Application of artificial neural networks to unit commitment

Artificial neural networks are currently being applied to a variety of complex combinatorial optimization and nonlinear programming problems. In this paper, a combination of Hopfield Tank type, and Chua-Lin type artificial neural networks is applied to solve simultaneously the unit commitment and the associated economic unit dispatch problems. The approach is based on imbedding the various constraints in a generalized energy function, and then defining the network dynamics in such a way that the generalized energy function is a Lyapunov function of the artificial neural network. The novel feature of the proposed approach is that the nonlinear programming and the combinatorial optimization problems are solved simultaneously by one network. An illustrative example is also presented