Cloud–Edge Collaborative Resource Allocation Optimization for Low-Latency Distribution Automation in Smart Grid

Efficient distribution automation management, which integrates data perception, real-time analysis, and operational decision-making for distribution-automation services, requires low-delay, high-reliability, and coordinated processing of the massive, multi-dimensional data generated by distribution terminal units (DTUs). However, challenges arise from the limited computing resources of DTUs, communication bottlenecks in cloud-only processing, and the dynamic, uncertain nature of wireless channels and distributed energy resources, all of which complicate real-time data analysis and decision processes. To overcome these challenges, this paper proposes a 3D backpressure learning-based cloud–edge collaborative dynamic resource allocation approach. Specifically, a cloud–edge coordinated data processing architecture and a full-link latency characterization model are constructed, and the joint optimization of terminal splitting ratio, computing paradigm determination, and computing resource allocation is formulated as a stochastic optimization formulation. The long-term delay-constrained optimization is transformed into a queue stability problem via Lyapunov optimization, and a dual-phase optimization scheme is designed: (i) a 3D backpressure-aware federated deep deterministic policy gradient (FDDPG) algorithm is constructed for optimizing terminal splitting and computing paradigm selection; (ii) a Lagrangian-based optimization strategy is adopted to allocate cloud–edge computing resources. To further enhance learning efficiency and robustness, a 3D backpressure-aware mechanism is integrated into the reward construction process, a differential contribution-based federated aggregation strategy is presented to enhance collaborative training reliability, and a dual experience pool mechanism is established to enhance constraint-boundary exploration capability and increase sample efficiency. Simulation results verify that the developed approach achieves better delay performance and faster convergence behavior, providing efficient and reliable resource scheduling support for distribution automation.