LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
Large language models (LLMs) and agentic AI systems have evolved from natural language tasks to using external tools to plan, retrieve, and act in technical domains. In smart grids, recent work applies agentic schemes to forecasting, optimization, and control, wrapping trusted solvers behind language interfaces and orchestrating multi-step workflows. The literature lacks a unified approach to designing and evaluating such systems. LLMs can produce numerically plausible yet physically infeasible outputs, evaluation protocols vary across tasks, and the boundary between what the model should and should not compute is implicit. This paper presents a solver-grounded design principle: a numeric...
| Authors | Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung et al. |
|---|---|
| Primary category | eess.SY |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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