Implementing soft computing techniques to solve economic dispatch problem in power systems
Soft computing is the state-of-the-art approach to artificial intelligence and it has showed an excellent performance in solving the combined optimization problems. In this paper, issues related to the implementation of the soft computing techniques are highlighted for a successful application to solve economic dispatch (ED) problem, which is a constrained optimization problem in power systems. First of all, a survey covering the basics of the techniques is presented and then implementation of the techniques in the ED problem is discussed. The soft computing techniques, namely tabu search (TS), genetic algorithm (GA), Hopfield neural network (HNN) and multi-layered perceptron (MLP) are applied to solve the ED problem. The techniques are tested on power systems consisting of 6 and 20 generating units and the results are compared to highlight the performance of the soft computing techniques. Future directions and open-ended problems in implementation of soft computing techniques for constrained optimization problems in power system are indicated. Suggestions are presented to improve soft computing techniques.
soft computing techniques economic dispatch constrained optimization tabu search genetic algorithm hopfield neural networks mlp neural networks