Two‐step method to reduce metro transit energy consumption by optimising speed profile and timetable

With the rising energy prices and increasing environmental awareness, the energy efficiency of metro transit system has attracted much attention in recent years. This study proposes a two-step optimisation method to optimise speed profile and timetable, aiming to reduce the operational energy consumption of metro transit system. First, a coasting point searching algorithm is designed to reduce tractive energy consumption by optimising speed profile and running time distribution scheme. Then, a mixed-integer linear programming model is constructed to maximise the overlap time between the accelerating and braking phases by optimising headway and dwell time, in order to improve the utilisation of regenerative braking energy (RBE). Furthermore, numerical simulations are presented based on the data from a Guangzhou Metro Line. The results show that the tractive energy consumption can be reduced by 8.46% and the utilisation of RBE can be improved by 11.6%.

[1]  Joaquin Rodriguez,et al.  Towards Eco-Aware Timetabling: Evolutionary Approach and Cascading Initialisation Strategy for the Bi-Objective Optimisation of Train Running Times , 2016 .

[2]  Tao Tang,et al.  A Cooperative Train Control Model for Energy Saving , 2015, IEEE Transactions on Intelligent Transportation Systems.

[3]  Xiang Li,et al.  A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy , 2012, IEEE Transactions on Intelligent Transportation Systems.

[4]  Chung-Fu Chang,et al.  Optimising train movements through coast control using genetic algorithms , 1997 .

[5]  P. Howlett.  An optimal strategy for the control of a train , 1990, The Journal of the Australian Mathematical Society. Series B. Applied Mathematics.

[6]  Phil Howlett,et al.  Optimal driving strategies for a train journey with speed limits , 1994 .

[7]  Clive Roberts,et al.  Field test of train trajectory optimisation on a metro line , 2017 .

[8]  Amie R. Albrecht,et al.  Energy-efficient train control: From local convexity to global optimization and uniqueness , 2013, Autom..

[9]  Xiang Li,et al.  An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems , 2015 .

[10]  Paul Batty,et al.  A systems approach to reduce urban rail energy consumption , 2014 .

[11]  Lei Chen,et al.  An integrated metro operation optimization to minimize energy consumption , 2017 .

[12]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[13]  Tin Kin Ho,et al.  Coast control for mass rapid transit railways with searching methods , 2004 .

[14]  Clive Roberts,et al.  Single-Train Trajectory Optimization , 2013, IEEE Transactions on Intelligent Transportation Systems.

[15]  Xiaobo Qu,et al.  Minimizing the Average Delay at Intersections via Presignals and Speed Control , 2018, Journal of Advanced Transportation.

[16]  Xiang Li,et al.  A Survey on Energy-Efficient Train Operation for Urban Rail Transit , 2016, IEEE Transactions on Intelligent Transportation Systems.

[17]  Hong Kam Lo,et al.  An energy-efficient scheduling and speed control approach for metro rail operations , 2014 .

[18]  Yonghua Zhou,et al.  A synergistic energy-efficient planning approach for urban rail transit operations , 2018 .

[19]  K. Ichikawa.  Application of Optimization Theory for Bounded State Variable Problems to the Operation of Train , 1968 .

[20]  Leo G. Kroon,et al.  Review of energy-efficient train control and timetabling , 2017, Eur. J. Oper. Res..