Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , |
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. |
id |
UNSP_d32883d8f5a35a282731288b32f1bcba |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/233224 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
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 |
|
_version_ |
1808128719548579840 |