Dynamic Multi-Stage Energy Storage Allocation Based on Power Grid Partitioning for Long-Term Node Overload Mitigation

The bidirectional characteristics of energy storage enable it to serve as a flexible resource for enhancing the regulation capacity of the power grid. However, with the large-scale deployment of energy storage systems, they face increasing risks of degradation. Conventional energy storage planning schemes primarily focus on early-stage tasks such as site selection and capacity determination, lacking considerations for long-term, multi-stage planning. Considering the safety and capacity optimization of energy storage power stations during long-term, multi-stage operation, this paper proposes a dynamic optimal allocation method for energy storage based on multi-stage grid partitioning. An improved Newman's Fast Algorithm is developed to design a grid partitioning method that adapts to multi-stage planning requirements. Based on the results of multi-stage grid partitioning, this study considers the gradual increase in the load at each node of the energy storage power station during long-term operation. A dynamic energy storage allocation model is established, using the minimization of the number of nodes exceeding a fixed load rate as the objective function and incorporating voltage constraints, with a one-year planning interval. Using the strategy set obtained from the previous stage as constraints, a heuristic multipath forward dynamic programming method is employed to determine the specific investment timing for each piece of equipment. A case study is presented to verify the effectiveness and economic benefits of the proposed method. This paper proposes a multi-stage spatiotemporal guidance model to dynamically plan energy storage capacity based on the requirements of different cycles or stages during the long-term, multi-stage operation of energy storage power stations.