sequential monte carlo simulation

By integrating the power and natural gas systems (IPGS), these two systems can synergistically provide electric and gas power through mutual energy sharing. However, disruptions occurring in one system can have detrimental effects on the optimal performance of the other system. Hence, the assessment of IPGS reliability becomes imperative. In this article, a reliability evaluation model is proposed that utilizes machine learning algorithms to tackle uncertainties associated with the failure rate of IPGS components in the presence of gas storage resources. To calculate system reliability indices, we employ Sequential Monte Carlo Simulation (SMCS) and an optimal load-shedding program. Moreover, a random forest (RF) algorithm is adopted to classify elements based on their importance in upholding the overall system's reliability. The proposed model is implemented using MATLAB, GAMS, and Python software, with the IEEE 14-bus system and the 10-node natural gas system as case studies. The simulation results illustrate that by considering the gas storage resources, the reliability of the power system and natural gas can be improved by 2.12 and 8.3%, respectively, and the overall reliability of IPGS is improved by 5.25%. Also, the prioritization of these resources is determined in IPGS.