Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation


Autoria(s): Maciel, Renan S.; Rosa, Mauro; Miranda, Vladimiro; Padilha-Feltrin, Antonio
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

20/05/2014

20/05/2014

01/08/2012

Resumo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Processo FAPESP: 06/06758-9

This paper proposes a multi-objective approach to a distribution network planning process that deals with the challenges derived from the integration of Distributed Generation (DG). The proposal consists of a multi-objective version of the well-known Evolutionary Particle Swarm Optimization method (MEPSO). A broad performance comparison is made between the MEPSO and other multi-objective optimization meta-heuristics, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a Multi-objective Tabu Search (MOTS), using two distribution networks in a given DG penetration scenario. Although the three methods proved to be applicable in distribution system planning, the MEPSO algorithm has presented promising attributes and a constant and high level performance when compared to other methods. (C) 2012 Elsevier BM. All rights reserved.

Formato

100-108

Identificador

http://dx.doi.org/10.1016/j.epsr.2012.02.018

Electric Power Systems Research. Lausanne: Elsevier B.V. Sa, v. 89, p. 100-108, 2012.

0378-7796

http://hdl.handle.net/11449/9806

10.1016/j.epsr.2012.02.018

WOS:000304787300012

Idioma(s)

eng

Publicador

Elsevier B.V. Sa

Relação

Electric Power Systems Research

Direitos

closedAccess

Palavras-Chave #Distributed generation planning #Multi-objective optimization #Evolutionary particle swarm optimization #Genetic Algorithm #Tabu Search
Tipo

info:eu-repo/semantics/article