PC Cluster based Parallel PSO Algorithm for Optimal Power Flow

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, the OPF problem has been intensively studied and widely used in power system operation and planning. To solve OPF problem, a number of conventional optimization techniques have been applied. In the past few decades, many heuristic optimization methods have been developed, such as genetic algorithm (GA), evolutionary programming (EP), evolution strategies (ES), and particle swarm optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallelization of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC- cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.