针对大规模可再生能源接入下电力系统频率稳定控制资源不足的问题，提出基于改进Benders分解的储能系统（energy storage system，ESS）容量多目标优化配置方法。首先，构建扩展系统频率响应模型，分析大功率缺额事故下系统有功功率响应与暂态最低频率间的耦合关系，基于最严重预想事故场景估计维持频率稳定所需的临界暂态调频能力（transient frequency regulation capability，TFRC）。其次，在满足临界TFRC的基础上，以全生命周期成本最小、运行收益最大为目标，建立ESS容量多目标优化配置模型。然后，在Benders分解方法中嵌入遗传算法（genetic algorithm，GA），将优化模型分解为主-子问题双层结构，在交替迭代求解的基础上，利用GA的全局搜索能力提升主问题可行解的搜索效率。算例分析表明，所提方法不仅能够提升运行经济性，而且能够降低低频减载的动作轮次，验证了其有效性。

Considering the insufficient control resource for power system frequency stability caused by the large-scale integration of renewable energies, a multi-objective optimal sizing scheme based on an improved Benders decomposition is proposed for the energy storage system (ESS). First, an extended frequency response model is built to analyze the coupling relation between power system active power response and frequency nadir under major disturbances. And the critical transient frequency regulation capability (TFRC) to ensure frequency stability is estimated according to the most severe contingency. Then, a multi-objective optimization model incorporating the TFRC constraint is established for the ESS to minimize the life cycle cost and maximize the operation profits. Additionally, the Benders decomposition method incorporates the genetic algorithm (GA) is used to decompose the optimization model into the master problem and subproblem, which are solved iteratively and alternately. And the global search capability of GA is utilized to enhance the efficiency of searching for feasible solutions in the master problem. Finally, case study results demonstrate that the proposed scheme can improve the operational economy and reduce the number of activated under-frequency load shedding steps, validating its effectiveness.