Spatial-Temporal model to estimate the load curves of charging stations for electric vehicles
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808128322222161920 |