Computational Methods for Power System Analysis - ECE51032
Learning Objective:
An introduction to modern power system analysis and computer methods used in planning and operating electric power systems. A student who successfully fulfills the course requirements will have demonstrated:
- An ability to explain how electricity markets work and how various computational methods are used in power system operations and planning.
- An ability to understand formulation and solution techniques applied to normal operation of large power systems.
- An ability to implement existing optimization packages to solve power system problems.
- An ability to use machine learning methods to answer questions about power system operations.
Topics Covered:
| 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 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. |
Prerequisites:
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.
Textbooks:
No required text.
Recommended texts:
- Applied Linear Regression Models, 4th Edition, M. Kutner, C. Nachtsheim & J. Neter, McGraw-Hill Education, 2004, ISBN No. 0073014664
- Class notes and technical journal papers
- Convex Optimization, S. Boyd & L. Vandenberghe, Cambridge University Press, 2004, ISBN No. 0521833787
- Power System Analysis, 4th Edition, J. Grainger & W. Stevenson, McGraw-Hill, 1994, ISBN No. 0-07-061293-5