Economic Load Dispatch Using Evolutionary Technique

This paper basically presents the use of evolutionary technique for economic scheduling problems. Various evolutionary techniques like genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO) have been implemented to solve problems of economic scheduling in which the objective function (i.e., fuel cost) is stochastic, non-differentiable and nonlinear. The effectiveness of the techniques has been performed on a test system consisting of six generation units considering the losses (i.e., transmission losses) and satisfying all its constraints. They have been compared individually with respect to each other on the basis of power allocation and convergence rate.