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.
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