Stochastic electric power system production costing and operations planning using a Hopfield artificial neural network

This paper presents a new method for stochastic electric power system production costing and operations planning. In the method we formulate a stochastic Hopfield/Chua-Kennedy neural network in which unit availability and system load demand are random parameters with known statistics. Unit outages are modeled as Markov processes. The unit commitment status variables are (0, 1) integers while the unit dispatch/loading levels take on decimal values. The unit commitment status variables together with the unit dispatch/loading levels are random processes satisfying appropriately derived deterministic equivalent differential equations. A review of the techniques that are currently employed by utilities for production costing and operations planning with particular emphasis on those for the stochastic problem is also presented.