面向节能的城市轨道交通列车运行图两阶段优化方法

Two-stage Optimization Method for Energy-oriented Train Running Diagram in Urban Rail Transit

  • 摘要:
    目的 随着线网规模不断扩张,城市轨道交通系统带来的巨大电能消耗问题不容忽视。列车运行图是影响城市轨道交通系统牵引能耗的重要因素,为降低系统牵引能耗,须对节能列车运行图进行研究。
    方法 基于区间运行时间与列车牵引能耗负相关关系、再生制动能利用原理,提出了寻求净牵引能耗最小化的节能列车运行图两阶段优化方法。第一阶段,基于ATS(列车自动监控)系统中预存的多运行等级信息,建立了以列车周转牵引能耗最小为目标的列车运行图标尺优化模型,实现了合理分配区间运行时间。第二阶段,以发车间隔为决策变量构建了混合整数非线性规划模型,实现了同一车站附近牵引制动列车重叠时间最大化,并进一步将模型处理为便于求解的线性化形式。选取我国某实际地铁线路进行案例分析,调用商业求解器GUROBI对模型进行求解。
    结果及结论  第一阶段优化在列车周转时间不变的前提下,将列车周转牵引能耗从650.30 kWh降至593.55 kWh,降幅为8.73%;第二阶段优化进一步将牵引制动重叠时间从1 005 s提升至1 710 s,增幅为70.10%。所构建方法实现了列车周转牵引能耗、再生制动能利用率的双重优化,提升了节能效果。

     

    Abstract:
    Objective With the continuous expansion of line network scale, the significant electricity consumption issue caused by urban rail transit system cannot be ignored. Train diagram is a crucial factor influencing the traction energy consumption of the system. It is necessary to carry out a research on energy-oriented train diagram to reduce the system traction energy consumption.
    Method Based on the negative correlation between inter-section running time and train traction energy consumption, and the principle of regenerative braking energy utilization, a two-stage optimization method for energy-oriented diagram is proposed to minimize net traction energy consumption. In the first stage, based on the pre-stored multi-operation level information in the ATS (automatic train supervision) system, a diagram scaling optimization model is established to minimize the turnaround traction energy consumption, thus achieving the effective allocation of train inter-section running time. In the second stage, a mixed-integer nonlinear programming model is constructed with train headways as the decision variables, aiming to maximize the overlap time of traction and braking trains near the same station. The model is then further processed into a linearized form for easier solution. A case study is conducted using an actual metro line in China, and the commercial solver GUROBI is used to solve this model.
    Result & Conclusion  The first stage optimization reduces the train turnaround traction energy consumption from 650.30 kWh to 593.55 kWh, achieving a reduction of 8.73%, while keeping the total train turnaround time unchanged. The second stage optimization further increases the overlap time of traction and braking trains from 1 005 seconds to 1 710 seconds, representing an increase of 70.10%. The proposed method achieves a dual optimization of both train turnaround traction energy consumption and regenerative braking energy utilization rate, thereby enhancing the energy-oriented performance.

     

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