{
  "$type": "site.standard.document",
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  "description": "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…",
  "path": "/patents/1114869",
  "publishedAt": "2026-05-28T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "H02J3/004",
    "Guangdong Shunde Electric Power Design Institute Co., Ltd."
  ],
  "textContent": "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.",
  "title": "Edge-Computation-Based Dynamic Optimization Control System for Energy Storage Cluster"
}