An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning

Detalhes bibliográficos
Autor(a) principal: Gallego, Luis A. [UNESP]
Data de Publicação: 2012
Outros Autores: Rider, Marcos J. [UNESP], Lavorato, Marina [UNESP], Paldilha-Feltrin, Antonio [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1155/2012/781041
http://hdl.handle.net/11449/73291
Resumo: An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem. Copyright © 2012 Luis A. Gallego et al.
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spelling An enhanced genetic algorithm to solve the static and multistage transmission network expansion planningComparative analysisEnhanced genetic algorithmsHigher efficiencyInitial populationLinear programming problemMultistage transmissionOptimal solutionsTransmission expansion planningGenetic algorithmsHeuristic algorithmsLinear programmingProblem solvingAn enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem. Copyright © 2012 Luis A. Gallego et al.Departamento de Engenharia Elétrica Faculdade de Engenharia de Ilha Solteira Universidade Estadual Paulista (UNESP), Avenida Brasil 56, 15385-000 Ilha Solteira, SPDepartamento de Engenharia Elétrica Faculdade de Engenharia de Ilha Solteira Universidade Estadual Paulista (UNESP), Avenida Brasil 56, 15385-000 Ilha Solteira, SPUniversidade Estadual Paulista (Unesp)Gallego, Luis A. [UNESP]Rider, Marcos J. [UNESP]Lavorato, Marina [UNESP]Paldilha-Feltrin, Antonio [UNESP]2014-05-27T11:26:27Z2014-05-27T11:26:27Z2012-04-23info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://dx.doi.org/10.1155/2012/781041Journal of Electrical and Computer Engineering.2090-01472090-0155http://hdl.handle.net/11449/7329110.1155/2012/7810412-s2.0-848598769102-s2.0-84859876910.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of Electrical and Computer Engineering0,1710,171info:eu-repo/semantics/openAccess2024-07-04T19:05:48Zoai:repositorio.unesp.br:11449/73291Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:36:48.523121Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
title An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
spellingShingle An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
Gallego, Luis A. [UNESP]
Comparative analysis
Enhanced genetic algorithms
Higher efficiency
Initial population
Linear programming problem
Multistage transmission
Optimal solutions
Transmission expansion planning
Genetic algorithms
Heuristic algorithms
Linear programming
Problem solving
title_short An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
title_full An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
title_fullStr An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
title_full_unstemmed An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
title_sort An enhanced genetic algorithm to solve the static and multistage transmission network expansion planning
author Gallego, Luis A. [UNESP]
author_facet Gallego, Luis A. [UNESP]
Rider, Marcos J. [UNESP]
Lavorato, Marina [UNESP]
Paldilha-Feltrin, Antonio [UNESP]
author_role author
author2 Rider, Marcos J. [UNESP]
Lavorato, Marina [UNESP]
Paldilha-Feltrin, Antonio [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Gallego, Luis A. [UNESP]
Rider, Marcos J. [UNESP]
Lavorato, Marina [UNESP]
Paldilha-Feltrin, Antonio [UNESP]
dc.subject.por.fl_str_mv Comparative analysis
Enhanced genetic algorithms
Higher efficiency
Initial population
Linear programming problem
Multistage transmission
Optimal solutions
Transmission expansion planning
Genetic algorithms
Heuristic algorithms
Linear programming
Problem solving
topic Comparative analysis
Enhanced genetic algorithms
Higher efficiency
Initial population
Linear programming problem
Multistage transmission
Optimal solutions
Transmission expansion planning
Genetic algorithms
Heuristic algorithms
Linear programming
Problem solving
description An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem. Copyright © 2012 Luis A. Gallego et al.
publishDate 2012
dc.date.none.fl_str_mv 2012-04-23
2014-05-27T11:26:27Z
2014-05-27T11:26:27Z
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/10.1155/2012/781041
Journal of Electrical and Computer Engineering.
2090-0147
2090-0155
http://hdl.handle.net/11449/73291
10.1155/2012/781041
2-s2.0-84859876910
2-s2.0-84859876910.pdf
url http://dx.doi.org/10.1155/2012/781041
http://hdl.handle.net/11449/73291
identifier_str_mv Journal of Electrical and Computer Engineering.
2090-0147
2090-0155
10.1155/2012/781041
2-s2.0-84859876910
2-s2.0-84859876910.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Journal of Electrical and Computer Engineering
0,171
0,171
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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_ 1808128387951099904

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