A two-step linear programming model for energy-efficient timetables in metro railway networks

In this paper we propose a novel two-step linear optimization model to calculate energy-efficient timetables in metro railway networks. The resultant timetable minimizes the total energy consumed by all trains and maximizes the utilization of regenerative energy produced by braking trains, subject to the constraints in the railway network. In contrast to other existing models, which are NP-hard, our model is computationally the most tractable one being a linear program. We apply our optimization model to different instances of service PES2-SFM2 of line 8 of Shanghai Metro network spanning a full service period of one day (18 h) with thousands of active trains. For every instance, our model finds an optimal timetable very quickly (largest runtime being less than 13 s) with significant reduction in effective energy consumption (the worst case being 19.27%). Code based on the model has been integrated with Thales Timetable Compiler - the industrial timetable compiler of Thales Inc that has the largest installed base of communication-based train control systems worldwide.

[1]  Christian Rehtanz,et al.  Balancing Renewable Electricity: Energy Storage, Demand Side Management, and Network Extension from an Interdisciplinary Perspective , 2012 .

[2]  Eugene Khmelnitsky,et al.  On an optimal control problem of train operation , 2000, IEEE Trans. Autom. Control..

[3]  Christian Liebchen,et al.  Periodic Timetable Optimization in Public Transport , 2006, OR.

[4]  Rongfang Rachel Liu,et al.  Energy-efficient operation of rail vehicles , 2003 .

[5]  W. J. DeCoursey,et al.  Introduction: Probability and Statistics , 2003 .

[6]  Peng Zhou,et al.  The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points , 2016 .

[7]  P. Howlett,et al.  A note on the calculation of optimal strategies for the minimization of fuel consumption in the control of trains , 1993, IEEE Trans. Autom. Control..

[8]  Mort Webster,et al.  An approximate dynamic programming approach for designing train timetables , 2011 .

[9]  James D. Stamey Modern Mathematical Statistics with Applications , 2008 .

[10]  Maite Pena-Alcaraz,et al.  Optimal underground timetable design based on power flow for maximizing the use of regenerative-braking energy , 2012 .

[11]  Ahmed Yousuf Saber,et al.  Intelligent unit commitment with vehicle-to-grid —A cost-emission optimization , 2010 .

[12]  Flavio Ciccarelli,et al.  Improvement of Energy Efficiency in Light Railway Vehicles Based on Power Management Control of Wayside Lithium-Ion Capacitor Storage , 2014, IEEE Transactions on Power Electronics.

[13]  Lino Guzzella,et al.  Cost and fuel-optimal selection of HEV topologies using Particle Swarm Optimization and Dynamic Programming , 2012, 2012 American Control Conference (ACC).

[14]  Xiang Li,et al.  A Cooperative Scheduling Model for Timetable Optimization in Subway Systems , 2013, IEEE Transactions on Intelligent Transportation Systems.

[15]  Martin Connors,et al.  Optimization Models , 2014 .

[16]  Keping Li,et al.  Optimizing the train timetable for a subway system , 2015 .

[17]  Hong Kam Lo,et al.  Energy minimization in dynamic train scheduling and control for metro rail operations , 2014 .

[18]  Steven Harrod,et al.  A tutorial on fundamental model structures for railway timetable optimization , 2012 .

[19]  Lacra Pavel,et al.  An optimization model to utilize regenerative braking energy in a railway network , 2015, 2015 American Control Conference (ACC).

[20]  Emil P. Vlad,et al.  R&M&A&S of communication based train control systems applied to Urban Rail Transportation — A way to improve city sustainability , 2011, 2011 Proceedings - Annual Reliability and Maintainability Symposium.

[21]  Hosam K. Fathy,et al.  A Framework for the Integrated Optimization of Charging and Power Management in Plug-in Hybrid Electric Vehicles , 2013, IEEE Transactions on Vehicular Technology.

[22]  Hosam K. Fathy,et al.  Plug-in hybrid electric vehicle charge pattern optimization for energy cost and battery longevity , 2011 .

[23]  Ian P. Milroy,et al.  Aspects of automatic train control , 1980 .

[24]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[25]  Iain Dunning,et al.  Computing in Operations Research Using Julia , 2013, INFORMS J. Comput..

[26]  Vadim I. Utkin,et al.  Recasting the HEV energy management problem into an infinite-time optimization problem including stability , 2013, 52nd IEEE Conference on Decision and Control.

[27]  K. Ramachandran,et al.  Mathematical Statistics with Applications. , 1992 .

[28]  Phil G. Howlett,et al.  Local energy minimization in optimal train control , 2009, Autom..

[29]  Leon W P Peeters,et al.  Cyclic Railway Timetable Optimization , 2003 .

[30]  F. Béguin,et al.  Supercapacitors : materials, systems, and applications , 2013 .

[31]  Walter Ukovich,et al.  A Mathematical Model for Periodic Scheduling Problems , 1989, SIAM J. Discret. Math..

[32]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[33]  Phil Howlett,et al.  The Optimal Control of a Train , 2000, Ann. Oper. Res..

[34]  Phil Howlett,et al.  Energy-efficient train control , 1994 .

[35]  Chris P. Tsokos,et al.  Mathematical Statistics with Applications , 2009 .

[36]  Z. Filipi,et al.  A framework for the integrated optimization of charging and power management in plug-in hybrid electric vehicles , 2012, 2012 American Control Conference (ACC).

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

[38]  Paul Batty,et al.  Sustainable urban rail systems: strategies and technologies for optimal management of regenerative braking energy , 2013 .

[39]  Avinash Balakrishnan,et al.  Nanostructured Ceramic Oxides for Supercapacitor Applications , 2014 .

[40]  Sanjoy Mahajan,et al.  Street-Fighting Mathematics , 2010 .