Safe Multi-Agent Collaborative Learning for Networked Grid Operation Under Power Network Coupling Constraints
Modern grid operation is increasingly a sequential collaborative control problem under renewable uncertainty, storage dynamics, flexible demand, transmission coupling, and carbon-aware corrective redispatch. This paper focuses on sub-hourly dynamic OPF assistance and safe regional redispatch after forecast updates or emergent network stress. We formulate networked grid operation as a constrained decentralized partially observable Markov decision process and propose a safe multi-agent collaborative learning framework. The method integrates dynamic transfer-stress tracing, a dual-view graph encoder over the physical grid and a real-time stress graph, consensus-based dual coordination for globally coupled constraints, and a differentiable safety projection that maps tentative decisions to executable actions. The framework aims to reduce operating cost, load shedding, renewable curtailment, and carbon-relevant corrective burden, while maintaining feasibility with respect to local linearized surrogate limits and empirically reducing violations in the full simulator. Experiments on chronological grid benchmarks evaluate comparison, ablation, robustness, efficiency, statistical evidence, visualization, and generalization, showing improved trade-offs among cost, reliability, safety, and renewable accommodation.