From Monitoring to Mitigation – Intelligent Battery Dispatch to Modulate Distribution-Transformer Load

The rapid decline in battery energy storage system (BESS) costs-falling over $40 \%$ to record lows in 2024 - is making distributed storage a cost-effective approach to mitigate distribution transformer thermal overload. However, effectively dispatching these assets requires real-time, asset-specific thermal visibility that is currently lacking. While standards such as IEEE Std C57.91 provide definitive physics-based models for thermal aging, their reliance on complex, asset-specific parameters makes them ill-suited for scalable, edge-based deployment. We present an agentic, edge-resident sentinel designed to address this gap. The agent fuses local electrical and thermo-mechanical sensing (voltage, current, tank temperature, vibration) with a machine-learning-derived lookup table to detect statistical anomalies in real-time. Instead of passive logging, it generates structured service requests (e.g., “Discharge-ESS at 5 kW “) intended to trigger immediate thermal relief via co-located battery storage or EV charging schedulers. A bench-scale hardware-in-the-loop evaluation demonstrates sub-second detection-to-message latency, verifying the agent’s ability to provide the requisite signal for closing the loop between aging grid assets and modern, economic energy storage systems.