Real-Time Energy System Optimization and Resilience Analysis in Low-Voltage Networks Using Intelligent Edge Computing

The transition toward active distribution networks requires advanced control solutions capable of handling the rapid dynamics of distributed energy resources. This paper proposes a low-cost, intelligent IoT architecture designed for the real-time optimization and analysis of energy systems within low-voltage networks. Unlike centralized monitoring approaches constrained by communication latency, the proposed solution leverages Intelligent Edge Processing (IEP) implemented on ESP32 embedded nodes to optimize data flow and decision-making. This architecture executes stability assessments directly at the network edge, calculating critical analysis indicators such as the Voltage Deviation Index (VDI) and Rate of Change of Frequency (RoCoF). The system was validated on the CIGRE European LV benchmark under severe stress scenarios, including rapid solar transients and voltage sags. The results demonstrate that the proposed architecture effectively coordinates storage interventions, ensuring voltage recovery within 300 ms and maintaining power quality within EN 50160 limits even during severe voltage sags. The study validates the feasibility of using industrial IoT edge computing as a resilient, non-wire alternative for modernizing complex energy systems.