Fuel constrained economic emission load dispatch using hopfield neural networks

Abstract

This paper presents a new approach using Hopfield neural networks for solving fuel constrained economic emission load dispatch problems of thermal generating units. This is a multi-objective minimization technique that includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints were ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that one of the two objective functions (fuel cost and emission level) may be increased while other may be decreased to serve the same power demand but this may well compensate for the penalty that might be otherwise imposed for not maintaining the fuel contract. Numerical results for an example system have been presented to illustrate the performance and applicability of the proposed method.