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Hybrid Metaheuristic Optimization Methods for Optimal Location and Sizing DGs in DC Networks

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Applied Computer Sciences in Engineering (WEA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1052))

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Abstract

In this paper is proposed a master-slave method for optimal location and sizing of distributed generators (DGs) in direct-current (DC) networks. In the master stage is used the genetic algorithm of Chu & Beasley (GA) for the location of DGs. In the slave stage three different continuous techniques are used: the Continuous genetic algorithm (CGA), the Black Hole optimization method (BH) and the particle swarm optimization (PSO) algorithm, in order to solve the problem of sizing. All of those techniques are combined to find the hybrid method that provides the best results in terms of power losses reduction and processing times. The reduction of the total power losses on the electrical network associated to the transport of energy is used as objective function, by also including a penalty to limit the power injected by the DGs on the grid, and considering all constraints associated to the DC grids. To verify the performance of the different hybrid methods studied, two test systems with 10 and 21 buses are implemented in MATLAB by considering the installation of three distributed generators. To solve the power flow equations, the slave stage uses successive approximations. The results obtained shown that the proposed methodology GA-BH provides the best trade-off between speed and power losses independent of the total power provided by the DGs and the network size.

This work was supported by the Instituto Tecnológico Metropolitano, Universidad Tecnologica de Bolívar, Universidad Nacional de Colombia, and Colciencias (Fondo nacional de financiamiento para ciencia, la tecnología y la innovación Francisco José de Caldas) under the National Scholarship Program (call for applications 727-2015), the Scholarship Program (Joven investigador ITM); and the projects P17211, C2018P02 and “Estrategia de transformación del sector energético Colombiano en el horizonte de 2030 - Energética 2030” - “Generación distribuida de energía eléctrica en Colombia a partir de energía solar y eólica” (Code: 58838, Hermes: 38945).

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Author information

Authors and Affiliations

  1. Departamento de Electromecánica y Mecatrónica, Instituto Tecnológico Metropolitano, Medellín, Colombia

    Luis Fernando Grisales-Noreña & Oscar Daniel Garzon-Rivera

  2. Programa de Ingeniería Eléctrica e Ingeniería Electrónica, Universidad Tecnológica de Bolívar, Cartagena, Colombia

    Oscar Danilo Montoya

  3. Facultad de Minas, Universidad Nacional de Colombia, Medellín, Colombia

    Carlos Andrés Ramos-Paja

Authors
  1. Luis Fernando Grisales-Noreña
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  2. Oscar Daniel Garzon-Rivera
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  3. Oscar Danilo Montoya
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  4. Carlos Andrés Ramos-Paja
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Corresponding author

Correspondence to Luis Fernando Grisales-Noreña .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Universidad Antonio Nariño, Bogotá, Colombia

    Mario Duarte-González

  3. Universidad Antonio Nariño, Bogotá, Colombia

    Sebastián Jaramillo-Isaza

  4. Universidad del Rosario, Bogotá, Colombia

    Alvaro David Orjuela-Cañon

  5. Corporación Unificada Nacional CUN, Bogotá, Colombia

    Yesid Díaz-Gutierrez

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Grisales-Noreña, L.F., Garzon-Rivera, O.D., Danilo Montoya, O., Ramos-Paja, C.A. (2019). Hybrid Metaheuristic Optimization Methods for Optimal Location and Sizing DGs in DC Networks. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_19

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  • DOI: https://doi.org/10.1007/978-3-030-31019-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31018-9

  • Online ISBN: 978-3-030-31019-6

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