EEE 102

Course Title: Advanced Topics in Energy Systems

Instructor: Dr. Parikshit
Semester: Spring 2025
Department: Electrical Engineering, IIT Roorkee

Course Description

This course explores advanced methodologies for solving complex energy system challenges. Topics include probabilistic power system operations, non-parametric methods, and the application of quantum computing to energy problems. Students will gain a deep understanding of theoretical principles and practical tools essential for modern power systems.

Learning Objectives

By the end of this course, students will be able to:

  • Understand and apply probabilistic approaches in energy systems.
  • Integrate machine learning techniques into power system operations.
  • Analyze the potential and limitations of quantum computing in solving energy problems.
  • Develop robust models for grid stability and optimal power flow.

Prerequisites

  • Basics of power systems (EEE 101 or equivalent).
  • Linear Algebra and Probability.
  • Experience with MATLAB or Python is recommended.

Course Topics

  1. Introduction to Modern Power Systems
  2. Learning-Integrated Power System Operations
    • Bayesian approaches
    • Machine learning frameworks for uncertainty modeling
  3. Probabilistic and Non-Parametric Methods
    • Stochastic optimization
    • Uncertainty quantification and contextualization
  4. Quantum Computing in Energy Systems
    • Quantum algorithms for optimization
    • Current challenges and opportunities

Grading Breakdown

  • Assignments: 30%
  • Midterm Exam: 20%
  • Project: 30%
  • Final Exam: 20%

Late submission policy: A penalty of 10% per day will be applied to late submissions, up to a maximum of 3 days.

Class Schedule

| Week | Topic | Notes/Readings |
|——|——————————————-|———————————|
| 1 | Introduction to Modern Power Systems | Chapter 1 of Textbook |
| 2 | Learning-Integrated Operations | Lecture slides and paper [1] |
| 3-4 | Probabilistic Modeling and Optimization | Chapters 3 & 4, Paper [2] |
| 5-6 | Quantum Computing for Energy Systems | Lecture notes and paper [3] |

Resources

  • Textbook: Power System Analysis and Design by Glover, Sarma, Overbye.
  • Papers: Links to research papers will be provided on Moodle.
  • Tools: Python (preferred), MATLAB, and Qiskit for quantum simulations.

Contact Information

  • Instructor: Dr. Parikshit
    • Office: Room 101, Electrical Engineering Building
    • Email: parikshit@iitr.ac.in
    • Office Hours: Monday & Wednesday, 3:00 PM - 4:00 PM
  • Teaching Assistant:
    • Name: [TA Name]
    • Email: [TA Email]

Announcements

Please check the course Moodle page regularly for updates on assignments, lecture notes, and deadlines.

Policies

  • Academic integrity is taken very seriously. Plagiarism and cheating will result in disciplinary action.
  • Participation is expected in lectures and group discussions.

Project Guidelines

  • Topics will be provided by the instructor, or students can propose their own (with approval).
  • Teams of up to 3 students are allowed.
  • Deliverables include a project proposal, midterm progress report, and final presentation.

Let’s make this a great semester of learning and discovery!

References