Meysam Rahmati ¹, Reza Effatnejad ², Amin Safari ³

Affiliations
1 Department of Electrical Engineering, Islamic Azad University, Ahar branch, Ahar, IR
2 Department of Electrical Engineering, Islamic Azad University-Karaj branch, Karaj, IR
3 3Department of Electrical Engineering, Faculty of Engineering, Azarbaijan Shahid Madani University, Tabriz, IR

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

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