True Multi-Objective Optimal Power Flow in a Deregulated Environment Using Intelligent Technique

Abstract

Abstract—in this paper, a Multi-Objective Particle Swarm optimization (MOPSO) technique is proposed for solving the Optimal Power Flow (OPF) problem in a deregulated environment. The OPF problem is formulated as a nonlinear constrained multiobjective optimization problem where the fuel cost and wheeling cost are to be optimized simultaneously. MVA-km method is used to calculate the wheeling cost in the system. The proposed approach handles the problem as a true multiobjective optimization problem. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal solutions of the multiobjective OPF problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multiobjective OPF problem are confirmed. IEEE 30 bus system is considered to demonstrate the suitability of this algorithm.