Multi-Objective Optimal Power Flow Using Differential Evolution

M. A. Abido; N. A. Al-Ali

doi:10.1007/s13369-012-0224-3

Multi-Objective Optimal Power Flow Using Differential Evolution

M. A. Abido, N. A. Al-Ali

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

This paper presents a multi-objective differential-evolution-based approach to solve the optimal power flow (OPF) problem. The OPF problem has been treated as a true multi-objective constrained optimization problem. Different objective functions and operational constraints have been considered in the problem formulation. A clustering algorithm is applied to manage the size of the Pareto set. In addition, an algorithm based on fuzzy set theory is used to extract the best compromise solution. Simulation results on IEEE 30-bus and IEEE 118-bus standard test systems show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well-distributed Pareto-optimal solutions.

Original language	English
Pages (from-to)	991-1005
Number of pages	15
Journal	Arabian Journal for Science and Engineering
Volume	37
Issue number	4
DOIs	https://doi.org/10.1007/s13369-012-0224-3
State	Published - Jun 2012

Keywords

Differential evolution
Evolutionary algorithms
Multi-objective optimization
Optimal power flow

ASJC Scopus subject areas

General

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10.1007/s13369-012-0224-3

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title = "Multi-Objective Optimal Power Flow Using Differential Evolution",

abstract = "This paper presents a multi-objective differential-evolution-based approach to solve the optimal power flow (OPF) problem. The OPF problem has been treated as a true multi-objective constrained optimization problem. Different objective functions and operational constraints have been considered in the problem formulation. A clustering algorithm is applied to manage the size of the Pareto set. In addition, an algorithm based on fuzzy set theory is used to extract the best compromise solution. Simulation results on IEEE 30-bus and IEEE 118-bus standard test systems show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well-distributed Pareto-optimal solutions.",

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AU - Al-Ali, N. A.

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AB - This paper presents a multi-objective differential-evolution-based approach to solve the optimal power flow (OPF) problem. The OPF problem has been treated as a true multi-objective constrained optimization problem. Different objective functions and operational constraints have been considered in the problem formulation. A clustering algorithm is applied to manage the size of the Pareto set. In addition, an algorithm based on fuzzy set theory is used to extract the best compromise solution. Simulation results on IEEE 30-bus and IEEE 118-bus standard test systems show the effectiveness of the proposed approach in solving true multi-objective OPF and also finding well-distributed Pareto-optimal solutions.

KW - Differential evolution

KW - Evolutionary algorithms

KW - Multi-objective optimization

KW - Optimal power flow

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JO - Arabian Journal for Science and Engineering

JF - Arabian Journal for Science and Engineering

IS - 4

ER -

Multi-Objective Optimal Power Flow Using Differential Evolution

Abstract

Keywords

ASJC Scopus subject areas

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