Recurrent neural networks solving a real large scale mid-term scheduling for power plants

This paper deals with an application of artificial neural network (ANN) to solve the operation planning problem of generation systems in the mid-term operation horizon. This problem is related to economic power dispatch that minimizes the overall production cost while satisfies the load demand. These kinds of problem are large scale optimization problems in which the complexity increases with the planning horizon and the accuracy of the system to be modeled. This paper considers the two-phase optimization neural network which solves linear and quadratic programming problems. These networks are based on the solution of a set of differential equations that are obtained from a transformation of an augmented Lagrange energy function. This network also provides the corresponding Lagrange multiplier associated with each constraint which is the marginal price. The results indicate that the developed ANN model provides optimal scheduling of hydro, thermal and wind power plant towards the minimal operation cost.