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

Bogdan Tomoiaga, Mircea Chindriş, Andreas Sumper, Antoni Sudria-Andreu and Roberto Villafafila-Robles
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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
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

Energies, 2013, vol. 6, issue 3, 1-17

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.

Keywords: power distribution systems; reconfiguration; genetic algorithms; multi-objective optimization; Pareto optimality (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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