Reliability assessment combining importance resampling and the cross entropy method

In the ongoing transition towards a sustainable energy system, the electric power system increases in complexity and must adapt to rapid changes and new uncertainties. Thus, development and application of appropriate probabilistic methods is all the more important. Reliability analysis based on Monte Carlo simulation (MCS) has become increasingly popular, and its strength lies in the ability to account for a large number of random variables, general stochastic processes and assessing the probability distribution of the output. However, power system reliability is governed by relatively rare interruption events which poses a fundamental challenge to MCS. This paper presents a variance reduction technique for Monte Carlo based reliability analysis which combines resampling and the cross entropy (CE) method. The motivation for the work is to mitigate the computational burden related to rare event sampling, while at the same time preserving the flexibility of MCS by introducing few assumptions on the stochastic model. The method is demonstrated on a synthetic test system and gives a speedup of about 10 times compared to a crude simulation.