A Two-level Optimization Model and Algorithm for Energy-Efficient Urban Train Operation

Metro takes the most part of energy consumption in urban public service systems, it's important to make the train movement in an energy-efficient way. Based on the characteristics of train movement and control rules of locomotive, an urban train running on an uneven rail with the specific run-time for minimal energy consumption can be formulated as a two-level hierarchical problem. On the first leve1, an optimization model is designed to decide the appropriate coasting point(s) and number(s) of inter-station run for energy-efficient urban train operation. On the second leve1, an optimization model of arranging the train travel time of inter-station run is presented for minimal energy consumption. Algorithms for solving the two-level optimization model are developed based on Genetic Algorithm. A case study shows that the two-level optimization model and algorithm are effective for energy-efficient urban train operation on a long-distance line with several sections. The result indicates that the two-level method can save energy 19.06% within scheduled run-time and enhance the recovery ability of train movement compared with traditional method.

摘要地铁运输系统是城市公共系统中最大的耗能系统，列车节能运行具有重要的意义. 结合地铁列车运行特点与机车操纵规则，提出了在起伏坡道与定时约束条件下地铁列车节能运行的两阶段优化方法. 第一阶段，建立了寻求站间最佳惰行控制次数及惰行控制点的优化模型；第二阶段，建立了合理分配各个站间区间列车运行时间的优化模型. 设计了基于遗传算法的优化模型求解算法. 与既有方法相比，在运行时分相同条件下，经过两阶段优化后，列车运行能耗下降了19.06%，列车运行恢复正点的能力也得到了提高.