Agent-driven anomaly detection in smart energy management systems: Taxonomy, evidence, and a deployment roadmap

Smart energy management systems are shifting from passive monitoring toward proactive and increasingly autonomous operation, which makes timely anomaly detection essential to their security and resilience. This survey reviews anomaly detection for these systems through an agent-driven lens, and its central clarification is to separate a detector, which maps signals to anomaly scores, from an agent, which closes a perception–reasoning–action loop by invoking detectors, reasoning over operational context, and acting within a safety envelope or escalating to human approval. Following a transparent review protocol, we synthesize 164 studies across smart grids, distributed energy resources, battery storage, and electric-vehicle systems. We organize the field with an operational taxonomy that links each system layer to its data sources, anomaly types, detection tasks, response actions, and evaluation metrics, and we compare detector families along latency, robustness, interpretability, and physical consistency. Grading the evidence on a four-level validation scale reveals a consistent gap: detection accuracy is well documented, whereas latency, computational cost, and field performance are reported unevenly, and federated and agent-based methods remain supported mainly by simulation. We formalize an agentic orchestration framework with explicit agent roles, human-in-the-loop approval, and edge-to-cloud deployment, and we set out a staged roadmap toward standardized benchmarks, digital-twin and hardware-in-the-loop validation, and real-time deployment. The review aims to help researchers and practitioners choose, evaluate, and responsibly deploy anomaly detection in energy operations.