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Open AccessArticle

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

1
Power Systems & Management Department, Technical University of Cluj-Napoca, Memorandumului st., No. 28, 400114 Cluj-Napoca, Romania
2
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
3
IREC Catalonia Institute for Energy Research, Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Barcelona, Spain
*
Author to whom correspondence should be addressed.
Energies 2013, 6(3), 1439-1455; https://doi.org/10.3390/en6031439
Received: 17 December 2012 / Revised: 2 February 2013 / Accepted: 19 February 2013 / Published: 6 March 2013
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. View Full-Text
Keywords: power distribution systems; reconfiguration; genetic algorithms; multi-objective optimization; Pareto optimality power distribution systems; reconfiguration; genetic algorithms; multi-objective optimization; Pareto optimality
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MDPI and ACS Style

Tomoiagă, B.; Chindriş, M.; Sumper, A.; Sudria-Andreu, A.; Villafafila-Robles, R. Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II. Energies 2013, 6, 1439-1455. https://doi.org/10.3390/en6031439

AMA Style

Tomoiagă B, Chindriş M, Sumper A, Sudria-Andreu A, Villafafila-Robles R. Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II. Energies. 2013; 6(3):1439-1455. https://doi.org/10.3390/en6031439

Chicago/Turabian Style

Tomoiagă, Bogdan; Chindriş, Mircea; Sumper, Andreas; Sudria-Andreu, Antoni; Villafafila-Robles, Roberto. 2013. "Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II" Energies 6, no. 3: 1439-1455. https://doi.org/10.3390/en6031439

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