Comparative Analyses of Particle Swarm Optimization for Non-convex Economic Dispatch Problems

An important criterion in power system operation is to meet an efficient approach for solving economic dispatch (ED) problems with non convex cost functions using particle swarm optimization (PSO). Although the Particle Swarm Optimization (PSO) approaches have several advantages suitable to heavily constrained non convex optimization problems, PSO framework employing chaotic sequences combined with the conventional linearly decreasing inertia weights and adopting a crossover operation scheme to increase both exploration and exploitation capability of the PSO. In addition, an effective constraint handling framework is employed for considering equality and inequality constraints. The proposed PSO is applied to three different non convex ED problems with valve-point effects, prohibited operating zones with ramp rate limits as well as transmission network losses, and multi-fuels with valvepoint effects. Additionally, it is applied to the large-scale power system. Also, the results are compared with those of the state-of-the-art methods. The effectiveness of these algorithms has been tested on systems of fifteen generating unit.