Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface

The Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem that involves both discrete and continuous vari...

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Autores:
Sánchez-Mora, Martin M.
Bernal-Romero, David Lionel
Montoya, Oscar Danilo
Villa Acevedo, Walter M.
López Lezama, Jesús M.
Tipo de recurso:
Fecha de publicación:
2022
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/11128
Acceso en línea:
https://hdl.handle.net/20.500.12585/11128
https://doi.org/10.3390/computation10080128
Palabra clave:
Combinatorial optimization
DIgSILENT software
Genetic algorithm
Mean variance mapping optimization
Optimal reactive power dispatch
Power losses minimization
Python programming language
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
title Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
spellingShingle Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
Combinatorial optimization
DIgSILENT software
Genetic algorithm
Mean variance mapping optimization
Optimal reactive power dispatch
Power losses minimization
Python programming language
LEMB
title_short Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
title_full Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
title_fullStr Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
title_full_unstemmed Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
title_sort Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface
dc.creator.fl_str_mv Sánchez-Mora, Martin M.
Bernal-Romero, David Lionel
Montoya, Oscar Danilo
Villa Acevedo, Walter M.
López Lezama, Jesús M.
dc.contributor.author.none.fl_str_mv Sánchez-Mora, Martin M.
Bernal-Romero, David Lionel
Montoya, Oscar Danilo
Villa Acevedo, Walter M.
López Lezama, Jesús M.
dc.subject.keywords.spa.fl_str_mv Combinatorial optimization
DIgSILENT software
Genetic algorithm
Mean variance mapping optimization
Optimal reactive power dispatch
Power losses minimization
Python programming language
topic Combinatorial optimization
DIgSILENT software
Genetic algorithm
Mean variance mapping optimization
Optimal reactive power dispatch
Power losses minimization
Python programming language
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description The Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem that involves both discrete and continuous variables; the former include transformer tap positions and settings of reactor banks, while the latter include voltage magnitude settings in generation buses. In this paper, the ORPD problem is modeled as a mixed integer nonlinear programming problem and solved through two different metaheuristic techniques, namely the Mean Variance Mapping Optimization and the genetic algorithm. As a novelty, the solution of the ORPD problem is implemented through a PythonDIgSILENT interface that combines the strengths of both software. Several tests were performed on the IEEE 6-, 14-, and 39-bus test systems evidencing the applicability of the proposed approach. The results were contrasted with those previously reported in the specialized literature, matching, and in some cases improving, the reported solutions with lower computational times.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-10-05T12:26:27Z
dc.date.available.none.fl_str_mv 2022-10-05T12:26:27Z
dc.date.issued.none.fl_str_mv 2022-07-25
dc.date.submitted.none.fl_str_mv 2022-09-30
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.spa.spa.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.citation.spa.fl_str_mv Sánchez-Mora, M.M.; Bernal-Romero, D.L.; Montoya, O.D.; Villa Acevedo, W.M.; López-Lezama, J.M. Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface. Computation 2022, 10, 128. https://doi.org/10.3390/computation10080128
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/11128
dc.identifier.doi.none.fl_str_mv https://doi.org/10.3390/computation10080128
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Sánchez-Mora, M.M.; Bernal-Romero, D.L.; Montoya, O.D.; Villa Acevedo, W.M.; López-Lezama, J.M. Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface. Computation 2022, 10, 128. https://doi.org/10.3390/computation10080128
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/11128
https://doi.org/10.3390/computation10080128
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.uri.*.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 24 Páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv Computation Vol.10 N° 8 (2022)
institution Universidad Tecnológica de Bolívar
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spelling Sánchez-Mora, Martin M.47f83a24-df85-4a58-8009-c0ece6312ce4Bernal-Romero, David Lionel92b4db2c-95be-4502-bb3c-74213711c838Montoya, Oscar Danilo9fa8a75a-58fa-436d-a6e2-d80f718a4ea8Villa Acevedo, Walter M.b3ac5a1a-9e8e-483f-99b8-f8f02774668cLópez Lezama, Jesús M.26e59177-5d26-454f-96f0-88275a529dc22022-10-05T12:26:27Z2022-10-05T12:26:27Z2022-07-252022-09-30Sánchez-Mora, M.M.; Bernal-Romero, D.L.; Montoya, O.D.; Villa Acevedo, W.M.; López-Lezama, J.M. Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface. Computation 2022, 10, 128. https://doi.org/10.3390/computation10080128https://hdl.handle.net/20.500.12585/11128https://doi.org/10.3390/computation10080128Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem that involves both discrete and continuous variables; the former include transformer tap positions and settings of reactor banks, while the latter include voltage magnitude settings in generation buses. In this paper, the ORPD problem is modeled as a mixed integer nonlinear programming problem and solved through two different metaheuristic techniques, namely the Mean Variance Mapping Optimization and the genetic algorithm. As a novelty, the solution of the ORPD problem is implemented through a PythonDIgSILENT interface that combines the strengths of both software. Several tests were performed on the IEEE 6-, 14-, and 39-bus test systems evidencing the applicability of the proposed approach. The results were contrasted with those previously reported in the specialized literature, matching, and in some cases improving, the reported solutions with lower computational times.24 Páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Computation Vol.10 N° 8 (2022)Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interfaceinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Combinatorial optimizationDIgSILENT softwareGenetic algorithmMean variance mapping optimizationOptimal reactive power dispatchPower losses minimizationPython programming languageLEMBCartagena de IndiasVilla-Acevedo, W.M.; López-Lezama, J.M.; Valencia-Velásquez, J.A. A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem. Energies 2018, 11, 2352Marín-Cano, C.C.; Sierra-Aguilar, J.E.; López-Lezama, J.M.; Jaramillo-Duque, Á.; Villegas, J.G. A Novel Strategy to Reduce Computational Burden of the Stochastic Security Constrained Unit Commitment Problem. Energies 2020, 13, 3777Sierra-Aguilar, J.E.; Marín-Cano, C.C.; López-Lezama, J.M.; Jaramillo-Duque, Á.; Villegas, J.G. A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty. Appl. Sci. 2021, 11, 3987Mota-Palomino, R.; Quintana, V.H. Sparse Reactive Power Scheduling by a Penalty Function - Linear Programming Technique. IEEE Trans. Power Syst. 1986, 1, 31–39.Quintana, V.; Santos-Nieto, M. Reactive-power dispatch by successive quadratic programming. IEEE D 1989, 4, 425–435Granville, S. Optimal reactive dispatch through interior point methods. IEEE Trans. Power Syst. 1994, 9, 136–146López-Lezama, J.M.; Cortina-Gómez, J.; Muñoz-Galeano, N. 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