Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs

This paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and wel...

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Detalles Bibliográficos
Autores: Danilo Montoya, Oscar, Chamorro Vera, Harold Rene, Alvarado Barrios, Lázaro, Gil González, Walter, Orozco Henao, César
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/4689
Acceso en línea:https://hdl.handle.net/20.500.12412/4689
Access Level:acceso abierto
Palabra clave:Annual operational cost minimization
Chu and Beasley genetic algorithm (CBGA)
Daily active and reactive demand curves
Distribution static compensators (D-STATCOMs)
Radial distribution networks
Reactive power compensation
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spelling Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMsDanilo Montoya, OscarChamorro Vera, Harold ReneAlvarado Barrios, LázaroGil González, WalterOrozco Henao, CésarAnnual operational cost minimizationChu and Beasley genetic algorithm (CBGA)Daily active and reactive demand curvesDistribution static compensators (D-STATCOMs)Radial distribution networksReactive power compensationThis paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and well-known Chu and Beasley genetic algorithm, which employs an integer codification to select the nodes where these will be installed. To determine the optimal sizes of the D-STATCOMs, a second-order cone programming reformulation of the optimal power flow problem is employed with the aim of minimizing the total costs of the daily energy losses. The objective function considered in this study is the minimization of the annual operative costs associated with energy losses and installation investments in D-STATCOMs. This objective function is subject to classical power balance constraints and device capabilities, which generates a mixed-integer nonlinear programming model that is solved with the proposed genetic-convex strategy. Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software.2021info:eu-repo/semantics/articlehttps://hdl.handle.net/20.500.12412/4689reponame:Brújulainstname:Universidad Loyola AndalucíaInglésThe first author was supported by the Centro de Investigación y Desarrollo Científico de la Universidad Distrital Francisco José de Caldas, under grant 1643-12-2020 associated with the project “Desarrollo de una metodología de optimización para la gestión óptima de recursos energéticos distribuidos en redes de distribución de energía eléctrica” and in part by the Dirección de Investigaciones de la Universidad Tecnológica de Bolívar, under grant PS2020002 associated with the project “Ubicación óptima de bancos de capacitores de paso fijo en redes eléctricas de distribución para reducción de costos y pérdidas de energía: Aplicación de métodos exactos y metaheurísticos.”http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/46892026-06-24T12:48:37Z
dc.title.none.fl_str_mv Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
title Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
spellingShingle Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
Danilo Montoya, Oscar
Annual operational cost minimization
Chu and Beasley genetic algorithm (CBGA)
Daily active and reactive demand curves
Distribution static compensators (D-STATCOMs)
Radial distribution networks
Reactive power compensation
title_short Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
title_full Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
title_fullStr Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
title_full_unstemmed Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
title_sort Genetic-Convex Model for Dynamic Reactive Power Compensation in Distribution Networks Using D-STATCOMs
dc.creator.none.fl_str_mv Danilo Montoya, Oscar
Chamorro Vera, Harold Rene
Alvarado Barrios, Lázaro
Gil González, Walter
Orozco Henao, César
author Danilo Montoya, Oscar
author_facet Danilo Montoya, Oscar
Chamorro Vera, Harold Rene
Alvarado Barrios, Lázaro
Gil González, Walter
Orozco Henao, César
author_role author
author2 Chamorro Vera, Harold Rene
Alvarado Barrios, Lázaro
Gil González, Walter
Orozco Henao, César
author2_role author
author
author
author
dc.subject.none.fl_str_mv Annual operational cost minimization
Chu and Beasley genetic algorithm (CBGA)
Daily active and reactive demand curves
Distribution static compensators (D-STATCOMs)
Radial distribution networks
Reactive power compensation
topic Annual operational cost minimization
Chu and Beasley genetic algorithm (CBGA)
Daily active and reactive demand curves
Distribution static compensators (D-STATCOMs)
Radial distribution networks
Reactive power compensation
description This paper proposes a new hybrid master–slave optimization approach to address the problem of the optimal placement and sizing of distribution static compensators (D-STATCOMs) in electrical distribution grids. The optimal location of the D-STATCOMs is identified by implementing the classical and well-known Chu and Beasley genetic algorithm, which employs an integer codification to select the nodes where these will be installed. To determine the optimal sizes of the D-STATCOMs, a second-order cone programming reformulation of the optimal power flow problem is employed with the aim of minimizing the total costs of the daily energy losses. The objective function considered in this study is the minimization of the annual operative costs associated with energy losses and installation investments in D-STATCOMs. This objective function is subject to classical power balance constraints and device capabilities, which generates a mixed-integer nonlinear programming model that is solved with the proposed genetic-convex strategy. Numerical validations in the 33-node test feeder with radial configuration show the proposed genetic-convex model’s effectiveness to minimize the annual operative costs of the grid when compared with the optimization solvers available in GAMS software.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12412/4689
url https://hdl.handle.net/20.500.12412/4689
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The first author was supported by the Centro de Investigación y Desarrollo Científico de la Universidad Distrital Francisco José de Caldas, under grant 1643-12-2020 associated with the project “Desarrollo de una metodología de optimización para la gestión óptima de recursos energéticos distribuidos en redes de distribución de energía eléctrica” and in part by the Dirección de Investigaciones de la Universidad Tecnológica de Bolívar, under grant PS2020002 associated with the project “Ubicación óptima de bancos de capacitores de paso fijo en redes eléctricas de distribución para reducción de costos y pérdidas de energía: Aplicación de métodos exactos y metaheurísticos.”
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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