共享储能是提高储能利用效率、发挥微能源网集群协同优势的有效措施，然而，现有研究大多将共享储能与微能源网集群间的能量传输通道简化为母线结构，存在仅能“定容”不能“选址”、配置后共享储能利用率不足等问题。在此背景下，建立了考虑电网承载能力的微能源网集群共享储能双层优化配置模型。首先，建立面向微能源网集群的共享储能双层配置模型：上层模型以大电网运行成本与共享储能规划运行成本最小为目标，考虑电网运行承载能力，优化决策共享储能的位置及容量；下层模型以各微能源网运行成本最小为目标，优化求解微能源网运行问题。其次，基于Karush Kuhn Tucker (KKT)条件将下层模型转换为上层模型的附加约束条件，将双层优化配置模型转换为单层优化问题。最后，利用包含3个微能源网的测试算例进行对比分析。结果表明所提模型能够充分考虑电网承载能力，获得经济效益更优、投资回收期更短的共享储能选址定容方案。

Shared energy storage is an effective measure to improve the efficiency of energy storage and play the synergistic advantages of micro-energy grid clusters, however, most of the existing studies have simplified the energy transmission channel between shared energy storage and micro-energy network clusters into a bus structure, which has problems such as 'fixed capacity’ but not 'siting’, insufficient utilisation of shared energy storage after configuration, and so on. There are problems such as only 'capacity-setting’ but not 'siting’ and insufficient utilisation of shared energy storage after configuration. In this context, a two-layer optimal configuration model of shared energy storage for micro-energy grid clusters is established, taking into account the carrying capacity of the power grid. Firstly, a two-layer configuration model of shared energy storage for micro-energy grid clusters is established: the upper model takes the minimisation of the operating cost of the power grid and the shared energy storage planning as the goal, and optimises the decision-making of the location and capacity of the shared energy storage by taking into account the carrying capacity of the power grid; the lower model takes the minimisation of the operating cost of the micro-energy grid as the goal, and optimises the solution to the micro-energy grid operation problem. Secondly, the lower-layer model is converted into the constraints of the upper-layer model based on the KKT condition, and the two-layer optimal allocation model is converted into a single-layer optimisation problem. Finally, a comparative analysis is conducted using a test case containing three micro-energy grids. The results show that the proposed model can fully consider the carrying capacity of the grid and obtain a shared energy storage siting and capacity-setting scheme with better economic benefits and shorter payback period.