Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/81029
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dc.contributor.authorGonçalves, José-
dc.contributor.authorNeves, Luís-
dc.contributor.authorMartins, António Gomes-
dc.date.accessioned2018-10-10T13:51:16Z-
dc.date.available2018-10-10T13:51:16Z-
dc.date.issued2015-
dc.identifier.isbn978-3-319-16548-6-
dc.identifier.isbn978-3-319-16549-3-
dc.identifier.urihttp://hdl.handle.net/10316/81029-
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.pt
dc.language.isoengpt
dc.publisherSpringerpt
dc.relationCENTRO-07-0224-FEDER-002004pt
dc.relationPEst-OE/EEI/UI0308/2014pt
dc.relation.ispartofseriesLecture Notes in Computer Science-
dc.rightsembargoedAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectGenetic algorithms Energy storage Power distribution networks Energy profiles Energy service NSGAIIpt
dc.titleMultiobjective Methodology for Assessing the Location of Distributed Electric Energy Storagept
dc.typearticle-
degois.publication.firstPage227pt
degois.publication.lastPage238pt
degois.publication.titleLecture Notes in Computer Sciencept
dc.peerreviewedyespt
dc.identifier.doi10.1007/978-3-319-16549-3_19pt
degois.publication.volume9028pt
dc.date.embargo2016-12-31*
dc.date.periodoembargo730pt
uc.date.periodoEmbargo730-
item.grantfulltextopen-
item.fulltextCom Texto completo-
Appears in Collections:I&D INESCC - Artigos em Revistas Internacionais
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