000848003 001__ 848003
000848003 005__ 20240711101429.0
000848003 0247_ $$2doi$$a10.1016/j.apenergy.2018.09.185
000848003 0247_ $$2ISSN$$a0306-2619
000848003 0247_ $$2ISSN$$a1872-9118
000848003 0247_ $$2WOS$$aWOS:000452345400081
000848003 037__ $$aFZJ-2018-03311
000848003 082__ $$a620
000848003 1001_ $$0P:(DE-HGF)0$$aCardoso, Goncarlo$$b0$$eCorresponding author
000848003 245__ $$aBattery Aging in Multi-Energy Microgrid Design Using Mixed Integer Linear Programming
000848003 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2018
000848003 3367_ $$2DRIVER$$aarticle
000848003 3367_ $$2DataCite$$aOutput Types/Journal article
000848003 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1547469295_23129
000848003 3367_ $$2BibTeX$$aARTICLE
000848003 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000848003 3367_ $$00$$2EndNote$$aJournal Article
000848003 520__ $$aThis paper introduces a linear battery aging and degradation model to a multi-energy microgrid sizing model using mixed integer linear programming. The battery aging model and its integration into a larger microgrid sizing formulation are described. A case study is provided to explore the impact of considering battery aging on key results: optimal photovoltaic and storage capacities, optimal distributed energy resources operations strategies, and annual cost and generation metrics.The case study results suggest that considering battery degradation in optimal microgrid sizing problems significantly impacts the perceived value of storage. Depending on capacity loss and lifetime targets, considering battery degradation is shown to decrease optimal storage capacities between 6 and 92% versus scenarios that do not consider battery health. When imposing constant distributed energy resource capacities, inclusion of degradation can decrease optimal annual battery cycling by as much as a factor five and reduce total annual electricity cost savings from otherwise identical photovoltaic and storage systems by 5–12%. These results emphasize that as batteries grow in maturity and ubiquity for distributed energy applications, considering battery health and capacity loss is an essential component of any analytical tool or model to guide system planning and decision-making.
000848003 536__ $$0G:(DE-HGF)POF3-134$$a134 - Electrolysis and Hydrogen (POF3-134)$$cPOF3-134$$fPOF III$$x0
000848003 536__ $$0G:(DE-Juel1)PHD-NO-GRANT-20170405$$aPhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)$$cPHD-NO-GRANT-20170405$$x1
000848003 588__ $$aDataset connected to CrossRef
000848003 7001_ $$0P:(DE-HGF)0$$aBrouhard, Thomas$$b1
000848003 7001_ $$0P:(DE-HGF)0$$aDeForest, Nicholas$$b2
000848003 7001_ $$0P:(DE-HGF)0$$aWang, Dai$$b3
000848003 7001_ $$0P:(DE-HGF)0$$aHeleno, Miguel$$b4
000848003 7001_ $$0P:(DE-Juel1)168451$$aKotzur, Leander$$b5$$ufzj
000848003 773__ $$0PERI:(DE-600)2000772-3$$a10.1016/j.apenergy.2018.09.185$$gVol. 231, p. 1059 - 1069$$p1059 - 1069$$tApplied energy$$v231$$x0306-2619$$y2018
000848003 8564_ $$uhttps://juser.fz-juelich.de/record/848003/files/1-s2.0-S0306261918315058-main.pdf$$yRestricted
000848003 8564_ $$uhttps://juser.fz-juelich.de/record/848003/files/1-s2.0-S0306261918315058-main.pdf?subformat=pdfa$$xpdfa$$yRestricted
000848003 909CO $$ooai:juser.fz-juelich.de:848003$$pVDB
000848003 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168451$$aForschungszentrum Jülich$$b5$$kFZJ
000848003 9131_ $$0G:(DE-HGF)POF3-134$$1G:(DE-HGF)POF3-130$$2G:(DE-HGF)POF3-100$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bEnergie$$lSpeicher und vernetzte Infrastrukturen$$vElectrolysis and Hydrogen$$x0
000848003 9141_ $$y2018
000848003 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bAPPL ENERG : 2015
000848003 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000848003 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000848003 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000848003 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000848003 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000848003 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000848003 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000848003 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000848003 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000848003 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology
000848003 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bAPPL ENERG : 2015
000848003 920__ $$lyes
000848003 9201_ $$0I:(DE-Juel1)IEK-3-20101013$$kIEK-3$$lElektrochemische Verfahrenstechnik$$x0
000848003 980__ $$ajournal
000848003 980__ $$aVDB
000848003 980__ $$aI:(DE-Juel1)IEK-3-20101013
000848003 980__ $$aUNRESTRICTED
000848003 981__ $$aI:(DE-Juel1)ICE-2-20101013