Edge-Computation-Based Dynamic Optimization Control System for Energy Storage Cluster

The present application discloses an edge-computation-based dynamic optimization control system for an energy storage cluster, and relates to the technical field of energy storage clusters. The edge-computation-based dynamic optimization control system includes: acquiring historical data of the energy storage cluster and local data of edge nodes; predicting a time sequence by using an ARIMA model, recognizing and correcting a non-linear error by using a BP neural network, and outputting a final power prediction value. By means of short-term trend prediction of ARIMA and error correction of the BP neural network, the stable operation of an energy storage system in a grid can be guaranteed; by performing local BP neural network model training on each edge node, distributed computation resources can be sufficiently utilized; and under a synergistic effect of a bi-level optimization model, a final power distribution solution and a transmission path optimization result will be outputted.