Application of Monte Carlo simulation to generating system well-being analysis

Abstract

System well-being analysis is a new approach to power system generation adequacy evaluation which incorporates deterministic criteria in a probabilistic framework and provides system operating information in addition to risk assessment. This approach not only provides a new perspective to generation adequacy studies but can also be useful in those situations in which conventional probabilistic techniques are not normally accepted, such as, in system operating capacity reserve assessment and in small isolated system planning. The probabilities of system health, margin and risk are the basic well-being indices and can be evaluated using analytical techniques. Monte Carlo simulation can also be used to estimate the indices by simulating the actual process and random behavior of the system and can include system effects which may not be possible without excessive approximation in a direct analytical approach. This paper illustrates the utilization of Monte Carlo simulation to evaluate additional well-being indices and their distributions and the significance of this additional information on capacity reserve evaluation.