这篇论文把LLM智能体怎么用在智能电网上讲得很清楚，给出了具体的数字对比，比如EVAgent减少了7.5倍未满足能量，适合做电网AI系统的实践参考。
本论文提出了一种基于求解器的设计原则，确保数值结果来自可信工具并通过明确验证。在四个案例研究中，EVAgent在EV充电调度中复现了CVXPY最优解，并将LLM-only的未满足能量减少了7.5-9.5倍；GridDebugAgent修复了17/39个应急案例，总违规减少52.3%。论文提出了一个四组评估框架，涵盖任务效用、求解器正确性、忠实性与安全失败、成本与延迟。核心结论是智能体系统负责编排、检索与解释，可信工具负责计算，验证门控决定报告内容。
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 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.