Efficient Formulations and Decomposition Approaches for Power Peak Reduction in Railway Traffic via Timetabling

Andreas Bärmann, Alexander Martin, Oskar Schneider
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引用次数: 11

Abstract

Over the last few years, optimization models for the energy-efficient operation of railway traffic have received more and more attention, particularly in connection with timetable design. In this work, we study the effect of load management via timetabling. The idea is to consider trains as time-flexible consumers in the railway power supply network and to use slight shifts in the departure times from the stations to avoid too many simultaneous departures. This limits peak consumption and can help to improve the stability of the power supply. To this end, we derive efficient formulations for the problem of an optimal timetable adjustment based on a given timetable draft, two of which even allow for totally unimodular polyhedral descriptions. The proper choice of the objective function allows the incorporation of the priorities of either the train operating companies or the infrastructure manager. These include the avoidance of large peaks in average or instantaneous consumption and the improved use of recuperated braking energy. To solve the arising optimization models efficiently, we develop specially tailored exact Benders decomposition schemes that allow for the computation of high-quality solutions within a very short time. In an extensive case study for German railway passenger traffic, we show that our methods are capable of solving the problem on a nationwide scale. We see that the optimal adjustment of timetables entails a tremendous potential for reducing energy consumption.
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铁道交通调度降峰的有效公式与分解方法
近年来,铁路交通节能运行的优化模型越来越受到人们的关注,特别是与列车时刻表设计相关的优化模型。在这项工作中,我们研究了通过时间表进行负荷管理的效果。这个想法是将列车视为铁路供电网络中时间灵活的消费者,并在车站的发车时间上进行轻微的调整,以避免同时发车的次数过多。这限制了峰值消耗,有助于提高电源的稳定性。为此,我们推导了基于给定时间表草案的最优时间表调整问题的有效公式,其中两个公式甚至允许完全单模多面体描述。目标函数的适当选择允许合并列车运营公司或基础设施管理者的优先级。这包括避免平均或瞬时消耗的大峰值,以及改进回收制动能量的使用。为了有效地解决出现的优化模型,我们开发了专门定制的精确Benders分解方案,允许在很短的时间内计算高质量的解决方案。在对德国铁路客运的广泛案例研究中,我们表明我们的方法能够在全国范围内解决问题。我们看到,对时间表的最佳调整具有减少能源消耗的巨大潜力。
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