%0 Journal Article %1 energies2013pareto %A Tomoiagă, Bogdan %A Chindriş, Mircea %A Sumper, Andreas %A Sudria-Andreu, Antoni %A Villafafila-Robles, Roberto %D 2013 %E Energies, %J Energies %K (NSGA-II) A Adaptation Andrea Angela Approach Chicco Distribution Extension Geometrical Gianfranco Heuristic Humberto Interruption Jorge Luís M M. Mazza Moshtagh Multi Multi-objective NSGA-II Neves Optimal P P. Pareto Power Ranking Reconfiguration Romeu Russo Supply System Vitorino address adjacent algorithm algorithms analyse and approach branches by cases conflicting considering consumers cost criteria crossover damage decision decision-making distribution due enhanced for front fronts genetic ghasemi graphs heuristic interruption losses making making-based methods metric multi multi-objective mutation new non-dominated objectives of operator operators optimality optimization power ranking reconfiguration represent sasan searching solution solutions sorting spanning supply system systems the to tree using with %N 3 %P 1439-1455 %T Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II %U http://www.mdpi.com/1996-1073/6/3/1439 %V 6 %X 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.

@article{energies2013pareto, 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.}, added-at = {2014-01-13T14:14:44.000+0100}, author = {Tomoiagă, Bogdan and Chindriş, Mircea and Sumper, Andreas and Sudria-Andreu, Antoni and Villafafila-Robles, Roberto}, biburl = {https://www.bibsonomy.org/bibtex/214380e50cbc83c89ded0ca82f4038c0d/smartgridsro}, editor = {Energies}, interhash = {c13c86e0daef294bcfe174e2bc03974d}, intrahash = {14380e50cbc83c89ded0ca82f4038c0d}, journal = {Energies}, keywords = {(NSGA-II) A Adaptation Andrea Angela Approach Chicco Distribution Extension Geometrical Gianfranco Heuristic Humberto Interruption Jorge Luís M M. Mazza Moshtagh Multi Multi-objective NSGA-II Neves Optimal P P. Pareto Power Ranking Reconfiguration Romeu Russo Supply System Vitorino address adjacent algorithm algorithms analyse and approach branches by cases conflicting considering consumers cost criteria crossover damage decision decision-making distribution due enhanced for front fronts genetic ghasemi graphs heuristic interruption losses making making-based methods metric multi multi-objective mutation new non-dominated objectives of operator operators optimality optimization power ranking reconfiguration represent sasan searching solution solutions sorting spanning supply system systems the to tree using with}, month = {March}, number = 3, pages = {1439-1455}, timestamp = {2014-02-18T13:43:57.000+0100}, title = {Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II}, url = {http://www.mdpi.com/1996-1073/6/3/1439}, volume = 6, year = 2013 }