LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
2026-07-20 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors look at how AI language models can work safely with specialized tools to solve power system problems like forecasting wind power and scheduling electric vehicle charging. They suggest only trusting answers that come from verified, trusted tools rather than from the AI alone. Their approach combines AI for planning and explaining tasks with reliable solvers for calculations, tested in four case studies showing better accuracy and safety than AI-only methods. They also propose a clear way to evaluate such systems on usefulness, correctness, and efficiency.
large language modelsagentic AIsmart gridspower system optimizationsolver verificationwind power forecastingEV charging schedulingpower flow analysiscontingency diagnosisevaluation framework
Authors
Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung, Jett Ngo, Ryan Luo, Peter R. Quawas, Junpyung Kim, Kangkai Liang, Mansi Nanavati, Jonathan Mai, Meng-Chi Tsai, Yun-Tong Tsai, Yize Chen, Yuanyuan Shi
Abstract
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 numerical result is reported only when it originates from a trusted tool and passes explicit verification. We review the building blocks of LLM and agentic AI systems for power systems: prompting strategies and agentic architectures. We instantiate the principle in four case studies: wind power forecasting, EV charging scheduling, power flow analysis, and contingency diagnosis, each comparing an LLM-only baseline against its solver-grounded counterpart on identical data and metrics. EVAgent reproduces the CVXPY optimum while reducing LLM-only unmet energy by 7.5-9.5x, and GridDebugAgent repairs 17/39 contingency cases while reducing total violations by 52.3%. We propose a four-group evaluation framework spanning task utility, solver-grounded correctness, faithfulness and safe failure, and cost and latency. A consistent division of labor emerges: the agentic system reliably orchestrates, retrieves, and explains, while trusted tools compute and a verification gate decides what is reported.