电池储能是高比例新能源电力系统中消纳新能源与保障频率安全的有效资源之一，随着其大规模接入电网，可以与火电机组配合参与调峰调频。储能同时参与调峰调频需要提前预留足够的调频容量，调频容量需求由频率和功率的隐式模型决定，现有方法根据机组组合的混合整数线性规划特点线性化调频容量模型，以一定近似误差保证计算效率。针对模型准确性和计算效率的矛盾，本文基于深度神经网络的通用近似理论，构建频率和功率的关系模型，既不改变机组组合问题结构，理论上又可按任意精度逼近原模型，并以此提出一种机组组合中电池储能调峰调频联合调度方法。首先，分析了多源联合调频机理，提出储能应对负荷阶跃扰动提供快速功率支撑调频的控制策略，根据该控制策略构建预测系统可承受最大扰动的深度神经网络，并提出储能的调频容量备用方法。其次，构建电池储能调峰与调频联合调度模型，以进行机组运行状态和储能调峰调频容量分配的统一优化。最后，IEEE39算例仿真结果表明，所提方法能够在最大阶跃扰动下保证系统的频率安全，并提高了系统运行的经济性，证明了数据驱动方法在频率波动表达方面的优越性。

Battery energy storage system (BESS) is one of the most effective resources to compensate demand-supply imbalance induced by renewable energy and frequency security. With large-scale access to the power grid, BESSs coordinate with thermal power units to participate in peak regulation and frequency regulation. To this end, BESSs need to reserve sufficient capacity in advance. The required frequency regulation capacity is determined by the implicit relationship between frequency variations and power imbalance. Existing methods linearize the frequency regulation capacity model according to the mixed integer linear programming characteristics of the unit commitment problem. Such linearization methods guarantee the computational efficiency at the cost of an increased approximation errors. In view of the trade-off between model accuracy and computational efficiency, this paper develops a model between frequency and power based on the Universal Approximation Theorem of deep neural network (DNN), which do not change the problem structure of unit commitment and might approach the original model with an arbitrary accuracy in theory. Based on such methods, we propose a joint scheduling method for battery energy storage in unit commitment. First, the mechanism of multi-source joint frequency regulation is analyzed, and the control strategy of energy storage to provide fast power support frequency modulation in response to load step disturbance is proposed. According to the control strategy, a DNN is constructed to predict that the system can withstand the maximum disturbance, and the standby method of frequency modulation capacity of energy storage is proposed. Second, the joint scheduling model of battery energy storage peak regulation and frequency regulation is constructed to optimize the unit operating state and the capacity allocation of energy storage peak regulation and frequency regulation. Last but not least, case studies based on IEEE 39-bus system are conducted to verify the proposed work. The results show that the proposed method can ensure the frequency security of the system under the maximum step disturbance, and improve the economy of the system operation, which proves the superiority of the data-driven method in the expression of frequency fluctuations.