Transmission expansion planning by using DC and AC models and particle swarm optimization

Detalhes bibliográficos
Autor(a) principal: Torres, Santiago P.
Data de Publicação: 2012
Outros Autores: Castro, Carlos A., Rider, Marcos J. [UNESP]
Tipo de documento: Capítulo de livro
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.4018/978-1-4666-2666-9.ch013
http://hdl.handle.net/11449/227710
Resumo: The Transmission Expansion Planning (TEP) entails to determine all the changes needed in the electric transmission system infrastructure in order to allow the balance between the projected demand and the power supply, at minimum investment and operational costs. In some type of TEP studies, the DC model is used for the medium and long term time frame, while the AC model is used for the short term. This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO, Evolutionary PSO, and Adaptive PSO. Comparisons among these PSO variants in terms of robustness, quality of the solution, and number of function evaluations are carried out. Tests, detailed analysis, guidelines, and particularities are shown in order to apply the PSO techniques for realistic systems. © 2013, IGI Global.
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spelling Transmission expansion planning by using DC and AC models and particle swarm optimizationThe Transmission Expansion Planning (TEP) entails to determine all the changes needed in the electric transmission system infrastructure in order to allow the balance between the projected demand and the power supply, at minimum investment and operational costs. In some type of TEP studies, the DC model is used for the medium and long term time frame, while the AC model is used for the short term. This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO, Evolutionary PSO, and Adaptive PSO. Comparisons among these PSO variants in terms of robustness, quality of the solution, and number of function evaluations are carried out. Tests, detailed analysis, guidelines, and particularities are shown in order to apply the PSO techniques for realistic systems. © 2013, IGI Global.Power Systems Department University of Campinas (UNICAMP)University of Campinas (UNICAMP)Electrical Engineering Department São Paulo State University (UNESP), Ilha SolteiraElectrical Engineering Department São Paulo State University (UNESP), Ilha SolteiraUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (UNESP)Torres, Santiago P.Castro, Carlos A.Rider, Marcos J. [UNESP]2022-04-29T07:14:47Z2022-04-29T07:14:47Z2012-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bookPart260-284http://dx.doi.org/10.4018/978-1-4666-2666-9.ch013Swarm Intelligence for Electric and Electronic Engineering, p. 260-284.http://hdl.handle.net/11449/22771010.4018/978-1-4666-2666-9.ch0132-s2.0-84899278462Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSwarm Intelligence for Electric and Electronic Engineeringinfo:eu-repo/semantics/openAccess2024-07-04T19:06:58Zoai:repositorio.unesp.br:11449/227710Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:53:58.499628Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Transmission expansion planning by using DC and AC models and particle swarm optimization
title Transmission expansion planning by using DC and AC models and particle swarm optimization
spellingShingle Transmission expansion planning by using DC and AC models and particle swarm optimization
Torres, Santiago P.
title_short Transmission expansion planning by using DC and AC models and particle swarm optimization
title_full Transmission expansion planning by using DC and AC models and particle swarm optimization
title_fullStr Transmission expansion planning by using DC and AC models and particle swarm optimization
title_full_unstemmed Transmission expansion planning by using DC and AC models and particle swarm optimization
title_sort Transmission expansion planning by using DC and AC models and particle swarm optimization
author Torres, Santiago P.
author_facet Torres, Santiago P.
Castro, Carlos A.
Rider, Marcos J. [UNESP]
author_role author
author2 Castro, Carlos A.
Rider, Marcos J. [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual de Campinas (UNICAMP)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Torres, Santiago P.
Castro, Carlos A.
Rider, Marcos J. [UNESP]
description The Transmission Expansion Planning (TEP) entails to determine all the changes needed in the electric transmission system infrastructure in order to allow the balance between the projected demand and the power supply, at minimum investment and operational costs. In some type of TEP studies, the DC model is used for the medium and long term time frame, while the AC model is used for the short term. This chapter proposes a load shedding based TEP formulation using the DC and AC model, and four Particle Swarm Optimization (PSO) based algorithms applied to the TEP problem: Global PSO, Local PSO, Evolutionary PSO, and Adaptive PSO. Comparisons among these PSO variants in terms of robustness, quality of the solution, and number of function evaluations are carried out. Tests, detailed analysis, guidelines, and particularities are shown in order to apply the PSO techniques for realistic systems. © 2013, IGI Global.
publishDate 2012
dc.date.none.fl_str_mv 2012-12-01
2022-04-29T07:14:47Z
2022-04-29T07:14:47Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.4018/978-1-4666-2666-9.ch013
Swarm Intelligence for Electric and Electronic Engineering, p. 260-284.
http://hdl.handle.net/11449/227710
10.4018/978-1-4666-2666-9.ch013
2-s2.0-84899278462
url http://dx.doi.org/10.4018/978-1-4666-2666-9.ch013
http://hdl.handle.net/11449/227710
identifier_str_mv Swarm Intelligence for Electric and Electronic Engineering, p. 260-284.
10.4018/978-1-4666-2666-9.ch013
2-s2.0-84899278462
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Swarm Intelligence for Electric and Electronic Engineering
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 260-284
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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