Research on Capacity Credit of Grid-connected Intermittent Energy Based on Improved Sequential Monte Carlo Sampling Method

As the world turns towards renewable energy sources, wind power has emerged as a crucial component of the new energy mix. However, due to its intermittently nature, it poses several challenges for reliable generation and managing wind farms. This paper addresses these challenges by analyzing the reliability model for systems containing intermittent energy sources and proposing the use of the improved sequential Monte Carlo sampling algorithm to accelerate simulation speed while maintaining effective load carrying capacity assessment accuracy when calculating loss of load expectation. Then, by combining the algorithm with the secant method, this paper calculates the wind farm capacity credit. The results show that the proposed algorithm effectively improves reliability, reduces computation time, and enhances calculation performance for larger and more intricate wind systems.