不同类型储能具有差异化成本和运行特性，适用平抑不同时间尺度的风、光等具有波动性的新能源发电。现有研究未充分考虑利益主体及优化目标差异，缺乏从源、网双端协同角度开展多元储能规划研究。针对这一问题，本文提出一种多元混合储能系统的源网协同双层优化框架。该方法首先利用经验模态分解方法（Empirical mode decomposition，EMD）将风光出力分解为不同频率分量，针对高频分量在源侧由电化学储能处理，低频部分由网侧压缩空气储能（Compressed-air energy storage，CAES）进行承担。进而建立兼顾源侧成本最小化及网侧考虑储能综合评价性能、经济性与稳定性的多目标优化模型，并采用分层迭代进行高效求解。算例结果表明，与传统单层储能配置方法相比，所提源网协同优化方法可将系统电压波动和净负荷波动分别降低24.86%和18.62%，显著提升电力系统的运行稳定性和可再生能源消纳水平。

Different types of energy storage have differentiated costs and operating characteristics, so they are suitable for stabilizing the volatility of wind, solar and other renewable energy in different time scales. The existing research does not fully consider the differences of stakeholders and optimization objectives, and lacks the research on multiple energy storage planning from the perspective of source and network coordination. To address this issue, this paper proposes a two-level optimization framework for multi-type hybrid energy storage systems. The method first employs Empirical Mode Decomposition (EMD) to decompose the wind–solar output into distinct frequency components. High-frequency components are absorbed on the source side by electrochemical energy storage, while low-frequency components are regulated on the grid side by compressed-air energy storage (CAES). On this basis, a multi-objective optimization model is formulated that simultaneously minimizes source-side costs and, on the grid side, accounts for the storage’s comprehensive performance assessment, economic efficiency, and system stability. The model is efficiently solved via a hierarchical iterative scheme. Case studies show that, compared with conventional single-layer storage configuration, the proposed source–grid collaborative scheme reduces voltage fluctuations and net load fluctuations by 24.86% and 18.62%, thereby significantly enhancing system stability and renewable accommodation capability.