Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s40313-014-0111-0 http://hdl.handle.net/11449/171615 |
Resumo: | This paper presents a spatial-temporal approach for estimating the load demand of battery electric vehicles (BEV) charging in small residential areas. This approach is especially suited for simulating the driving pattern of BEVs in cities without this kind of information. The service zone is divided into several sub-zones; each of these has a probability that represents how likely it is for a BEVs to cross the sub-zone. The driving pattern of BEVs is simulated using a multi-agent framework, which estimates the spatial distribution of these in a city. To determine the hourly charge in each place identified in the spatial area, the model considers the battery charging profile via two charging scenarios. The main contribution of this method is the estimation of BEV charging in feeders or transformers using small-scale simulation. The proposed approach was tested on a real distribution system of a mid-sized city in Brazil. For this specific system, the simulation was able to identify two different levels of agglomerations; when the worst-case scenario with a 20 % penetration is analyzed, an increase in peak demand up to 34.04 % was determined in the most affected part of the distribution system while the rest of the distribution system is almost unaffected. © 2014 Brazilian Society for Automatics - SBA. |
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Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areasBattery electric vehicleDistribution planningLoad estimationMulti-agentPercolation theory.This paper presents a spatial-temporal approach for estimating the load demand of battery electric vehicles (BEV) charging in small residential areas. This approach is especially suited for simulating the driving pattern of BEVs in cities without this kind of information. The service zone is divided into several sub-zones; each of these has a probability that represents how likely it is for a BEVs to cross the sub-zone. The driving pattern of BEVs is simulated using a multi-agent framework, which estimates the spatial distribution of these in a city. To determine the hourly charge in each place identified in the spatial area, the model considers the battery charging profile via two charging scenarios. The main contribution of this method is the estimation of BEV charging in feeders or transformers using small-scale simulation. The proposed approach was tested on a real distribution system of a mid-sized city in Brazil. For this specific system, the simulation was able to identify two different levels of agglomerations; when the worst-case scenario with a 20 % penetration is analyzed, an increase in peak demand up to 34.04 % was determined in the most affected part of the distribution system while the rest of the distribution system is almost unaffected. © 2014 Brazilian Society for Automatics - SBA.Department of Electrical Engineering, University of the State of Sao Paulo-UNESP, Ilha Solteira SPCenter for Engineering and Mathematical Sciences-CECE, University of the State University of West Parana-UNIOESTE, Iguaçu PRDepartment of Electrical Engineering, University of the State of Sao Paulo-UNESP, Ilha Solteira SPUniversidade Estadual Paulista (Unesp)Center for Engineering and Mathematical Sciences-CECE, University of the State University of West Parana-UNIOESTEMelo, Joel D. [UNESP]Carreno, Edgar M.Padilha-Feltrin, Antonio [UNESP]2018-12-11T16:56:15Z2018-12-11T16:56:15Z2014-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article470-480application/pdfhttp://dx.doi.org/10.1007/s40313-014-0111-0Journal of Control, Automation and Electrical Systems, v. 25, n. 4, p. 470-480, 2014.2195-38992195-3880http://hdl.handle.net/11449/17161510.1007/s40313-014-0111-02-s2.0-849037278072-s2.0-84903727807.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Control, Automation and Electrical Systems0,2740,274info:eu-repo/semantics/openAccess2024-07-04T19:06:26Zoai:repositorio.unesp.br:11449/171615Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:43:13.224551Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas |
title |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas |
spellingShingle |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas Melo, Joel D. [UNESP] Battery electric vehicle Distribution planning Load estimation Multi-agent Percolation theory. |
title_short |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas |
title_full |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas |
title_fullStr |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas |
title_full_unstemmed |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas |
title_sort |
Spatial-temporal simulation to estimate the load demand of battery electric vehicles charging in small residential areas |
author |
Melo, Joel D. [UNESP] |
author_facet |
Melo, Joel D. [UNESP] Carreno, Edgar M. Padilha-Feltrin, Antonio [UNESP] |
author_role |
author |
author2 |
Carreno, Edgar M. Padilha-Feltrin, Antonio [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Center for Engineering and Mathematical Sciences-CECE, University of the State University of West Parana-UNIOESTE |
dc.contributor.author.fl_str_mv |
Melo, Joel D. [UNESP] Carreno, Edgar M. Padilha-Feltrin, Antonio [UNESP] |
dc.subject.por.fl_str_mv |
Battery electric vehicle Distribution planning Load estimation Multi-agent Percolation theory. |
topic |
Battery electric vehicle Distribution planning Load estimation Multi-agent Percolation theory. |
description |
This paper presents a spatial-temporal approach for estimating the load demand of battery electric vehicles (BEV) charging in small residential areas. This approach is especially suited for simulating the driving pattern of BEVs in cities without this kind of information. The service zone is divided into several sub-zones; each of these has a probability that represents how likely it is for a BEVs to cross the sub-zone. The driving pattern of BEVs is simulated using a multi-agent framework, which estimates the spatial distribution of these in a city. To determine the hourly charge in each place identified in the spatial area, the model considers the battery charging profile via two charging scenarios. The main contribution of this method is the estimation of BEV charging in feeders or transformers using small-scale simulation. The proposed approach was tested on a real distribution system of a mid-sized city in Brazil. For this specific system, the simulation was able to identify two different levels of agglomerations; when the worst-case scenario with a 20 % penetration is analyzed, an increase in peak demand up to 34.04 % was determined in the most affected part of the distribution system while the rest of the distribution system is almost unaffected. © 2014 Brazilian Society for Automatics - SBA. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01 2018-12-11T16:56:15Z 2018-12-11T16:56:15Z |
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://dx.doi.org/10.1007/s40313-014-0111-0 Journal of Control, Automation and Electrical Systems, v. 25, n. 4, p. 470-480, 2014. 2195-3899 2195-3880 http://hdl.handle.net/11449/171615 10.1007/s40313-014-0111-0 2-s2.0-84903727807 2-s2.0-84903727807.pdf |
url |
http://dx.doi.org/10.1007/s40313-014-0111-0 http://hdl.handle.net/11449/171615 |
identifier_str_mv |
Journal of Control, Automation and Electrical Systems, v. 25, n. 4, p. 470-480, 2014. 2195-3899 2195-3880 10.1007/s40313-014-0111-0 2-s2.0-84903727807 2-s2.0-84903727807.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of Control, Automation and Electrical Systems 0,274 0,274 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
470-480 application/pdf |
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_ |
1808129109377679360 |