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Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

Author

Listed:
  • Damir Jakus

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split 21000, Croatia)

  • Rade Čađenović

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split 21000, Croatia)

  • Josip Vasilj

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split 21000, Croatia)

  • Petar Sarajčev

    (Department of Power Engineering, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split 21000, Croatia)

Abstract

This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

Suggested Citation

  • Damir Jakus & Rade Čađenović & Josip Vasilj & Petar Sarajčev, 2020. "Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm," Energies, MDPI, vol. 13(7), pages 1-21, March.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1544-:d:337096
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    References listed on IDEAS

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    1. Bogdan Tomoiagă & Mircea Chindriş & Andreas Sumper & Antoni Sudria-Andreu & Roberto Villafafila-Robles, 2013. "Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II," Energies, MDPI, vol. 6(3), pages 1-17, March.
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    Cited by:

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    2. Thuan Thanh Nguyen & Bach Hoang Dinh & Thai Dinh Pham & Thang Trung Nguyen, 2020. "Active Power Loss Reduction for Radial Distribution Systems by Placing Capacitors and PV Systems with Geography Location Constraints," Sustainability, MDPI, vol. 12(18), pages 1-30, September.
    3. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.
    4. Aroa González Fuentes & Nélida M. Busto Serrano & Fernando Sánchez Lasheras & Gregorio Fidalgo Valverde & Ana Suárez Sánchez, 2020. "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms," Energies, MDPI, vol. 13(10), pages 1-16, May.
    5. Adil Amin & Wajahat Ullah Khan Tareen & Muhammad Usman & Kamran Ali Memon & Ben Horan & Anzar Mahmood & Saad Mekhilef, 2020. "An Integrated Approach to Optimal Charging Scheduling of Electric Vehicles Integrated with Improved Medium-Voltage Network Reconfiguration for Power Loss Minimization," Sustainability, MDPI, vol. 12(21), pages 1-15, November.
    6. 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.

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