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

Detalhes bibliográficos
Autor(a) principal: Soares, João
Data de Publicação: 2016
Outros Autores: Borges, Nuno, Ghazvini, Mohammad Ali Fotouhi, Vale, Zita, Oliveira, P.B. de Moura
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/9385
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 environmentBig dataElectric vehiclesFuzzy logicMonte Carlo simulationSmart cityThis 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.ElsevierRepositório Científico do Instituto Politécnico do PortoSoares, JoãoBorges, NunoGhazvini, Mohammad Ali FotouhiVale, ZitaOliveira, P.B. de Moura20162117-01-01T00:00:00Z2016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/9385enghttp://dx.doi.org/10.1016/j.energy.2016.06.011info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T12:50:46ZPortal AgregadorONG
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, João
Big data
Electric vehicles
Fuzzy logic
Monte Carlo simulation
Smart city
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, João
author_facet Soares, João
Borges, Nuno
Ghazvini, Mohammad Ali Fotouhi
Vale, Zita
Oliveira, P.B. de Moura
author_role author
author2 Borges, Nuno
Ghazvini, Mohammad Ali Fotouhi
Vale, Zita
Oliveira, P.B. de Moura
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Soares, João
Borges, Nuno
Ghazvini, Mohammad Ali Fotouhi
Vale, Zita
Oliveira, P.B. de Moura
dc.subject.por.fl_str_mv Big data
Electric vehicles
Fuzzy logic
Monte Carlo simulation
Smart city
topic Big data
Electric vehicles
Fuzzy logic
Monte Carlo simulation
Smart city
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
2016-01-01T00:00:00Z
2117-01-01T00:00:00Z
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://hdl.handle.net/10400.22/9385
url http://hdl.handle.net/10400.22/9385
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://dx.doi.org/10.1016/j.energy.2016.06.011
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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