Agent-driven anomaly detection reshapes smart grid security

⚡ AI Executive Summary

Researchers have synthesized findings from 164 studies on anomaly detection in smart energy systems, proposing a framework that separates detection algorithms from autonomous agents that interpret signals, reason about grid conditions, and take corrective action or escalate to operators. The work spans smart grids, distributed energy resources, battery storage, and electric vehicles, organizing detection methods by operational layer and comparing them on latency, robustness, and physical consistency. For power system engineers, this taxonomy and validation roadmap highlight a critical gap in the field: while detection accuracy is well-proven, real-world deployment remains constrained by incomplete reporting of latency and computational costs, and agent-based methods lack field validation beyond simulation. The proposed staged roadmap—from standardized benchmarks to hardware-in-the-loop testing—suggests the industry is moving toward autonomous grid operations that can detect and respond to faults faster than human operators, but widespread adoption depends on closing the gap between laboratory performance and field reliability.