This course will teach students constrained optimization problems and associated solution methods, how to implement and apply linear and mixed integer linear programs to solve such problems using Julia/JuMP, and the practical application of such techniques in energy systems engineering.
The course will first introduce students to the theory and mathematics of constrained optimization problems and provide a brief introduction to linear programming, including problem formation and solution algorithms.
Next, to build hands-on experience with optimization methods for energy systems engineering, the course will introduce students to several canonical problems in electric power systems planning and operations, including: economic dispatch, unit commitment, optimal network power flow, and capacity planning.
Finally, several datasets of realistic power systems are provided which students will use in conjunction with building a model for a course project that answers a specific power systems question.
The course will consist of lectures, labs, and seminars where students will engage with foundational concepts, hands-on applications in an algebraic modeling language, and methods and limitations of the state-of-the-art.
Course materials and sample code will be available publicly at the course repo throughout the term. These are being jointly developed with Prof. Jesse Jenkins for use also in a course at a Princeton (MAE / ENE 539).
This course will be taught remote in Fall 2021.