Comparison of Classical Method and Soft Computing Optimization Algorithm applied to Economic load Dispatch Problem

This paper presents a comparative study of classical method and soft computing optimization algorithm applied to solve Economic Load Dispatch problem with non-smooth fuel cost curves considering transmission losses, power balance and capacity constraints. The soft computing algorithm varies from the classical method in terms of the following basic factors. The genetic algorithm based approach produces significantly better solutions compared against those obtained using the standard economic dispatch approach. It also proves the robustness of this algorithm in solving this type of optimization problem. GA differs from Classical optimization techniques in that it works on a population of solutions and searching is on a bit string encoding of the real parameters rather than the parameters themselves. Also GA uses probabilistic transition rules. The performance of the soft computing algorithm is investigated and tested with a three generator power system. Simulation results are presented to show the comparative performance of these methods.