Edge-Cloud Collaborative Digital Twin and Hierarchical Intelligent Decision-Making Model for Low-Latency Energy Regulation

Abstract

High penetrations of distributed energy resources require energy regulation that combines cloud-level global optimization with edge-level fast response. This paper proposes EC-HDT, a device-edge-cloud hierarchical digital twin in which a lightweight graph-attention-temporal-convolution estimator reconstructs local states under asynchronous, noisy, and missing measurements, while a cloud predictor and model predictive controller perform rolling economic optimization. A five-factor decision weight based on communication latency, information freshness, estimation confidence, operational risk, and edge computational load continuously allocates control authority between edge and cloud, and a quadratic-programming safety layer enforces physical constraints. On the IEEE 33-bus system, EC-HDT achieves a nodal-voltage MAE of 0.0076 p.u., mean/P95 end-to-end latencies of 56.4/89.4 ms, and a 99.2% control success rate; the daily operating cost is 3.51% lower than that of the fixed-fusion scheme. The results indicate that state-aware edge-cloud coordination can improve the latency-economy-safety trade-off in distribution-system regulation.