ESE 562: AI-Driven Smart Grids (Fall 2026)

Graduate
Published

Jun, 2026

Course Information

  • Instructors: Yifan Zhou
  • Lecture time: 6:30pm-9:20pm Wednesday
  • Location: Frank Melville Jr. Memorial Library E4315
  • Office hours: Wednesday 1 pm-5 pm (in-person or online)

Course Description

This course focuses on applications of artificial intelligence (AI) to power system modeling, analysis, and operation. Topics include the fundamentals of AI and smart grids; AI-based modeling methods for load and generation forecasting, dynamic model discovery, and system identification; AI-assisted power system analysis, including dynamic simulation, stability assessment, and security assessment; and AI-enabled operation, including optimal dispatch and emergency control. Emerging topics such as generative AI, quantum machine learning, and trustworthy AI will also be discussed.

This course is part of the Engineering Artificial Intelligence (EAI) program at Stony Brook University. The course emphasizes practical AI algorithms for power system applications, with hands-on experiments designed to help students develop AI-enabled power system analysis and operation programs.

Course Learning Objective

The goal of this course is to help students build a solid foundation in AI applications to smart grids. Students will study the basic principles of data science and practical AI algorithms for data-driven power grid analysis and operation. Homework assignments and term projects are designed to strengthen students’ ability to develop data-driven programs for important power system problems.

Upon completion of this course, students will acquire knowledge and skills in AI-driven smart grid modeling, analysis, and operation, including:

  • Fundamental concepts in smart grid operation and control.
  • Core machine learning and AI methods relevant to power system applications.
  • Understanding of key opportunities, challenges, and future directions of AI-driven smart grids.
  • Understanding of representative AI algorithms for power system modeling, analysis, and operation, including regression, classification, clustering, reinforcement learning, and transfer learning.
  • Understanding of physics-informed machine learning methods that integrate power system models, constraints, and domain knowledge into AI algorithms.
  • Hands-on experience in developing and implementing data-driven power system applications.

Grading

The grading weights for the course are as follows:

  • Quiz: 9% (3 quizzes in total)
  • Homework: 24% (3 assignments in total)
  • Presentation 1: 15%
  • Presentation 2: 20%
  • Presentation 3: 25%
  • Class Participation: 7%

Quiz

Quizzes mainly consist of short-answer questions related to the previous lecture.

Hands-on Experiments

For most lectures, Python code will be provided to give students hands-on experience with the topics discussed. These codes typically serve as foundational examples to help students understand the lecture material and as starting points for related homework assignments.

We will work through the programs together at the end of the lecture.

Homework

Homework mainly consists of short-answer questions, mathematical problems, and simple programming problems for applying fundamental machine learning methods to power system problems. Homework assignments are posted on Wednesdays and are typically due by midnight the following Wednesday.

Course Project

Students will complete three presentations during the semester:

  • Presentation 1: Survey presentation. Each student will present a survey of a selected topic related to AI-enabled smart grids. Topics to be determined.
  • Presentation 2: Mini-lecture. Each student will give a short lecture-style presentation on a selected emerging technology, method, or research direction related to AI-enabled smart grids. The goal is to introduce new ideas, broaden the class’s perspective, and stimulate discussion beyond the regular lecture topics.
  • Presentation 3: Programming project and AI-agent validation. Students will develop and present a programming project for AI-driven smart grids. Students will work in teams of two on the same topic, while first developing their own solutions independently, possibly with the assistance of AI chatbots or coding assistants. They will then cross-validate each other’s results and examine whether the solutions are technically sound, complete, and properly justified. The goal is to help students learn how to guide, evaluate, and refine AI-agent outputs rather than simply accept them. Topics to be determined.

Prerequisites

No formal prerequisites.

Course Schedule

To be determined.

Student Accessibility Support Center Statement

If you have a physical, psychological, medical, or learning disability that may impact your course work, please contact the Student Accessibility Support Center, Stony Brook Union Suite 107, (631) 632-6748, or at sasc@stonybrook.edu. They will determine with you what accommodation is necessary and appropriate. All information and documentation are confidential.

Academic Integrity Statement

Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person’s work as your own is always wrong. Faculty is required to report any suspected instances of academic dishonesty to the Academic Judiciary. Faculty in the Health Sciences Center (School of Health Technology and Management, Nursing, Social Welfare, Dental Medicine) and School of Medicine are required to follow their school-specific procedures. For more comprehensive information on academic integrity, including categories of academic dishonesty please refer to the academic judiciary website at http://www.stonybrook.edu/commcms/academic_ integrity/index.html.

Critical Incident Management

Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of University Community Standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students’ ability to learn. Faculty in the HSC Schools and the School of Medicine are required to follow their school-specific procedures. Further information about most academic matters can be found in the Undergraduate Bulletin, the Undergraduate Class Schedule, and the Faculty-Employee Handbook.

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