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A Novel Solution Method for the Distribution Network Reconfiguration Problem Based on a Search Mechanism Enhancement of the Improved Harmony Search Algorithm

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Listed:
  • Josephy Dias Santos

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goias, Av. Universitária, No. 1488, Goiania 74605-010, Brazil)

  • Frederico Marques

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goias, Av. Universitária, No. 1488, Goiania 74605-010, Brazil)

  • Lina Paola Garcés Negrete

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goias, Av. Universitária, No. 1488, Goiania 74605-010, Brazil)

  • Gelson A. Andrêa Brigatto

    (Electrical, Mechanical and Computer Engineering School, Federal University of Goias, Av. Universitária, No. 1488, Goiania 74605-010, Brazil)

  • Jesús M. López-Lezama

    (Research Group on Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Medellin 050010, Colombia)

  • Nicolás Muñoz-Galeano

    (Research Group on Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia (UdeA), Medellin 050010, Colombia)

Abstract

This paper addresses the problem of Distribution Systems Reconfiguration (DSR), which consists of finding the state of switching devices (open or closed) in a given distribution network, aiming to minimize active power loses. DSR is modeled as a mixed-integer non-linear optimization problem, in which the integer variables represent the state of the switches, and the continuous variables represent the power flowing through the branches. Given the multi-modal and non-convex nature of the problem, an improved harmony search (IHS) algorithm is proposed to solve the DSR problem. The main novelty of this approach is the inclusion of a Path Relinking phase which accelerates convergence of the DSR problem. Several tests were carried out in four benchmark distribution systems, evidencing the effectiveness and applicability of the proposed approach.

Suggested Citation

  • Josephy Dias Santos & Frederico Marques & Lina Paola Garcés Negrete & Gelson A. Andrêa Brigatto & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2022. "A Novel Solution Method for the Distribution Network Reconfiguration Problem Based on a Search Mechanism Enhancement of the Improved Harmony Search Algorithm," Energies, MDPI, vol. 15(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2083-:d:769875
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    References listed on IDEAS

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    1. Santos Coelho, Leandro dos & de Andrade Bernert, Diego Luis, 2009. "An improved harmony search algorithm for synchronization of discrete-time chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 41(5), pages 2526-2532.
    2. Sergio Danilo Saldarriaga-Zuluaga & Jesús María López-Lezama & Nicolás Muñoz-Galeano, 2020. "Optimal Coordination of Overcurrent Relays in Microgrids Considering a Non-Standard Characteristic," Energies, MDPI, vol. 13(4), pages 1-18, February.
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    Cited by:

    1. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "Optimal Feeder Reconfiguration and Placement of Voltage Regulators in Electrical Distribution Networks Using a Linear Mathematical Model," Sustainability, MDPI, vol. 15(1), pages 1-20, January.
    2. Soheil Younesi & Bahman Ahmadi & Oguzhan Ceylan & Aydogan Ozdemir, 2022. "Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems," Energies, MDPI, vol. 15(24), pages 1-18, December.
    3. Mohammed Alqahtani & Ponnusamy Marimuthu & Veerasamy Moorthy & B. Pangedaiah & Ch. Rami Reddy & M. Kiran Kumar & Muhammad Khalid, 2023. "Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization," Energies, MDPI, vol. 16(12), pages 1-15, June.
    4. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.
    5. Zifa Liu & Jieyu Li & Yunyang Liu & Puyang Yu & Junteng Shao, 2022. "Collaborative Optimized Operation Model of Multi-Character Distribution Network Considering Multiple Uncertain Factors and Demand Response," Energies, MDPI, vol. 15(12), pages 1-19, June.

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