电力行业想学AI但怕门槛高的，可以看看这个开源框架，从基础DNN到CNN潮流代理再到强化学习，都有Jupyter Notebook能直接跑。
该论文提出一个面向电力系统AI教育的开源可执行框架，包含渐进式难度模块。框架从基础DNN模板（函数逼近、负荷曲线拟合）起步，提供5节点系统的CNN潮流代理模型，并拓展到DNN辅助优化、电池储能DRL控制及摆动方程的PINN。作者基于社区调查，92%受访者表示运行AI模型前至少遇到一个障碍，94%希望有电力专用的动手课程。模块以Jupyter Notebook形式发布，支持本地或Google Colab运行，并通过IEEE在线课程和PES网络研讨会交付。相关网络研讨会吸引超过590名直播观众，位居IEEE PES网络研讨会前十。
Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly rely on large language models rather than building their own. This gap points to a need for engineering-grounded AI (EGAI), in which AI workflows follow established engineering and power-system domain rules rather than acting as task-agnostic black boxes. Motivated by a community survey of researchers and practitioners, which shows 92% report at least one barrier before running an AI model and 94% want a power-specific hands-on course. This paper presents a framework consisting of open, executable module library that lowers the entry barrier for AI in power systems. The modules follow a progressive difficulty ladder that maps core AI concepts onto representative power-system tasks: (i) foundational deep neural network (DNN) templates for function approximation and load-curve fitting; (ii) a domain-coupled convolutional neural network (CNN) power-flow surrogate for a 5-bus system; and (iii) frontier modules on DNN-assisted optimization, deep reinforcement learning (DRL) for battery storage control, and physics-informed neural networks (PINNs) for the swing equation. All modules are released as Jupyter notebooks that run locally or on Google Colab and are delivered through an IEEE online course and IEEE Power & Energy Society (PES) webinar series. The webinar drew more than 590 live attendees, which is among the ten most-attended IEEE PES webinars, and over 344 repository visits within two weeks, reinforcing the survey-based motivation.