Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory

Detalhes bibliográficos
Autor(a) principal: Morro-Mello, Igoor [UNESP]
Data de Publicação: 2021
Outros Autores: Padilha-Feltrin, Antonio [UNESP], Melo, Joel D., Heymann, Fabian
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.energy.2021.121380
http://hdl.handle.net/11449/233224
Resumo: Fast charging stations for electric vehicles require a high-power demand, meaning that electricity distribution companies must define the connection locations within the distribution network to guarantee adequate power supply levels. Due to electric vehicle users' driving patterns and equipment's high costs, these stations must be concentrated in certain regions. This paper presents a methodology for assisting electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. First, possible connection points are analyzed with graph theory to find the least costly connection; this strategy prioritizes the current network elements' unused capacity. As a second step, the electric distribution network is analyzed after fast-charging stations have been connected, evaluating the networks' operational limits. The methodology is applied in a Brazilian city combining spatial information with a realistic representation of the network and network total supply capability to connect new loads. Model outcomes are spatial maps that help identify suitable connection locations, determine new capacity values, and calculate the necessary investment. We compare the proposed methodology with other conventional approaches, demonstrating how the developed methodology can assist distribution companies in reducing overall investment and operational costs of fast charging stations for electric vehicles.
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spelling Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theoryCharging stationsGeographic information systemGraph theoryPower distribution system planningSpatial analysisFast charging stations for electric vehicles require a high-power demand, meaning that electricity distribution companies must define the connection locations within the distribution network to guarantee adequate power supply levels. Due to electric vehicle users' driving patterns and equipment's high costs, these stations must be concentrated in certain regions. This paper presents a methodology for assisting electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. First, possible connection points are analyzed with graph theory to find the least costly connection; this strategy prioritizes the current network elements' unused capacity. As a second step, the electric distribution network is analyzed after fast-charging stations have been connected, evaluating the networks' operational limits. The methodology is applied in a Brazilian city combining spatial information with a realistic representation of the network and network total supply capability to connect new loads. Model outcomes are spatial maps that help identify suitable connection locations, determine new capacity values, and calculate the necessary investment. We compare the proposed methodology with other conventional approaches, demonstrating how the developed methodology can assist distribution companies in reducing overall investment and operational costs of fast charging stations for electric vehicles.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Estadual Paulista (UNESP)Instituto SENAI de Tecnologia em Automação (IST Automação)Universidade Estadual de Campinas (UNICAMP)Universidade Federal Do ABC (UFABC)Institute for Systems and Computer Engineering Technology and Science (INESC TEC)Universidade Estadual Paulista (UNESP)FAPESP: 2015/21972–6FAPESP: 2017/01909–3FAPESP: 2017/22577–9FAPESP: 2019/00466–6Universidade Estadual Paulista (UNESP)Instituto SENAI de Tecnologia em Automação (IST Automação)Universidade Estadual de Campinas (UNICAMP)Universidade Federal do ABC (UFABC)Technology and Science (INESC TEC)Morro-Mello, Igoor [UNESP]Padilha-Feltrin, Antonio [UNESP]Melo, Joel D.Heymann, Fabian2022-05-01T06:02:10Z2022-05-01T06:02:10Z2021-11-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.energy.2021.121380Energy, v. 235.0360-5442http://hdl.handle.net/11449/23322410.1016/j.energy.2021.1213802-s2.0-85109106088Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEnergyinfo:eu-repo/semantics/openAccess2024-07-04T19:06:04Zoai:repositorio.unesp.br:11449/233224Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:54:40.048933Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
title Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
spellingShingle Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
Morro-Mello, Igoor [UNESP]
Charging stations
Geographic information system
Graph theory
Power distribution system planning
Spatial analysis
title_short Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
title_full Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
title_fullStr Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
title_full_unstemmed Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
title_sort Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
author Morro-Mello, Igoor [UNESP]
author_facet Morro-Mello, Igoor [UNESP]
Padilha-Feltrin, Antonio [UNESP]
Melo, Joel D.
Heymann, Fabian
author_role author
author2 Padilha-Feltrin, Antonio [UNESP]
Melo, Joel D.
Heymann, Fabian
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Instituto SENAI de Tecnologia em Automação (IST Automação)
Universidade Estadual de Campinas (UNICAMP)
Universidade Federal do ABC (UFABC)
Technology and Science (INESC TEC)
dc.contributor.author.fl_str_mv Morro-Mello, Igoor [UNESP]
Padilha-Feltrin, Antonio [UNESP]
Melo, Joel D.
Heymann, Fabian
dc.subject.por.fl_str_mv Charging stations
Geographic information system
Graph theory
Power distribution system planning
Spatial analysis
topic Charging stations
Geographic information system
Graph theory
Power distribution system planning
Spatial analysis
description Fast charging stations for electric vehicles require a high-power demand, meaning that electricity distribution companies must define the connection locations within the distribution network to guarantee adequate power supply levels. Due to electric vehicle users' driving patterns and equipment's high costs, these stations must be concentrated in certain regions. This paper presents a methodology for assisting electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. First, possible connection points are analyzed with graph theory to find the least costly connection; this strategy prioritizes the current network elements' unused capacity. As a second step, the electric distribution network is analyzed after fast-charging stations have been connected, evaluating the networks' operational limits. The methodology is applied in a Brazilian city combining spatial information with a realistic representation of the network and network total supply capability to connect new loads. Model outcomes are spatial maps that help identify suitable connection locations, determine new capacity values, and calculate the necessary investment. We compare the proposed methodology with other conventional approaches, demonstrating how the developed methodology can assist distribution companies in reducing overall investment and operational costs of fast charging stations for electric vehicles.
publishDate 2021
dc.date.none.fl_str_mv 2021-11-15
2022-05-01T06:02:10Z
2022-05-01T06:02:10Z
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/10.1016/j.energy.2021.121380
Energy, v. 235.
0360-5442
http://hdl.handle.net/11449/233224
10.1016/j.energy.2021.121380
2-s2.0-85109106088
url http://dx.doi.org/10.1016/j.energy.2021.121380
http://hdl.handle.net/11449/233224
identifier_str_mv Energy, v. 235.
0360-5442
10.1016/j.energy.2021.121380
2-s2.0-85109106088
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Energy
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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