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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.typeArtigopt_BR
dc.titleMetaheuristic Optimization Algorithms For The Optimal Coordination Of Plug-in Electric Vehicle Charging In Distribution Systems With Distributed Generationen
dc.contributor.authorArias N.B.pt_BR
dc.contributor.authorFranco J.F.pt_BR
dc.contributor.authorLavorato M.pt_BR
dc.contributor.authorRomero R.pt_BR
unicamp.author.externalUNESP – Universidade Estadual Paulista, Faculdade de Engenharia de Ilha Solteira, Departamento de Engenharia Elétrica, Ilha Solteira, São Paulo, Brazilpt_BR
unicamp.author.externalPUC -Campinas - Pontifícia Universidade Católica de Campinas, Faculdade de Engenharia Elétrica, Campinas, Sao Paulo, Brazilpt_BR
dc.subjectElectrical Distribution Systemen
dc.subjectHybrid Algorithmen
dc.subjectMetaheuristicen
dc.subjectPlug-in Electric Vehicle Charging Coordinationen
dc.description.abstractThis paper proposes three metaheuristic optimization techniques to solve the plug-in electric vehicle (PEV) charging coordination problem in electrical distribution systems (EDSs). Optimization algorithms based on tabu search, greedy randomized adaptive search procedure, and a novel hybrid optimization algorithm are developed with the objective of minimizing the total operational costs of the EDS, considering the impact of charging the electric vehicle batteries during a specific time period. The proposed methodologies determine a charging schedule for the electric vehicle batteries considering priorities according to the PEV owners charging preferences. A 449-node system with two distributed generation units was used to demonstrate the efficiency of the proposed methodologies, taking into account different PEV penetration levels. The results show that the charging schedule found makes the economic operation of the EDS possible, while satisfying operational and priority constraints. © 2016 Elsevier B.V.en
dc.relation.ispartofElectric Power Systems Researchpt_BR
dc.publisherElsevier Ltdpt_BR
dc.date.issued2017pt_BR
dc.identifier.citationElectric Power Systems Research. Elsevier Ltd, v. 142, p. 351 - 361, 2017.pt_BR
dc.language.isoEnglishpt_BR
dc.description.volume142pt_BR
dc.description.firstpage351pt_BR
dc.description.lastpage361pt_BR
dc.rightsfechadopt_BR
dc.sourceScopuspt_BR
dc.identifier.issn0378-7796pt_BR
dc.identifier.doi10.1016/j.epsr.2016.09.018pt_BR
dc.identifier.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84992166058&doi=10.1016%2fj.epsr.2016.09.018&partnerID=40&md5=0bb2dfa7c54e065cd093a2d43454c664pt_BR
dc.description.sponsorshipCAPES, Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.description.sponsorshipCNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPESP, Fundação de Amparo à Pesquisa do Estado de São Paulopt_BR
dc.description.sponsorship1Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)pt_BR
dc.description.sponsorship1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)pt_BR
dc.description.sponsorship1Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)pt_BR
dc.date.available2017-08-17T19:17:30Z-
dc.date.accessioned2017-08-17T19:17:30Z-
dc.description.provenanceMade available in DSpace on 2017-08-17T19:17:30Z (GMT). No. of bitstreams: 1 2-s2.0-84992166058.pdf: 2328588 bytes, checksum: 923ebe782ee5a2a0619599fdd312c941 (MD5) Previous issue date: 2017en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/324077-
dc.identifier.idScopus2-s2.0-84992166058pt_BR
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