energies Article Hydropower Advantages over Batteries in Energy Storage of Off-Grid Systems: A Case Study Prajwal S. M. Guruprasad 1, Emanuele Quaranta 2 , Oscar E. Coronado-Hernández 3 and Helena M. Ramos 4,* 1 Energy Technologies Dual Degree Program, Instituto Superior Tecnico and Karlsruhe Institute of Technology, CERIS at IST, 1049-001 Lisbon, Portugal; prajwalshandilya6227@gmail.com 2 European Commission Joint Research Centre, 21027 Ispra, Italy; emanuele.quaranta@ec.europa.eu 3 Facultad de Ingeniería, Universidad Tecnológica de Bolívar, Cartagena 131001, Colombia; ocoronado@utb.edu.co 4 Civil Engineering Research and Innovation for Sustainability (CERIS), Instituto Superior Técnico, Department of Civil Engineering, Architecture and Environment, University of Lisbon, 1049-001 Lisbon, Portugal * Correspondence: hramos.ist@gmail.com or helena.ramos@tecnico.ulisboa.pt Abstract: Microgrids are decentralized power production systems, where the energy production and consumption are very close to each other. Microgrids generally exploit renewable energy sources, encountering a problem of storage, as the power production from solar and wind is intermittent. This research presents a new integrated methodology and discusses a comparison of batteries and pumped storage hydropower (PSH) as energy storage systems with the integration of wind and solar PV energy sources, which are the major upcoming technologies in the renewable energy sector. We implemented the simulator and optimizer model (HOMER), which develops energy availability usage to obtain optimized renewable energy integration in the microgrid, showing its economic added value. Two scenarios are run with this model—one considers batteries as an energy storage technology and the other considers PSH—in order to obtain the best economic and technical results for the analyzed microgrid. The economic analysis showed a lower net present cost (NPC) and levelized cost of energy (LCOE) for the microgrid with PSH. The results showed that the microgrid with the storage of PSH was economical, with an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh, in comparison with the scenario with batteries, which had an NPC of 95.2 M€ and an LCOE of 0.786 €/kWh. The role of storage was understood by differentiating the data into different seasons, Citation: Guruprasad, P.S.M.; Quaranta, E.; Coronado-Hernández, using a Python model. Furthermore, a sensitivity analysis was conducted by varying the capital cost O.E.; Ramos, H.M. Hydropower multiplier of solar PV and wind turbines to obtain the best optimal economic solutions. Advantages over Batteries in Energy Storage of Off-Grid Systems: A Case Keywords: hydropower; energy storage; pumped storage hydropower (PSH); batteries; net present Study. Energies 2023, 16, 6309. cost (NPC); levelized cost of energy (LCOE); microgrids https://doi.org/10.3390/en16176309 Academic Editor: Wencheng Guo Received: 1 August 2023 1. Introduction Revised: 17 August 2023 Climate change and global warming are topics of major interest in the current decade. Accepted: 29 August 2023 Furthermore, there has been an ever-increasing demand for energy over the past few Published: 30 August 2023 decades. Renewable energy technologies play a major role in satisfying the energy demand, as well as in decreasing CO2 emissions, especially in the European Union, where the target of net zero emissions has been set for 2050. The global electricity demand is expected to Copyright: © 2023 by the authors. reach 30,621 TWh in 2030 and 43,762 TWh in 2050, from 24,700 TWh, which was recorded Licensee MDPI, Basel, Switzerland. in 2021, with a major part of consumption in buildings and an expected increasing demand This article is an open access article from Electric Vehicles (EVs) and hybrid industries [1]. Additionally, the invasion of Russia distributed under the terms and in the Ukraine caused an energy crisis in many countries. The rising prices of energy in conditions of the Creative Commons Europe placed a major burden on consumers and had a great impact on the economy, Attribution (CC BY) license (https:// which was recovering from the COVID-19 pandemic [2]. Net zero emissions and sustain- creativecommons.org/licenses/by/ able energy can be achieved with the implementation of different types of renewables 4.0/). (such as solar photovoltaics (PV), wind, hydropower, geothermal power, ocean power, Energies 2023, 16, 6309. https://doi.org/10.3390/en16176309 https://www.mdpi.com/journal/energies Energies 2023, 16, 6309 2 of 28 Energies 2023, 16, x FOR PEER REVIEW 2 of 30 bioenergy). The global renewable energy capacity by the end of 2020 was 2802 GW [3]. Portugal generated 24.27 TWh of energy in 2022, accounting for 59.4% of the total electricity pemroidsusicotniosn a[n4d]. sustainable energy can be achieved with the implementation of different typesH oyfd rreonpeowwaebrlehsa s(stuhcehl aarsg essotlacro npthroibtouvtioolntaaicms o(nPgV)a,l lwreinnde,w haybdleroepnoewrgeire,s .geItostheneremrgayl gpeonweerra,t ioocneaisn gproewateerr, tbhioanentehregrye)s. tTohfet ghleorbeanl erwenaebwleabenlee ergnyertgeyc hcnapolaocgitiye sbcyo tmheb einnedd o. fO 2v0e2r0- awlla,s1 278%02o fGtWhe [g3l]o. bPaolrteulegcatlr igceitnyerwataesdp 2r4o.d2u7 cTeWd hby ofh yenderrogpyo wine 2r0i2n22, 0a2cc0o. uTnhteinpgo ftoern t5i9a.l4o%f hoyf dthreo ptootwale er,lewctitrhicilotyw pcraordbuocntieolne c[t4r]i.c i ty and a very high generation capacity, will be very impoHrtyandtroinpothweejro huarns etyheto lwaragredsst tchoenetrniberugtyiotnr aanmsiotinogn .aHll yrednreowpoawbleer ecnaenrgbieesp.r Iotds uecneedrgayt bgoentherlaatrigoen aisn dgrsemataelrl tshcaanle tsh.eT rheest loafr gtheeo rremneewdaiubmle escnaelregsyt ytepcihcnalollyoginiecslu cdoemtbhienecdo.n Ostvruerc-- tailol,n 1o7f%d aomf tshaen gdlorbesael revloecirs to cowhile small-scale hydropotwriecritcya w nvaesrt potential and kinetic energy intn als poriondculucdeed ebnye rhgyyderxotproawctieorn infr o2m020 o. eTlehcet rpical energy,water diostterinbtuiatli oonf shyysdterompsoawnedr, iwrriitgha ltoiown csayrsbtoenm esl,euctsriincgitye xainsdti nag vienryfr ahsigtrhu cgteunreersatainodn cthapuascaitdyh, ewriinllg beto vtehrye cimircpuolratraencto inno tmhey jcoounrcneepyt .toHwyadrrdosp othwee erncearngayl storabnesietxiopnlo. iHteyddwroitphowwaetre crahna mbem perroadnudcePdS Hat tbeocthhn loalroggey a, ncodm smbianleld scwalieths. oTthhee rlarregnee wora mbleeds isuumch scaaslseos ltayrpPicValalny dinwcliunddee tnheer gcoyn[s5t]r.uction of daTmhse ainndc rreeasseirnvgoidrse tmo acnodnvfeorrt penoteerngtyiaal nadndt hkeintertaicn esinteiorgnyt oinwtoa redlesctcrliecaanl eenneerrggyy, whhavilee isnmtraoldl-usccaelde nheywdrcohpaollwenegre csa, nsu aclhsoa sininccluredaes eedneerngeyrg eyxetrffiacctiieonnc yfraonmd swtoartaegr ed. iEstnreibrguytisotno rsaygse- itsema sc raitnidca ilrrairgeaatioofnr esysesatermchs,t huastinisg nexeeisdtiendgf ionrfrtahsetriunctetugrreasti aonndo tfhruesn eawdhaebrlienegn teor tghiees c.irTchue- plairv oetcaolnroomleyo fceonnecregpyt.s Htoyradgreopinowadedr rceasnsi naglstoh ebep reoxbplleomiteodf twheithin tweramteirt theanmcymoefrr eannedw PaSbHle tteecchhnnoollooggyie, scsoumcbhinaseds owlairtha ontdhewri rnednewwilalbbleeso sfugcrhe aatsi smoplaorr PtaVn caendin waicnhdie evninerggtyh [e5g].o al of mitigTathien gingclroebaaslinwga rdmeminagntdo 1fo.5r deengerregeys .aTnhde rtehea rteramnasnityioenn etorgwyasrtdosr acgleeatne cehnneorlgoyg iheasv, seu icnh- atrsomdueccehda nniecwal ,cehlaelclternog-cehse, msuiccha la, sc hinecmreicaasle,dth eenremrgayl aenffidceielencctyr iacnald( sFtiogruargee1. aE)n[e6r]g. yB asttoterraigees aisn ad cPrSitHicaalr aertehae omf roesstecaormchm thoant tiesc nheneodloedgi efosrt hthaet ainreteugsraetdiofno roef nreerngeywsatbolrea geneeirngoiefsf-. gTrhide spyivstoetmals raonled omf eicnreorggryid sst.oCraognes iidne ardindgrethsseincug rtrheen tpsrtoobrlaegme toefc thhneo ilnotgeirems,itttheensctyo roafg reencaepwaacbitlye itnecwhnatoelroagnieds hsyudchro apso wsoelrarr easnedrv woiirnsdis wbyillf abret ohfe glarregaet sitm(Fpiogrutraen1cbe )i[n7 ]a.cIhniecvoimngp atrhieso gnowali tohf bmaitttiegraytisntgo rgalgoeb,aPl SwHarcmaninagl stoo g1e.5n edreagterepeos.s iTtihveeree navrier omnamneyn etanle–rsgoyc isatlo–rtaegche ntieccahlnboelnoegfiietss,, csruecaht iansg mcoecnhdaintiiocnasl, teoleacdtrdor-ecshsemcliimcaal,t echcehmanicgael,, twheartemraslc aanrcdi teyl,eflctoroicdalc (oFnigtruorle, fi1are) fi[6g]h. tBinatg- steurpiepso artn,dth PeSaHv aairlea bthileit ymoofswt caotmermfoornd treicnhkninoglo, girireisg athtiaotn aaren dusiend ufosrt reianleprgroyc setsosreasg, eb uint coaffn- aglrsiod gseynsetermates nanegda mtiviceriomgpriadcst.s ,Csouncshidaesrninagtu trhael lcaunrdretnrat nsstfoorramgea ttieocnh[n8o].loMgiiense, Sthtoer astgoeraisgae coampapcaitnyy inth wataitseirn avnodlv heyddirnoipmopwlemr reensteirnvgoPirSsH isp bryo jfeacrt sthine olaldrgmesitn (eFdigaurerea s1,bw) h[7ic]h. Ihna csotmhe- poatreisnotnia lwtoitho vbearttcoermye sthoreamgee,n PtiSoHne cdanp raolbsole gmenoefrnaatetu praolsiltainvde etrnavnisrfoonrmeantitoanl–.social–tech- nicalT bheenecfiotms,b cirneattiionng ocof nstdoirtiaogness tyos atedmdrsecsas nclaimlsaoteb echuasnegde, ,s wucahtear sschayrbcirtiyd, flpouomdp ceodntarnodl, bfiarettfiegryhtsiynsgt esmupsp. PorStH, thaen davbaaitltaebriileitsyc aonf wsuapteprl efomre dnrtineakcinhgo, tihrerirg, atniodnt haendsy isntedmusmtrianl apgreos- ecnesesregsy, vbaurti actainon aslsino agepnroermatisei ngeghaytbivreid imtecphancotsl,o sguycahn adsc nanatbueraul sleadndin toraffn-gsfroidrmreantieowna [b8l]e. eMnienreg yStsoyrsatgeem is [a9 ]c.oFmopraenxya mthpalte ,ist hinevsotulvdeyd pinre ismenptleedmienn[t1in0g] uPsSeHd tphreojgeecntse tiinc oaldg omriitnhemd taoreoapst, iwmhiziceha hraesn ethwea pboleteennteiarlg tyos oyvsetermcomusei nthges molaenr tPioVn, ePdS Hpraonbdlemba totfe rniaetsutroali nlacnreda streatnhse- rfeolrimabaitliotyn.o f the system. (a) Figure 1. Cont. Energies 2023, 16, x FOR PEER REVIEW 3 of 30 E nergies 2023, 16, 6309 3 of 28 (b) FFiigguurree1 1. .G Geenneeraral lc lcalsassisfiicfiactaiotinono fodfi fdfeiffreenret nent eerngeyrgstyo rsatogreatgeech tneochlongoileosg(iae)sa (nad) caanpda bcailpitayboilfietyle octfr ieclietcy- sttroicraitgye sgtolorabgaell yglboybablalytt ebryie bsaattnedrihesy danrodp hoywderroipno2w0e2r0 i(nb )20[72]0. (b) [7]. DTehcee ncotrmalbizineadtipono wofe rstsoyrsatgeem ssysthteamt su sceano nallsyo rbeen euwseadb,l esuecnhe ragsy hsyybsrtiedm psuamspeende ragnyd sboauttrecerys esnycsoteumntse. rPtShHe parnodb lbeamtteorfiienst ecramn istutepnpt leenmeregnyt eparcohd uoctthioern, farnodm tshoel asrysatnedmw minadnaagneds tehnisercgany bvearoivateirocnosm ine uas pinrogmstiosriangge hsyybsrteidm tse.cWhnitohliongtyh iasnsde tctainng b, eth uissereds iena rocffh-tgrrieids troenaedwdraebslse tehneeprgroyb slyesmteomfsa n[9e].f fiFcoire nextaemneprlgey, tshteo rsatguedyte pchrensoelnotgeyd fionr [m10in] iu-gserdid tshien greenmeotitce alolgcoartiiothnms atnod oipstliamndizse. Ta hriesnieswaavbelrey erneelergvya nstytsotepmic, uessipnegc isaolllyari nPVth, ePESuHr oapneda nbaUttneiroiens. tToh ienEcrueraospee tahne Creolmiambiilsitsyio onf itshien sdyesetdemsu. pporting a just and sustainable transition, which means ensuring that reDgeiocennstararelinzeodt lepfot wbeehri nsdysitnemthse cthleaatn uesnee rognylytr arnensfeowrmabalteio enn. eMrgoyre osyvsetre,mthse Easu reonpeeragny Csoumrmceiss seinocnouisncteorm tmhei tpterdobtloemen osuf riinntgertmhaittternutr aelnaerregays pbreondeuficttiforno mfrothme snoelwar eacnodn owminicd oapnpdo trhtuisn citaiens boef orevneerwcoamblee uensienrg isetso.rRaegnee swysatbelmessa. rWe witheilnl s tuhiitse dsefttoirndge, ctehnistr raeliszeeadrcahn dtrileosc atol gaednderreastsio tnh,ei npcrroebalseimng otfh aenn ueffimcbiernot fesnmeraglyl- ssctaolreagene etregcyhnporloojegcyt sfotor pmrionmi-gorteidssu isnta rienmaboltee elnoecargtiyonpsr oadnudc itsiolann.ds. This is a very relevant topic, especially in the European Union. The European Commission is indeed supporting a just and sustainable transition, which 1m.1e.aPnusm epnesduSritnogra tgheaHt ryedgroiopnows earre(P nSoHt )leffotr bMehediniudm in- atnhde Lclaeragne- eSncaelregEyn terragnysfSotromraagteion. More- aonvdePr,r tohdeu cEtuiornopean Commission is committed to ensuring that rural areas benefit from the new PeScoHncoamnicb eopcponorstiudneriteidesa osf arelnaregweabbalett eenryerbgaiensk. Rtheanteswtoarbelsesw aarete wr aeltl hsiugihteedr feoler vdaetcieonn- ftrroamlizaedlo awnedr lroecsaelr vgoenireorartwioant,e irnbcoredays.inWgh tehnee nvuemr tbheerr eofi ssma dalelm-scaanlde efonreregleyc tprricoijteyc,tws taot eprrios- dmisocthea srugestdaifnroamblet heeneurpgpye pr rroesdeurcvtoioirnt.o the lower reservoir. The water discharged from the upper reservoir runs a hydro-turbine that generates electricity. Whenever there is a surplus o1f.1e.l Pecutmripceitdy Sptorroadgue cHeyddrfroopmowerre n(PeSwHa)b floers Msuecdhiuams- wanidn dLaorgres-oSlcaarl,e oErnewrghye nStothraegee laencdtr icity dPermodauncdtioisn low, the water is pumped back from the lower reservoir to the upper one. This methoPdSHof ceanne rbgey csotonrsaigdeerbeedc oams eas lcarrigtiec ablaittneorfyf- gbraindke ntheargt ystsoyrsetse mwsatwerh eant hthigehreeris ealedveaatriothn offropmow a elor wpreord ruescetirovnoifrr oomr wraetneerw baobdlye.s Winhaenloewve-rs uthnesrhei nise ao dreamloawnd-s fpoere edlewctirnicditcyo, nwdaitteiorn is, wdhisicchhacragnendo ftrsatisfby varying theoflmo wth yet hueto tp dpeemr and for electricity. The electricity produced can be controlledhe turrbeisneervaocicro trod itnhge tloowtheer erleescetrrivcoitiyr. dTehmea wndat.er discharged from the uTphpeewr orrelsde’rsvloarirg ersutnbsa tat ehryydteroch-tnuorlboignye itshpaut mgepneedrsattoersa egleechtyrdicriotyp.o Wwehre, nwehviecrh tahcecroeu inst sa fsourrmplourse othf aenle9c4t%ricoitfyt hperiondsutacleledd fgrolomb arleenneewrgayblsetso rsaugcehc aasp awciintyd[ 1o1r, 1so2]la. rT, hoerr ewahreentw thoew ealeycs- itnriwcihtyic dhePmSaHndca ins bloewef, ftehceti vwealyteur siesd pfuomr spteodra bgaec. kIn froopmen t-hloeo lpowPSeHr r,ewseartveroifrr otmo tahen autpupraelr boondey. Tishius smedetthoofidl ol fo enneeorgf yth setotrwagoer beseecrovmoeirss carintidcaaln inu opffp-egrrirdes eenrevrogiyr issyustseemdst wo hsteonr ethtehree pisu ma dpeeadrtwh aotfe rp.oIwn ecrlo psreodd-luocotpionP SfHro,mth reensyeswteambleiss inno at cloownn-seuctnesdhitnoea onra at ulorawl-wspaeteedr bwoidnyd bcuotnfdoirtmiosn,a wcohnicnhe cctaionnnboet tswateiesnfyt wthoe rdeseemrvaonidrs f(ourp epleerctarnicditylo. wTehre) oelnelcyt.rTichiteye pnrvoirdouncmeden ctaanl ibmep caocnttorof lolepde nb-ylo voapryPiSnHg tohne aflqouwa ttioc tahned tuterrbriensetr aiaclcohradbiintagt stois thgere ealteecrtrtihcaitny tdheamt oafncdl.o s ed- loop PTShHe ,wboercladu’ss elatrhgeensta tbuarttaelrryiv teerchisnaollsoogayf fiesc pteudm[p11e]d. Cstloorsaegde- lhoyodprPoSpHowiseru, swedhiicnh thace- ccuorurenntst froers emarocrhe ftohranH 9O4M%E oRf tshime iunlsattailolneds. global energy storage capacity [11,12]. There are two Iwna2y0s2 i2n, twhheitcoht aPlSinHs tcaalnle bdet ueffrbeicnteivpeolyw uesrecda pfoarc isttyorfraogme. PInS HopiennP-loorotpu gPaSlHw,a wsa3t.e7r1 fGroWm, aan ndaFtuigrualr ebo2dsyh oisw usstehde two efielkl loynaev oerf atghee etlwecot rriecsiteyrvporiords uacntdio anni nupPpoertru rgeasleruvsoinirg ids iuffseerde ntto rsetsoorue rtchees apnudmtpheedc ownatrtiebru. tIino ncloofsPedS-Hlotoopt hPeSHto,t atlheel escytsrticeimty igse nnoert actoionnne[1c3te].d to a natural Energies 2023, 16, x FOR PEER REVIEW 4 of 30 water body but forms a connection between two reservoirs (upper and lower) only. The environmental impact of open-loop PSH on aquatic and terrestrial habitats is greater than that of closed-loop PSH, because the natural river is also affected [11]. Closed-loop PSH is used in the current research for HOMER simulations. In 2022, the total installed turbine power capacity from PSH in Portugal was 3.71 GW, Energies 2023, 16, 6309 and Figure 2 shows the weekly average electricity production in Portugal using differe4not f 28 resources and the contribution of PSH to the total electricity generation [13]. Figure 2. Average total net electricity generation during one week in Portugal in 2022. Figure 2. Average total net electricity generation during one week in Portugal in 2022. 1.12..2B. Batattteerrieiessa ass SSttoorraaggee iinn MMiiccrrooggrriiddss oorr OOfff-fG-Grirdid BBaatttteerireiessa arreet thhee cchheemmiiccaall eenneerrggyy ssttoorraaggee tetechchnnoloolgoigeise tshtahta htahvaev beebene ernevroelvuotliountiiozninizgi ng thtehew woorlrdld, , wwitithh ssttoorraaggee ccaappaabbiilliittiieess iinn ggrrididss, ,mmoobbiliiltiyt ysyssytsetmems, se,leecletrcotnroicn idcevdiecveisc easnda nd mmananyym moorree.. TThhee ccrritiitciacal lfafcatcotrosr tso tcooncosindseird ienr ai nbaattebrayt taerrey itasr eeffiitcsieenfcfiyc,i eitns clyif,ei ctsyclliefe, tchyec le, thteemtepmerpaeturaretu orfe oopfeorapteiorna,t itohne, dthepethd eopf tdhisocfhdarigsceh (aDrOgeD()D, tOheD d),enthsietyd oenf seinteyrgoyf eannedr gseylfa- nd sedlifs-cdhisacrhgea r[g1e4][.1 T4h].erTeh aerree dairffeedreinffte rcelanstsicfilacastsiiofincsa toiof nbsattoefrby attetcehrynotleocghienso lboagsieeds obna stehde on thcehecmheismtriys toryf tohfe tbhaettbearytt,e sruyc,hs uacsh leaasdl–eaacdid–,a Lcii-dio, nL,i -nioicnk,enl–iccakdeml–icuamdm, sioudmiu,mso–sduiulfmur–, svual-fur, vannaadium redox batteries and more. The study in [15] analyzed the different types of batter-ies fdoiru emnerrgedy ostxorbaagtete arnieds caonndclumdoerde t.hTath seodstiuumdy–siunlf[u1r5 b] aattnearliyesz eadre tmheosdt iuffseerde nint ltayrpgee-s of bascttaelrei estsofroargee nsyersgteymsst,o arnagde thaen dprcoodnuccltuiodne dcotshtsa tosf oLdi-iiuomn –ansudl fsuordibuamtt–esruielsfuarr beamtteorsitesu asered in lavregrey-s hciagleh sinto croagmepsayrsistoemn sw, iathn dotthheer pbraottdeurcyt itoecnhcnoosltosgoiefsL. iH-ioown eavnedr,s Loid-iiounm b–asttuelfruiersb haattveer ies arheigvhe reyffihciiegnhcyin, hciogmh penaerirsgoyn dwenisthityo athnedr ablsaott ae rlyontgecehr nliofel ocgyiceles,. wHhoicwhe mveark,eLsi -thioenmb aa tptoe-ries hatevnetihail gehneerfgfiyc isetnorcayg, eh isgyhsteenme rfogry gdreidn saiptyplaicnadtioanls (obaotlho nogffe-grrliidfe acnydc loen,-wgrhidic)h [1m6]a. kLeesadth–em aapcoidte bnatttiaelrieense arrgey aslstoor pagoteensytisatle cmomfopretgitroidrs afoprp elinceartgioyn st(obroatghe oinff -ogffri-dgraidn dsyostne-mgrsi dan) d[1 6]. Lmeaidcr–oagcriiddsb adtutee rtioe sthaerier alolwso cpoostt.e Wnthiaelnc loemadp–eatciitdo rbsafttoerrieense arrgey costmorpaagreedi nwoitfhf- Lgir-iidons ybsatte-ms antedrimesi,c Lroi-giornid bsadttuereietos sthhoewir lao lwoncgoesrt .liWfe hcyenclele, agdre–aatceird ebffiactiteenriceys aanrde cboemttepr acrheadrgwinitgh aLnid-i on badtitsecrhiaersg, iLnig- icoynclbeas;t taeltrhieosusghho twhe auplofrnognetr cloisfte ocfy lcelaed,–garceiadt beartteeffiriceise sneceymasn tod bbee ltotewrecrh tharagni ng anthdadt oisfc Lhia-riognin bgacttyecrileess,; oavltehro tuhge hliftehteimuep forfo tnhtec boasttt eorfieles,a Ldi–-aiocnid bbattatetreireise sarsee tehme smtoosbt eecloow- er thnaonmtihcaatl oopf tLioi-nio [n17b]a. tteries, over the lifetime of the batteries, Li-ion batteries are the most economical option [17]. 1.3. Microgrids or Decentralized Power Production 1.3. MTichreorger iadrseo srevDeercaeln ctoranlsitzreadinPtosw ine rexPtreondduicntgio ennergy access to remote places and islands, as tThhise rceaanr ebsee vae rparlocbolenmst raeicnotnsoimn iecxatlelyn dainndg ecnaenr gaylsaoc cceasusseto ernevmirootnempleancteasl ainmdpiascltasn. ds, as this can be a problem economically and can also cause environmental impacts. Decen- tralized power production with distributed energy resources is a promising system where power generation and consumption occur at the same time. This system can achieve zero emissions by using renewable sources only. Wind and solar are major resources for the production of electricity; however, the problem of the intermittency of power production in solar PV and wind energy is a major obstacle in the use of only renewables as a source to satisfy the demand. Therefore, the usage of energy storage technologies such as batteries and PSH becomes inevitable to satisfy the continuous demand for electricity. Batteries and PSH have both advantages and disadvantages when used as storage technologies. Reference [18] gives an overview of the different software that can be used for the design of hybrid energy systems. A hybrid renewable energy system (HRES) is one where there Energies 2023, 16, 6309 5 of 28 is a combination of more than one energy source [19]. HOMER is a tool that is used to design power systems and was developed by the National Renewable Energy Laboratory of the US. The software can be used to design microgrid systems using various energy generation technologies with resources that can be taken from a specific geographic location. The software performs simulation, optimization and sensitivity analysis. The simulation is conducted to consider the technical aspects and life cycle costs, and the optimization process of HOMER selects the scenario that satisfies the technical aspects with the lowest cost over the period of the project. The sensitivity analysis considers the uncertainties and performs a range of simulations and optimization considering the variability in inputs [20]. Table 1 shows the different research that has been undertaken using the HOMER model. Table 1. Different research on microgrids using HOMER software at different locations. Research Optimal Energy System Location Economics Demiroren and Yilmaz (2010) [21] Wind Energy System(PV/Battery) Gokceada, Turkey NPC: 32,537,056 $ LCOE: 0.174 $/kWh Yimen et al. (2018) [22] PV/Biogas/PSH Djoundé, Cameroon NPC: 370,426 €LCOE: 0.256 €/kWh Dalton et al. (2009) [23] Wind Energy System with Coastal Area ofBattery Queensland, Australia NPC: 19.1 M$ Scenario 1: Generator/Wind NPC: 1,834,996 € Energy/ LCOE: 0.1658 €/kWh Ioakimidis et al. (2016) [24] Battery NPC: 2,249,666 €Scenario 2: Wind/Solar PV/ An Island in Greece LCOE: 0.2047 €/kWh Generator/Battery NPC: 6.5 million € Scenario 3: Wind/PV/Battery LCOE: 0.61 €/kWh Standalone: Solar PV/Wind Energy/Battery/Diesel NPC: 16,806,238 $ He et al. (2018) [25] Generator LCOE: 0.133 $/kWhGrid-Connected: Solar Beijing, China NPC: 9,034,966 $ PV/Wind LCOE: 0.055 $/kWh Energy/Battery/Grid Solar PV/Wind Sen and Bhattacharyya (2014) [26] Energy/Battery/Bio-Diesel Chhattisgarh, India NPC: 673,147 $ Generator/Hydropower LCOE: 0.420 $/kWh 1.4. Data Preparation and Cleaning of the Obtained Load Data The load data are obtained for each hour from the REN website [27] of Portugal and are scaled down to obtain a suitable load based on the EDM information for the analyzed microgrid. The load data obtained from the website are further used in the simulations of the HOMER software. The quality of the data is enhanced by identifying and clearing the errors and data that are not consistent [28]. There are different types of data, but quantitative data are the data that can be used to measure the topic of interest with numbers such as integers or decimals, and consistent data will result in a reliable analysis of the data [29]. To avoid any unwanted results regarding the usage of the data, the data must be cleaned before using them. With the use of the Python language for data analysis, the load data that are obtained from the REN website are prepared. The zero and missing values of power are replaced by the average values of power consumption. The duplicate values in the collected data are removed in the data preparation process. Outliers are data that significantly deviate from normal values or patterns in the obtained data. 2. Methodology This research uses an integrated model based on an energy simulator of different alternatives based on renewable energy sources (pre-defined) and an optimizer model for microgrid energy sources with economic analysis integration. The model uses a general grid search algorithm for all possible solutions and then uses its proprietary derivative, Energies 2023, 16, 6309 6 of 28 a free algorithm, to find the lowest-cost microgrid system (Figure 3). The combination of different parameters from the source availability, different scenarios’ simulation and technical and economic optimization, as an integrated model, represents the novelty of this research. This study uses HOMER as the optimization model to find the most economical storage system and to further analyze the obtained results through a new model developed in Python. The integration of HOMER and Python allows us to analyze the results in different seasons and to assess the role of energy storage in satisfying the energy demand. Energies 2023, 16, x FOR PEER REVIETWh e assumptions made in this study are (1) scaling down the Portugal demand data f7o roft h3e0 load and using the same demand pattern for the project; (2) considering the higher end costs of solar PV; (3) considering the selected site viable for a PSH and microgrid project. Figure 3.. Developed methodology of the integrated modell.. 2.1. LTohade design of a distributed energy resource (DER) system requires a careful analysis of the available resources in the desired location before installing the renewable energy systemThse. Mloiacdro fgorri dths/em cuinrir-egnrtid ssywsteilml b ethcoatm ies cursiteidca floirn tphreo voipdtiinmgizeanteiorgny otfo trheem iontteegprlaacteeds amnoddiesll aisn tdaskwenh ferroemg rtihde ehxotuenrlsyi ocnonissunmotpfteioansi bdlaet.a Coof nMsatrduecitrian, gPoarftruagmale wfroomrk tthhea tthuen RifiEeNs awlletbhseitaev [a2i7l]a b(Fleigruenree w4aa)b alenden iet rsghyoswyss ttehme smwoinththoluyt asvaecrraifigcei nvgartihaetiroenli athbrioliutyghofouthte thsye syteemar i2s0q2u2;i tiet aclhsoal gleivnegsi ningf.oErmneartgioyns atobroaugte thsye sctoemmps lpemlayenatacrriutyc ibael trwoleeenin edneecrgenyt sroaulirzceeds einn eeragcyh smolounttihon. sT.hTeh eelesicmtruicl altoiaodnso afrdee cthene tpraolwizeerd ceonnesrugmy seyds teevmersy/ rheonuewr ainb lkeWen. eHrgoyusrylys tdematsa caarne bime pacehraietivveed inw riuthnnminang yscseonftawriaorse toto ooblsta, isnu cahccausraHteO rMesEuRlt,sP. TVhsey sdtaotar oatrhe etrask.eTnh ceoHncOerMniEnRg sthofet wcoamrepiasriussoend oifn btahtteecruiersr eanntdr ePsSeHar cahs etoneorbgtya isntoorpatgiem saollruetsiounltss .aTndh ewHe OanMaElyRzes othftew caorset uanseds ftehaesibloilaidty daastpaecgtisv. eFnigaunrde 4c oshlloewctss tthhee grreaspohu rocfe thdea tlaoafdro. mThteh peelaokc alotiaodn isin 15w91h.i0c8h ktWhe; mthiec raovgerriadgeis etnoerbgeyi cnosntaslulemde.dT ihs e25H,6O70M.3E5R kWreshu/dltasys. hTohwe ltohaede fcaocntoorm oifc 0f.e3a3s iisb ciloitnysiodfetrhede for both simulations. The load is low during the months of summer as there is no heating required. Energies 2023, 16, 6309 7 of 28 project. The results from the HOMER calculations show the NPC, LCOE, operation and maintenance costs (O&M) and autonomy of storage. There are two scenarios considered in this research that are simulated in HOMER and then treated in a Python model. The scenarios compare the costs of using batteries and pumped storage hydropower for the given load and renewable energy resources. The following sections explain the details of the components and the simulation characteristics. 2.1. Load The load for the current system that is used for the optimization of the integrated model is taken from the hourly consumption data of Madeira, Portugal from the the REN website [27] (Figure 4a) and it shows the monthly average variation throughout the year 2022; it also gives information about the complementarity between energy sources in each month. The electric loads are the power consumed every hour in kW. Hourly data are imperative in running scenarios to obtain accurate results. The data are taken concerning the comparison of batteries and PSH as energy storage solutions and we analyze the cost and feasibility aspects. Figure 4 shows the graph of the load. The peak load is 1591.08 kW; the average energy consumed is 25,670.35 kWh/day. The load factor of 0.33 is considered for both simulations. The load is low during the months of summer as there is no heating required. 2.2. Resources The location used for the HOMER simulations is Madeira, Portugal. The HOMER software downloads the data of the solar irradiance, wind speed and temperature from different sources, such as NREL or NASA satellites. Portugal is in the southern part of Europe and obtains much more solar power in comparison with most other countries in Europe. 2.2.1. Solar Energy Data The solar irradiation data are taken from the NASA Prediction of Worldwide Energy Resource (POWER), in the specified location, where the annual average daily radiation is 5.12 kWhm2 /day. Figure 5 shows the distribution of the solar data in different months. The peak daily radiation is seen in the month of July, with 7.22 kWhm2 /day. The lowest daily radiation is in the month of December, with 2.580 kWhm2 /day. The clearness index shows the clearness in the sky. The portion of incoming radiation that is incident on the Earth is the clearness index. The higher the clearness index value, the greater the available energy to convert it into electricity [30]. The highest clearness index is seen in the month of August, with a value of 0.663, indicating that there are many clear sky days in this month, whereas the lowest clearness index is in the month of December, with a clearness index of 0.510, indicating that there are many cloudy days in this month. 2.2.2. Wind Energy Data The model collects the wind speed data from the NASA POWER database. Figure 6 shows the distribution of wind speeds in different months of the year. The anemometer measures the wind speed at a height of 10 m. The wind data are of 50 m altitude above sea level and measured over a period of 30 years. The average annual wind speed is 6.42 m/s. The highest wind speed is seen in the month of December, with an average speed of 7.10 m/s. The lowest wind speed is in September, with a wind speed of 5.369 m/s. The optimized model considers for the wind speed data a surface roughness length of 0.01 m. The wind speed at a specific height can be calculated using the Prandtl law, as shown in Equation (1). ( ) z1 u(z1) ln ( z0= ) (1) u(z2) ln z2z0 where Energies 2023, 16, 6309 8 of 28 u = average wind speed; z0 = surface roughness; Energies 2023, 16, x FOR PEER REVIEW z1 = height 1; 8 of 30 z2 = height 2. (a) (b) FFiigguurree 44.. HHoouurrllyy llooaadd ddiissttrriibbuuttiioonni nink kWWt hthrorouugghhouout tth tehey eyaera2r0 22022(2a )(,aa),n adnmd omnothnltyhalyv earvaegreagvea rviaatriioan- ttihorno uthgrhoouugthtohuety tehaer y2e0a2r2 2(0b2)2. (b). 2..23.. RBeasoicurTceersm inology 2.3.1.TNhe tloPcraetsieonnt Cusoesdt (fNorP Cth)e HOMER simulations is Madeira, Portugal. The HOMER softwTahre NdoPwCnilsotahdes dthifefe dreantac eobf etthwe eseonlatrh eircraudrriaentcev,a wluiendo fsaplel ethde acnods ttse,minpceluradtiunrge Ofr&omM dcoiffstesr,ernetp lsaocuermcesn, tscuocshts afso rNthReELlif eotri mNeAoSfAth seapterlolijetecst.a Pnodrttuhegaplr eiss eint tvhael useouotfhtehrenr epvaernt uoef Ethuartotphee apnrdoj eocbtteaainrns smouvcehr imtsoernet isroelalirf eptiomwee.rT ihne cpormojpeactrishoonu wlditbhe mdeositg ontehderin cosucnhtraiews ainy Easurtopred. u ce the NPC [31]. HOMER uses the discount rate to calculate the NPC. The NPC can be calculated using the following formula [32]: 2.2.1. Solar Energy Data The solar irradiation data are takeNn PfrCom= t ThAe NCASA Prediction of Worldwide Energ(2y) Resource/ (dPaOyWER), in the specified location, wChReFre the annual average daily radiation is N5.12 . Figure 5 shows the diCstributioi(n1 o+f it)RF = he solar data inN peak daily radiation is seen in the month o(f1 J+ulyi), with/ d7.a2y2 / ddaiffyerent months. T(h3e) . The lowest daily rwahdeiaretion is in the month of December, with 2.580 . The clearness index shows TAC = total annualized cost; the clCeaRrFne=scs aipni tthaler sekcyo.v Terhye fpaoctrotiro;n of incoming radiation that is incident on the Earth is the clearness index. The higher the clearness index value, the greater the available energy to convert it into electricity [30]. The highest clearness index is seen in the month of Au- gust, with a value of 0.663, indicating that there are many clear sky days in this month, whereas the lowest clearness index is in the month of December, with a clearness index of 0.510, indicating that there are many cloudy days in this month. Energies 2023, 16, x FOR PEER REVIEW 9 of 30 Energies 2023, 16, 6309 9 of 28 Energies 2023, 16, x FOR PEER REVIEW N = number of years; 9 of 30 Figurie= 5.a Snonlaura rlaidnitaetiroens tdraattae o(f% th).e location with radiation in kWh/m /day for different months of an average year and with the clearness index for different months. 2.2.2. Wind Energy Data The model collects the wind speed data from the NASA POWER database. Figure 6 shows the distribution of wind speeds in different months of the year. The anemometer measures the wind speed at a height of 10 m. The wind data are of 50 m altitude above sea level and measured over a period of 30 years. The average annual wind speed is 6.42 m/s. The highest wind speed is seen in the month of December, with an average speed of 7.10 m/s. The lowest wind speed is in September, with a wind speed of 5.369 m/s. The optimized model considers for the wind speed data a surface roughness length of 0.01 m. The wind speed at a specific height can be calculated using the Prandtl law, as shown in Equation (1). (( )) = ( ) (1) where u = average wind speed; FFiigguurrzee0 55 ..= SS sooullaarrrf arracaded iiraaottiiuoognnh ddnaaettaas soo;ff tthhee llooccaattiioonn wwiitthhr raaddiiaattioionni nink kWWhh//mm2//ddaayy ffoorr ddiiffffeerreenntt mmoonntthhss ooff aann aavveezrr1aa gg=ee h yyeeeiaagrrh aatnn 1dd; wwiitthh tthhee cclleeaarrnneessss iinnddeexx ffoorr ddiiffffeerreenntt mmoonntthhss.. z2 = height 2. 2.2.2. Wind Energy Data The model collects the wind speed data from the NASA POWER database. Figure 6 shows the distribution of wind speeds in different months of the year. The anemometer measures the wind speed at a height of 10 m. The wind data are of 50 m altitude above sea level and measured over a period of 30 years. The average annual wind speed is 6.42 m/s. The highest wind speed is seen in the month of December, with an average speed of 7.10 m/s. The lowest wind speed is in September, with a wind speed of 5.369 m/s. The optimized model considers for the wind speed data a surface roughness length of 0.01 m. The wind speed at a specific height can be calculated using the Prandtl law, as shown in Equation (1). (( )) = ( ) (1) where u = average wind speed; FFiigguurrzee0 66 .=. Asuverrfaagcee rwoiungd hsnpeesesd; for all the months considering surface roughness length of 0.01 m Average wind speed for all the months considering surface roughness length of 0.01 m and and wzi1n d= shpeeiegdhst m1;e asured at a height of 10 m. wind speeds measured at a height of 10 m. z2 = height 2. 2.3.2. Levelized Cost of Energy (LCOE) The LCOE is the ratio of the average cost of electrical energy produced and the electrical energy produced by the system. This can be calculated using the following equation [33]: C LCOE ann,tot= (4) Eserved where Cann,tot = total annualized cost, in EUR; Eserved = total electric load served. 2.3.3. Operation and Maintenance (O&M) Cost The O&M costs are the costs associated with the wear and tear of the equipment, such as wind turbines, solar PV and hydropower equipment. These can be variable or fixed costs Figure 6. Average wind speed for all the months considering surface roughness length of 0.01 m and wind speeds measured at a height of 10 m. Energies 2023, 16, 6309 10 of 28 associated with the project. These usually cover the maintenance of dams in hydropower, hydro-turbines and other equipment in wind turbines. HOMER receives the O&M costs for different systems as input and calculates the total O&M cost of the system over the complete cycle of the project [34]. 2.3.4. Autonomy of the Storage Autonomy can be defined as the physical quantity of time that the load can serve in the absence of an energy source. It is measured in hours or days. Higher autonomy of storage will increase the reliability of the energy system by serving the load. Autonomy of storage is an important factor that serves as a backup power source for the energy system that is installed and covers the variations in power production to maintain the system’s operation. 2.4. Simulations There are two simulation scenarios developed in this research. The aim is to determine the economic feasibility of this case study of a microgrid. A comparison between batteries and PSH and an economic assessment are also carried out. The HOMER model optimizes different scenarios to suggest the lowest NPC scenario. This research concentrates on two scenarios, where, in Scenario 1, solar PV, wind turbines and batteries are used to meet the electrical load demand, as shown in Figure 7. In Scenario 2, solar PV, wind turbines and pumped storage hydropower are used to satisfy the electrical load, as shown in Figure 8. The electrical load is the same for the both scenarios and the same company’s PV and wind turbines are used, without altering the costs, in both scenarios in order to obtain a suitable comparison. 2.4.1. Solar PV Solar PV is an economical source of power when designing a renewable energy-based Energies 2023, 16, x FOR PEER REVIEmWic rogrid system. Table 2 shows the specifications of the solar PV used for the simul1a1t iofn s3.0 The costs of the solar PV are considered as 3000 €/kW with a replacement cost of 3000 €/kW and an O&M cost of 10 €/kW. The Peimar SG340P is a multi-crystalline solar PV system asnhdowthne einffi Fciiegnucrye o8f. mThoen oecleryctsrtiaclaliln leosaodl airs PthVe issagmreea tfeorr tthhaen bthoatht osfcemnualrtiiocsr yasntadl litnhee ssoalmare PcVo;mhpoawneyv’esr P, tVh eancods wt oinf md tounrobcirnyesst aalrlein uesPeVd, iws hitihgohuetr aalstewrienllg. Tthhee cpooswts,e irnf rboomtht hsceesnoalrairoPs Vin coarndbeer tcoa locbutlaaitned a ussuiintagbEleq ucoamtiopnar(i5s)o. n. FFigiguurere7 7. .S Scceennaarrioio1 1w witihthw wininddt uturrbbininee, ,s soolalarrP PVVa annddb baatttteerryya asss stotoraraggee. . Figure 8. Scenario 2 with wind turbine, solar PV and PSH as storage. 2.4.1. Solar PV Solar PV is an economical source of power when designing a renewable energy-based microgrid system. Table 2 shows the specifications of the solar PV used for the simula- tions. The costs of the solar PV are considered as 3000 €/kW with a replacement cost of 3000 €/kW and an O&M cost of 10 €/kW. The Peimar SG340P is a multi-crystalline solar PV system and the efficiency of monocrystalline solar PV is greater than that of multicrys- talline solar PV; however, the cost of monocrystalline PV is higher as well. The power from the solar PV can be calcula𝑃ted= u s𝑌ing𝑓 Equation (, [ 51).+ 𝛼 𝑇 − 𝑇 , ] (5) wher𝑓𝑌e 𝐺 = power output during standard mtest conditions in kW; = derating factor of solar PV; = incident solar irradiance in kW/ ; Energies 2023, 16, x FOR PEER REVIEW 11 of 30 shown in Figure 8. The electrical load is the same for the both scenarios and the same company’s PV and wind turbines are used, without altering the costs, in both scenarios in order to obtain a suitable comparison. Energies 2023, 16, 6309 11 of 28 Figure 7. Scenario 1 with wind turbine, solar PV and battery as storage. FFigiguurere8 8. .S Scceennaarrioio2 2w witihthw wininddt uturrbbininee, ,s (soolalarrP PVVa an)nddP PSSHHa asss tsotoraraggee. . 2.4.1. Solar PV GPPV = Y T PV fPV [1 + αP(Tc − Tc,STC)] (5) Solar PV is an economical source GofT p,SoTwC er when designing a renewable energy-based wmhiecreogrid system. Table 2 shows the specifications of the solar PV used for the simula- tionsY.P TVh=e pcoswtse roof uthtpeu stodlaurr iPnVg satraen cdoanrdsidt esrtecdo ansd i3t0io0n0 s€i/nkWkW w; ith a replacement cost of 3000f P€V/k=Wd aenradt ianng Ofa&ctMor coofssto olaf r1P0V €;/kW. The Peimar SG340P is a multi-crystalline solar PV sGyTste=min acni de tnhtes eoffilarciiernracdy ioafn mceoinokcWry/stmal2li;ne solar PV is greater than that of multicrys- tallinGeT s,SoTlCar= PiVn;c hidoewnetvseorl,a trhier rcaodsita onfc me aotnsotcarnydsatarldlitnees tPcVo insd hitigiohnesr, ai.se w., 1elkl.W Th/em p2o;wer from the sαoPla=r tPeVm cpaenra bteu rcealccouelfafitceide nutsoinfgp Eowquear;tion (5). T ◦c = cell temperature of solar PV in C; Tc,STC = cell temperatu𝑃re u=n d𝑌er𝑓standa,rd te[s1t +co𝛼ndi𝑇tio−ns𝑇, i,.e., 2]5 ◦C. (5) Twabhleer2𝑓𝑌e. Technical specifications of all the renewable energy systems used.𝐺 = power output during standard test conditions in kW; Solar PV—Peimer SG3 4=0 Pderating factor of solar PV; Wind Turbine—Enercon E-48 [800 kW]Parameter = inVcaidlueent solar irradiance in kWPa/rmam;e ter Value PV Model Peimar SG340P Wind Turbine Model Enercon E-48 Vmp 38.3 V Rated Capacity 800 kW Imp 8.88 A Rotor Diameter 48 m Rated Capacity 1500 kW Cut-In windspeed 2.5 m/s Efficiency 17.5 Cut-Out windspeed 34 m/s Operating Temperature 25 ◦C Generator Direct Driven Generator Temperature Coefficient −0.43 Convertor—Leonics MTP-413 F Battery—Fortress Power eVault LFP-15 Battery Parameter Value Parameter Value Inverter Model Leonics MTP-413F 25 kW Nominal Voltage (V) 48 External DC Charger 240 V Nominal Capacity (kWh) 14.4 Phase 3 phases Nominal Capacity (Ah) 300 Maximum Efficiency 95% Roundtrip Efficiency (%) 98 AC Output 240 V AC Maximum Charge Rate(A/Ah) 0.4 Maximum Charge Current (A) 130 Maximum Discharge Current (A) 150 Energies 2023, 16, 6309 12 of 28 2.4.2. Wind Turbine Wind turbines are one of the major renewable energy conversion systems in Europe. The wind turbine used in this case study simulation is the Enercon E-48 with a capacity of 800 kW. Figure 9 shows the annual energy yield of the wind turbine with the variation in the wind speed [35]. The cost of the wind turbine is considered to be 3800 €/kW with an O&M cost of 25,600 €/year [36]. The turbine specifications are given in Table 2 [35]. The Energies 2023, 16, x FOR PEER REVIEWm odel calculates the power produced by the wind turbine by calculating the pow13e roaf t3t0h e hub height with the power curve and then multiplies the power produced with the air density ratio. Figure 9. Annual energy yield of Enercon E-48 wind turbine. Figure 9. Annual energy yield of Enercon E-48 wind turbine. 2.4.3. Converter and Batteries 2.4.3. CAoncvoenrvteerr taenr dis Baanttiemripeosr tant power electronics device that converts AC to DC or DC to ACA. Tchoenvseorlaterrp isa naenl simprpoodrutacnet opuotwpuert einleDctCroannicds tdheivsince etdhsatt ocobnevceortnsv AerCte tdo tDoCA oCr wDhCe n to pArCov. iTdhine gsoiltatro ptahneerlse spirdoednutsceo or uthtpeugtr iind .DTCh aensdp tehcisfi ncaeteidosn stoo bfet hceoncvoenrvtedrt etor AusCe dwihnenth e prsoivmiduilnatgi oint taor ethseh orwesnidiennTtsa bolre t2h.eT hgreidco. sTthceo nsspiedceirfiecdatfioorntsh eofi nthve rcteornivse6r0te0r €u/skeWd iwn ithen o simOu&lMaticoons ta.rTe hseholiwfenti mine Tiasbalses u2.m Tehde tcoobset c1o0nyseidaresr.eTdh fiosri sthaeb iindvireerctetiro insa 6l0in0v €e/rktWer wiitth tnhore e O&phMa sceoss. tT. hTehien vlieferttiemr cea ins baessuusmededfo tros oblea r1a0n ydeaortsh.e Tr hreisn eisw a bbliedeirneecrtgioynsaolu irncvees.rter with three pThhaseebsa. tTtehrey icnhvaerratcetre criasnti cbsea uresesdp efocirfi seodlainr aTnadbl eot2h,ewr hrenreewFoarbtrle sesnPeorwgye rsoeuVracuelst.L FP-15 batTtehreie bsaattreercyh cohsaernacfoterrtihstiiscsre asreea srcphe.cified in Table 2, where Fortress Power eVault LFP- 15 batteries are chosen for this research. 3. Results and Discussion 3. 3R.1e.suSlctesn aarniod 1Discussion 3.1. SceInnarSicoe 1n a rio 1, batteries are considered for storage along with solar PV and wind energy as renewable sources of energy. Scenario 1 uses batteries to satisfy the demand when there is aInd rSocpeninartihoe 1r, ebnaettweraibelse asroeu crocenss’idperroeddu fcotiro sntoorfaeglee catlroicnagl weniethrg syolfaror mPVs oalnadr PwVinodr weni-nd ergtuyr absin reesn.eTwhaebcloe msopuornceens tosf uesneedrginy.S Scceennaarrioio1 1a uresetsh beaPtteeimrieasr tSoG s3a4ti0sPfys tohlaer dpeamnaenlsd, Ewnheercno n theEr-e4 8is8 a0 0dkroWp wini nthdet urernbeinweasb, eleV saouultrLceFsP’ -p1r5obdautctetiroyna onfd eLleecotnriiccaslM enTePr-g4y1 3fFro2m5 ksoWlacro PnVv eorrt er. wiTnhde tcuorsbtionfetsh. eTbhaet tceormiespoisnceonntss idueseredd ians S1c4e,n00a0ri€o/ 1U anriet. tAheft ePrerimunanri nSgG3th4e0Psi msoulalart ipoannsewlsi, th Entehrecoinnp Eu-t4d8 a8t0a0g kivWe nwiinndTa tbulreb2in, eths,e eHVaOuMlt ELRFPs-o1f5tw baattreerryu nasndd iLffeeornenictss MceTnPa-r4io1s3Fto 2g5 ikvWe a n coonpvteirmteirz.e Tdhree scuolstt. oAfc tchoer dbianttgetroietsh ies sciomnusildateiroends ,atsh 1e4b,0e0s0t s€c/eUnnairti.o Aifstechr orusennnifnogr tthhee asnimaluy-sis. latTiohnesr ewsuitlhts thoef Sincepnuatr dioat1as ghiovwent hina tTtahbeleN 2P, Cthwe iHllObMe 9E5R.2 sMoft€wwairteh raunnsL CdiOffEeroefn0t .s7c8e6n€a/rikoWs h. to Fgiigvuer ean1 0opantidmTizaebdle r3essuhlot.w Atchceorcdoisntsg atsos othceia steimd uwlaitthiotnhsis, tshcee nbaersito s. cenario is chosen for the analysis. The results of Scenario 1 show that the NPC will be 95.2 M € with an LCOE of 0.786 €/kWh. Figure 10 and Table 3 show the costs associated with this scenario. The electric load is satisfied with 5331 kW of solar PV capacity, 33,955 kWh of battery capacity and 3200 kW of wind energy capacity, with three wind turbines. The HOMER simulation calculates the annual operating costs as 2.38 M €/year. The autonomy of stor- age in Scenario 1 is 30.2 h. In Tables 4–6, we present the technical specifications of each component under Scenario 1’s conditions. Energies 2023, 16, 6309 13 of 28 The electric load is satisfied with 5331 kW of solar PV capacity, 33,955 kWh of battery capacity and 3200 kW of wind energy capacity, with three wind turbines. The HOMER simulation calculates the annual operating costs as 2.38 M €/year. The autonomy of Energies 2023, 16, x FOR PEER REVIE s Wto rage in Scenario 1 is 30.2 h. In Tables 4–6, we present the technical specifications o1f4 eoafc 3h0 component under Scenario 1’s conditions. FFiigguurree 1100.. Reprresenttattiion off capiittall,, rrepllacceemeentt,, O&M and sallvage costt vallues off PV,, wiind tturrbiine,, bbaatttteerryy,,c coonnvveerrtteerra annddt thheew whhooleles syysstetemmf oforrS Scceennaarrioio1 1. . TTaabbllee3 3.. CCoossttss ooff ddiiffffeerreennttc coomppoonneennttssu usseeddi ninS Scceennaarrioio1 1. . CompCoonmepnotn ent Capital Capital ReplaRceepmlaecnetm ent O&OM& M SaSlvalavgaeg e TToottaall Enercon E-48 Enercon E-48 [800 kW€ 1] 2,160,0€0102.,01060 ,000.00€ 3,87€6,36,8976.3,689 7.38 € 1,3€213,3,72737,7.769 € −2,184,767.59 € 15,175,707.48 F[o8r0tr0 kW] 7.69 € −2,184, 67.59 € 15,175,7 ess Power eVault Fortress PLoFwP-e1r5 € 33,012,000.00 € 29,164,042.96 € 0.00 € −3,954,141.87 € 58,221,901.08 eVault LFP-15 € 33,012,000.00 € 29,164,042.96 € 0.00 € −3,954,141.87 € 58,221,901.08 Leonics MTP-413F 25 kW € 3,260,278.43 € 2,880,252.64 € 0.00 € −390,512.65 € 5,750,018.43 LeonPicesim MarTSPG- 340P € 15,993,758.47 € 0.00 € 689,198.61 € −638,571.73 € 16,044,385.35 413F 25S kyWste m € 3,260,2€7684.4,432 6,036.90€ 2,88€03,25,592.06,499 2.98 €€2 0,0.0102 ,976.31 € €−3−970,1,56172,9.9635. 84 €€ 59,57,5109,20,01182.4.334 Peimar SG340P € 15,993,758.47 € 0.00 € 689,198.61 € −638,571.73 € 16,044,385.35 System € 64,426T,0a3b6le.940. Electric€a 3l 5sp,9e2c0ifi,9c9at2i.o9n8s of Sce€n a2r,i0o112.,976.31 € −7,167,993.84 € 95,192,012.34 Table 4. EleQcturiacnatl istpyecifications of Scenario 1k. Wh/yr Percentage % Peimar SG340P 8,841,858 48.7 Enercon E-48Q[8u00anktWit]y 9,299,602 kWh/yr Pe5r1c.e3ntage % AC PrimPaerimy Laora SdG340P 9,362,5098,841,858 10048.7 ExcEensseErcleocntr Eic-i4ty8 [800 kW] 8,540,9319,299,602 47.151.3 Unmet AElCec tPrircimLoaardy Load 7169 9,362,509 0.0765Capacity Shortage 9355 0.0991800 Excess Electricity 8,540,931 47.1 Unmet Electric Load 7169 0.0765 As a wiCnadptaucritbyi nSehoursteasgteh e kinetic energy of w93in55d to produce energy0,.0b9a9s8e d on the wind profile in the selected location, the wind specification is as seen in the Table 6 for STcaebnlae r5i.o T1e.chFnoiucarl tsuprebciinfiecastaiornesc ohfo bsaettne,rwy fitohr Sacneninasritoa l1l.e d power capacity of 800 kW each, for a total power capacity of 3200 kW. The average output of the power from the wind turbine is 1062 kW per year. TQhuisanistiftuyr ther used to find the capaVciatlyufea ctor, which isUonbita ined by dividing the mean ouBtpauttteroifesth e wind power and total c2a3p5a8c ity of the windqptyo wer [37]. The total energy prodSutrcitniogn Stihzreo ugh the wind turbine is 9,2919 ,602 kWh/yr.bTahtteermieisn imum and maximum ouSttpruintsgsf oirn tPhaerwalilneld turbine are the lowes2t3a5n8d highest amosutnritnsgosf power Bus Voltage 48 V Autonomy 30.2 hr Storage Wear Cost 0.236 €/kWh Nominal Capacity 33,955 kWh Usable Nominal Capacity 32,257 kWh Energies 2023, 16, 6309 14 of 28 obtained over the year, which are 0 kW and 2573 kW, respectively. The wind penetration is the average power output of the wind turbine divided by the average primary load, which is expressed as a percentage, whereas the percentage of Enercon E-48 in Table 4 is the percentage of wind energy in the total production of energy. Table 5. Technical specifications of battery for Scenario 1. Quantity Value Unit Batteries 2358 qty String Size 1 batteries Strings in Parallel 2358 strings Bus Voltage 48 V Autonomy 30.2 hr Storage Wear Cost 0.236 €/kWh Nominal Capacity 33,955 kWh Usable Nominal Capacity 32,257 kWh Lifetime Throughput 25,136,156 kWh Expected Life 10 yr Average Energy Cost 0 €/kWh Energy In 2,538,404 kWh/yr Energy Out 2,488,352 kWh/yr Storage Depletion 724 kWh/yr Losses 50,775 kWh/yr Annual Throughput 2,513,616 kWh/yr Table 6. Technical specifications of solar PV and wind turbine for Scenario 1. Peimar SG340P Enercon E-48 [800 kW] Quantity Value Unit Quantity Value Unit Rated Capacity 5331 kW Total Rated Capacity 3200 kW Mean Output 1009 kW Mean Output 1062 kW Mean Output 24,224 kWh/d Capacity Factor 33.2 % Capacity Factor 18.9 % Total Production 9,299,602 kWh/yr Total Production 8,841,858 kWh/yr Minimum Output 0 kW Minimum Output 0 kW Maximum Output 2573 kW Maximum Output 5410 kW Wind Penetration 99.3 % PV Penetration 94.4 % Hours of Operation 8339 h/yr Hours of Operation 4385 h/yr Levelized Cost 0.126 €/kWh Levelized Cost 0.14 €/kWh Clipped Production 0 kWh 3.2. Scenario 2 Scenario 2 is simulated with solar PV, wind turbines and pumped storage hydropower (PSH). The generic units of the PSHu parameters are defined by the reservoir capacity of 1000 m3 of water to be discharged over a 12 h time period. The cost of PSHu is considered as 2200 €/kW. HOMER calculates the energy by considering an effective head of 100 m with turbine generator efficiency of 90%. The discharge flow rate is calculated by 1000 m3/(12 × 60 × 60) = 0.0231 m3/s (6) The power generation is calculated using the equation below, considering 90% effi- ciency: 9.81 × 100 × 0.0231 × 0.90 ∼= 20.44 kW (7) Energy = 20.44 kW × 12 h = 245.25 kWh (8) Energies 2023, 16, 6309 15 of 28 For the charging cycle of the PSHu, the model considers the turbine generator as a pump in reverse mode to pump the water back to the upper reservoir. The flow rate of the pump is calculated and 0.01875 m3/s is obtained as the pumped flow. The period of time required to completely fill the reservoir and the electrical energy with pump efficiency of Energies 2023, 16, x FOR PEER REVIEW8 0% are 14.6 h and 302.7 kWh, respectively. The model considers a 22 kW generator. The16 of 30 nominal voltage is 240 V with a maximum discharge current of 91.6 A. The capacity of the PSHu is given by dividing the power by the nominal voltage, which is found to be 1059 Ah. The cost of PSH is considered as 2200 €/kW, with an operating cost of 4000 €/year. FoSrc etnhaer icoh2acrogninsigd ecrysctlhee osfa mthmees Po/lSsaHr PuV, tahned mwoinddetlu crobninseidceorms ptohnee tnutsr,bwinhei cgheanrertahteor as a puPmeipm einr SrGev3e4r0sPe amndodEen etorc opnumE-p48 t,hwei twhaatnere qbuaicvka lteon tthlaer ugeppPSeHr roebsetarivnoeidr.f rTohme 1fl5o9wP SrHatue. of the puSmcepn airsi oca2lchualsaatendN aPnCd o0f.04158.875M € and iasn oLbCtaOinEeodf a0s.3 t7h9e€ p/ukWmhp.edFi gfluorwe .1 T1haen dpeTraibolde 7of time resqhuoiwredth etoc ocsotms opfltehteeldyi ffifellr etnhtec roemseprovnoeinrt sanofdS tcheen aerlieoc2tr. ical energy with pump efficiency of 80T%ab alere7. 1C4o.s6t sho fadnidff e3re0n2t.7co kmWpohn,e nretssupseecdtiinveSlcyen. aTrhioe2 .model considers a 22 kW generator. The nominal voltage is 240 V with a maximum discharge current of 91.6 A. The capacity of the Component PSHuC iasp gitiavlen by diRveipdliancegm tehnet power bOy& tMhe nominal vSoalvtage , which is fTooutanld to be 1059 Enercon E-48 [800 kW] Ah€. 1T2h,1e6 0c,0o0s0t. 0o0f PSH€ i3s, 8c7o6n,6s9i7d.3e8red as€ 212,30203 ,7€7/7k.W69 , wit€h− a2n,1 o84p,7e6r7a.5ti9ng co€s1t5 o,1f7 54,07070. 4€8/year. Generic 245 kWh Pumped Hydro € 7 S,6c9e5n,6a0r0i.o00 2 conside€rs0 .0th0 e same so€ l8a,2r2 P1,V90 0a.n52d wind€ −tu69r1b,i3n2e8. 0c2ompo€n1e5n,2t2s6, ,1w72h.i5c0h are the Peimer SG340P and Enercon E-48, with an equivalent l−arge PSH obtained from 159 PSHu. Leonics MTP-413F 25 kW € 1,220,369.05 € 1,078,119.94 € 0.00 € 146,174.49 € 2,152,314.50 Peimar SG340P Sce€n1a3r,2i3o1 ,26 6h7.a7s2 an NPC€ o0.f0 045.8 M € a€n5d7 0a,1n7 5L.3C6 OE of€ 0−.357289, 2€9/1k.6W5 h. Fi€g1u3r,2e7 31,155 a1.n43d Table 7 System sho€w34 ,t3h0e7, 6c3o6s.7ts7 of th€e 4d,9i5ff4e,8r1e7n.3t2 comp€o1n0e,1n15ts,8 5o3f. 5S8cenar€i−o 32,5. 5 0,561.75 € 45,827,745.91 Figure 11. Representation of capital, replacement, O&M and salvage cost values of PV, wind turbine, Figure 11. Representation of capital, replacement, O&M and salvage cost values of PV, wind turbine, PSH, converter and the whole system for Scenario 2. PSH, converter and the whole system for Scenario 2. The load, being the same as in Scenario 1, is satisfied with 4411 kW of solar PV, 3200 kW Taobflew 7in. Cdoesntse rogfy dciffaperaecnitty coamndpo4n0,e4n1t1s kuWsedh ionf SPcSeHnacraiop a2c. ity. The autonomy for Scenario 2 Component is 37.8 hC. aFpigituarle 12 shRowepsltahceempeerncte ntage of Oth&e MPS H state of chaSraglevadguer ing differentTdoatyasl of the year. In Tables 8–10, we present the characteristics of each component obtained for Enercon E-48 [800 kW] Scen€a r1i2o,126.0,000.00 € 3,876,697.38 € 1,323,777.69 € −2,184,767.59 € 15,175,707.48 Generic 245 kWh Pumped Hydro € 7,695,600.00 € 0.00 € 8,221,900.52 € −691,328.02 € 15,226,172.50 Leonics MTP-413F 25 kW € 1,220,369.05 € 1,078,119.94 € 0.00 € −146,174.49 € 2,152,314.50 Peimar SG340P € 13,231,667.72 € 0.00 € 570,175.36 € −528,291.65 € 13,273,551.43 System € 34,307,636.77 € 4,954,817.32 € 10,115,853.58 € −3,550,561.75 € 45,827,745.91 The load, being the same as in Scenario 1, is satisfied with 4411 kW of solar PV, 3200 kW of wind energy capacity and 40,411 kWh of PSH capacity. The autonomy for Scenario 2 is 37.8 h. Figure 12 shows the percentage of the PSH state of charge during different days of the year. In Tables 8–10, we present the characteristics of each component obtained for Scenario 2. EnEerngeiregsi2es0 230,2136, ,163, 0x9 FOR PEER REVIEW 1167o fo2f 830 Figure 12. State of charge of PSH for different hours of the day and different days of the year. Figure 12. State of charge of PSH for different hours of the day and different days of the year. Table 8. Electrical specifications of Scenario 2. Table 8. Electrical specifications of Scenario 2. Quantity kWh/yr Percentage % PeimaQr SuGa3n4t0iPty 7,31k4,W886h/yr Per4c2e.n6tage % EnercoPneEim-48ar[ 8S0G0 3k4W0]P 9,8471,,341740,886 574.42.6 EAnCePrcroimna Ery-4L8o [a8d00 kW] 9,3691,8,64012,470 10507.4 ExAceCss PErleimctraicityUnmet ElectricrLyo aLdoad 7,006,065 40.8 890,73761,602 0.0816020 CapEaxcciteysSs hEolretcatgreicity 973,60506,065 0.094909.8 Unmet Electric Load 8077 0.0862 Capacity Shortage 9365 0.0999 Table 9. Technical specifications of PSH for Scenario 2. Table 9. TeQcuhannictaitly specifications of PSH for SVceanluaerio 2. Unit Bus VoltagQeuantity 240 Value V Unit AutonoBmuys Voltage 37.8 240 hr V Storage Wear Cost 0 €/kWh Nominal CaApauctiotynomy 40,411 37.8 kWh hr Usable NomSitnoarlaCgeap Waceitayr Cost 40,411 0 kWh€/kWh Lifetime TNhoromuignhapl uCtapacity 109,828,332 40,411 kWhkWh EUxpseacbteled NLiofeminal Capacity 40 40,411 yr kWh Average Energy Cost EnLeirfgeytimIne Throughput 0 3,050,767 109,828,332 €/kWh kWh/ykrWh EnergyEOxuptected Life 2,471,137 40 kWh/yryr StoragAevDeerpalgetei oEnnergy Cost 18.3 0 kWh/€y/kr Wh LossesEnergy In 579,647 3,050,767 kWhk/Wyr h/yr Annual ThroEungehrpguyt Out 2,745,708 2,471,137 kWhk/Wyr h/yr Storage Depletion 18.3 kWh/yr Table 10. Technical spLecoisfisceasti ons of solar PV and wind tu5r7b9in,6e4fo7r Scenario 2. kWh/yr Solar PV—Peimar SG340P Annual Throughput Wind Turb2i,7n4e5—,7E0n8e rcon E-48 [800 kWkW] h/yr Quantity TVaablluee 10. Technical Uspneictifications of solarQ PuVa anntidty wind turbine for VSacleuneario 2. Unit Rated Capacity 4411 kW Total Rated Capacity 3200 kW Mean Output Solar PV—P8e3im5 ar SG340P kW MeWaniOnudt pTuutrbine—Enerc1o1n2 3E-48 [800 kW]k W Mean OuQtpuuatntity 20,041Value kWh/Udnit CapacitQyuFacnttoirty 35.V1 alue U%nit CapaciRtyaFteadct oCrapacity 18.9 4411 % kW TTotoatlaPl rRodatuecdti oCnapacity 9,841,4372000 kWkWh/ yr Total Product MinimumMOeaun io Onutput 7,314,886835 kWh/kyWr MinimMumeaOn uOtput 0 kWtput 0 kW Maximum Outpuutpt ut 26991123 kkWW MaximumMOeauntp Oututput 4476 20,041 kWkWh/d WindCPaepnaectirtayt iFonactor 10535.1 % PV PeCneatpraatcioitny Factor 78.1 18.9 % % HourTsootfaOl Pperroadtiuocntion 8393,8941,470 kWh/hy/ryr Hours oTfoOtpael rPartioodnuction 43875,314,886 h/kyWr h/yr LeMveilnizimeduCmo sOt utput 0.119 0 €/kkWW h Levelized Cost ClippedMPirnoidmuuctmio nOutput 0.14 0 €/kWh0 kWhkW Maximum Output 2699 kW Maximum Output 4476 kW Wind Penetration 105 % Energies 2023, 16, 6309 17 of 28 Table 10 states the technical specifications of the solar PV and wind turbines. As in Scenario 1, four wind turbines with 800 kW each are selected according to the optimized result. The mean output here is 1123 kW, with a capacity factor of 35.1%. The total production is obtained as 9,841,470 kWh/yr. The wind penetration in Scenario 2 is 105%. The working hours are the same as in Scenario 1, namely 8339 h. 3.3. Comparison between Scenario 1 and Scenario 2 The model optimizes the microgrid with a grid search algorithm to find all the feasible solutions and then uses the derivative-free algorithm, which is implemented in HOMER, to identify the system with the lowest cost. The scenarios covering the load demands with the available renewable resources are inserted into the model to provide an optimized solution with the lowest NPC. The optimized result for Scenario 1 has an NPC of 95.2 M €, whereas Scenario 2, which considers the PSH, has an NPC of 45.8 M €. Hence, the scenario that uses PSH, Scenario 2, had an NPC that is 51.8% lower than the one using batteries, i.e., Scenario 1. This can be attributed to the replacement costs of batteries in the long run, since the lifetimes of batteries are less than those of PSH, and the replacement costs will contribute to the long-term costs of the project. Considering the LCOE, in Scenario 1, the LCOE is 0.786, which is higher than that of Scenario 2, with an LCOE of 0.379. The LCOE, as a defined measure of the cost of energy, shows the economical viability and indicates which is the better project. Hence, the NPC and LCOE are excellent measurement parameters to compare the economic aspects of the two scenarios. The capacity for solar PV in Scenario 2 is 4411 kW, which is less than the capacity of Scenario 1, which is 5331 kW. Scenario 2 has more PSH power being generated, with an annual throughput of 2,745,708 kWh/yr, whereas, in Scenario 1, with batteries, the annual throughput (amount of energy that cycles though the storage bank in one year) is 2,513,616 kWh/yr. Due to the high cost of batteries and the shorter life cycle, the model requires a greater capacity of renewable energy components (solar PV), which is an economic solution in which a larger storage capacity is used to satisfy the demand for electricity. Figures 13 and 14 show the states of charge of the storage systems in Scenarios 1 and 2 and the solar PV and wind turbines satisfying the load demand. 3.4. Technical Analysis of Scenario 2 with Python 3.4.1. Energy Demand and Production in Different Seasons The hourly results obtained from the modeling for demand and production are ex- ported and used in the data analysis of a developed Python model. The whole energy demand and production is divided into different seasons, which are winter, spring, summer and autumn. The winter season ranges from the start of January to the end of February to facilitate the data grouping. The spring period is from the start of March until the end of May. The summer season is considered from the start of June till the end of August. Autumn is considered from the start of September until the end of December. It can be observed that the solar irradiance is lower in the seasons of winter and autumn, which leads to a greater role for PSH during the daytime. It is also observed that the load during these seasons is high due to heating. Figures 15–18 show the graphs of power production during the different seasons. Energies 2023, 16, x FOR PEER REVIEW 19 of 30 Eneerrgiieess 22002233,, 1166,, 6x3 F09OR PEER REVIEW 1198 off 2380 Figure 13. Scenario 2: solar PV, wind turbine and PSH power output along with the power demand Fain gdu rPeS1H3 .stSacteen oafr icoh2a:rgsoe.l ar PV, wind turbine and PSH power output along with the power demand Figure 13. Scenario 2: solar PV, wind turbine and PSH power output along with the power demand and PSH state of charge. and PSH state of charge. FFiigguurree 1144.. SScceennaarrioio1 1: :s osolalrarP VP,Vw, iwndintdu rtbuirnbeinaen danbda tbtearttyepryow peorwoeurt pouuttpaluotn aglownigth wthitehp tohwe eprodwemera dned- mand and battery state of charge. aFnigdubraet t1e4r.y Sscteantearoiof c1h: asroglea.r PV, wind turbine and battery power output along with the power de- mand and battery state of charge. Energies 2023, 16, x FOR PEER REVIEW 20 of 30 3.4. Technical Analysis of Scenario 2 with Python 3.4.1. Energy Demand and Production in Different Seasons The hourly results obtained from the modeling for demand and production are ex- ported and used in the data analysis of a developed Python model. The whole energy demand and production is divided into different seasons, which are winter, spring, sum- mer and autumn. The winter season ranges from the start of January to the end of Febru- ary to facilitate the data grouping. The spring period is from the start of March until the end of May. The summer season is considered from the start of June till the end of August. Autumn is considered from the start of September until the end of December. It can be observed that the solar irradiance is lower in the seasons of winter and autumn, which leads to a greater role for PSH during the daytime. It is also observed that the load during Energies 2023, 16, 6309 these seasons is high due to heating. Figures 15–18 show the graphs of power pro1d9uocf t2i8on during the different seasons. Energies 2023, 16, x FOR PEER REVIEWFi gure 15. Power 21 of 30 Figure 15. Power pr pordoudcuticotniofnr ofrmomso slaorlaPrV P,Vw, iwndintdu rtubirnbeinaen adnpdu pmupmepdesdt osrtaograeghey hdyrdoproopwoewrecrh cahrgaerge anadnddi sdcihscahrgaergpeo pwoewrearl oanlognwg iwthitthh ethloe alodadde dmeamnadnfdo rfothr ethsee asseoansoonf owfi wntienrt.er. Figure 16. Power production from solar PV, wind turbine and pumped storage hydropower char ge and discharge power along with the load demand for the season of spring. Figure 16. Power production from solar PV, wind turbine and pumped storage hydropower charge and discharge power along with the load demand for the season of spring. Figure 17. Power production from solar PV, wind turbine and pumped storage hydropower charge and discharge power along with the load demand for the season of summer. Energies 2023, 16, x FOR PEER REVIEW 21 of 30 Energies 2023, 16, 6309 Figure 16. Power production from solar PV, wind turbine and pumped storage hydropower2 0chofa2rg8e and discharge power along with the load demand for the season of spring. Figure 17. Power production from solar PV, wind turbine and pumped storage hydropower charge anFdigduirsec h17a.r gPeowpoewr perroadlouncgtiowni tfhrotmhe sloolaadr PdVem, waninddf oturrtbhienese aansdon poufmspuemdm steor.rage hydropower charge and discharge power along with the load demand for the season of summer. Based on the analysis of the different seasons, the pumped storage hydropower working conditions vary. This is mainly due to the highly variable output of the wind energy, which depends on the wind speed. Most of the time, the shadow effects on the solar panels also necessitate PSH to maintain the demand for a power supply. Figure 19 shows a bar graph of the average power production in each month of the analyzed year. Energies 2023, 16, x FOR PEER REVIEW Since solar PV works only during the day for a specific time, while the wind pow 2e2 rocf a3n0 keep working, the average value of the wind power per month is comparably greater than that of solar PV. Figure 18. Power production from solar PV, wind turbine and pumped storage hydropower c harge aFnigdudreis 1ch8.a Prgoewpeor wpreordaulocntigonw firthomth seolloaard PdVe, mwainndd tfuorrbtihnee saenadso pnuomfpaeudtu smtonr.age hydropower charge and discharge power along with the load demand for the season of autumn. Based on the analysis of the different seasons, the pumped storage hydropower working conditions vary. This is mainly due to the highly variable output of the wind energy, which depends on the wind speed. Most of the time, the shadow effects on the solar panels also necessitate PSH to maintain the demand for a power supply. Figure 19 shows a bar graph of the average power production in each month of the analyzed year. Since solar PV works only during the day for a specific time, while the wind power can keep working, the average value of the wind power per month is comparably greater than that of solar PV. Figure 19. Power production of solar PV, wind turbine and PSH along with load demand of hourly data averaged across months of the analyzed year. Energies 2023, 16, x FOR PEER REVIEW 22 of 30 Figure 18. Power production from solar PV, wind turbine and pumped storage hydropower charge and discharge power along with the load demand for the season of autumn. Based on the analysis of the different seasons, the pumped storage hydropower working conditions vary. This is mainly due to the highly variable output of the wind energy, which depends on the wind speed. Most of the time, the shadow effects on the solar panels also necessitate PSH to maintain the demand for a power supply. Figure 19 shows a bar graph of the average power production in each month of the analyzed year. Since solar PV works only during the day for a specific time, while the wind power can Energies 2023, 16, 6309 keep working, the average value of the wind power per month is comparably greater2 1thoaf n28 that of solar PV. Energies 2023, 16, x FOR PEER REVIEW 23 of 30 FFiigguurree 1199. .PPoowweerr pprroodduucctitoionn oof fssoolalarr PPVV, ,wwinindd ttuurrbbininee aanndd PPSSHH aalolonngg wwitithh loloaadd ddeemmaanndd oof fhhoouurrlyly ddaattaa aavveerraaggeedd aaccrroossss mmoonntthhss ooff tthhee aannaallyyzzeedd yyeeaarr.. 3.34..42..2 T. hTeh eNNeeede dfofro Er nEenregryg yStSotroargaeg e ToTo uunnddeersrtsatanndd ththee nneeedd foforr eenneerrggyy ssttoorraaggee,, tthee grraph of the power production gives a a cllearr piicctturree ooff tthhee vvaarriiaabbiilliittyy ooff tthhee ppooweerrg geenneerraattioionnb byyw winindda annddt htheen noonn-p-prorodduuctcitoionno f ofs osloalrarP VPVd udruinrigntgh ethnei gnhigt.hTt.h Tishcias ncabne sbeee nseiennF iing uFriegu20re, w 20h,i cwhhsihcohw sshtohwesp tohwe epropwroedr upcrtoio-n duovcteiron48 ohveinr 4J8a nhu ianr yJ.anuary. FiFgiugruer e202.0 P. oPwowere pr rpordoudcutciotino nono n2n2dn–d3–r3dr dofo JfaJnaunaurayr y2022022 2wwithit hsosloalra PrVP,V w, winidn dtutrubribnien,e P, SPHSH chcahragreg e anadn didsicshcahragreg epopwowere arlaolnogn gwwithit hthteh ededmemanadn dfofro rpopwowere.r . ItI itsi snontoicteicde dthtaht aitn ionrdoredr etro tkoeekpe etphet hdeemdeamnda nsdatissafiteisdfi, ewdh, ewnheevnere vthererteh eisr ea idsraopd rino p poinwpeor wfreormf rsoomlars oalnadr awnidndw tiunrdbitnuer bsionuercseosu, rtchees ,PtShHe PsaStHisfiseasti sthfie sdtehmeadnedm. Iatn ids .alIstoi sreacls-o ognized that whenever there is excessive power production from solar PV, usually during the daytime, the PSH is pumped to recharge the storage. The pumping of water to the upper reservoir ensures that PSH can be used again when there is a demand for power. This can be clearly visualized with profile graphs, and the variability in the wind power can also be realized. Figures 21–23 show the profiles of solar PV, wind turbine power out- put and PSH input power. EEnneregrgieise s22002233, ,1166, ,x6 F3O09R PEER REVIEW 24 2o2fo f302 8 recognized that whenever there is excessive power production from solar PV, usually during the daytime, the PSH is pumped to recharge the storage. The pumping of water to the upper reservoir ensures that PSH can be used again when there is a demand for power. This can be clearly visualized with profile graphs, and the variability in the wind power can Energies 2023, 16, x FOR PEER REVIE a Wls o be realized. Figures 21–23 show the profiles of solar PV, wind turbine power o24u topf u3t0 and PSH input power. Figure 21. Average daily profile of solar PV output for different months of the year of 2022. It can be noticed that the production of power from solar PV is low, with a peak close to 2000 kW during the winter season. The production of electricity also can be seen only during the daytime, approximately from 7 a.m. to 6 p.m. There is also a gradual increase and decrease in power production over time, which leaves the demand for power vulner- able, and it needs to be satisfied with wind power and PSH. The variability in the wind pFoFiiwgguuerree c 22a11n.. Abveve esrreaagegnee idnaai iltlyhy epp rproorfifiollefie oloeff sosofol latahrre PP VwVo ionuudttp pouuuttf tfoporurd td iiifffnfe erFreeingntutm mreoo n2nt2thh.s so offt htheey yeeaarro off2 2002222. . It can be noticed that the production of power from solar PV is low, with a peak close to 2000 kW during the winter season. The production of electricity also can be seen only during the daytime, approximately from 7 a.m. to 6 p.m. There is also a gradual increase and decrease in power production over time, which leaves the demand for power vulner- able, and it needs to be satisfied with wind power and PSH. The variability in the wind power can be seen in the profile of the wind output in Figure 22. FFigiguurere 2222. .AAvveerraaggee ddaaiillyy pprroofifillee ooff wwiinnddt uturbrbinineeE EnenrecrocnonE -E4-84o8u otuptuptufot rfodri fdfeirffeenrtemnto mntohnstohfst hoef ytheea r yoefar2 0o2f 22,0w22h,i cwhhsihcho wshsotwhes vthaeri vabariliiatbyiilnityp oinw peorwgeenr egreantieorna.tion. Figure 22. Average daily profile of wind turbine Enercon E-48 output for different months of the year of 2022, which shows the variability in power generation. Energies 2023, 16, x FOR PEER REVIEW 25 of 30 The variability in the power production in the wind turbine is attributed to the con- Energies 2023, 16, 6309 tinuous variability in the wind speed. Due to this variability, the profile of the PSH s2h3oofw2s8 power being produced at night, where power from solar PV is not available. FFiigguurree 2233.. Avveerraaggee ddaaiillyy pprroofifillee ooff PPSSH iinnppuutt aanndd oouuttppuutt ppooweerr ffoorr ddiiffffeerreenntt moonntthhss ooff tthhee yyeeaarr ooff 2022.. IFtigcuanre b2e3 ncloetaicrelyd inthdaitcatthees pthraotd tuhcet iionnpuotf ppoowweerr fforro PmSHso olacrcuPrVs disurloinwg, twheit hdaaytpimeaek, cwlohseeret oth2e0 p00owkWer idsu prriondguthceedw frinomter ssoelaars oPnV.. TDhuerpinrgo dthuec taiuotnuomfne laencdtr iwciitnytearls mo ocnanthbse, tsheeerne oisn glyredauterri nugsatghee odfa pyotiwmeer, farpopmr oPxSimH,a taesl ythfer opmow7ear. mfro. mto s6olpar.m P.VT ihs erreediuscaelds oanadg PraSdHu aisl irnecqrueiarseeda tnod codmecpreeansseatine fpoorw theer pvaroridaubcleti opnowoveer rptriomdeu, cwedhi fcrholmea tvhees wthiendd etmurabnindef.o r power vulnerable, and it needs to be satisfied with wind power and PSH. The variability in the w3.5in. dSepnosiwtievritcya Annbaelysseise n in the profile of the wind output in Figure 22. The variability in the power production in the wind turbine is attributed to the contin- uousAv asreinasbiitliivtyityin anthaelywsiisn ids psperefeodr.mDeude wtoithth Sicsevnaarriiaob 2il,i tays, itth ies pthreo fiolpetiomf athl esoPlSuHtiosnh. oItw iss ppoerwfoerrmbeeidn gwphreordeu vcaerdiaabtlne igvhaltu, wesh feorre pdoiffweerrenfrto pmarsaomlaertPerVs icsanno tbaev gaiivlaebnl.e .The scenario, whicFhi gcounresi2d3ercsl eaa 1rl0y0%in dsitcaatete osft hchaatrtghee fionrp PuSt Hpo awnedr tfhoer cPaSpHitaolc ccousrts odfu trhien gSGth-e34d0aPy stiomlaer, wPVh earnedth Eenpeorcwoenr Eis-4p8r,o ids uvcaerdiedfr,o ams sshoolawr nP Vin. DTaubrilne g11th. e autumn and winter months, there is greater usage of power from PSH, as the power from solar PV is reduced and PSH is rTeaqbulei r1e1d. Ctoapciotaml cpoesnt smauteltfipolrietrh feovr aserniasbitlieviptyo awnearlypsirso. duced from the wind turbine. SG 340P Solar PV Enercon E-48 Wind Turbine 3.5. Sensitivity Ana0l.y2s is 0.2 A sensitivity0a.n8 alysis is performed with Scenario 2, as it0i.s8 the optimal solution. It is performed wher1e variable values for different parameters ca1n be given. The scenario, which considers a 120 0% state of charge for PSH and the capital co2s t of the SG-340P solar PV and Enercon E-48, i3s varied, as shown in Table 11. 3 Table 11. Capital cost4m ultiplier for sensitivity analysis. 4 Figure 24S sGho3w40sP tShoel oarpPtiVmal sensitivity solutions.E Tnheerc coanpEit-a48l cWosint disT cuornbsinideered with variations both less t0h.2an and greater than the unit multiplier. Th0e.2 main reason for this consideration is that 0th.8ere have been a number of investments in0 r.8enewable energy pro- jects and also the effec1ts of war, which result in inflation. The grap1h covers the variation 2 2 3 3 4 4 Energies 2023, 16, 6309 24 of 28 Figure 24 shows the optimal sensitivity solutions. The capital cost is considered with variations both less than and greater than the unit multiplier. The main reason for this consideration is that there have been a number of investments in renewable energy projects and also the effects of war, which result in inflation. The graph covers the variation in the solar PV SG 340P and Enercon E-48 wind turbine for the range of values from 0.2 to 4 in their capital cost multiplier. The various areas show the coverage of the NPC for an optimal solution. The sensitivity analysis covers the different solutions in calculation for microgrids. In Figure 24, we present 36 values of the NPC (in white) for different simulations of the model, resulting from six sensitivity variables for the solar PV and wind turbine; it also shows the representation domain over the given capital cost multiplier range. It is important to ensure that the scenarios or projects in the simulation are as close as possible to the expected reality. The orange region is the area with solar PV and PSH as a feasible option in terms of Energies 2023, 16, x FOR PEER REVIEeWco nomics, and the blue region suggests a combination of solar PV, wind turbines an 2d6 PoSf H30. The green region represents the region where the wind turbine-based model dominates. These regions in the graph can be attributed to the capital cost multipliers used. The region winit thhae lsowlarw PinVd StGu r3b4i0nPe caanpdi tEanl ecorcsot nm Eu-l4ti8p wlieirnbdu ttuarbhinigeh feorrs tohlea rraPnVgcea opfi tvaallcuoesst fmroumlt i0p.l2i etro r4e siunl ttshienirt hcaepwitianld cotusrt bminuel-tbipaslieedr.m Tihcero vgarridiomuso adreelaasn sdhvoiwce tvher scao.vTerhaegger eoef nthre gNioPnC, w foitrh aan sooplatirmPaVl csoapluittaiolnco. sTthme useltnipsiltiievritrya nagnianlgysaips pcroovxeirms athteel ydfirffoemren1.t9 stoolu2t.2io,nasn dina cwalicnudlattuiornbi nfoer cmapicitraolgcroidsts.m Inu lFtiipgluierre f2r4o,m w2e. 8ptroes3e.n10t ,3r6e svualltuseisn oaf wthine dNtPuCrb (iinne -wbhasiteed) mfoirc droiffgreirdenwti sthimPuSlHa- atsiosntos roafg tehe(E m-4o8d/ePlH, re2s4u5l)t,indgu efrotomt hseixl osewnesritNivPitCy vcaormiapbalerse dfotro tohteh seorlasor lPuVti oannsdi nwvionldv itnugr- sboilnaer;P itV a+lsoP SshHoawnsd t/hoer rseoplraersPenVta+tiwonin ddotmurabinin oevse+r PthSeH g. iTvhene sceanpsiittailv citoystg mrauplhtiipslioebrt raainnegde. bIyt icsh iomopsionrgtasnpte tcoifi ecnpsouirnet sth(a1t,2 t,h3,e4 ,s5c)e,naasrsihoso worn pinroFjeigctusr ein2 4th, ea nsdimoublsaetrivoinn garteh easd ciflfoesree nats spoolusstiiobnles ttoh atht ea reexppoecstseibdl er,eaalsitsyh.o wn in Figure 25a–e. FFiigguurree 2244. . SSeennssiittiivviittyy aannaallyyssiiss ssupeerriimpoosseed wiitthh ttoottaall NPC ffoorr ccoosstt muullttiipplliieerrss ooff ssoollaarr PPVV aanndd wwininddt tuurrbbininee. .* *s sppeecciifificcv vaalluueessu usseeddi innc caappititaallc coossttssm muultltipiplileierri nine eaacchhs soolulutitoionn. . The orange region is the area with solar PV and PSH as a feasible option in terms of economics, and the blue region suggests a combination of solar PV, wind turbines and PSH. The green region represents the region where the wind turbine-based model domi- nates. These regions in the graph can be attributed to the capital cost multipliers used. The region with a low wind turbine capital cost multiplier but a higher solar PV capital cost multiplier results in the wind turbine-based microgrid model and vice versa. The green region, with a solar PV capital cost multiplier ranging approximately from 1.9 to 2.2, and a wind turbine capital cost multiplier from 2.8 to 3.10, results in a wind turbine-based microgrid with PSH as storage (E-48/PH 245), due to the lower NPC compared to other solutions involving solar PV + PSH and/or solar PV + wind turbines + PSH. The sensitivity graph is obtained by choosing specific points (1,2,3,4,5), as shown in Figure 24, and ob- serving the different solutions that are possible, as shown in Figure 25a–e. EnEeernrggeiriegessi e 22s002220332,, 3 11,661,, 6 xx, 6F3O09R PEER REVIIEW 222577o fof2f 8 3300 ((a)) ((b)) ((c)) ((d)) ((e)) FFiigiguurrree 225.5. I.IntItnertreproplloatltaetde dvavllualeuss e osff cocfapciaittpalil t ccaolssctt o mstumllttiiupllltiiieprrlssi e ffrosrr sfsoorllasrro PlaVr aPnVd a wnidindw ttiunrdrbiitnuer b iiinn ttehien dtehveell-- oped ssenssiittiiviitty anallyssiiss att ((a)) poiintt 1;; ((b)) poiintt 2;; ((cc)) poiintt 3;; ((d)) poiintt 4;; ((e)) poiintt 5;; ** sspecciificc valluess udsseevd e iilno pcceadpiisttealnl csciotsisvttsist ymaunlltatiilpyllsiiiesrr a iitn( ae)acpcho issnotll1u;tti(iobn)..p oint 2; (c) point 3; (d) point 4; (e) point 5; * specific values used in capital costs multiplier in each solution. 44.. .CCoonnccllluussiiioonnss Wiititth ttthe eeverr--iincrreasiing demaanndd ffoorr e enneerrrggyya arrroouunnddt ththeew woorlrrdlld, ,d, dueuet o ttoc lcilmliimataettec h cahnagnege aanndd ggllloobbaall l waarrrmiiing iissues,, tthe need fforr aann eenneerrrggyy t trtrraannsistiititoiionnh hasasb ebceocmome ec rcirtriitctiaicla. llT.. hTehrei rsrieise iinin ssoolllaarrr PV and wiiind eenneerrrggyyr rernenewewabalbellsehs ahsalse ldletdo ttpoo pteonttteinatltiipalrl o pbrrloembllsemsusc hsuacshv asr i avbarirliiiatybiiillniitty iinp opwoewreprrr opdrruodctuioctntii.oTnh.. eTshtoe rsattgoerraogf e noeff r genyeirsrgnye ciies s nsaercyestosaarrdy d ttroe sasdtdhrerepsrso ttbhle m prroofbilnletmer m ofif t itiennttetrr-- mpiiottttweenrtt pproowduerrc tpiorrnodfruocmttiiorenn fferrwomab rlreesn. eBwatatbelrleies.s. aBnattdtteprruiiems paendd s ptourmagpeehdy dsttroorrpaogwe ehryadrrreospoomweerr aorrfe tshoemsteo orfaf gttheet e sctthornraogloeg ttieecshtnhaoltloagrieiebs e ttihnagtt caurrre r ebneitinlyg u csuerrdrr.enttlly used.. TThhiiiss rrreesseeaarrrcchh ddisiicsucusssesdedt h ttehaen aalnyaslliys soiifs twoffo ttswceon asrcieonsaurrisioins g uasniiningt eagnr aiitnetdtemgrroadtteedl b masoeddell boansetdh eonH ttOhMe HEROManEdRP aynthd o Pnytsthofotnw saorfefttwtoaorrles . ttoTohlles.. mThoed eml owdaesll rwuans trroufin n ttdo fiannde caonn eocmonicoaml -- iicsaolll ustoilolunttiifoonr ftfhorer tmthiec rmogiicrrridogtrhriiadt ttshaatitts fisaettsiisthfieesd ttehme adnedmoafnthd e olffo ttahde. lSlocaedn..a rSicoe1naurrsiieod 1 b uastteedr ibesatt-- ttearrsiiestso arsa gsteto,rrwagiteh,, swoiilttahr PsoVllaarr n PdVa awnidn da wtuiirnbdi n tteurarbsiirneen aews rraebnleewsoaublrlec e ssooufrrcpeosw oeff r pgoewneerrr a gtieonne.rr-- attiion.. An iinverrtterr was used tto converrtt DC tto AC.. Scenarriio 2 used PSH wiitth tthe same Energies 2023, 16, 6309 26 of 28 An inverter was used to convert DC to AC. Scenario 2 used PSH with the same renewable sources as Scenario 1 and from the same manufacturers. The simulations resulted in an NPC of 95.2 M € and an LCOE of 0.786 €/kWh for Scenario 1, whereas Scenario 2 resulted in an NPC of 45.8 M€ and an LCOE of 0.379 €/kWh, clearly indicating, for the analyzed case study, that Scenario 2 with PSH was the most economical solution. Real projects should be chosen based on the location and taking into consideration that batteries have a small lifetime in comparison with PSH, which results in replacement costs. Although the cost of batteries is being reduced over time, it is still difficult for them to compete with PSH economically. The technical analysis of Scenario 2 indicates the need for the storage of the data obtained from the model. Data analysis with Python was performed to place distinct data into different seasons and to better understand the role of PSH in the winter and autumn seasons, where the power from PV is reduced. A sensitivity analysis was carried out to investigate different projects by varying the characteristic parameters. The optimal solutions with different capital cost multipliers for the solar PV—SG-340P—and the wind turbine—Enercon E-48—resulted from the combination of solar PV, wind turbines and PSH. However, this study presents some limitations. In this research, the integrated model does not consider the constraints of the area available to install PSH for the analyzed study. With a constraint on the area, batteries could be a more suitable solution, despite their higher cost, but also their occupied area should be considered. The model performed the simulations using the NASA POWER system to acquire the renewable resource data. The obtained electrical results cannot be exactly the same as real-time results and the margin of error needs to be considered. Moreover, the consideration of buying the land for PSH should also be addressed. Whenever there is a shift in the system from batteries to wind or wind to solar, there is always a challenge in addressing the shifting period from one renewable technology to other, which will require special smart devices and should need be considered also in the optimization and cost analysis. The analyzed topic also falls within the field of machine learning techniques, used to forecast the energy demand and weather data. Modern digital technologies are used to collect data and develop smart solutions and smart grids with the Internet of Things (IoT), where the lowest cost of energy is inserted into the grid automatically and the PSH is considered based on the power demand and weather forecast conditions. The other advantages of hydropower as a storage solution against batteries that are worth mentioning are that (i) hydropower is a huge “water battery” that can provide the flexibility to integrate other renewables in a complementary way, allowing us to better address climate change and water scarcity and provide a water supply, but, on the other hand, social and environmental impacts can be generated; (ii) the use of hydropower as a multi-purpose system and its hybridization with other energy sources in the water– energy nexus will be of the utmost importance in the near future; (iii) the lifetime of PSH is comparably higher than that of batteries, guaranteeing a long-term solution; (iv) the number of cycles of charging and discharging can be high for PSH, without shortening the lifespan of the system, when compared with batteries. Thus, these advantages and the economic analysis here performed suggest that PSH can be a better solution in terms of storage for microgrid and off-grid systems in comparison with batteries. However, in the near future, a solution that incorporates both components could be more convenient. Author Contributions: Conceptualization, H.M.R.; methodology, H.M.R. and P.S.M.G.; drawings, P.S.M.G. and H.M.R.; software and calculus, P.S.M.G.; writing—original draft preparation, P.S.M.G. and H.M.R.; review and editing, P.S.M.G., H.M.R., E.Q. and O.E.C.-H.; supervision and final prepara- tion, H.M.R., E.Q. and O.E.C.-H. All authors have read and agreed to the published version of the manuscript. Funding: The authors are grateful for the Foundation for Science and Technology’s support of the first author through the funding UIDB/04625/2020 from the research unit CERIS. Energies 2023, 16, 6309 27 of 28 Data Availability Statement: The used data are available in the manuscript and can be shared upon request. Acknowledgments: The authors would like to thank CERIS for the funding support through the Foundation for Science and Technology through the funding UIDB/04625/2020 and the opportunity to develop this research. The Hydropower course at Instituto Superior Tecnico, University of Lisbon, provided the platform for the development and initiation of the idea for this research. Conflicts of Interest: The authors declare no conflict of interest. References 1. IEA. World Energy Outlook 2022; IEA: Paris, France, 2022. Available online: https://www.iea.org/reports/world-energy-outlook- 2022 (accessed on 10 April 2023). 2. IEA. Electricity Market Report–July 2022; IEA: Paris, France, 2022. Available online: https://www.iea.org/reports/electricity- market-report-july-2022 (accessed on 10 April 2023). 3. IRENA. Renewable Energy Statistics 2021; The International Renewable Energy Agency: Abu Dhabi, United Arab Emirates, 2021. 4. Energy-Charts. Public Net Electricity Generation in Portugal in 2022. Available online: https://energy-charts.info/charts/ energy_pie/chart.htm?l=en&c=PT&year=2022&interval=year (accessed on 10 April 2023). 5. Ramos, H.M.; Vargas, B.; Saldanha, J.R. New Integrated Energy Solution Idealization: Hybrid for Renewable Energy Network (Hy4REN). Energies 2022, 15, 3921. [CrossRef] 6. Rahman, M.; Oni, A.O.; Gemechu, E.; Kumar, A. Assessment of energy storage technologies: A review. Energy Convers. Manag. 2020, 223, 113295. [CrossRef] 7. IEA. Executive Summary–Hydropower Special Market Report–Analysis. Available online: https://www.iea.org/reports/ hydropower-special-market-report/executive-summary (accessed on 10 April 2023). 8. Immendoerfer, A.; Tietze, I.; Hottenroth, H.; Viere, T. Life-cycle impacts of pumped hydropower storage and battery storage. Int. J. Energy Environ. Eng. 2017, 8, 231–245. [CrossRef] 9. Javed, M.S.; Zhong, D.; Ma, T.; Song, A.; Ahmed, S. Hybrid pumped hydro and battery storage for renewable energy based power supply system. Appl. Energy 2020, 257, 114026. [CrossRef] 10. Ghanjati, C.; Tnani, S. Optimal sizing and energy management of a stand-alone photovoltaic/pumped storage hy- dropower/battery hybrid system using Genetic Algorithm for reducing cost and increasing reliability. Energy Environ. 2022. [CrossRef] 11. PNNL. Open or Closed: Pumped Storage Hydropower Is on the Rise. Available online: https://www.pnnl.gov/news-media/ open-or-closed-pumped-storage-hydropower-rise#:~:text=Open%2Dloop%20versus%20closed%2Dloop,to%20a%20natural% 20water%20source (accessed on 16 May 2023). 12. Pumped Storage Hydropower. Available online: https://www.hydropower.org/factsheets/pumped-storage (accessed on 6 February 2023). 13. Energy-Charts. Public Net Electricity Generation in Portugal in Week 35 2023. Available online: https://energy-charts.info/ charts/power/chart.htm?l=en&c=PT (accessed on 6 February 2023). 14. Divya, K.C.; Østergaard, J. Battery energy storage technology for power systems—An overview. Electr. Power Syst. Res. 2009, 79, 511–520. [CrossRef] 15. Poullikkas, A. A comparative overview of large-scale battery systems for electricity storage. Renew. Sustain. Energy Rev. 2013, 27, 778–788. [CrossRef] 16. Chen, T.; Jin, Y.; Lv, H.; Yang, A.; Liu, M.; Chen, B.; Xie, Y.; Chen, Q. Applications of Lithium-Ion Batteries in Grid-Scale Energy Storage Systems. Trans. Tianjin Univ. 2020, 26, 208–217. [CrossRef] 17. Keshan, H.; Thornburg, J.; Ustun, T. Comparison of lead-acid and lithium ion batteries for stationary storage in off-grid energy systems. In Proceedings of the 4th IET Clean Energy and Technology Conference (CEAT 2016), Kuala Lumpur, Malaysia, 14–15 November 2016. 18. Sinha, S.; Chandel, S. Review of software tools for hybrid renewable energy systems. Renew. Sustain. Energy Rev. 2014, 32, 192–205. [CrossRef] 19. Krishna, K.S.; Kumar, K.S. A review on hybrid renewable energy systems. Renew. Sustain. Energy Rev. 2015, 52, 907–916. [CrossRef] 20. Lambert, T.; Gilman, P.; Lilienthal, P. Micropower system modeling with HOMER. In Integration of Alternative Sources of Energy; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2006; Volume 1, pp. 379–385. 21. Demiroren, A.; Yilmaz, U. Analysis of change in electric energy cost with using renewable energy sources in Gökceada, Turkey: An island example. Renew. Sustain. Energy Rev. 2010, 14, 323–333. [CrossRef] 22. Yimen, N.; Hamandjoda, O.; Meva’a, L.; Ndzana, B.; Nganhou, J. Analyzing of a Photovoltaic/Wind/Biogas/Pumped-Hydro Off-Grid Hybrid System for Rural Electrification in Sub-Saharan Africa—Case Study of Djoundé in Northern Cameroon. Energies 2018, 11, 2644. [CrossRef] 23. Dalton, G.; Lockington, D.; Baldock, T. Feasibility analysis of renewable energy supply options for a grid-connected large hotel. Renew. Energy 2009, 34, 955–964. [CrossRef] Energies 2023, 16, 6309 28 of 28 24. Thomas, D.; Deblecker, O.; Ioakimidis, C.S. Optimal design and techno-economic analysis of an autonomous small isolated microgrid aiming at high RES penetration. Energy 2016, 116, 364–379. [CrossRef] 25. He, L.; Zhang, S.; Chen, Y.; Ren, L.; Li, J. Techno-economic potential of a renewable energy-based microgrid system for a sustainable large-scale residential community in Beijing, China. Renew. Sustain. Energy Rev. 2018, 93, 631–641. [CrossRef] 26. Sen, R.; Bhattacharyya, S.C. Off-grid electricity generation with renewable energy technologies in India: An application of HOMER. Renew. Energy 2014, 62, 388–398. [CrossRef] 27. Energy Market Information System. Available online: https://mercado.ren.pt/EN/Gas/MarketInfo/Load/Actual/Pages/ Hourly.aspx (accessed on 10 April 2023). 28. Rahm, E.; Do, H.H. Data cleaning: Problems and current approaches. IEEE Data Eng. Bull. 2000, 23, 3–13. 29. Hellerstein, J.M. Quantitative Data Cleaning for Large Databases; United Nations Economic Commission for Europe (UNECE): Geneva, Switzerland, 2008; Volume 25, pp. 1–42. 30. How HOMER Calculates Clearness Index. Available online: https://www.homerenergy.com/products/pro/docs/3.9/how_ homer_calculates_clearness_index.html (accessed on 10 April 2023). 31. Abdelhady, S. Techno-economic study and the optimal hybrid renewable energy system design for a hotel building with net zero energy and net zero carbon emissions. Energy Convers. Manag. 2023, 289, 117195. [CrossRef] 32. Net Present Cost. Available online: https://www.homerenergy.com/products/grid/docs/1.8/net_present_cost.html (accessed on 10 April 2023). 33. Levelized Cost of Energy. Available online: https://www.homerenergy.com/products/pro/docs/3.11/levelized_cost_of_energy. html (accessed on 8 May 2023). 34. Operation and Maintenance Cost. Available online: https://www.homerenergy.com/products/pro/docs/3.11/operation_and_ maintenance_cost.html (accessed on 14 June 2023). 35. E-48–ENERCON GmbH–Wind Turbine Datasheet|GlobalSpec. Available online: https://datasheets.globalspec.com/ds/ enercon/e-48/965dc900-6d47-4188-8415-d3e10b84b523 (accessed on 8 May 2023). 36. Distributed Generation Energy Technology Capital Costs. Energy Analysis|NREL. Available online: https://www.nrel.gov/ analysis/tech-cost-dg.html (accessed on 8 May 2023). 37. Wind Turbine Outputs. Available online: https://www.homerenergy.com/products/pro/docs/3.10/wind_turbine_outputs.html (accessed on 16 May 2023). Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). 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