingonthetimeof
theyear,isundertakentoperformthestudy.Theintroductioninthesemi‐empiricaldegradation
modelsofthebatteryoperationparameters(SOCevolutions,exchangedcurrent,voltagestand‐by
levels,andothers)obtainedatthesimulations,deliverslifetimeexpectanciesforthedifferent
batteriesunderthevariousscenarios.Ingeneral,itcanbeobservedhowthecycleageingimpacts
moreNMCcellsthanLFPones,whichmakesLFPbatteriesoutfitNMCmodelswhensmallbattery
packsareimplementedinthehomestoragesolution.However,asbatterycapacitygetslarger,
lifetimeexpectanciesconverge.Additionally,notethatthedegradationexperiencedforthedifferent
loadprofilesdoesnotvarysignificantly,whichimpliesthatthewarrantycanbegranted,regardless
ofthetypeofconsumer.Additionally,similarly,thelifetimeexpectancythatisobtainedatthe
differentlocationsisalsoquiteregular,whichalsoimpliesthatthewarrantycanbegrantedatany
location.Infact,noneofthecasesanalyzedprovidedlifetimesbelow10yearsbeingthebatterypack
temperatureofsimulationsalwaysbelowthatofthewarrantyforstate‐of‐the‐artmodels.Neither
chargethroughputwentbeyondthatofferedinthemanufacturers’datasheets.Therefore,itcanbe
finallyconcludedthatcurrentcommercialbatterymodelspresentlifetimeexpectanciesunderreal
operationconditionsbeyondthebreak‐evenpointthatisestimated,dependingonthecountryand
itscorrespondingelectricitytariffs’structures,between8and12years.
AuthorContributions:Conceptualization,HectorBeltran;methodology,HectorBeltran,PabloAyusoand
EmilioPerez;software,PabloAyusoandEmilioPerez;validation,PabloAyusoandHectorBeltran;formal
analysis,HectorBeltranandPabloAyuso;investigation,HectorBeltran,PabloAyusoandEmilioPerez;
resources,PabloAyusoandEmilioPerez;datacuration,PabloAyuso;writing—originaldraftpreparation,
HectorBeltranandPabloAyuso;writing—reviewandediting,HectorBeltranandEmilioPerez;visualization,
HectorBeltran,PabloAyusoandEmilioPerez;supervision,HectorBeltranandEmilioPerez;project
administration,HectorBeltran;fundingacquisition,HectorBeltran”.Allauthorshavereadandagreedtothe
publishedversionofthemanuscript.
Funding:ThisresearchwasfundedbyUniversitatJaumeI(Spain),projectUJI‐B2017‐26,andbythe
GeneralitatValenciana,projectGV‐2019‐087.
Acknowledgements:
ConflictsofInterest:Theauthorsdeclarenoconflictofinterest.
Energies2020,13,56816of18
References
1. REN21.Renewables2019GlobalStatusReport;REN21:Paris,France,2019.
2. InternationalEnergyAgency(IEA).TrackingProgressonPV.2019.Availableonline:
https://www.iea.org/tcep/power/renewables/solarpv/(accessedon15thJanuary2020).
3. JointResearchCentre.EuropeanComission.PVStatusReport2018;PublicationsOfficeoftheEuropean
Union:Brussels,Belgium,2018.
4. Bermudez,V.ElectricitystoragesupportingPVcompetitivenessinareliableandsustainableelectric
network.J.Renew.Sustain.Energy2017,9,12301.
5. Leadbetter,J.;Swan,L.Batterystoragesystemforresidentialelectricitypeakdemandshaving.Energy
Build.2012,55,685–692.
6. Teng,F.;Strbac,G.Businesscasesforenergystoragewithmultipleserviceprovision.J.Mod.Power
Syst.CleanEnergy,2016,4,615–625.
7. Hesse,H.C.;Schimpe,M.;Kucevic,D.;Jossen,A.Lithium‐ionbatterystorageforthegrid—Areviewof
stationarybatterystoragesystemdesigntailoredforapplicationsinmodernpowergrids.Energies
2017,10,2107.
8. WoodMackenzie.GlobalEnergyStorageOutlook:Q32019;WoodMackenzie:Edinburgh,UK,2019.
9. InternationalRenewableEnergyAgency.InnovationLandscapeBrief:Behind‐the‐MeterBatteries;
InternationalRenewableEnergyAgency:AbuDhabi,UnitedArabEmirates,2019.
10. WoodMackenzie.EuropeResidentialEnergyStorageOutlook2019–2024;WoodMackenzie:Edinburgh,
UK,2019.
11. Weniger,J.;Tjaden,T.;Quaschning,V.SizingofresidentialPVbatterysystems.EnergyProcedia2014,
46,78–87.
12. Tant,J.;Geth,F.;Six,D.;Tant,P.;Driesen,J.MultiobjectivebatterystoragetoimprovePVintegration
inresidentialdistributiongrids.IEEETrans.Sustain.Energy2013,4,182–191.
13. Hafiz,F.;deQueiroz,A.R.;Husain,I.Multi‐stagestochasticoptimizationforaPV‐storagehybridunit
inahousehold.InProceedingsofthe2017IEEEIndustryApplicationsSocietyAnnualMeeting,
Cincinnati,OH,USA,1–5October2017.
14. Naumann,M.;Karl,R.C.;Truong,C.N.;Jossen,A.;Hesse,H.C.Lithium‐ionbatterycostanalysisin
PV‐householdapplicationEnergyProcedia2015,73,37–47.
15. Sun,C.;Sun,F.;Moura,S.J.Nonlinearpredictiveenergymanagementofresidentialbuildingswith
photovoltaics&batteriesJ.PowerSources2016,325,723–731.
16. Segarra‐Tamarit,J.;Perez,E.;Alfonso‐Gil,J.C.;Arino,C.;Aparicio,N.;Beltran,H.Optimized
managementofaresidentialmicrogridusingasolarpowerestimationdatabase.InProceedingsofthe
2017IEEE26thInternationalSymposiumonIndustrialElectronics,Edinburgh,UK,19–21June2017.
17. Barcellona,S.;Piegari,L.;Musolino,V.;Ballif,C.Economicviabilityforresidentialbatterystorage
systemsingrid‐connectedPVplants.IETRenew.PowerGener.2018,12,135–142.
18. Munzke,N.;Schwarz,B.;Barry,J.TheImpactofControlStrategiesonthePerformanceand
ProfitabilityofLi‐IonHomeStorageSystems.EnergyProcedia2017,135,472–481.
19. Beltran,H.;Garcia,I.T.;Alfonso‐Gil,J.C.;Perez,E.LevelizedCostofStorageforLi‐IonBatteriesUsed
inPVPowerPlantsforRamp‐RateControl.IEEETrans.EnergyConvers.2019,34,554–561.
20. Kakimoto,N.;Satoh,H.;Takayama,S.;Nakamura,K.Ramp‐RateControlofPhotovoltaicGenerator
WithElectricDouble‐LayerCapacitor.IEEETrans.EnergyConvers.2009,24,465–473.
21. Du,Y.;Jain,R.;Lukic,S.M.Anovelapproachtowardsenergystoragesystemsizingconsidering
batterydegradation.InProceedingsofthe2016IEEEEnergyConversionCongressandExposition,
Milwaukee,WI,USA,18–22September2016;pp.1–8.
22. Swierczynski,M.;Stroe,D.I.;Stan,A.I.;Teodorescu,R.;Sauer,D.U.Selectionand
performance‐degradationmodelingofLiMo2/Li4Ti5O12andLiFePO4/Cbatterycellsassuitable
energystoragesystemsforgridintegrationwithwindpowerplants:Anexamplefortheprimary
frequencyregulationservice.IEEETrans.Sustain.Energy2014,5,90–101.
23. Wankmüller,F.;Thimmapuram,P.R.;Gallagher,K.G.;Botterud,A.Impactofbatterydegradationon
energyarbitragerevenueofgrid‐levelenergystorage.J.EnergyStorage2017,10,56–66.
24. Berglund,F.;Zaferanlouei,S.;Korpas,M.;Uhlen,K.Optimaloperationofbatterystoragefora
subscribedcapacity‐basedpowertariffprosumer—ANorwegiancasestudy.Energies2019,12,4450.
Energies2020,13,56817of18
25. Hoke,A.;Brissette,A.;Smith,K.;Pratt,A.;Maksimovic,D.Accountingforlithium‐ionbattery
degradationinelectricvehiclechargingoptimization.IEEEJ.Emerg.Sel.Top.PowerElectron.2014,2,
691–700.
26. Grün,T.;Stella,K.;Wollersheim,O.ImpactsonloaddistributionandageinginLithium‐ionhome
storagesystems.EnergyProcedia2017,135,236–248.
27. Angenendt,G.;Zurmühlen,S.;Mir‐Montazeri,R.;Magnor,D.;Sauer,D.U.EnhancingBatteryLifetime
inPVBatteryHomeStorageSystemUsingForecastBasedOperatingStrategies.EnergyProcedia2016,
99,80–88.
28. Abdulla,K.;DeHoog,J.;Muenzel,V.;Suits,F.;Steer,K.;Wirth,A.;Halgamuge,S.OptimalOperation
ofEnergyStorageSystemsConsideringForecastsandBatteryDegradation.IEEETrans.SmartGrid
2018,9,2086–2096.
29. Hesse,H.C.;Martins,R.;Musilek,P.;Naumann,M.;Truong,C.N.;Jossen,A.Economicoptimizationof
componentsizingforresidentialbatterystoragesystems.Energies2017,10,835.
30. Vieira,F.M.;Moura,P.S.;deAlmeida,A.T.Energystoragesystemforself‐consumptionof
photovoltaicenergyinresidentialzeroenergybuildings.Renew.Energy2017,103,308–320.
31. Castillo‐Cagigal,M.;Caamano‐Martín,E.;Matallanas,E.;Masa‐Bote,D.;