Scenario generation for electric vehicles' uncertain behavior in a smart city environment

Detalhes bibliográficos
Autor(a) principal: Soares,J
Data de Publicação: 2016
Outros Autores: Borges,N, Ghazvini,MAF, Vale,Z, Paulo Moura Oliveira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/6500
http://dx.doi.org/10.1016/j.energy.2016.06.011
Resumo: This paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand. Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location.
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spelling Scenario generation for electric vehicles' uncertain behavior in a smart city environmentThis paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand. Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location.2018-01-16T19:17:03Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/6500http://dx.doi.org/10.1016/j.energy.2016.06.011engSoares,JBorges,NGhazvini,MAFVale,ZPaulo Moura Oliveirainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:31Zoai:repositorio.inesctec.pt:123456789/6500Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:15.924759Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Scenario generation for electric vehicles' uncertain behavior in a smart city environment
title Scenario generation for electric vehicles' uncertain behavior in a smart city environment
spellingShingle Scenario generation for electric vehicles' uncertain behavior in a smart city environment
Soares,J
title_short Scenario generation for electric vehicles' uncertain behavior in a smart city environment
title_full Scenario generation for electric vehicles' uncertain behavior in a smart city environment
title_fullStr Scenario generation for electric vehicles' uncertain behavior in a smart city environment
title_full_unstemmed Scenario generation for electric vehicles' uncertain behavior in a smart city environment
title_sort Scenario generation for electric vehicles' uncertain behavior in a smart city environment
author Soares,J
author_facet Soares,J
Borges,N
Ghazvini,MAF
Vale,Z
Paulo Moura Oliveira
author_role author
author2 Borges,N
Ghazvini,MAF
Vale,Z
Paulo Moura Oliveira
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Soares,J
Borges,N
Ghazvini,MAF
Vale,Z
Paulo Moura Oliveira
description This paper presents a framework and methods to estimate electric vehicles' possible states, regarding their demand, location and grid connection periods. The proposed methods use the Monte Carlo simulation to estimate the probability of occurrence for each state and a fuzzy logic probabilistic approach to characterize the uncertainty of electric vehicles' demand. Day-ahead and hour-ahead methodologies are proposed to support the smart grids' operational decisions. A numerical example is presented using an electric vehicles fleet in a smart city environment to obtain each electric vehicle possible states regarding their grid location.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2018-01-16T19:17:03Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/6500
http://dx.doi.org/10.1016/j.energy.2016.06.011
url http://repositorio.inesctec.pt/handle/123456789/6500
http://dx.doi.org/10.1016/j.energy.2016.06.011
dc.language.iso.fl_str_mv eng
language eng
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dc.format.none.fl_str_mv application/pdf
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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