Stochastic models for power generation system production costs

Production costing models are widely used in the electric power industry for the purpose of generation capacity expansion planning, fuel management, and operational planning. These models account for the load variation over time and generator outages. A widely used model, due to Balériaux and Booth, yields a prediction of the expected production costs and is based on the load duration curve and forced outage rate of the generating units. This paper highlights the fact that, in order to obtain a more detailed characterization of the probability distribution costs beyond the expected value, a model involving the stochastic processes underlying the generator outages is necessary. A stochastic model is considered as an enhancement to the traditional Balériaux model. It is shown that Monte Carlo simulation can be routinely used on the enhanced model to provide answers concerning the distribution of production costs. Monte Carlo methods avoid the problems associated with the complexity of the analytical methods. Numerical examples are given using the enhanced model where load is considered to be either a deterministic or stochastic time-varying function. An example is given using decision analysis where a possible use of the more detailed information on the probability distribution of production costs in generation system planning is illustrated.