Non-parametric Stratified Importance Sampling Method for Reliability Evaluation of Power System with Renewable Energy

Abstract

The share of intermittent renewable energy is continuously increasing in modern power systems, which significantly declines the efficiency of traditional methods for power system reliability evaluation. Therefore, a non-parametric stratified importance sampling method for reliability evaluation of the power system is proposed, which solves the problem that it is difficult to control sample correlation when Latin hypercube sampling is combined with non-parametric importance sampling, and realizes the organic combination of these two methods. This method can concentrate the system state samples in important areas that have a large contribution to the reliability index, and reduce the repetition of samples, so it can significantly improve the efficiency of reliability evaluation of modern power systems with multiple renewable energy plants. The IEEE RTS-79 standard test system is modified and simulation examples are carried out by using the historical output data of actual wind farms, photovoltaic power plants and hydropower plants. The results show that the calculation speed of the proposed non-parametric stratified importance sampling method is significantly faster than that of individual importance sampling method. The effectiveness of the method has been proven.