Neural computation using discrete and continuous Hopfield networks for power system economic dispatch and unit commitment

A new method using artificial neural networks for the solution of the unit commitment (UC) and economic dispatch (ED) using Hopfield neural network (HNN) is presented. Discrete and Continuous Hopfield Networks have been used earlier to solve UC and ED problems separately. But these two problems are completely interdependent. Due to their inseparable nature, both the problems must be solved simultaneously. The difficulty in combining these problems is that while the first one requires a discrete neuron model, the latter requires a continuous neuron model. The combined solution of these problems using HNN requires the interconnection of discrete and continuous neural network models and the formulation of a unified energy function, which is quite complicated. The important contribution of this work is the proposal of a new architecture for the discrete HNN for UC and the output of the UC module is used as input to the continuous HNN for ED. The associated advantage of using HNN for the combined solution of UC and ED is the decoupling of their interdependency, i.e., both the UC and ED are iteratively solved using respective HNN for the particular period. The implementation of the proposed method causes a considerable reduction in the HNN size and hence complexity and computation requirements, compared to earlier attempts. The method was successfully tested for different cases (3, 5, 6, 10 and 26 generator units), with varying load pattern of different durations (24 and 168 h) on Matlab on P-IV machine in windows enviroment. Each case study is done with an aim to bring out the important features of the proposed method. The results for the case studies are presented and important observations are discussed.