Optimization of Underground Train Systems
Hager L, Kuen T (2024)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2024
Publisher: Springer
Edited Volumes: Unlocking Artificial Intelligence. From Theory to Applications
City/Town: Cham
Pages Range: 303-319
ISBN: 978-3-031-64831-1
DOI: 10.1007/978-3-031-64832-8_16
Abstract
This chapter presents two approaches for enhancing the sustainability and efficiency of underground train systems. The first approach focuses on the optimization of DC railway power systems, employing a novel Mixed-Integer Quadratically Constrained Quadratic Program (MIQCQP) to control substation feed-in voltages effectively. By minimizing energy losses, this optimization approach demonstrates substantial potential for cost and emission reduction, contributing to a more energyefficient underground train network. Validation results confirm the accuracy of the proposed model, and realistic instances reveal significant energy savings. The second approach deals with energy-efficient timetabling, a critical aspect in reducing the environmental impact of railway operations. The presented approach seeks to minimize energy consumption through the implementation of two key strategies: promoting energy-efficient driving patterns and optimizing recuperated energy from braking. Leveraging operational data, including power consumption profiles and travel time distributions, the optimization methods demonstrate remarkable potential in reducing energy consumption, subsequently leading to lower electricity costs and environmental benefits. This chapter is largely based on previous work of Hager and Koop on optimization of DC railway power systems and of Bärmann et al. [1] on energy-efficient timetabling.
Authors with CRIS profile
Involved external institutions
Germany (DE)How to cite
APA:
Hager, L., & Kuen, T. (2024). Optimization of Underground Train Systems. In Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann, Alexander Martin (Eds.), Unlocking Artificial Intelligence. From Theory to Applications. (pp. 303-319). Cham: Springer.
MLA:
Hager, Lukas, and Tobias Kuen. "Optimization of Underground Train Systems." Unlocking Artificial Intelligence. From Theory to Applications. Ed. Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann, Alexander Martin, Cham: Springer, 2024. 303-319.
BibTeX: Download