Class Syllabus

This course introduces the operation of large-scale power systems from a modern point of view. We will define and discuss the major problems in power system analysis, operations, dynamics and economics. We will talk about different formulations and tools that are commonly used in practice, and how they are changing when large amount of renewable resources are present in the grid. The open problems and current approaches are discussed in each of the these topics. No previous power engineering background is required. Familiarity with senior level undergraduate linear algebra and calculus is assumed. For more information, see syllabus.

Class: WF, 10:30am to 12:00pm, MGH 248
Instructor: Baosen Zhang, zhangbao@uw.edu, Office: EEB M310
Office Hours: Tuesday 3:15pm-4:15pm

Textbook and References:

  1. We will use the following textbook:
    • Power System Analysis, A Mathematical Approach, by Steven Low. It can be found at https://netlab.caltech.edu/book_reg/. You may need to register to get access. Please note that this book is still in the draft stage, so there might be some errors and typos.
  2. These references can be helpful:
    • Power System Generation, Operation and Control, by A. Wood & B. Wollenberg
    • Computational Methods for Electric Power Systems, M. L. Crow
    • Convex Optimization of Power Systems, J. Taylor
    • Introduction to Applied Linear Algebra, Boyd and Vandenberghe
  3. We will sometimes use the material provided by Tom Overbye, Ned Mohan and Bruce Wollenberg and Marija Ilic. Many thanks to them for making their course materials publicly available.
  4. I will try to respond to emails within 24 hours. Please write EE554 in the subject.

Schedule of Classes:

  1. Logistics of the course, introduction to power system analysis. Chapters 1.1, 1.3 in the textbook
  2. Power system models, power flow equations. Chapters 2.2, 4.2, in the textbook.
  3. Solving power flow equations, Newton methods. Chapters 4.3, 4.4, 4.5
  4. Convexity, Chapter 11.1. The book does not cover linear programming on its own. A good reference for LPs is ``Bertsimas D, Tsitsiklis JN. Introduction to linear optimization. Belmont, MA: Athena Scientific; 1997.’’

Optimization Software

  1. You can use whatever software you like. Newer languages like Python and Julia are recommended. Both have active communities developing power system toolboxes, like PyPSA and PowerModels. Some of you maybe more familiar with Matlab, and it has established tools like MatPower, although some people in academia and industry are moving away from it. If you use LLM tools for coding, please clearly describe your prompts (e.g., saying that ``I asked ChatGPT and this is the code’’ is not sufficient for homework solutions.)

Grading Structure:

  1. There will be weekly homework assignments. No late homework would be accepted.
  2. Grade distribution: homework 80%, class participation 20%.
  3. Bonus points for finding typos and mistakes in the textbook.
  4. You’re encouraged to discuss homework questions with other students. However, the solutions must be your own.

Homework Assignments:

  1. Homework 1, due Oct 9th, 11:59pm. Submit to CANVAS. Solution
  2. Homework 2, due Oct 21st, 11:59pm. Submit to CANVAS. Solution
  3. Homework 3, due Oct 28th, 11:59pm. Submit to CANVAS
  4. Homework 4, due Nov 7th, 11:59pm. Submit to CANVAS
  5. Homework 5, due Nov 25th, 11:59pm. Submit to CANVAS

    AI Policy:

    AI content generators, such as ChatGPT, present opportunities that can contribute to your learning and academic work. If you use such tools, you must clearly state where they are used and how they contributed to your work. Otherwise, using these technologies may violate academic standards of the University.

For other course policies, please see the syllabus.