Risk Assessment Method for Renewable Energy Uncertainty Considering Energy Storage and Automatic Generation Control

With the increasing penetration of renewable energy in the power system, the low inertia and uncertainty of its output power pose new challenges to the security and reliability of the system. In this paper, we propose a risk assessment method for renewable energy uncertainty considering energy storage and automatic generator control, and analyze the power fluctuation of renewable power sources in the IEEE system models. The overall risk probability of the system is calculated by setting different upper limits of branch power transmission and sampling the amount of power fluctuation of the distributed power source using the ALOE (At Least One rare Event) method and the CMCS (Conventional Monte Carlo Sampling) method, respectively. The results show that the ALOE method can effectively capture rare events, and the standard deviation of its calculated risk probability is smaller than that of the CMCS, which leads to a more accurate assessment of system risk. This study provides a more effective risk assessment tool for power systems containing renewable energy sources.