Optimization for sustainability

Course goals

In this course students learn to:

• understand discrete-event simulation models and statistical analysis methods for these models
• create a discrete-event simulation model for a given system
• perform a scientific sound simulation study including statistical analysis
• understand optimization models for sustainable mobility and energy systems
• understand applications of optimization algorithms and simulation to sustainable mobility and energy systems
• assess and present scientific papers on optimization algorithms and simulation to sustainable mobility and energy systems
• identify and describe possibilities for applications of optimization algorithms and simulation to sustainable mobility and energy systems

Assessment
The grading consists of the following parts:

• hand-in exercises: 1) simulation model (5% of t he final mark), 2) input analysis (5%), 3) optimization challenge energy and mobility (10%)
• simulation assignment (40%)
• seminar (40%)

To obtain a pass grade

• you have handed in all the assignments and the preparation documents for the seminar
• you have given a seminar presentation
• the grade for the optimization challenge exercise has to be at least 5.0
• you attended the milestone and feedback meeting of the simulation meeting in person. It is not sufficient if the other member of your group attended.
• you have attended the seminar sessions (exceptions for valid reasons are possible, contact teacher before the session)

Minimum effort to qualify for 2nd chance exam:             

• your final grade is at least 4
• you delivered the hand-in exercises on time.
• you received a pass for the milestone of the simulation assignment

You can take an additional exam for at most one out of hand-in exercises, simulation assignment, seminar. Which one is decided in discussion with the teacher.

Content

The current energy transition leads to many changes in the energy as well as the mobility system. In this course, we study algorithmic techniques to optimize the performance of future energy systems and discuss topics from sustainable public transportation.
We discuss Mixed Integer Linear Programming (MIP) formulations, branch-and-cut, and simulation.
Students learn how to apply these techniques to different optimization problem related to energy systems, such as network design, unit commitment, load flow, demand response, and storage optimization.
Moreover, we discuss question around electric buses, environmental friendly bus driving, and multi-modal route planning.
The topics in this course are related to recent research in the Algorithms and Complexity group.
In this course you will learn algorithms and methods that are also used by companies to solve real-world problems.

This course will not deal with political or policy issues in the domain of `sustainability', but is devoted entirely to (a selection of) the computational models and optimizing algorithms that are developing in the field.

Course form
Lectures, seminar: discussion and presentation of papers

Literature
Material will be made available through MS Teams.