Multi-objective particle swarm optimization for optimal power flow in a deregulated environment of power systems

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 multi-objective 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 multi-objective optimization problem. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal solutions of the multi-objective OPF problem in one single run. In addition, the effectiveness of the proposed approach and its potential to solve the multi-objective OPF problem are confirmed. IEEE 30 bus system is considered to demonstrate the suitability of this algorithm.