A bi-level energy-efficient optimization method with an extended solution space for urban railway train running

Purpose Energy consumption is one of the key topics of urban railway systems from the perspective of operating costs and environmental friendliness. To reinforce the optimization effect, a bi-level energy-efficient optimization method is proposed in this study to associate advantages of speed profile optimization and timetable optimization. Design/methodology/approach The proposed bi-level framework includes two stages. In the lower-level optimization, an interstation speed profile model is established based on multiple train running schemes. A multi-objective evolutionary algorithm combined with analytic functions is proposed to obtain Pareto front solutions. In the upper-level optimization, an energy-efficient timetable optimization model is constructed based on Pareto front solutions of each running section acquired from lower-level optimization. Accordingly, the solving method with an evolutionary algorithm is proposed to minimize total net energy consumption. Findings The case study on Beijing’s Yizhuang Line shows the effectiveness of the proposed method, and 27.56% overall energy is saved. In addition, the results with different scenes revealed the influence of each level optimization on the overall results. Originality/value This study presents a novel bi-level optimization framework that systematically combines speed profile and timetable optimization, and the solving method with an evolutionary algorithm is proposed to minimize total net energy consumption.