Probabilistic Uncertainty Analysis of Monte-Carlo Simulation for Bulk Power System Reliability Evaluation

Random sampling of system state is the fundamental procedure in Monte-Carlo simulation of bulk power system reliability evaluation, and the sampling size has significant effects on simulation accuracy and calculation time. So the probabilistic forecasting technique for simulation accuracy and sampling size is the key to balance the calculation accuracy and cost. The probabilistic forecasting method of the error between the sample mean and expectation value of the random variable utilizing the central limit theory was researched, then the mathematical formula for the relation between sampling size and variance coefficient of loss of load probability (LOLP) was analyzed. Furthermore, the formulas for the relation between confidence intervals of variance coefficient and sampling size are deduced. These formulas are valuable to realize the probabilistic quantitative analysis between simulation accuracy and sampling size. Finally, the RBTS and IEEE-RTS96 power systems are evaluated to verify their validity, and some valuable conclusions are drawn.