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., IEEE
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://hdl.handle.net/11449/185110
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.
id UNSP_4b376886378d913920ab51d24c9ee45f
oai_identifier_str oai:repositorio.unesp.br:11449/185110
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Spatial-Temporal Model to Estimate the Load Curves of Charging Stations for Electric VehiclesCharging stationsElectric vehiclesMulti-agent systemsPower system planningThe 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.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Sao Paulo State Univ UNESP, Dept Elect Engn, Ilha Solteira, BrazilUniv Fed Abc, Engn Ctr Modeling & Appl Social Sci, Santo Andre, BrazilSao Paulo State Univ UNESP, Dept Elect Engn, Ilha Solteira, BrazilIeeeUniversidade Estadual Paulista (Unesp)Universidade Federal do ABC (UFABC)Morro-Mello, I. [UNESP]Padilha-Feltrin, A. [UNESP]Melo, J. D.IEEE2019-10-04T12:32:44Z2019-10-04T12:32:44Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject62017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America). New York: Ieee, 6 p., 2017.http://hdl.handle.net/11449/185110WOS:000451380200013Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America)info:eu-repo/semantics/openAccess2021-10-23T02:05:56Zoai:repositorio.unesp.br:11449/185110Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T02:05:56Repositório 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.
IEEE
author_role author
author2 Padilha-Feltrin, A. [UNESP]
Melo, J. D.
IEEE
author2_role author
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.
IEEE
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 2017-01-01
2019-10-04T12:32:44Z
2019-10-04T12:32:44Z
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 2017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America). New York: Ieee, 6 p., 2017.
http://hdl.handle.net/11449/185110
WOS:000451380200013
identifier_str_mv 2017 Ieee Pes Innovative Smart Grid Technologies Conference - Latin America (isgt Latin America). New York: Ieee, 6 p., 2017.
WOS:000451380200013
url http://hdl.handle.net/11449/185110
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)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 6
dc.publisher.none.fl_str_mv Ieee
publisher.none.fl_str_mv Ieee
dc.source.none.fl_str_mv Web of Science
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
_version_ 1803046605483409408

Registros relacionados