Hybrid Particle Swarm Optimization Technique For Optimal Power Flow

The Optimal Power Flow (OPF) plays an important role in power system operation and control due to depleting energy resources, and increasing power generation cost and ever growing demand for electric energy. As the size of the power system increases, load may be varying. The generators should share the total demand plus losses among themselves. The sharing should be based on the fuel cost of the total generation with respect to some security constraints. Conventional optimization methods that make use of derivatives and gradients are, in general, not able to locate or identify the global optimum. Heuristic algorithms such as genetic algorithms (GA) and evolutionary programming and PSO have been proposed for solving the OPF problem. Recently, a new evolutionary computation technique, called Adaptive Particle Swarm Optimization (APSO), has been proposed and introd uced. In this paper, a novel Adaptive PSO based approach is presented to solve Optimal Power Flow problem to satisfy objectives such as minimizing generation fuel cost and transmission line losses. A hybrid OPF is proposed by combining the positive aspects of Interior point method and APSO.The proposed algorithms are tested on IEEE 30 bus system using MATLAB.