A Robust Mixed Integer Optimization Model to Utilize Regenerative Energy of Trains in a Railway Network

TL;DR

This paper introduces a robust mixed integer optimization model that efficiently creates train timetables to maximize the use of regenerative braking energy, significantly improving energy utilization in railway networks.

Contribution

It presents a novel optimization approach that maximizes regenerative energy transfer between trains, outperforming existing timetables in energy utilization.

Findings

Optimal timetables are generated quickly (up to 86.64 seconds).

Significant increase in regenerative energy utilization over existing schedules.

Model effective across different railway network instances.

Abstract

In this paper we present a robust mixed integer optimization model to utilize regenerative braking energy produced by trains in a railway network. An electric train produces regenerative energy during braking, which is often lost in present technology. To utilize this energy we calculate a timetable which maximizes the total overlapping time between the braking and accelerating phases of suitable train pairs, so that the regenerative energy of a braking train can be transferred to a suitable accelerating one. We apply our optimization model to different instances of a railway network for a time horizon spanning six hours. For each instance, our model finds an optimal timetable very quickly (largest running time being 86.64 seconds). Compared to the existing timetables, we observe significant increase in utilization of regenerative energy for every instance.