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Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II

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  • Bogdan Tomoiagă

    (Power Systems & Management Department, Technical University of Cluj-Napoca, Memorandumului st., No. 28, 400114 Cluj-Napoca, Romania)

  • Mircea Chindriş

    (Power Systems & Management Department, Technical University of Cluj-Napoca, Memorandumului st., No. 28, 400114 Cluj-Napoca, Romania)

  • Andreas Sumper

    (Centre of Technological Innovation in Static Converters and Drives, Department of Electrical Engineering, College of Industrial Engineering of Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Carrer Comte d'Urgell, 187-08036 Barcelona, Spain
    IREC Catalonia Institute for Energy Research, Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Barcelona, Spain)

  • Antoni Sudria-Andreu

    (Centre of Technological Innovation in Static Converters and Drives, Department of Electrical Engineering, College of Industrial Engineering of Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Carrer Comte d'Urgell, 187-08036 Barcelona, Spain)

  • Roberto Villafafila-Robles

    (Centre of Technological Innovation in Static Converters and Drives, Department of Electrical Engineering, College of Industrial Engineering of Barcelona, Universitat Politècnica de Catalunya-BarcelonaTech, Carrer Comte d'Urgell, 187-08036 Barcelona, Spain)

Abstract

Reconfiguration, by exchanging the functional links between the elements of the system, represents one of the most important measures which can improve the operational performance of a distribution system. The authors propose an original method, aiming at achieving such optimization through the reconfiguration of distribution systems taking into account various criteria in a flexible and robust approach. The novelty of the method consists in: the criteria for optimization are evaluated on active power distribution systems (containing distributed generators connected directly to the main distribution system and microgrids operated in grid-connected mode); the original formulation (Pareto optimality) of the optimization problem and an original genetic algorithm (based on NSGA-II) to solve the problem in a non-prohibitive execution time. The comparative tests performed on test systems have demonstrated the accuracy and promptness of the proposed algorithm.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:3:p:1439-1455:d:24012
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    References listed on IDEAS

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    1. Martinez-Rojas, Marcela & Sumper, Andreas & Gomis-Bellmunt, Oriol & Sudrià-Andreu, Antoni, 2011. "Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search," Applied Energy, Elsevier, vol. 88(12), pages 4678-4686.
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    Cited by:

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    2. Wang, Hong-Jiang & Pan, Jeng-Shyang & Nguyen, Trong-The & Weng, Shaowei, 2022. "Distribution network reconfiguration with distributed generation based on parallel slime mould algorithm," Energy, Elsevier, vol. 244(PB).
    3. Ippolito, M.G. & Di Silvestre, M.L. & Riva Sanseverino, E. & Zizzo, G. & Graditi, G., 2014. "Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sources under different design scenarios," Energy, Elsevier, vol. 64(C), pages 648-662.
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    6. 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.
    7. Zhang, Yongfeng & Zhang, Yi & Friedman, Daniel, 2017. "Economic recommendation based on pareto efficient resource allocation," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2017-503, WZB Berlin Social Science Center.
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    9. Sultana, Beenish & Mustafa, M.W. & Sultana, U. & Bhatti, Abdul Rauf, 2016. "Review on reliability improvement and power loss reduction in distribution system via network reconfiguration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 297-310.
    10. Firas M. F. Flaih & Xiangning Lin & Mohammed Kdair Abd & Samir M. Dawoud & Zhengtian Li & Owolabi Sunday Adio, 2017. "A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands," Energies, MDPI, vol. 10(4), pages 1-19, April.
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    13. Zhao, Xin-gang & Li, Ang, 2016. "A multi-objective sustainable location model for biomass power plants: Case of China," Energy, Elsevier, vol. 112(C), pages 1184-1193.
    14. Gianfranco Chicco & Andrea Mazza, 2020. "Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’," Energies, MDPI, vol. 13(19), pages 1-38, September.
    15. Badran, Ola & Mekhilef, Saad & Mokhlis, Hazlie & Dahalan, Wardiah, 2017. "Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 854-867.
    16. Carlos Henrique Valério de Moraes & Jonas Lopes de Vilas Boas & Germano Lambert-Torres & Gilberto Capistrano Cunha de Andrade & Claudio Inácio de Almeida Costa, 2022. "Intelligent Power Distribution Restoration Based on a Multi-Objective Bacterial Foraging Optimization Algorithm," Energies, MDPI, vol. 15(4), pages 1-23, February.
    17. Rade Čađenović & Damir Jakus & Petar Sarajčev & Josip Vasilj, 2018. "Optimal Distribution Network Reconfiguration through Integration of Cycle-Break and Genetic Algorithms," Energies, MDPI, vol. 11(5), pages 1-19, May.

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