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In power grids, the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate attacks. This has sparked discussions on the resilience of power grids. Energy-storage systems (ESSs) are critical for enhancing the resilience of power grids. ESSs, with their mechanism of flexible charging and discharging, adjust energy usage as needed during disasters, thereby mitigating the impact on the grid and enhancing security and resilience. This, in turn, ensures the power system's stable operation. Currently, there is limited systematic research quantifying the economic value of energy storage in resilience scenarios. Therefore, a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events. A two-stage stochastic optimization mathematical model was developed. The first stage involves pre-deployment based on day-ahead expectations, and the second stage involves simulating potential failure scenarios through real-time scheduling. Considering the temporal dimension, the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of load-shedding. Here, a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity, as reflected in the emergency dispatch model. This index helps determine the balance between the energy storage investment cost and resilience value. Finally, an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed method. The findings revealed a significant improvement in the resilience value, with a 23.49% increase observed when energy storage systems were implemented compared to the scenario without energy storage systems. The optimal capacity configurations for the flywheel, lithium-ion batteries, and pumped hydro storage were 10 MW, 11 MW, and 12 MW, respectively, highlight their potential to maximize value in experimental system.
Wu et al. (Fri,) studied this question.