High-performance hybrid genetic algorithm to solve transmission network expansion planning
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1049/iet-gtd.2016.0511 http://hdl.handle.net/11449/162776 |
Resumo: | In this study, a high-performance hybrid genetic algorithm (HGA) is proposed to solve static and multistage transmission network expansion planning (TNEP) problem. The main features of the HGA are: (i) it avoids homogenised solutions by using a special genetic algorithm as the backbone of the procedure, (ii) uses a powerful path-relinking algorithm for the deep exploration of local solutions, (iii) employs an efficient constructive heuristic algorithm for finding high-quality initial solutions and for improving solution qualities and (iv) uses a fast relaxation strategy for solving the linear programming problems required for calculating the fitness functions. This procedure will result in an intelligent exploration of a large search space in less amount of time. The proposed methodology is tested with three electrical systems: South Brazilian 46-bus, Colombian 93-bus and the North-Northeast Brazilian 87-bus. |
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Repositório Institucional da UNESP |
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High-performance hybrid genetic algorithm to solve transmission network expansion planningpower transmission planninggenetic algorithmslinear programminghigh-performance hybrid genetic algorithmtransmission network expansion planningstatic transmission network expansion planning problemmultistage TNEP problemhigh-performance HGApath-relinking algorithmconstructive heuristic algorithmfast relaxation strategylinear programming problemfitness functionSouth Brazilian 46-bus electrical systemColombian 93-bus electrical systemNorth-Northeast Brazilian 87-bus electrical systemIn this study, a high-performance hybrid genetic algorithm (HGA) is proposed to solve static and multistage transmission network expansion planning (TNEP) problem. The main features of the HGA are: (i) it avoids homogenised solutions by using a special genetic algorithm as the backbone of the procedure, (ii) uses a powerful path-relinking algorithm for the deep exploration of local solutions, (iii) employs an efficient constructive heuristic algorithm for finding high-quality initial solutions and for improving solution qualities and (iv) uses a fast relaxation strategy for solving the linear programming problems required for calculating the fitness functions. This procedure will result in an intelligent exploration of a large search space in less amount of time. The proposed methodology is tested with three electrical systems: South Brazilian 46-bus, Colombian 93-bus and the North-Northeast Brazilian 87-bus.Univ Estadual Londrina, Dept Elect Engn, Londrina, BrazilUniv Fed Goias, Elect Mech & Comp Engn Sch, Goiania, Go, BrazilCarnegie Mellon Univ, Engn & Publ Policy Dept, Pittsburgh, PA 15213 USAUniv Estadual Paulista, Dept Elect Engn, Ilha Solteira, BrazilUniv Estadual Paulista, Dept Elect Engn, Ilha Solteira, BrazilInst Engineering Technology-ietUniversidade Estadual de Londrina (UEL)Universidade Federal de Goiás (UFG)Carnegie Mellon UnivUniversidade Estadual Paulista (Unesp)Gallego, Luis A.Garces, Lina P.Rahmani, MohsenRomero, Ruben A. [UNESP]2018-11-26T17:31:19Z2018-11-26T17:31:19Z2017-03-30info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1111-1118http://dx.doi.org/10.1049/iet-gtd.2016.0511Iet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 11, n. 5, p. 1111-1118, 2017.1751-8687http://hdl.handle.net/11449/16277610.1049/iet-gtd.2016.0511WOS:000400850600004Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengIet Generation Transmission & Distribution0,907info:eu-repo/semantics/openAccess2021-10-23T14:54:22Zoai:repositorio.unesp.br:11449/162776Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T14:54:22Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
High-performance hybrid genetic algorithm to solve transmission network expansion planning |
title |
High-performance hybrid genetic algorithm to solve transmission network expansion planning |
spellingShingle |
High-performance hybrid genetic algorithm to solve transmission network expansion planning Gallego, Luis A. power transmission planning genetic algorithms linear programming high-performance hybrid genetic algorithm transmission network expansion planning static transmission network expansion planning problem multistage TNEP problem high-performance HGA path-relinking algorithm constructive heuristic algorithm fast relaxation strategy linear programming problem fitness function South Brazilian 46-bus electrical system Colombian 93-bus electrical system North-Northeast Brazilian 87-bus electrical system |
title_short |
High-performance hybrid genetic algorithm to solve transmission network expansion planning |
title_full |
High-performance hybrid genetic algorithm to solve transmission network expansion planning |
title_fullStr |
High-performance hybrid genetic algorithm to solve transmission network expansion planning |
title_full_unstemmed |
High-performance hybrid genetic algorithm to solve transmission network expansion planning |
title_sort |
High-performance hybrid genetic algorithm to solve transmission network expansion planning |
author |
Gallego, Luis A. |
author_facet |
Gallego, Luis A. Garces, Lina P. Rahmani, Mohsen Romero, Ruben A. [UNESP] |
author_role |
author |
author2 |
Garces, Lina P. Rahmani, Mohsen Romero, Ruben A. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Londrina (UEL) Universidade Federal de Goiás (UFG) Carnegie Mellon Univ Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Gallego, Luis A. Garces, Lina P. Rahmani, Mohsen Romero, Ruben A. [UNESP] |
dc.subject.por.fl_str_mv |
power transmission planning genetic algorithms linear programming high-performance hybrid genetic algorithm transmission network expansion planning static transmission network expansion planning problem multistage TNEP problem high-performance HGA path-relinking algorithm constructive heuristic algorithm fast relaxation strategy linear programming problem fitness function South Brazilian 46-bus electrical system Colombian 93-bus electrical system North-Northeast Brazilian 87-bus electrical system |
topic |
power transmission planning genetic algorithms linear programming high-performance hybrid genetic algorithm transmission network expansion planning static transmission network expansion planning problem multistage TNEP problem high-performance HGA path-relinking algorithm constructive heuristic algorithm fast relaxation strategy linear programming problem fitness function South Brazilian 46-bus electrical system Colombian 93-bus electrical system North-Northeast Brazilian 87-bus electrical system |
description |
In this study, a high-performance hybrid genetic algorithm (HGA) is proposed to solve static and multistage transmission network expansion planning (TNEP) problem. The main features of the HGA are: (i) it avoids homogenised solutions by using a special genetic algorithm as the backbone of the procedure, (ii) uses a powerful path-relinking algorithm for the deep exploration of local solutions, (iii) employs an efficient constructive heuristic algorithm for finding high-quality initial solutions and for improving solution qualities and (iv) uses a fast relaxation strategy for solving the linear programming problems required for calculating the fitness functions. This procedure will result in an intelligent exploration of a large search space in less amount of time. The proposed methodology is tested with three electrical systems: South Brazilian 46-bus, Colombian 93-bus and the North-Northeast Brazilian 87-bus. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-03-30 2018-11-26T17:31:19Z 2018-11-26T17:31:19Z |
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/10.1049/iet-gtd.2016.0511 Iet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 11, n. 5, p. 1111-1118, 2017. 1751-8687 http://hdl.handle.net/11449/162776 10.1049/iet-gtd.2016.0511 WOS:000400850600004 |
url |
http://dx.doi.org/10.1049/iet-gtd.2016.0511 http://hdl.handle.net/11449/162776 |
identifier_str_mv |
Iet Generation Transmission & Distribution. Hertford: Inst Engineering Technology-iet, v. 11, n. 5, p. 1111-1118, 2017. 1751-8687 10.1049/iet-gtd.2016.0511 WOS:000400850600004 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Iet Generation Transmission & Distribution 0,907 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1111-1118 |
dc.publisher.none.fl_str_mv |
Inst Engineering Technology-iet |
publisher.none.fl_str_mv |
Inst Engineering Technology-iet |
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_ |
1803046833535057920 |