Bridging Artificial Intelligence and Power Systems Education Using a Hands-On Executable Framework
2026-08-03 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors noticed that many people new to AI in power systems find it hard to get started and want more practical learning resources. To help, they created a set of easy-to-use, step-by-step AI modules that teach key concepts through real power system examples, like modeling and controlling batteries. These modules use popular tools like Jupyter notebooks and can be accessed online, making them accessible for beginners. Their approach was supported by a survey and attracted a large audience in webinars, showing strong interest in this type of hands-on education.
Artificial IntelligencePower SystemsDeep Neural NetworksConvolutional Neural NetworksReinforcement LearningPhysics-Informed Neural NetworksLoad ForecastingOptimizationJupyter NotebooksPower Flow
Authors
Junjie Yin, Buxin She, Xinyu Feng, Fangxing, Li
Abstract
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.