Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method

Abstract

The reliability of energy systems is assessed to control their operation and expansion. An effective method for reliability assessment is the Monte Carlo method. This process, however, is often time-consuming due to the large size of the power system. This interferes with subsequent control problems. The speed of reliability assessment and the accuracy of the result for the Monte Carlo method directly depend on the number of randomly generated states of the system, their quality and the complexity of the subproblem to be solved for each state. When solving such a subproblem for reliability assessment, random states can be defined as a shortage and shortage-free ones. To assess the reliability of power systems using the Monte Carlo method, one should analyze only the state of the system with a shortage. We suggest the use of machine learning methods to eliminate or sort the shortage and shortage-free states. The paper demonstrates the effectiveness of two methods: a support vector machine and a random forest. It also shows their performance when the Monte Carlo and quasi-Monte Carlo methods are used.