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Multiobjective Methodology for Assessing the Location of Distributed Electric Energy Storage

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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorGonçalves, José
dc.contributor.authorNeves, Luís
dc.contributor.authorMartins, António Gomes
dc.date.accessioned2025-12-16T19:36:29Z
dc.date.available2025-12-16T19:36:29Z
dc.date.issued2015
dc.description18th European Conference on the Applications of Evolutionary Computation, Evo Applications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings
dc.description.abstractThe perception of the associated impacts among possible management schemes introduces a new way to assess energy storage systems. The ability to define a specific management scheme considering the different stakeholder objectives, both technical and economic, will increase the perception of available installation options. This paper presents a multiobjective feasibility assessment methodology using an improved version of the Non-dominated Sorting Genetic Algorithm II, to optimize the placement of electric energy storage units in order to improve the operation of distribution networks. The model is applied to a case study, using lithium-ion battery technology as an example. The results show the influence of different charging/discharging profiles on the choice of the best battery location, as well as the influence that these choices may have on the different network management objectives, e.g. increasing the integration of renewable generation. As an additional outcome, the authors propose a pricing scheme for filling the present regulatory gap regarding the pricing scheme to be applied to energy storage in order to allow the exploitation of viable business models.eng
dc.description.sponsorshipThis work has been framed under the Energy for Sustainability Initiative of the University of Coimbra, and supported by the Energy and Mobility for Sustainable Regions Project CENTRO-07-0224-FEDER-002004, co-funded by the European Regional Development Fund (ERDF) through the «Programa Operacional Regional do Centro 2007–2013 (PORC)», in the framework of the «Sistema de Apoio a Entidades do Sistema Científico e Tecnológico Nacional». The work was also funded by the «Fundação para a Ciência e Tecnologia» under PEst-OE/EEI/UI0308/2014.
dc.identifier.doi10.1007/978-3-319-16549-3_19
dc.identifier.isbn9783319165486
dc.identifier.isbn9783319165493
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.urihttp://hdl.handle.net/10400.8/15108
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.ispartofLecture Notes in Computer Science
dc.relation.ispartofApplications of Evolutionary Computation
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEnergy profiles
dc.subjectEnergy service
dc.subjectEnergy storage
dc.subjectGenetic algorithms
dc.subjectNSGAII
dc.subjectPower distribution networks
dc.titleMultiobjective Methodology for Assessing the Location of Distributed Electric Energy Storageeng
dc.typebook part
dspace.entity.typePublication
oaire.citation.titleApplications of Evolutionary Computation
oaire.citation.volume9028
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePires Neves
person.givenNameLuís
person.identifier.ciencia-id591E-30D4-2C97
person.identifier.orcid0000-0002-2600-5622
person.identifier.scopus-author-id34977315800
relation.isAuthorOfPublication5315d446-6d51-4d95-aeaa-72b6a44a4838
relation.isAuthorOfPublication.latestForDiscovery5315d446-6d51-4d95-aeaa-72b6a44a4838

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Chapter: Multiobjective Methodology for Assessing the Location of Distributed Electric Energy Storage, pp 227–238 Book: Applications of Evolutionary Computation Part of the book series: Lecture Notes in Computer Science (LNTCS,volume 9028)
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