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.
%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
}