Decentralized optimum power flow using evolutionary multi-objective evolutionary algorithms

被引：0

作者：

De Andrade Amorim, Elizete ^{[1
]}

Romero, Rubén ^{[2
]}

Mantovani, José R. S. ^{[2
]}

机构：

[1] Universidade Federal de Mato Grosso do Sul - UFMS, Departamento de Engenharia Elétrica, Campus de Campo Grande, CEP 79070-900 - Campo Grande MS

[2] Universidade Estadual Paulista Júlio de Mesquista Filho, Grupo de Pesquisa em Planejamento de Sistemas Elétricos, Departamento de Engenharia Elétrica - UNESP, CEP 15385-000 - Ilha Solteira SP

来源：

Controle y Automacao | 2009年 / 20卷 / 02期

关键词：

Decentralized optimal power flow; Decomposition technical; Evolutionary algorithm; Fuzzy set; Multiobjective optimization;

D O I：

10.1590/s0103-17592009000200009

中图分类号：

学科分类号：

摘要：

This work presents the development of a computational tool for decentralized optimal power flow (OPF) solution. For this purpose, the OPF problem is decoupled into areas defining several regional OPF subproblems. The OPF is modeled as a constrained nonlinear optimization problem, non-convex, in that the active power losses and optimal dispatch of active and reactive power are minimized simultaneously. Regional OPF subproblems are solved by multiobjective evolutionary algorithm based on the Pareto theory. The proposed approach employs a diversity-preserving mechanism to overcome the premature convergence of algorithm and local optimal solutions. Fuzzy set theory is employed to extract the best compromises of the Pareto set. In addition, a hierarchical clustering algorithm is implemented for reducing Pareto set. To validate the efficiency of the model and the proposed solution technique, the results e analyses of the simulations with the RTS-96 e IEEE-354 test systems are presented.

引用

页码：217 / 232

页数：15

共 26 条

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