Transmission expansion planning by using DC and AC models and particle swarm optimization
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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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 |
|
_version_ |
1808129371231223808 |