Real-Time State Estimation in Smart Grids over 5G Networks: Experimental Validation Using Raspberry Pis and Typhoon HIL

Reliable, low-latency communication is critical for real-time monitoring and control in modern Smart Grids (SGs). The emergence of 5G networks, with enhanced reliability, significantly lower latency, and native support for massive machine-type communication, offers strong potential to enable advanced grid applications such as state estimation (SE) and fault detection. While existing studies investigate 5G for SG use cases, most rely on simulations or analytical models; experimental validation using real hardware and SG data remains limited. This paper fills this gap by presenting a fully experimental validation of real-time SE over a commercial 5G network using a 5G-based multi-node testbed built with Raspberry Pi (RPi)-based SG nodes and a Typhoon Hardware-in-the-Loop (HIL) real-time simulator. We first characterize 5G communication performance using simulated SG data under varying reporting rates and deployment environments by evaluating Key Performance Indicators (KPIs) such as end-to-end delay, jitter, and frame loss. Experimental results show that the worst-case mean delay observed for the 5G is approximately 6.5x lower than that of our previous LTE cat-M study at the corresponding reporting rate. We then stream real-time voltage, current, and phase-angle measurements-generated by an IEEE 4-node feeder model in Typhoon HIL simulator-to a remote Phasor Data Concentrator (PDC) for SE and fault detection. Results demonstrate that 5G-enabled measurements support accurate SE under both steady-state and dynamic load variations. Furthermore, fault-detection experiments confirm reliable and prompt fault detection, with detection delays as low as 0.80 s.

Threshold Optimization and Dynamic Adaptation of Distributed Optimal Power Flow in 5G Networks

In this paper, we present an experimental evaluation study of the Alternating Direction Method of Multipliers (ADMM), which is a widely used technique in the distributed optimization of power distribution networks. The focus of this study is on how real 5G communication performance affects ADMM in a fully experimental platform that features commercial 5G connectivity and real-time control. The ADMM-based Distributed Optimal Power Flow (DOPF) problem is solved using the IEEE 123-bus unbalanced distribution feeder subdivided into five areas, each managed by a local controller implemented on a Raspberry Pi. To mitigate the impact of the communication network variability, we propose a delay threshold-based mechanism that yields a 7.75% reduction in convergence time compared to a no-threshold baseline. We also devised a policy to dynamically update the threshold value based on communication and computation conditions, achieving a 26.42% reduction in the convergence time compared with the static optimal threshold. These results demonstrate the potential of adaptive, communication-aware control strategies for real-world Smart Grid (SG) deployments.