The Study Of Algorithms For Power System Reliability Evaluation Based On Monte Carlo Simulation

With the rapid growth of China’s economic development and the people’s living standard, the electricity demand in the community is gradually increasing, and the security and stability of power supply has become a growing concern. Power system reliability is the important index of measuring the operation level and the power supply reliability of power system. The related research about power system reliability is meaningful for power system planning, design and operation. This paper is mainly about:1) Simply introduce the development of power system reliability, several existing reliability evaluation methods and several common reliability evaluation indexes. Then, it expounded the theoretical basis of several common variance reduction technique used in Monte Carlo method, such as importance sampling method, stratified sampling method, control variable method, etc. And it analyzed the advantages and disadvantages of these methods and their applicable scope.2) Considering the influence of different system components on the system reliability, this paper proposed an improved component importance analysis method based on component fault impact analysis. By taking the influence of the network topology into consideration, this method can not only be handled easily, but also improve the assessment accuracy at the same time.3) With a comprehensive application of importance sampling method and control variable method, an importance-control method (ICM) for power system reliability evaluation was proposed. Based on the recognition result of the important components in the system and the characteristics of system state analysis, ICM can construct important control variable which meets the requirements for the calculation. With the constructed important control variable, the reconfiguration of system important state function can be realized. This method can effectively reduce the sampling variance and speed up the convergence of Monte Carlo method. 4) After realizing the reconfiguration of system important state function, ICM failed to further optimize its probability distribution. This limited the application of the method to some extent. Therefore, on the basis of studying several existing important sampling technique, this paper realized the optimization of system state space probability distribution by using the adaptive sampling technique. This method can further reduce the variance and improve the applicability of ICM.Based on the algorithm in this paper, IEEE-RTS79system and its modified systems were analyzed. The results verified the effectiveness of the proposed algorithm in this paper.