Monte Carlo simulation is becoming an attractive alternative to analytical approaches for reliability evaluation in large electric power systems. Monte Carlo simulation is generally more flexible than an analytical technique when complex operating conditions and system considerations such as multi-derated states, chronology, reservoir operating rules, bus load uncertainty and weather effects need to be incorporated. Monte Carlo techniques are usually classified as being either a sequential or nonsequential method.
The basic non-sequential Monte Carlo approach is known as the state sampling method in which the actual frequency of failure is estimated from the number of failures encountered during the simulation process. The actual frequency of failure can be more accurately obtained by using a sequential approach which models the component up and down cycles together with the system load.
This paper presents and illustrates the application of the state transition sampling technique. This method can be used to estimate the actual frequency index without requiring an additional enumeration procedure or sampling the component up and down cycles and storing chronological information on the overall state of the system. In this approach the next system state is obtained by allowing a component to undergo transitions from its present state. The procedure focuses on transitions of the whole system rather than on component states or state durations. This technique is usually much faster than the traditional sequential simulation approach.
The state transition sampling technique will be illustrated by application to generating capacity and composite generation and transmission system reliability assessment in a representative electric power system.