EET 110: Power and Energy Management II

Course Information

• Semester: Spring Semester 2025
• Instructor: Parikshit Pareek (email: pareek AT ee.iitr.ac.in)
• Lectures: Tue • 09:00 AM – 11:00 AM; 05:00 PM – 07:00 PM @ EED 109
• Venue: EED 109
• Piazza: Same as EET 109 Course
• TA: Ayushi Jolotia(ayushi_j AT ee.iitr.ac.in)

📌 Announcements

• 2025‑02‑02: The Term Paper timeline and deliverables are published in the final section of this webpage.
• 2026‑01‑20: Term paper form is available on Piazza. Submission deadline: next Tuesday.
• 2025‑12‑10: Initial Course website launched.

🎯 Course Objectives

This is not a traditional classroom-based course, nor is it a lab course focused on running experiments. This is a TEC: Talent Enhancement Course, Part II.

Designed for individuals who are already highly capable, this course aims to challenge your thinking and expand your potential. It will be largely hands-off in terms of direct implementation, encouraging independent exploration, creative problem-solving, and pushing beyond your current limits. Briefly, our objectives in this course are:

1. Develop a solid understanding of optimization algorithms behind operations of power grid.
2. Implement various optimal power flow methods on CPU and GPU with parallelization capabilities for speed & accuracy.
3. Explore advanced ML surrogates for Constrained Optimization Problem.

Course Content

📝 Assignments

• Python and Julia are default programming languages for the course. You should use any of these for programming your assignments unless otherwise explicitly allowed.
• Submit via Moodle or GitHub—- as specified in each assignment.
• Viva will accompany each assignment — your explanation during the viva carries significant weight in grading.
• Honor Code: Any cases of copying will be awarded a zero on the assignment. More severe penalties may follow.
• Late submissions will incur penalties, as annouced with assignment.

📚 References & Resources

Course Material: There is no required textbook for this class. Slides will be shared.

Reference Books:

• S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
  • Available for free: stanford.edu/~boyd/cvxbook/
• H.P. Williams. Model Building in Mathematical Programming, 5th Edition. Wiley, 2013.

🧾 Grading Policy (Tentative)

• CWS+PRE+PRS (50 Marks)
  • Lab Assignments — 20 Marks
  • Term Paper / Project — 30 Marks
• Mid Term (20 Marks) — Exam
• End Term (30 Marks) — Exam

Lab Assignments (Coding Tasks):

• Lab 1: Linear Programming (Dispatch)
• Lab 2: MILP (Unit Commitment)
• Lab 3: NLP (AC-OPF)
• Lab 4: Relaxations (SOCP/SDP)
• Lab 5: Optimization Proxies

🧾 Term Paper Timeline and Deliverables

🧾 Exam Papers

• Quiz 1