Comprehensive Learning Particle Swarm Optimization (CLPSO) for Multi-objective Optimal Power Flow

Meysam Rahmati; Reza Effatnejad   and Amin Safari

doi:10.17485/ijst/2014/v7i3.7

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2014/v7i3.7

Year: 2014, Volume: 7, Issue: 3, Pages: 262-270

Original Article

Comprehensive Learning Particle Swarm Optimization (CLPSO) for Multi-objective Optimal Power Flow

Meysam Rahmati¹ , Reza Effatnejad^2* and Amin Safari³

¹Department of Electrical Engineering, Islamic Azad University, Ahar branch, Ahar, Iran; ²Department of Electrical Engineering, Islamic Azad University-Karaj branch, Karaj, Iran; ³Department of Electrical Engineering, Faculty of Engineering, Azarbaijan Shahid Madani University

*Author for the correspondence:
Reza Effatnejad
Department of Electrical Engineering, Islamic Azad University-Karaj branch, Karaj, Iran
E-mail: [email protected]

Received Date:01 February 2014, Accepted Date:23 February 2014, Published Date:03 March 2014

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The Optimal Power Flow (OPF) problem has been widely used in power system operation and planning for determining electricity prices and amount of emission. This paper presents a Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm to solve the highly constrained multi-objective OPF involving conflicting objectives, considering fuel cost and emission level functions. The proposed technique has been carried out on IEEE 30-bus test system. The results demonstrate the capability of the proposed CLPSO approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective OPF problem. The results show that the approaches developed are feasible and efficient.

Keywords: Comprehensive Learning, Multi-objective Optimization, Particle Swarm Optimization, Optimal Power Flow