随着高比例可再生能源并网，电力系统由确定性形态向强随机性系统转变，对电力系统的灵活性调节能力提出了更高要求。储能作为一种灵活性调节资源，在新型电力系统中将逐渐替代传统火电机组，承担更多的调频调峰任务。然而，由于当前储能的价格高昂，投资者需要合理地配置储能容量以获取最大的经济效益。因此，本文以风电场站配置储能参与调峰调频服务为背景，考虑风储联合系统参与灵活性调节的容量规划问题，使得储能多组合应用盈利最大化。首先，为处理风电与负荷出力的不确定性，本文基于风电与负荷的历史数据，采用K-means聚类方法生成各自的典型场景，并由笛卡尔积生成风-荷联合典型场景及其相应概率。其次，结合调峰调频需求的特点，对风储系统参与辅助服务过程中的成本与效益进行量化建模，包含辅助调峰与调频的收益、储能的初始投资成本、储能的容量衰减成本，构建基于数学期望的随机优化模型。最后，以某地区风电场为算例，进行上述优化问题求解，仿真结果验证了本文所提方法的可行性与有效性。

The high proportion of renewable energy connected to the grid puts forward higher requirements for the flexibility and regulation ability of the power system. As a flexible regulation resource, energy storage will gradually replace the traditional thermal power units in the new power system and undertake more tasks of frequency modulation and peak load regulation. However, the high price of energy storage limits its large-scale application. Therefore, from the technical and economic point of view, this paper considers the energy storage capacity planning under the background that the wind storage combined system participates in the peak shaving and frequency modulation auxiliary service. Firstly, in order to deal with the uncertainty of wind power and load output, based on the historical data of wind power and load, this paper uses K-means clustering method to generate their own typical scenarios, and generates wind load typical joint scenarios and their corresponding probabilities by Cartesian product. Secondly, combined with the characteristics of peak shaving and frequency modulation demand, the various costs and benefits of the wind storage system in the process of participating in auxiliary services are quantitatively modeled, including the benefits of auxiliary peak shaving and frequency modulation, the initial investment cost of energy storage, and the life attenuation cost of energy storage. Finally, a wind farm in a certain area is taken as an example to verify the feasibility and effectiveness of the proposed method.