A method for urban high-voltage distribution network partitioning and energy storage planning

Centralized energy storage plays a critical role in grid applications such as peak shaving， valley filling， and balancing supply and demand across different partitions. However， existing energy storage station planning often lacks research on the supply-demand balance within specific partitions. To address this issue， a method for urban high-voltage distribution network partitioning and energy storage planning is proposed. First， the network partitioning is optimized using a Newman’s fast algorithm based on the electrical coupling strength （ECS） matrix. Then， an energy storage configuration model for the urban distribution networks is developed， with economic efficiency and grid vulnerability as optimization objectives， considering constraints such as energy storage limits and power flow constraints. A multi-objective particle swarm optimization （MOPSO） is employed to obtain the Pareto frontier， and the rank sum ratio （RSR） is used to determine the optimal site and capacity of energy storage for each partition. Finally， case studies based on the IEEE 39-node grid are conducted to verify that the proposed method enables rational planning of grid-side energy storage stations， effectively enhancing the autonomy and economy of partition-based operation of urban distribution networks.