Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva

Introducción: El problema del despacho óptimo de potencia reactiva (DOPR) consiste en encontrar la configuración óptima de diferentes recursos de potencia reactiva para minimizar las pérdidas de potencia del sistema. El DOPR es un problema complejo de optimización combinatorial que involucra variabl...

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Autores:: Londoño Tamayo, Daniel Camilo
López Lezama, Jesús María
Villa Acevedo, Walter Mauricio

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Corporación Universidad de la Costa

Repositorio:: REDICUC - Repositorio CUC

Idioma:: spa

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dc.title.spa.fl_str_mv	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva
dc.title.translated.eng.fl_str_mv	Mean-Variance Mapping Optimization Algorithm Applied to the Optimal Reactive Power Dispatch
title	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva
spellingShingle	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva metaheuristic techniques power loss minimization constraint handling mean-variance mapping optimization reactive power potencia reactiva optimización de mapeo de media-varianza técnicas metaheurísticas minimización de pérdidas manejo de restricciones
title_short	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva
title_full	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva
title_fullStr	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva
title_full_unstemmed	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva
title_sort	Algoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia Reactiva
dc.creator.fl_str_mv	Londoño Tamayo, Daniel Camilo López Lezama, Jesús María Villa Acevedo, Walter Mauricio
dc.contributor.author.spa.fl_str_mv	Londoño Tamayo, Daniel Camilo López Lezama, Jesús María Villa Acevedo, Walter Mauricio
dc.subject.eng.fl_str_mv	metaheuristic techniques power loss minimization constraint handling mean-variance mapping optimization reactive power
topic	metaheuristic techniques power loss minimization constraint handling mean-variance mapping optimization reactive power potencia reactiva optimización de mapeo de media-varianza técnicas metaheurísticas minimización de pérdidas manejo de restricciones
dc.subject.spa.fl_str_mv	potencia reactiva optimización de mapeo de media-varianza técnicas metaheurísticas minimización de pérdidas manejo de restricciones
description	Introducción: El problema del despacho óptimo de potencia reactiva (DOPR) consiste en encontrar la configuración óptima de diferentes recursos de potencia reactiva para minimizar las pérdidas de potencia del sistema. El DOPR es un problema complejo de optimización combinatorial que involucra variables discretas y continuas, así como una función objetivo no lineal y restricciones no lineales. Objetivo: En este artículo se busca comparar el desempeño del algoritmo de optimización de mapeo de media varianza (MVMO, por sus siglas en inglés) con otras técnicas reportadas en la literatura especializada aplicadas a la solución del DOPR. Metodología: En el algoritmo MVMO se aplican dos enfoques diferentes de manejo de restricciones: penalización convencional de las desviaciones de las soluciones factibles y penalización por medio del producto de subfunciones que sirve para identificar cuándo una solución es óptima y factible. Se realizan simulaciones en sistemas de prueba IEEE de 30 y 57 barras. Conclusiones: El algoritmo MVMO es efectivo para solucionar el DOPR. Los resultados evidencian que el algoritmo MVMO supera o iguala a varias técnicas reportadas en la literatura técnica en la calidad de soluciones. El manejo alternativo de restricciones propuesto para el MVMO reduce el tiempo de cálculo y garantiza tanto factibilidad como optimalidad de las soluciones encontradas.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-10-28 00:00:00 2024-04-09T20:17:59Z
dc.date.available.none.fl_str_mv	2020-10-28 00:00:00 2024-04-09T20:17:59Z
dc.date.issued.none.fl_str_mv	2020-10-28
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.references.spa.fl_str_mv	S. M. Mohseni-Bonab & A. Rabiee, “Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi-objective model at the presence of uncertain wind power generation,” IET Gener Transm Distrib, vol. 11, no. 4, pp. 815–829, Mar. 2017. https://doi.org/10.1049/iet-gtd.2016.1545 R. Mota-Palomino & V. H. Quintana, “Sparse Reactive Power Scheduling by a Penalty Function - Linear Programming Technique,” IEEE Trans Power Syst, vol. 1, no. 3, pp. 31–39, Ago. 1986. https://doi.org/10.1109/TPWRS.1986.4334951 K. Aoki, M. Fan, & A. Nishikori, “Optimal VAr planning by approximation method for recursive mixed-integer linear programming,” IEEE Trans Power Syst, vol. 3, no. 4, pp. 1741–1747, Nov. 1988. https://doi.org/10.1109/59.192990 V. H. Quintana & M. Santos-Nieto, “Reactive-power dispatch by successive quadratic programming,” IEEE Trans Energy Convers, vol. 4, no. 3, pp. 425–435, Sep. 1989. https://doi.org/10.1109/60.43245 F. C. Lu & Y. Y. Hsu, “Reactive power/voltage control in a distribution substation using dynamic programming,” Transm Distrib IEE Proc - Gener, vol. 142, no. 6, pp. 639–645, Nov. 1995. https://doi.org/10.1049/ip-gtd:19952210 S. Granville, “Optimal reactive dispatch through interior point methods,” IEEE Trans Power Syst, vol. 9, no. 1, pp. 136–146, Feb. 1994. https://doi.org/10.1109/59.317548 A. A. A. E. Ela, M. A. Abido, & S. R. Spea, “Differential evolution algorithm for optimal reactive power dispatch,” Electr Power Syst Res, vol. 81, no. 2, pp. 458–464, Feb. 2011. https://doi.org/10.1016/j.epsr.2010.10.005 A. Mukherjee & V. Mukherjee, “Solution of optimal reactive power dispatch by chaotic krill herd algorithm,” Transm Distrib IET Gener, vol. 9, no. 15, pp. 2351–2362, 2015. https://doi.org/10.1049/iet-gtd.2015.0077 A. H. Gandomi & A. H. Alavi, “Krill herd: A new bio-inspired optimization algorithm,” Commun Nonlinear Sci Numer Simul, vol. 17, no. 12, pp. 4831–4845, Dic. 2012. https://doi.org/10.1016/j.cnsns.2012.05.010 H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama & Y. Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,” presented at IEEE Power Engineering Society Winter Meeting. Conference Proceedings, Cat. No.01CH37194, COLO, USA, 2001. https://doi.org/10.1109/PESW.2001.916897 A. A. A. Esmin, G. Lambert-Torres & A. C. Zambroni de Souza, “A hybrid particle swarm optimization applied to loss power minimization,” IEEE Trans Power Syst, vol. 20, no. 2, pp. 859–866, May. 2005. https://doi.org/10.1109/TPWRS.2005.846049 D. Gutiérrez, W. M. Villa, & J. M. López-Lezama, “Flujo Óptimo Reactivo mediante Optimización por Enjambre de Partículas,” Inf Tecnol, vol. 28, no. 5, pp. 215–224, 2017. https://doi.org/10.4067/S0718-07642017000500020 K. Mahadevan & P. S. Kannan, “Comprehensive learning particle swarm optimization for reactive power dispatch,” Appl Soft Comput, vol. 10, no. 2, pp. 641–652, Mar. 2010. https://doi.org/10.1016/j.asoc.2009.08.038 R. P. Singh, V. Mukherjee & S. P. Ghoshal, “Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers,” Appl Soft Comput, vol. 29, pp. 298–309, Apr. 2015. https://doi.org/10.1016/j.asoc.2015.01.006 S. Duman, Y. Sönmez, U. Güvenç & N. Yörükeren, “Optimal reactive power dispatch using a gravitational search algorithm,” Transm Distrib IET Gener, vol. 6, no. 6, pp. 563–576, Jun. 2012. https://doi.org/10.1049/iet-gtd.2011.0681 E. Rashedi, H. Nezamabadi-pour & S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Inf Sci, vol. 179, no. 13, pp. 2232–2248, Jun. 2009. https://doi.org/10.1016/j.ins.2009.03.004 G. Chen, L. Liu, Z. Zhang, & S. Huang, “Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints,” Appl Soft Comput, vol. 50, pp. 58–70, Jan. 2017. https://doi.org/10.1016/j.asoc.2016.11.008 B. Shaw, V. Mukherjee & S. P. Ghoshal, “Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm,” Int J Electr Power Energy Syst, vol. 55, pp. 29–40, Feb. 2014. https://doi.org/10.1016/j.ijepes.2013.08.010 A. Rajan & T. Malakar, “Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm,” Int J Electr Power Energy Syst, vol. 66, pp. 9–24, Mar. 2015. https://doi.org/10.1016/j.ijepes.2014.10.041 M. Ettappan, V. Vimala, S. Ramesh & V. T. Kesavan, “Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using Artificial Bee Colony Algorithm,” Microprocess Microsyst, vol. 76, Jul. 2020. https://doi.org/10.1016/j.micpro.2020.103085 C. Dai, W. Chen, Y. Zhu, & X. Zhang, “Seeker Optimization Algorithm for Optimal Reactive Power Dispatch,” IEEE Trans Power Syst, vol. 24, no. 3, pp. 1218–1231, Aug. 2009. https://doi.org/10.1109/TPWRS.2009.2021226 A. Bhattacharya & P. K. Chattopadhyay, “Biogeography-Based Optimization for solution of Optimal Power Flow problem,” ECTI, ECTI-CON2010, CNX, pp. 435–439, May. 2010. Available: https://ieeexplore.ieee.org/document/5491454 R. Ng Shin Mei, M. H. Sulaiman, Z. Mustaffa & H. Daniyal, “Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique,” Appl Soft Comput, vol. 59, pp. 210–222, Oct. 2017. https://doi.org/10.1016/j.asoc.2017.05.057 A. A. Heidari, R. Ali Abbaspour & A. Rezaee Jordehi, “Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems,” Appl Soft Comput, vol. 57, pp. 657–671, Aug. 2017. https://doi.org/10.1016/j.asoc.2017.04.048 D. Gutierrez Rojas, J. Lopez Lezama & W. Villa, “Metaheuristic Techniques Applied to the Optimal Reactive Power Dispatch: a Review,” IEEE Lat Am Trans, vol. 14, no. 5, pp. 2253–2263, May. 2016. https://doi.org/10.1109/TLA.2016.7530421 W. M. Villa-Acevedo, J. M. López-Lezama, & J. A. Valencia-Velásquez, “A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem,” Energies, vol. 11, no. 9, pp. 1–23, 2018. https://doi.org/10.3390/en11092352 I. Erlich, G. K. Venayagamoorthy & N. Worawat, “A Mean-Variance Optimization algorithm,” presented at IEEE CEC, CEC, BCN, pp. 1–6, 18-23 Jul. 2010. https://doi.org/10.1109/CEC.2010.5586027 I. Erlich, “Mean-variance mapping optimization algorithm home page,” UDE, 2018. Available: https://www.uni-due.de/mvmo/ J. L. Rueda & I. Erlich, “Optimal dispatch of reactive power sources by using MVMOs optimization,” presented at IEEE CIASG, CIASG, SG, 16-19 Apr. 2013. https://doi.org/10.1109/CIASG.2013.6611495 J. C. Cepeda, J. L. Rueda & I. Erlich, “Identification of dynamic equivalents based on heuristic optimization for smart grid applications,” IEEE CEC, CEC, Bris, QLD, AU, 10-15 Jun. 2012. https://doi.org/10.1109/CEC.2012.6256493 J. L. Rueda & I. Erlich, “Evaluation of the mean-variance mapping optimization for solving multimodal problems,” IEEE SIS, SIS, SG, 16-19 Apr. 2013. https://doi.org/10.1109/SIS.2013.6615153
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spelling	Londoño Tamayo, Daniel CamiloLópez Lezama, Jesús MaríaVilla Acevedo, Walter Mauricio2020-10-28 00:00:002024-04-09T20:17:59Z2020-10-28 00:00:002024-04-09T20:17:59Z2020-10-280122-6517https://hdl.handle.net/11323/12286https://doi.org/10.17981/ingecuc.17.1.2021.1910.17981/ingecuc.17.1.2021.192382-4700Introducción: El problema del despacho óptimo de potencia reactiva (DOPR) consiste en encontrar la configuración óptima de diferentes recursos de potencia reactiva para minimizar las pérdidas de potencia del sistema. El DOPR es un problema complejo de optimización combinatorial que involucra variables discretas y continuas, así como una función objetivo no lineal y restricciones no lineales. Objetivo: En este artículo se busca comparar el desempeño del algoritmo de optimización de mapeo de media varianza (MVMO, por sus siglas en inglés) con otras técnicas reportadas en la literatura especializada aplicadas a la solución del DOPR. Metodología: En el algoritmo MVMO se aplican dos enfoques diferentes de manejo de restricciones: penalización convencional de las desviaciones de las soluciones factibles y penalización por medio del producto de subfunciones que sirve para identificar cuándo una solución es óptima y factible. Se realizan simulaciones en sistemas de prueba IEEE de 30 y 57 barras. Conclusiones: El algoritmo MVMO es efectivo para solucionar el DOPR. Los resultados evidencian que el algoritmo MVMO supera o iguala a varias técnicas reportadas en la literatura técnica en la calidad de soluciones. El manejo alternativo de restricciones propuesto para el MVMO reduce el tiempo de cálculo y garantiza tanto factibilidad como optimalidad de las soluciones encontradas. Introduction: The optimal reactive power dispatch (ORPD) problem consists on finding the optimal settings of several reactive power resources in order to minimize system power losses. The ORPD is a complex combinatorial optimization problem that involves discrete and continuous variables as well as a nonlinear objective function and nonlinear constraints. Objective: This article seeks to compare the performance of the mean-variance mapping optimization (MVMO) algorithm with other techniques reported in the specialized literature applied to the ORPD solution. Methodology: Two different constraint handling approaches are implemented within the MVMO algorithm: a conventional penalization of deviations from feasible solutions and a penalization by means of a product of subfunctions that serves to identify both when a solution is optimal and feasible. Several tests are carried out in IEEE benchmark power systems of 30 and 57 buses. Conclusions: The MVMO algorithm is effective in solving the ORPD problem. Results evidence that the MVMO algorithm outperforms or matches the quality of solutions reported by several solution techniques reported in the technical literature. The alternative handling constraint proposed for the MVMO reduces the computation time and guarantees both feasibility and optimality of the solutions found.application/pdftext/htmlapplication/xmlspaUniversidad de la CostaINGE CUC - 2021http://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/ingecuc/article/view/3109metaheuristic techniquespower loss minimizationconstraint handlingmean-variance mapping optimizationreactive powerpotencia reactivaoptimización de mapeo de media-varianzatécnicas metaheurísticasminimización de pérdidasmanejo de restriccionesAlgoritmo de Optimización de Mapeo de Media Varianza Aplicado al Despacho Óptimo de Potencia ReactivaMean-Variance Mapping Optimization Algorithm Applied to the Optimal Reactive Power DispatchArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Inge Cuc S. M. Mohseni-Bonab & A. Rabiee, “Optimal reactive power dispatch: a review, and a new stochastic voltage stability constrained multi-objective model at the presence of uncertain wind power generation,” IET Gener Transm Distrib, vol. 11, no. 4, pp. 815–829, Mar. 2017. https://doi.org/10.1049/iet-gtd.2016.1545 R. Mota-Palomino & V. H. Quintana, “Sparse Reactive Power Scheduling by a Penalty Function - Linear Programming Technique,” IEEE Trans Power Syst, vol. 1, no. 3, pp. 31–39, Ago. 1986. https://doi.org/10.1109/TPWRS.1986.4334951 K. Aoki, M. Fan, & A. Nishikori, “Optimal VAr planning by approximation method for recursive mixed-integer linear programming,” IEEE Trans Power Syst, vol. 3, no. 4, pp. 1741–1747, Nov. 1988. https://doi.org/10.1109/59.192990 V. H. Quintana & M. Santos-Nieto, “Reactive-power dispatch by successive quadratic programming,” IEEE Trans Energy Convers, vol. 4, no. 3, pp. 425–435, Sep. 1989. https://doi.org/10.1109/60.43245 F. C. Lu & Y. Y. Hsu, “Reactive power/voltage control in a distribution substation using dynamic programming,” Transm Distrib IEE Proc - Gener, vol. 142, no. 6, pp. 639–645, Nov. 1995. https://doi.org/10.1049/ip-gtd:19952210 S. Granville, “Optimal reactive dispatch through interior point methods,” IEEE Trans Power Syst, vol. 9, no. 1, pp. 136–146, Feb. 1994. https://doi.org/10.1109/59.317548 A. A. A. E. Ela, M. A. Abido, & S. R. Spea, “Differential evolution algorithm for optimal reactive power dispatch,” Electr Power Syst Res, vol. 81, no. 2, pp. 458–464, Feb. 2011. https://doi.org/10.1016/j.epsr.2010.10.005 A. Mukherjee & V. Mukherjee, “Solution of optimal reactive power dispatch by chaotic krill herd algorithm,” Transm Distrib IET Gener, vol. 9, no. 15, pp. 2351–2362, 2015. https://doi.org/10.1049/iet-gtd.2015.0077 A. H. Gandomi & A. H. Alavi, “Krill herd: A new bio-inspired optimization algorithm,” Commun Nonlinear Sci Numer Simul, vol. 17, no. 12, pp. 4831–4845, Dic. 2012. https://doi.org/10.1016/j.cnsns.2012.05.010 H. Yoshida, K. Kawata, Y. Fukuyama, S. Takayama & Y. Nakanishi, “A particle swarm optimization for reactive power and voltage control considering voltage security assessment,” presented at IEEE Power Engineering Society Winter Meeting. Conference Proceedings, Cat. No.01CH37194, COLO, USA, 2001. https://doi.org/10.1109/PESW.2001.916897 A. A. A. Esmin, G. Lambert-Torres & A. C. Zambroni de Souza, “A hybrid particle swarm optimization applied to loss power minimization,” IEEE Trans Power Syst, vol. 20, no. 2, pp. 859–866, May. 2005. https://doi.org/10.1109/TPWRS.2005.846049 D. Gutiérrez, W. M. Villa, & J. M. López-Lezama, “Flujo Óptimo Reactivo mediante Optimización por Enjambre de Partículas,” Inf Tecnol, vol. 28, no. 5, pp. 215–224, 2017. https://doi.org/10.4067/S0718-07642017000500020 K. Mahadevan & P. S. Kannan, “Comprehensive learning particle swarm optimization for reactive power dispatch,” Appl Soft Comput, vol. 10, no. 2, pp. 641–652, Mar. 2010. https://doi.org/10.1016/j.asoc.2009.08.038 R. P. Singh, V. Mukherjee & S. P. Ghoshal, “Optimal reactive power dispatch by particle swarm optimization with an aging leader and challengers,” Appl Soft Comput, vol. 29, pp. 298–309, Apr. 2015. https://doi.org/10.1016/j.asoc.2015.01.006 S. Duman, Y. Sönmez, U. Güvenç & N. Yörükeren, “Optimal reactive power dispatch using a gravitational search algorithm,” Transm Distrib IET Gener, vol. 6, no. 6, pp. 563–576, Jun. 2012. https://doi.org/10.1049/iet-gtd.2011.0681 E. Rashedi, H. Nezamabadi-pour & S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Inf Sci, vol. 179, no. 13, pp. 2232–2248, Jun. 2009. https://doi.org/10.1016/j.ins.2009.03.004 G. Chen, L. Liu, Z. Zhang, & S. Huang, “Optimal reactive power dispatch by improved GSA-based algorithm with the novel strategies to handle constraints,” Appl Soft Comput, vol. 50, pp. 58–70, Jan. 2017. https://doi.org/10.1016/j.asoc.2016.11.008 B. Shaw, V. Mukherjee & S. P. Ghoshal, “Solution of reactive power dispatch of power systems by an opposition-based gravitational search algorithm,” Int J Electr Power Energy Syst, vol. 55, pp. 29–40, Feb. 2014. https://doi.org/10.1016/j.ijepes.2013.08.010 A. Rajan & T. Malakar, “Optimal reactive power dispatch using hybrid Nelder–Mead simplex based firefly algorithm,” Int J Electr Power Energy Syst, vol. 66, pp. 9–24, Mar. 2015. https://doi.org/10.1016/j.ijepes.2014.10.041 M. Ettappan, V. Vimala, S. Ramesh & V. T. Kesavan, “Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using Artificial Bee Colony Algorithm,” Microprocess Microsyst, vol. 76, Jul. 2020. https://doi.org/10.1016/j.micpro.2020.103085 C. Dai, W. Chen, Y. Zhu, & X. Zhang, “Seeker Optimization Algorithm for Optimal Reactive Power Dispatch,” IEEE Trans Power Syst, vol. 24, no. 3, pp. 1218–1231, Aug. 2009. https://doi.org/10.1109/TPWRS.2009.2021226 A. Bhattacharya & P. K. Chattopadhyay, “Biogeography-Based Optimization for solution of Optimal Power Flow problem,” ECTI, ECTI-CON2010, CNX, pp. 435–439, May. 2010. Available: https://ieeexplore.ieee.org/document/5491454 R. Ng Shin Mei, M. H. Sulaiman, Z. Mustaffa & H. Daniyal, “Optimal reactive power dispatch solution by loss minimization using moth-flame optimization technique,” Appl Soft Comput, vol. 59, pp. 210–222, Oct. 2017. https://doi.org/10.1016/j.asoc.2017.05.057 A. A. Heidari, R. Ali Abbaspour & A. Rezaee Jordehi, “Gaussian bare-bones water cycle algorithm for optimal reactive power dispatch in electrical power systems,” Appl Soft Comput, vol. 57, pp. 657–671, Aug. 2017. https://doi.org/10.1016/j.asoc.2017.04.048 D. Gutierrez Rojas, J. Lopez Lezama & W. 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