Economic Load Dispatch is the scheduling of generators to minimize the total operating cost depending on equality and inequality constraints. The Hopfield neural networks have been successfully applied for solving optimization problems in power systems. This paper presents a summary of algorithms that have been proposed for the application of the Hopfield Neural Network to the Economic Load Dispatch problem. The algorithms attempt to minimize the system operation cost, while satisfying the system operating constraints, e.g., power balance and unit generation limits. The major difficulties in applying the networks in solving the economic load dispatch problem were that there were no algorithms to determine the weights in the energy function, and the process also involved a large number of iterations. Some methodologies for improving the performance of combinatorial-optimization problems have already been published.
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