Comprehensive learning particle swarm optimization (CLPSO) for multi-objective optimal power flow

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.