Distributed energy storage technology can effectively solve the load peak-to-valley difference and voltage quality problems faced by distribution networks. Reasonable and efficient scheduling of distributed energy storage in distribution networks is an important means to play its role. The study proposes a power prediction-based optimized scheduling strategy for distributed energy storage in distribution grids with hierarchical zoning. Firstly, power prediction is carried out using GWO-EEMDBP neural network. Then partition optimization is carried out according to distributed power and load distribution, and the energy storage scheduling strategy is formulated based on the energy storage power prediction interval. Finally, experiments and arithmetic examples are analyzed based on the data related to the distribution system of the IEEE-33 distribution node. The predicted SOC values based on GWO-EEMD-BP neural network are basically consistent with the real SOC values. After applying the energy storage scheduling strategy designed in this paper, the system power loss decreased by 260.86 kW∙h and the load volatility decreased by 67.5%. In addition, this strategy has significant advantages in terms of system operation economic efficiency and voltage quality improvement, and it is capable of scheduling distributed energy storage in the distribution network in a reasonable manner.