Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles

Detalhes bibliográficos
Autor(a) principal: Morro-Mello, I. [UNESP]
Data de Publicação: 2017
Outros Autores: Padilha-Feltrin, A. [UNESP], Melo, J. D.
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/ISGT-LA.2017.8126693
http://hdl.handle.net/11449/179658
Resumo: The use of electric vehicles in urban zones is an alternative to reduce the emission of gases that enhance the greenhouse effect. For promoting and encouraging this use, charging stations should be built due to the low autonomy of electric vehicles. Therefore, it is necessary to allocate these stations throughout the city, which will increase the load demand in the distribution system. In order to determine this increase, this paper presents a spatial-Temporal model based on multi-Agent systems. The results of the proposed model are the growth load on distribution feeders. The proposal was applied in a mid-sized city in Brazil with a penetration of 3.87% of EVs and ETs (3362) and the largest impact was an increase of 26.62% at peak load of a feeder. The determination of this growth is important information for the distribution utilities in order to perform the expansion planning of the distribution network.
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spelling Spatial-Temporal model to estimate the load curves of charging stations for electric vehiclesCharging stationsElectric vehiclesMulti-Agent systemsPower system planning.The use of electric vehicles in urban zones is an alternative to reduce the emission of gases that enhance the greenhouse effect. For promoting and encouraging this use, charging stations should be built due to the low autonomy of electric vehicles. Therefore, it is necessary to allocate these stations throughout the city, which will increase the load demand in the distribution system. In order to determine this increase, this paper presents a spatial-Temporal model based on multi-Agent systems. The results of the proposed model are the growth load on distribution feeders. The proposal was applied in a mid-sized city in Brazil with a penetration of 3.87% of EVs and ETs (3362) and the largest impact was an increase of 26.62% at peak load of a feeder. The determination of this growth is important information for the distribution utilities in order to perform the expansion planning of the distribution network.Department of Electrical Engineering-DEE São Paulo State University-UNESPEngineering Center Modeling and Applied Social Sciences Federal University of ABC-UFABCDepartment of Electrical Engineering-DEE São Paulo State University-UNESPUniversidade Estadual Paulista (Unesp)Universidade Federal do ABC (UFABC)Morro-Mello, I. [UNESP]Padilha-Feltrin, A. [UNESP]Melo, J. D.2018-12-11T17:36:13Z2018-12-11T17:36:13Z2017-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1-6http://dx.doi.org/10.1109/ISGT-LA.2017.81266932017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017, v. 2017-January, p. 1-6.http://hdl.handle.net/11449/17965810.1109/ISGT-LA.2017.81266932-s2.0-85043473852Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017info:eu-repo/semantics/openAccess2024-07-04T19:11:14Zoai:repositorio.unesp.br:11449/179658Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:08:51.476033Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
title Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
spellingShingle Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
Morro-Mello, I. [UNESP]
Charging stations
Electric vehicles
Multi-Agent systems
Power system planning.
title_short Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
title_full Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
title_fullStr Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
title_full_unstemmed Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
title_sort Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
author Morro-Mello, I. [UNESP]
author_facet Morro-Mello, I. [UNESP]
Padilha-Feltrin, A. [UNESP]
Melo, J. D.
author_role author
author2 Padilha-Feltrin, A. [UNESP]
Melo, J. D.
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
Universidade Federal do ABC (UFABC)
dc.contributor.author.fl_str_mv Morro-Mello, I. [UNESP]
Padilha-Feltrin, A. [UNESP]
Melo, J. D.
dc.subject.por.fl_str_mv Charging stations
Electric vehicles
Multi-Agent systems
Power system planning.
topic Charging stations
Electric vehicles
Multi-Agent systems
Power system planning.
description The use of electric vehicles in urban zones is an alternative to reduce the emission of gases that enhance the greenhouse effect. For promoting and encouraging this use, charging stations should be built due to the low autonomy of electric vehicles. Therefore, it is necessary to allocate these stations throughout the city, which will increase the load demand in the distribution system. In order to determine this increase, this paper presents a spatial-Temporal model based on multi-Agent systems. The results of the proposed model are the growth load on distribution feeders. The proposal was applied in a mid-sized city in Brazil with a penetration of 3.87% of EVs and ETs (3362) and the largest impact was an increase of 26.62% at peak load of a feeder. The determination of this growth is important information for the distribution utilities in order to perform the expansion planning of the distribution network.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-01
2018-12-11T17:36:13Z
2018-12-11T17:36:13Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/ISGT-LA.2017.8126693
2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017, v. 2017-January, p. 1-6.
http://hdl.handle.net/11449/179658
10.1109/ISGT-LA.2017.8126693
2-s2.0-85043473852
url http://dx.doi.org/10.1109/ISGT-LA.2017.8126693
http://hdl.handle.net/11449/179658
identifier_str_mv 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017, v. 2017-January, p. 1-6.
10.1109/ISGT-LA.2017.8126693
2-s2.0-85043473852
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1-6
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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