Building a smarter electrical grid, providing sustainable and reliable energy for the future, is one of the largest challenges facing society. This course will provide an introduction to the algorithms and computational methods that underly this growing area of interest. We will study methods from optimization, machine learning, and control, and see how these techniques apply to real-world problems in the smart grid. In particular, students will become familiar with topics such as circuit and power flow analysis, linear and non-linear regression, convex optimization, and model predictive control. Although the course will cover topics in power systems, optimization, and machine learning, no prior background in these areas is required, as the goal of the course is to provide an applied introduction to all these areas; we therefore expect the course to be of substantial interest to majors in CS, ECE, MechE, CEE, and EPP.
This course is cross listed with 15-484 and 15-884. ECE cross-lists only the lowest level listed for the same course. ECE students are not permitted to register for 15-484 or 15-884 only 18-473. This course does not fall under any of the ECE areas for undergraduate courses.
Prerequisite(s): There are no formal prerequisites for the course, but students should have some programming experience (exercises will be done in MATLAB), and a working knowledge of linear algebra (Math 341 is more than sufficient, but not necessary).
This course is currently being offered.