Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1016/j.epsr.2022.108068 http://hdl.handle.net/11449/240084 |
Resumo: | The growth of subtransmission network aims at satisfying load growth, maintaining a contingency level, and providing a high quality and reliable electricity service. Utilities direct the investments to reinforce this system and thus a meshed network with multiple-point feeding to the transmission system arises. At this point, an efficient alternative to achieve these objectives is to carry out a diagnosis of the network architecture and, taking advantage of the switching capability, to plan the switching of the subtransmission lines. An optimal subtransmission switching approach is proposed based on constrained multi-objective optimization that deals with energy losses and reliability, in addition to using information on the characteristics of loads and generation. A simulation-based optimization framework is constructed using the non-dominated genetic classification algorithm NSGA-II in the optimization phase and reliability assessment during simulation phase. As a result, a set of non-dominated solutions approximating the Pareto front is obtained, which allows the planner to make decisions based on its priorities and needs. The performance of the proposal is assessed with a real subtransmission system of an Ecuadorian power utility. This approach to the operational planning of a meshed subtransmission network constitutes a powerful decision-making tool that could be adopted by distribution utilities. |
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Optimal subtransmission switching using a reliability simulation-based multi-objective optimization modelDistribution planningNSGA-II, optimal switchingreliabilitysubtransmission systemThe growth of subtransmission network aims at satisfying load growth, maintaining a contingency level, and providing a high quality and reliable electricity service. Utilities direct the investments to reinforce this system and thus a meshed network with multiple-point feeding to the transmission system arises. At this point, an efficient alternative to achieve these objectives is to carry out a diagnosis of the network architecture and, taking advantage of the switching capability, to plan the switching of the subtransmission lines. An optimal subtransmission switching approach is proposed based on constrained multi-objective optimization that deals with energy losses and reliability, in addition to using information on the characteristics of loads and generation. A simulation-based optimization framework is constructed using the non-dominated genetic classification algorithm NSGA-II in the optimization phase and reliability assessment during simulation phase. As a result, a set of non-dominated solutions approximating the Pareto front is obtained, which allows the planner to make decisions based on its priorities and needs. The performance of the proposal is assessed with a real subtransmission system of an Ecuadorian power utility. This approach to the operational planning of a meshed subtransmission network constitutes a powerful decision-making tool that could be adopted by distribution utilities.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Department of Electrical Engineering São Paulo State University – UNESP, SPDepartment of Planning CENTROSUR Electric Distribution UtilitySchool of Electrical Engineering University of CuencaSchool of Energy Engineering São Paulo State University – UNESPDepartment of Electrical Engineering São Paulo State University – UNESP, SPSchool of Energy Engineering São Paulo State University – UNESPFAPESP: 2015/21972-6FAPESP: 2017/02831-8Universidade Estadual Paulista (UNESP)CENTROSUR Electric Distribution UtilityUniversity of CuencaSergio, Zambrano-Asanza [UNESP]Tatiana, Proano BarrosStalin, Banegas DutanEdwin, Lema GuamanFredy, Franco John [UNESP]2023-03-01T20:00:49Z2023-03-01T20:00:49Z2022-09-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.epsr.2022.108068Electric Power Systems Research, v. 210.0378-7796http://hdl.handle.net/11449/24008410.1016/j.epsr.2022.1080682-s2.0-85130379388Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengElectric Power Systems Researchinfo:eu-repo/semantics/openAccess2023-03-01T20:00:49Zoai:repositorio.unesp.br:11449/240084Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:17:11.432653Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model |
title |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model |
spellingShingle |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model Sergio, Zambrano-Asanza [UNESP] Distribution planning NSGA-II, optimal switching reliability subtransmission system |
title_short |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model |
title_full |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model |
title_fullStr |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model |
title_full_unstemmed |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model |
title_sort |
Optimal subtransmission switching using a reliability simulation-based multi-objective optimization model |
author |
Sergio, Zambrano-Asanza [UNESP] |
author_facet |
Sergio, Zambrano-Asanza [UNESP] Tatiana, Proano Barros Stalin, Banegas Dutan Edwin, Lema Guaman Fredy, Franco John [UNESP] |
author_role |
author |
author2 |
Tatiana, Proano Barros Stalin, Banegas Dutan Edwin, Lema Guaman Fredy, Franco John [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) CENTROSUR Electric Distribution Utility University of Cuenca |
dc.contributor.author.fl_str_mv |
Sergio, Zambrano-Asanza [UNESP] Tatiana, Proano Barros Stalin, Banegas Dutan Edwin, Lema Guaman Fredy, Franco John [UNESP] |
dc.subject.por.fl_str_mv |
Distribution planning NSGA-II, optimal switching reliability subtransmission system |
topic |
Distribution planning NSGA-II, optimal switching reliability subtransmission system |
description |
The growth of subtransmission network aims at satisfying load growth, maintaining a contingency level, and providing a high quality and reliable electricity service. Utilities direct the investments to reinforce this system and thus a meshed network with multiple-point feeding to the transmission system arises. At this point, an efficient alternative to achieve these objectives is to carry out a diagnosis of the network architecture and, taking advantage of the switching capability, to plan the switching of the subtransmission lines. An optimal subtransmission switching approach is proposed based on constrained multi-objective optimization that deals with energy losses and reliability, in addition to using information on the characteristics of loads and generation. A simulation-based optimization framework is constructed using the non-dominated genetic classification algorithm NSGA-II in the optimization phase and reliability assessment during simulation phase. As a result, a set of non-dominated solutions approximating the Pareto front is obtained, which allows the planner to make decisions based on its priorities and needs. The performance of the proposal is assessed with a real subtransmission system of an Ecuadorian power utility. This approach to the operational planning of a meshed subtransmission network constitutes a powerful decision-making tool that could be adopted by distribution utilities. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-09-01 2023-03-01T20:00:49Z 2023-03-01T20:00:49Z |
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/10.1016/j.epsr.2022.108068 Electric Power Systems Research, v. 210. 0378-7796 http://hdl.handle.net/11449/240084 10.1016/j.epsr.2022.108068 2-s2.0-85130379388 |
url |
http://dx.doi.org/10.1016/j.epsr.2022.108068 http://hdl.handle.net/11449/240084 |
identifier_str_mv |
Electric Power Systems Research, v. 210. 0378-7796 10.1016/j.epsr.2022.108068 2-s2.0-85130379388 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Electric Power Systems Research |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1808128628482899968 |