Power grids are becoming more connected with 5G networks and edge-computing systems, including in civilian, emergency, and military critical-infrastructure environments. Because of this, optimization is no longer only a power-system problem or only a communication-network problem. It now involves energy, network, and computing resources simultaneously. This review focuses on grid telemetry supported by 5G network slicing and edge analytics. In this setting, data from PMUs, SCADA systems, IEDs, and AMI devices are used not only for monitoring but also for supporting state estimation, anomaly detection, and control decisions. The article reviews several AI-based optimization methods. These include learning-to-optimize, reinforcement learning, safe learning, multi-agent learning, federated learning, AirComp, graph-based models, and hybrid approaches. The review discusses these methods in relation to smart energy control, network slicing, military-resilient power and communication service, edge orchestration, and end-to-end system evaluation. Particular attention is given to tail latency, jitter, reliability, runtime, compute limits, and SLA violations, since average metrics alone are insufficient for critical grid operations. The review also proposes a practical roadmap from pilot co-simulation to edge-first analytics, slicing assurance, security hardening, and continuous monitoring, aiming to support reliable, sustainable and military-relevant smart-grid operation.
Gazis et al. (2026) studied this question.