Improve particle swarm optimization algorithm to optimize the profit of a thermal power plant using different revenue models

Abstract

In this research, three versions of particle swarm optimization algorithm such as conventional particle swarm optimization (PSO), particle swarm optimization with inertia weight and particle swarm optimization with constriction factor are applied for handling the economic load dispatch problem under the competitive electric market. The main work of the PSO algorithms is to determine the most optimal power output of generators to obtain total profit as much as possible for the power companies without violation of constraints. These algorithms are tested on three and ten generators system using two different revenue models. The results obtained from the algorithm simulation are compared to each other as well as to the other methods to evaluate the algorithm efficiency and robustness. As a result, the improved PSO algorithms are very strong to solve the economic load dispatch problem for profit optimization because they can obtain the highest profit, fast converge speed and simulation time.