Indian Journal of Science and Technology
Year: 2014, Volume: 7, Issue: 3, Pages: 262-270
Meysam Rahmati1 , Reza Effatnejad2* 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:
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
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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