A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/JSYST.2013.2290973 http://hdl.handle.net/11449/160993 |
Resumo: | This paper presents an enhanced evolutionary algorithm to solve the static distribution substation planning problem within large distribution networks. It is based on a deterministic heuristic algorithm to find the approximate substation service areas for each substation and an expert selection strategy that increases the convergence chance to a global optimal solution. The introduced algorithm takes different electrical constraints such as voltage drops, power flow, radial flow constraints, and all prevalent cost indices into consideration. In addition, effects of unreliability within network feeders and substations are investigated on the obtained layouts. The developed method is applied to four benchmark test systems and an actual large-scale distribution system with about 140 000 customers, followed by a discussion on results. |
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A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation PlanningDistribution expansion planning (DEP)evolutionary algorithm (EA)heuristic algorithmsubstation planningThis paper presents an enhanced evolutionary algorithm to solve the static distribution substation planning problem within large distribution networks. It is based on a deterministic heuristic algorithm to find the approximate substation service areas for each substation and an expert selection strategy that increases the convergence chance to a global optimal solution. The introduced algorithm takes different electrical constraints such as voltage drops, power flow, radial flow constraints, and all prevalent cost indices into consideration. In addition, effects of unreliability within network feeders and substations are investigated on the obtained layouts. The developed method is applied to four benchmark test systems and an actual large-scale distribution system with about 140 000 customers, followed by a discussion on results.Research Center of Power System Operation and Planning Studies, University of TehranConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Amirkabir Univ Technol, Sch Elect Engn, Tehran, IranUniv Tehran, Sch Elect & Comp Engn, Tehran, IranUniv Estadual Paulista, Dept Elect Engn, Fac Engn Ilha Solteira, BR-15385000 Ilha Solteira, SP, BrazilUniv Estadual Paulista, Dept Elect Engn, Fac Engn Ilha Solteira, BR-15385000 Ilha Solteira, SP, BrazilIeee-inst Electrical Electronics Engineers IncAmirkabir Univ TechnolUniv TehranUniversidade Estadual Paulista (Unesp)Mazhari, Seyed MahdiMonsef, HassanRomero, Ruben [UNESP]2018-11-26T16:18:43Z2018-11-26T16:18:43Z2015-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1396-1408application/pdfhttp://dx.doi.org/10.1109/JSYST.2013.2290973Ieee Systems Journal. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 9, n. 4, p. 1396-1408, 2015.1932-8184http://hdl.handle.net/11449/16099310.1109/JSYST.2013.2290973WOS:000365406200028WOS000365406200028.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIeee Systems Journal0,595info:eu-repo/semantics/openAccess2024-07-04T19:06:13Zoai:repositorio.unesp.br:11449/160993Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-07-04T19:06:13Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning |
title |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning |
spellingShingle |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning Mazhari, Seyed Mahdi Distribution expansion planning (DEP) evolutionary algorithm (EA) heuristic algorithm substation planning |
title_short |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning |
title_full |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning |
title_fullStr |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning |
title_full_unstemmed |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning |
title_sort |
A Hybrid Heuristic and Evolutionary Algorithm for Distribution Substation Planning |
author |
Mazhari, Seyed Mahdi |
author_facet |
Mazhari, Seyed Mahdi Monsef, Hassan Romero, Ruben [UNESP] |
author_role |
author |
author2 |
Monsef, Hassan Romero, Ruben [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Amirkabir Univ Technol Univ Tehran Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Mazhari, Seyed Mahdi Monsef, Hassan Romero, Ruben [UNESP] |
dc.subject.por.fl_str_mv |
Distribution expansion planning (DEP) evolutionary algorithm (EA) heuristic algorithm substation planning |
topic |
Distribution expansion planning (DEP) evolutionary algorithm (EA) heuristic algorithm substation planning |
description |
This paper presents an enhanced evolutionary algorithm to solve the static distribution substation planning problem within large distribution networks. It is based on a deterministic heuristic algorithm to find the approximate substation service areas for each substation and an expert selection strategy that increases the convergence chance to a global optimal solution. The introduced algorithm takes different electrical constraints such as voltage drops, power flow, radial flow constraints, and all prevalent cost indices into consideration. In addition, effects of unreliability within network feeders and substations are investigated on the obtained layouts. The developed method is applied to four benchmark test systems and an actual large-scale distribution system with about 140 000 customers, followed by a discussion on results. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-12-01 2018-11-26T16:18:43Z 2018-11-26T16:18:43Z |
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/10.1109/JSYST.2013.2290973 Ieee Systems Journal. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 9, n. 4, p. 1396-1408, 2015. 1932-8184 http://hdl.handle.net/11449/160993 10.1109/JSYST.2013.2290973 WOS:000365406200028 WOS000365406200028.pdf |
url |
http://dx.doi.org/10.1109/JSYST.2013.2290973 http://hdl.handle.net/11449/160993 |
identifier_str_mv |
Ieee Systems Journal. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 9, n. 4, p. 1396-1408, 2015. 1932-8184 10.1109/JSYST.2013.2290973 WOS:000365406200028 WOS000365406200028.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Ieee Systems Journal 0,595 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1396-1408 application/pdf |
dc.publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
publisher.none.fl_str_mv |
Ieee-inst Electrical Electronics Engineers Inc |
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_ |
1807278724658233344 |