ECE 51032 - Computational Methods for Power System Analysis

Course Details

Lecture Hours: 3 Credits: 3

Counts as:

  • EE Elective
  • CMPE Selective - Special Content

Normally Offered:

Each Spring


On-campus only


ECE 31032 and must be enrolled as a junior, senior or graduate classification.

Requisites by Topic:

This class requires basic knowledge of power systems, probability, linear algebra, and calculus. Familiarity with a programming language such as MATLAB or Python is preferred. Some knowledge of optimization is helpful but not necessary.

Catalog Description:

System modeling of power networks. Description of modern electricity markets. Analysis of the economic dispatch problem using optimality conditions. Planning of distributed energy resources. Smart grid applications. Machine learning applications to power systems (forecasting, demand-side management, and fault detection). Assigned projects will involve implementing some of the methods using realistic power system models.

Course Objectives:

An introduction to modern power system analysis and computer methods used in planning and operating electric power systems.

Required Text(s):


Recommended Text(s):

  1. Applied Linear Regression Models , 4th Edition , M. Kutner, C. Nachtsheim & J. Neter , McGraw-Hill Education , 2004 , ISBN No. 0073014664
  2. Class notes and technical journal papers
  3. Convex Optimization , S. Boyd & L. Vandenberghe , Cambridge University Press , 2004 , ISBN No. 0521833787
  4. Power System Analysis , 4th Edition , J. Grainger & W. Stevenson , McGraw-Hill , 1994 , ISBN No. 0-07-061293-5

Learning Outcomes:

A student who successfully fulfills the course requirements will have demonstrated:
  1. An ability to explain how electricity markets work and how various computational methods are used in power system operations and planning. . [3]
  2. An ability to understand formulation and solution techniques applied to normal operation of large power systems.. [1,2,3]
  3. An ability to implement existing optimization packages to solve power system problems. . [1,2,3]
  4. An ability to use machine learning methods to answer questions about power system operations.. [1,2,3,6]

Lecture Outline:

Weeks Topics
1 Introduction, steady-state power network models, electricity markets.
2 Overview of optimization. Analysis of economic dispatch using optimality conditions.
3 Planning methods for distributed energy resources (DERs): sizing and placement of solar PV and storage.
3 Smart grid applications: Control of energy storage, distribution system analysis with DERs.
3 Overview of supervised learning methods. Applications to renewable/load forecasting, fault detection.
3 Overview of unsupervised learning methods. Applications to demand-side management.

Assessment Method:

Exams, homework, projects, and presentations. 1/23