Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Liu, Jiang; Li, Tiantian; Cai, Baigen; Zhang, Jiao

doi:10.3390/en13082111

Cited by 1 publication

(1 citation statement)

References 47 publications

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“…Lv et al [30] analyzed the relationship between the transfer and utilization of regenerative braking energy among trains running in opposite directions and established a mixed integer linear programming model. Liu et al [31] calculated the energysaving effect of the timetable for the Batong Metro Line in Beijing in batches using the bacterial foraging optimization strategy and proposed a boundary identification method to measure the traction energy conservation capability of urban rail timetables. Zhang et al [32] established an overlapping time calculation model to optimize the speed curve, dwell time, and departure interval in the operation scenarios of two trains and three trains, which effectively improved the utilization efficiency of regenerative braking energy.…”

Section: Related Literaturementioning

confidence: 99%

Energy-Saving Metro Train Timetable Optimization Method Based on a Dynamic Passenger Flow Distribution

Pan

Tong

et al. 2022

Journal of Advanced Transportation

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The operation of metro trains with a focus on energy savings can effectively reduce operating costs and carbon emissions. Reducing traction energy consumption and increasing the utilization efficiency of regenerative braking energy are two important energy-saving approaches that are closely related to the metro train interstation running strategy and timetable. Changes in train mass caused by dynamic changes in passenger flow represent one of the important factors affecting the energy consumption and energy-saving operation of metro trains. In this study, the differences in the temporal and spatial distributions of metro line passenger flow were specifically considered, and an energy-saving metro train timetable optimization method focused on the dissipative regenerative braking energy utilization mode was studied. First, a logistic function is used to fit the passenger flow pattern of the origin-destination (OD) station pairs, and the number of passengers getting on and off at each station is derived by establishing the OD dynamic demand matrix for the entire metro line. Then, the passenger load in each station segment is calculated. Next, a timetable optimization model is established to minimize the net energy consumption based on the load difference between station segments and the train motion equation. The interstation running time and dwell time of the metro train are optimized to increase the amount of regenerative braking energy used during the overlap time between the traction and braking actions of adjacent trains in the train operation timetable. A particle swarm optimization and genetic algorithm (PSO-GA) structure is designed to solve the model. The PSO-GA structure has PSO as the main body and integrates the chromosome crossover and mutation operations of the GA into the iterative process to improve the search efficiency of the algorithm. Finally, the proposed method and model are tested based on the actual data of a metro line in Qingdao, China. The goodness of fit of the passenger flow pattern is 0.997. The energy consumption during the study period is reduced by 5169.67 kW h using the optimized timetable. The energy-saving efficiency decreases by 12.18% at a constant OD ratio during the entire travel time and by 20.23% at the same constant load for all station segments. The results of the case analysis prove the effectiveness of the proposed method and model. In addition, the energy-saving timetable can be better optimized by considering the differences in temporal and spatial distributions of dynamic passenger flow.

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Section: Related Literaturementioning

confidence: 99%