Cooperative eco-driving of multi-train combined with dc railway system can achieve energy saving at the substation level for the metro system. This article presents an integrated optimization method for eco-driving by combining energy-efficient train control and dc traction power supply system (TPSS) model, to minimize the substation energy consumption. The coupled time-varying TPSS models for single train and multi-train are established, and a novel power flow calculation (PFC) method is proposed, to directly obtain the precise power flow distribution without iterations. Then, customized dynamic programming (DP) algorithms for specific TPSS models are developed, and optimal speed profiles of the tracking train and the preceding train can be respectively obtained by the multi-phase DP method. The former maximizes the utilization of a distributed regenerative braking energy (RBE), and the latter actively generates RBE to be fully utilized. Finally, the performance of the proposed method is analyzed by implementing the numerical experiments with field data from Guangzhou Metro Line 8, and the effectiveness is verified by comparing it with a previous study-based heuristic algorithm (HA). The simulation result indicates that the proposed method can also effectively reduce the lost energy on the catenary, suppress catenary voltage fluctuations, and precise match power during the RBE transmission phase.

中文翻译：

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直流牵引网下多列列车协同生态驾驶

多列列车协同生态驾驶结合直流铁路系统，可实现地铁系统变电站级节能。本文提出了一种结合节能列车控制和直流牵引供电系统（TPSS）模型的生态驾驶综合优化方法，以最小化变电站能耗。建立了单列和多列耦合时变TPSS模型，提出了一种新的潮流计算（PFC）方法，无需迭代即可直接获得精确的潮流分布。然后，开发针对特定TPSS模型的定制动态规划（DP）算法，并通过多相DP方法分别获得跟踪列车和前列列车的最佳速度曲线。前者最大限度地利用分布式再生制动能量 (RBE)，后者主动生成 RBE 以得到充分利用。最后，通过对广州地铁 8 号线的现场数据进行数值实验来分析所提出方法的性能，并通过与先前基于研究的启发式算法 (HA) 进行比较来验证其有效性。仿真结果表明，该方法还可以有效减少接触网能量损失，抑制接触网电压波动，精确匹配RBE传输阶段的功率。并通过将其与先前基于研究的启发式算法 (HA) 进行比较来验证其有效性。仿真结果表明，该方法还可以有效减少接触网能量损失，抑制接触网电压波动，精确匹配RBE传输阶段的功率。并通过将其与先前基于研究的启发式算法 (HA) 进行比较来验证其有效性。仿真结果表明，该方法还可以有效减少接触网能量损失，抑制接触网电压波动，精确匹配RBE传输阶段的功率。