Monte Carlo stochastic simulation of electric power generation system production costs under time-dependent constraints

Probabilistic production costing models are widely used in the electric power industry to forecast the cost of producing electricity. A widely used model due to Balériaux and Booth provides an analytical formula for the expected production costs using the load duration curve (LDC) in place of chronological sequence of loads and the forced outage rates of the generating units. Since the chronological information is lost in the LDC, it cannot accurately simulate those aspects of production cost that are time dependent in nature. The paper points out that, in addition to the need for a chronological simulation of load to capture the time-dependent constraints, it is also necessary to model the frequency and duration of the generation outages. Monte Carlo results are given for a Markovian model for the frequency and duration of the outages where several unit commitment constraints are considered. It is shown that the mean and variance of the production costs may differ significantly if the failure and the repair rates of the generating units are changed although the respective forced outage rates remain unaltered. The paper also highlights the simplicity of using continuous-time simulation in the Markov model.