Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model

Detalhes bibliográficos
Autor(a) principal: Pourakbari-Kasmaei, Mandi [UNESP]
Data de Publicação: 2018
Outros Autores: Sanches Mantovani, Jose Roberto [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1016/j.ijepes.2017.11.010
http://hdl.handle.net/11449/163833
Resumo: There is increasing evidence of the shortage of solver-based models for solving logically-constrained AC optimal power flow problem (LCOPF). Although in the literature the heuristic-based models have been widely used to handle the LCOPF problems with logical terms such as conditional statements, logical-and, logical-or, etc., their requirement of several trials and adjustments plagues finding a trustworthy solution. On the other hand, a well-defined solver-based model is of much interest in practice, due to rapidity and precision in finding an optimal solution. To remedy this shortcoming, in this paper we provide a solver-friendly procedure to recast the logical constraints to solver-based mixed-integer nonlinear programming (MINLP) terms. We specifically investigate the recasting of logical constraints into the terms of the objective function, so it facilitates the pre-solving and probing techniques of commercial solvers and consequently results in a higher computational efficiency. By applying this recast method to the problem, two sub-power- and sub-function-based MINLP models, namely SP-MINLP and SF-MINLP, respectively, are proposed. Results not only show the superiority of the proposed models in finding a better optimal solution, compared to the existing approaches in the literature, but also the effectiveness and computational tractability in solving large-scale power systems under different configurations.
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spelling Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming modelFACTS devicesLogical constraintMixed-integer nonlinear programmingOptimal power flowSolver-based modelNon-smooth termsThere is increasing evidence of the shortage of solver-based models for solving logically-constrained AC optimal power flow problem (LCOPF). Although in the literature the heuristic-based models have been widely used to handle the LCOPF problems with logical terms such as conditional statements, logical-and, logical-or, etc., their requirement of several trials and adjustments plagues finding a trustworthy solution. On the other hand, a well-defined solver-based model is of much interest in practice, due to rapidity and precision in finding an optimal solution. To remedy this shortcoming, in this paper we provide a solver-friendly procedure to recast the logical constraints to solver-based mixed-integer nonlinear programming (MINLP) terms. We specifically investigate the recasting of logical constraints into the terms of the objective function, so it facilitates the pre-solving and probing techniques of commercial solvers and consequently results in a higher computational efficiency. By applying this recast method to the problem, two sub-power- and sub-function-based MINLP models, namely SP-MINLP and SF-MINLP, respectively, are proposed. Results not only show the superiority of the proposed models in finding a better optimal solution, compared to the existing approaches in the literature, but also the effectiveness and computational tractability in solving large-scale power systems under different configurations.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)State Univ Sao Paulo, Dept Elect Engn, Ilha Solteira, BrazilState Univ Sao Paulo, Dept Elect Engn, Ilha Solteira, BrazilFAPESP: 2014/22828-3FAPESP: 2016/14319-7CNPq: 305371/2012-6Elsevier B.V.Universidade Estadual Paulista (Unesp)Pourakbari-Kasmaei, Mandi [UNESP]Sanches Mantovani, Jose Roberto [UNESP]2018-11-26T17:45:08Z2018-11-26T17:45:08Z2018-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article240-249application/pdfhttp://dx.doi.org/10.1016/j.ijepes.2017.11.010International Journal Of Electrical Power & Energy Systems. Oxford: Elsevier Sci Ltd, v. 97, p. 240-249, 2018.0142-0615http://hdl.handle.net/11449/16383310.1016/j.ijepes.2017.11.010WOS:000424720900023WOS000424720900023.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal Of Electrical Power & Energy Systems1,276info:eu-repo/semantics/openAccess2024-07-04T19:06:03Zoai:repositorio.unesp.br:11449/163833Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:57:23.606027Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
title Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
spellingShingle Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
Pourakbari-Kasmaei, Mandi [UNESP]
FACTS devices
Logical constraint
Mixed-integer nonlinear programming
Optimal power flow
Solver-based model
Non-smooth terms
title_short Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
title_full Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
title_fullStr Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
title_full_unstemmed Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
title_sort Logically constrained optimal power flow: Solver-based mixed-integer nonlinear programming model
author Pourakbari-Kasmaei, Mandi [UNESP]
author_facet Pourakbari-Kasmaei, Mandi [UNESP]
Sanches Mantovani, Jose Roberto [UNESP]
author_role author
author2 Sanches Mantovani, Jose Roberto [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv Pourakbari-Kasmaei, Mandi [UNESP]
Sanches Mantovani, Jose Roberto [UNESP]
dc.subject.por.fl_str_mv FACTS devices
Logical constraint
Mixed-integer nonlinear programming
Optimal power flow
Solver-based model
Non-smooth terms
topic FACTS devices
Logical constraint
Mixed-integer nonlinear programming
Optimal power flow
Solver-based model
Non-smooth terms
description There is increasing evidence of the shortage of solver-based models for solving logically-constrained AC optimal power flow problem (LCOPF). Although in the literature the heuristic-based models have been widely used to handle the LCOPF problems with logical terms such as conditional statements, logical-and, logical-or, etc., their requirement of several trials and adjustments plagues finding a trustworthy solution. On the other hand, a well-defined solver-based model is of much interest in practice, due to rapidity and precision in finding an optimal solution. To remedy this shortcoming, in this paper we provide a solver-friendly procedure to recast the logical constraints to solver-based mixed-integer nonlinear programming (MINLP) terms. We specifically investigate the recasting of logical constraints into the terms of the objective function, so it facilitates the pre-solving and probing techniques of commercial solvers and consequently results in a higher computational efficiency. By applying this recast method to the problem, two sub-power- and sub-function-based MINLP models, namely SP-MINLP and SF-MINLP, respectively, are proposed. Results not only show the superiority of the proposed models in finding a better optimal solution, compared to the existing approaches in the literature, but also the effectiveness and computational tractability in solving large-scale power systems under different configurations.
publishDate 2018
dc.date.none.fl_str_mv 2018-11-26T17:45:08Z
2018-11-26T17:45:08Z
2018-04-01
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.ijepes.2017.11.010
International Journal Of Electrical Power & Energy Systems. Oxford: Elsevier Sci Ltd, v. 97, p. 240-249, 2018.
0142-0615
http://hdl.handle.net/11449/163833
10.1016/j.ijepes.2017.11.010
WOS:000424720900023
WOS000424720900023.pdf
url http://dx.doi.org/10.1016/j.ijepes.2017.11.010
http://hdl.handle.net/11449/163833
identifier_str_mv International Journal Of Electrical Power & Energy Systems. Oxford: Elsevier Sci Ltd, v. 97, p. 240-249, 2018.
0142-0615
10.1016/j.ijepes.2017.11.010
WOS:000424720900023
WOS000424720900023.pdf
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv International Journal Of Electrical Power & Energy Systems
1,276
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 240-249
application/pdf
dc.publisher.none.fl_str_mv Elsevier B.V.
publisher.none.fl_str_mv Elsevier B.V.
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
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