DOI:

https://doi.org/10.14483/22487638.18342

Publicado:

2022-09-25

Número:

Vol. 26 Núm. 74 (2022): Octubre - Diciembre

Sección:

Investigación

Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization

Métodos comparativos para la solución óptima del flujo de energía en redes de distribución considerando generadores distribuidos: metaheurística vs. optimización convexa

Autores/as

  • Oscar Danilo Montoya Giraldo Universidad Distrital Francisco José de Caldas https://orcid.org/0000-0001-6051-4925
  • Karen Julieth Bohórquez-Bautista Universidad Distrital Francisco José de Caldas https://orcid.org/0000-0001-9725-889X
  • Daniel Alejandro Moreno-Arias Universidad Distrital Francisco José de Caldas
  • Walter Gil-González Universidad Tecnológica de Pereira

Palabras clave:

Optimal power flow problem, metaheuristic optimization, second-order cone programming, convex optimization, distributed generation, branch power flow (en).

Palabras clave:

flujo de potencia óptimo, optimización metaheurística, programación cónica de segundo orden, optimización convexa, generación distribuida, flujo de potencia (es).

Descargas

Resumen (en)

Objective: This article presents an analysis of different optimization methodologies, which aims to make an objective comparison between metaheuristic and convex optimization methods in distribution networks, focusing on the inclusion of distributed generation (DG). The MATLAB software is used as a tool for implementation and obtaining results. The objective was to determine the optimal size of the DGs to be integrated into the networks, with the purpose of reducing the active power losses (objective function).

Methodology: Based on the specialized literature, the methodologies are selected, and the bases and conditions for the implementation of the optimization techniques are determined. In the case of second-order cone programming (SOCP), the relaxation of the nonlinear optimal power flow (OPF) problem is performed in order to use convex optimization. Then, the structures of each technique are established and applied in the MATLAB software. Due to the iterative nature of metaheuristic methods, the data corresponding to 100 compilations for each algorithm are collected. Finally, by means of a statistical analysis, the optimal solutions for the objective function in each methodology are determined, and, with these results, the different methods applied to the networks are compared.

Results: By analyzing 33- and 69-node systems, it is demonstrated that metaheuristic methods are able to effectively size DGs in distribution systems and yield good results that are similar and comparable to SOCP regarding the OPF problem. Genetic algorithms (GA) showed the best results for the studied implementation, even surpassing the SOCP.

Conclusions: Metaheuristic methods proved to be algorithms with a high computational efficiency and are suitable for real-time applications if implemented in distribution systems with well-defined conditions. These techniques provide innovative ideas because they are not rigid algorithms, which makes them very versatile methods that can be adapted to any combinatorial optimization problem and software, yielding results even at the convex optimization level.

Resumen (es)

Objetivo: Este artículo presenta un análisis de diferentes metodologías de optimización, cuyo fin es realizar una comparación objetiva entre métodos de optimización metaheurística y convexa en redes de distribución con énfasis en la inclusión de generación distribuida (DG). Se utiliza el software MATLAB como herramienta para la implementación y la obtención de resultados. El objetivo es determinar el tamaño óptimo de las DG a integrar en las redes, con el fin de reducir las pérdidas de potencia activa (función objetivo).

Metodología: A partir de la literatura especializada, se seleccionan las metodologías y se determinan las bases y condiciones para la implementación de las técnicas de optimización. En el caso de la programación cónica de segundo orden (SOCP), se realiza la relajación del problema de flujo de potencia óptimo (OPF) no lineal para utilizar optimización convexa. Luego, las estructuras de cada técnica se establecen y aplican en el software MATLAB. Debido al carácter iterativo de los métodos metaheurísticos, se recolectan los datos correspondientes a 100 compilaciones para cada algoritmo. Finalmente, mediante un análisis estadístico, se determinan las soluciones óptimas para la función objetivo en cada metodología y, con estos resultados, se comparan los diferentes métodos aplicados a las redes.

Resultados: A partir del análisis de sistemas de 33 y 69 nodos, se demuestra que los métodos metaheurísticos son capaces de dimensionar DGs manera efectiva en sistemas de distribución y dan buenos resultados, similares y comparables a la SOCP en el problema OPF. El algoritmo genético (GA) mostró los mejores resultados para la implementación realizada, superando incluso a la SOCP.

Conclusiones: Los métodos metaheurísticos demostraron ser algoritmos de alta eficiencia computacional y son adecuados para aplicaciones en tiempo real si se implementan en sistemas de distribución con condiciones correctamente definidas. Estas técnicas aportan ideas innovadoras porque no son algoritmos rígidos, lo que las convierte en métodos muy versátiles que pueden adaptarse a cualquier problema de optimización combinatoria y a cualquier software, dando resultados incluso a nivel de optimización convexa.

Biografía del autor/a

Oscar Danilo Montoya Giraldo, Universidad Distrital Francisco José de Caldas

Ingeniero de proyectos, Controller, empresa NETWORKS FOREVER, Bogotá DC, Colombia.

Karen Julieth Bohórquez-Bautista , Universidad Distrital Francisco José de Caldas

Estudiante de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas. Bogotá D.C

Daniel Alejandro Moreno-Arias, Universidad Distrital Francisco José de Caldas

Estudiante de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas. Bogotá D.C

Walter Gil-González, Universidad Tecnológica de Pereira

Ingeniero Eléctrico, Magíster en Ingeniería Eléctrica, Doctorado en Ingeniería. Profesor asistente en el Departamento de Ingeniería.

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Cómo citar

APA

Montoya Giraldo, O. D. ., Bohórquez-Bautista , K. J. ., Moreno-Arias, D. A. . ., & Gil-González, W. . (2022). Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization. Tecnura, 26(74), 87–129. https://doi.org/10.14483/22487638.18342

ACM

[1]
Montoya Giraldo, O.D. , Bohórquez-Bautista , K.J. , Moreno-Arias, D.A. y Gil-González, W. 2022. Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization. Tecnura. 26, 74 (sep. 2022), 87–129. DOI:https://doi.org/10.14483/22487638.18342.

ACS

(1)
Montoya Giraldo, O. D. .; Bohórquez-Bautista , K. J. .; Moreno-Arias, D. A. . .; Gil-González, W. . Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization. Tecnura 2022, 26, 87-129.

ABNT

MONTOYA GIRALDO, O. D. .; BOHÓRQUEZ-BAUTISTA , K. J. .; MORENO-ARIAS, D. A. . .; GIL-GONZÁLEZ, W. . Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization. Tecnura, [S. l.], v. 26, n. 74, p. 87–129, 2022. DOI: 10.14483/22487638.18342. Disponível em: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/18342. Acesso em: 26 sep. 2022.

Chicago

Montoya Giraldo, Oscar Danilo, Karen Julieth Bohórquez-Bautista, Daniel Alejandro Moreno-Arias, y Walter Gil-González. 2022. «Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization». Tecnura 26 (74):87-129. https://doi.org/10.14483/22487638.18342.

Harvard

Montoya Giraldo, O. D. ., Bohórquez-Bautista , K. J. ., Moreno-Arias, D. A. . . y Gil-González, W. . (2022) «Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization», Tecnura, 26(74), pp. 87–129. doi: 10.14483/22487638.18342.

IEEE

[1]
O. D. . Montoya Giraldo, K. J. . Bohórquez-Bautista, D. A. . . Moreno-Arias, y W. . Gil-González, «Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization», Tecnura, vol. 26, n.º 74, pp. 87–129, sep. 2022.

MLA

Montoya Giraldo, O. D. ., K. J. . Bohórquez-Bautista, D. A. . . Moreno-Arias, y W. . Gil-González. «Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization». Tecnura, vol. 26, n.º 74, septiembre de 2022, pp. 87-129, doi:10.14483/22487638.18342.

Turabian

Montoya Giraldo, Oscar Danilo, Karen Julieth Bohórquez-Bautista, Daniel Alejandro Moreno-Arias, y Walter Gil-González. «Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization». Tecnura 26, no. 74 (septiembre 25, 2022): 87–129. Accedido septiembre 26, 2022. https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/18342.

Vancouver

1.
Montoya Giraldo OD, Bohórquez-Bautista KJ, Moreno-Arias DA, Gil-González W. Comparative Methods for Solving Optimal Power Flow in Distribution Networks Considering Distributed Generators: Metaheuristics vs. Convex Optimization. Tecnura [Internet]. 25 de septiembre de 2022 [citado 26 de septiembre de 2022];26(74):87-129. Disponible en: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/18342

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