Focusing on the energy-conservation train operation issues, this paper proposes an effective real-time train regulation scheme for metro systems with energy storage devices. Specifically, to minimize train timetable deviation, passenger waiting and energy consumption, we formulate a mixed-integer nonlinear programming model to generate energy-efficient train regulation strategies. This model explicitly considers the train traffic, passenger load and storage, immediate and delayed uses of regenerative energy. Carefully tailored to the proposed model, we devise an efficient decomposition algorithm to split the original problem into small-scale subproblems. In the algorithm, specific values of binary variables, passenger-flow estimates and logic-based cuts are consecutively identified and updated. Besides, bound contraction and bilinear-specific warming start procedures are particularly designed for further acceleration. Numerical experiments are conducted to validate the proposed model and method. Our energy-efficient train regulation strategies can improve train departure punctuality, headway regularity, reduce passenger waiting times, and achieve energy savings. Furthermore, the solution algorithm exhibits promising computational efficiency in real-world experiments, thereby facilitating an online implementation.

本文针对列车节能运行问题，提出了一种有效的储能装置地铁系统实时列车调控方案。具体来说，为了最大限度地减少列车时刻表偏差、乘客等待和能源消耗，我们制定了混合整数非线性规划模型来生成节能的列车调控策略。该模型明确考虑了列车交通、乘客负载和存储、再生能源的即时和延迟使用。我们根据所提出的模型精心定制，设计了一种有效的分解算法，将原始问题分解为小规模的子问题。在算法中，二元变量的具体值、客流估计和基于逻辑的切割被连续识别和更新。此外，束缚收缩和双线性特定的升温启动程序是专门为进一步加速而设计的。进行数值实验来验证所提出的模型和方法。我们的节能列车调控策略可以提高列车发车准点性、车头发车规律性、减少乘客等待时间并实现节能。此外，该求解算法在现实世界的实验中表现出良好的计算效率，从而促进了在线实现。