LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications

Tutorial on LLM and agentic AI architectures for smart grid applications with solver-grounded design principle

A new arXiv preprint presents a tutorial on applying LLMs and agentic AI systems to power grid tasks including wind forecasting, EV charging scheduling, power flow analysis, and contingency diagnosis. The paper proposes a 'solver-grounded' design principle where numerical outputs are only reported when they originate from a trusted external solver and pass explicit verification, addressing the problem of LLMs producing physically infeasible outputs. Four case studies compare LLM-only baselines against solver-grounded agents, with EVAgent reducing unmet energy by 7.5-9.5x and GridDebugAgent repairing 17/39 contingency cases while cutting violations by 52.3%. A four-group evaluation framework covering task utility, solver correctness, faithfulness, and cost/latency is also proposed.