Statistical analysis of electric power production costs

Abstract

Electricity cannot be conviently stored. Thus there should be sufficient production at all times to meet the demand for electric power. If a low-cost generating unit fails this will lead to its substitution by a higher cost unit. The cost of producing electric power is a random variable because it depends upon two uncertain quantities, demand and the availability of the generating units. Analytical computation of the mean and the variance of the production costs can become quite cumbersome and time consuming for large systems, and therefore Monte Carlo simulation becomes an attractive alternative. A simulation study based on time series analysis of actual load data is described in which the primary objective was to determine the respective contributions of the demand and the generator availabilities to the variability of the estimates of the production cost. A secondary objective was to find out the extent to which an accurate temperature forecast reduces this variability. The results show that demand is a significant source of variation, and an accurate temperature forecast mitigates the effect of load uncertainty in the forecast of production costs.