An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems

Abstract Regenerative braking is an energy recovery mechanism that converts the kinetic energy during braking into electricity, also known as regenerative energy. In general, most of the regenerative energy is transmitted backward along the pantograph and fed back into the overhead contact line. To reduce the trains’ energy consumption, this paper develops a scheduling approach to coordinate the arrivals and departures of all trains located in the same electricity supply interval so that the energy regenerated from braking trains can be more effectively utilized to accelerate trains. Firstly, we formulate an integer programming model with real-world speed profiles to minimize the trains’ energy consumption with dwell time control. Secondly, we design a genetic algorithm and an allocation algorithm to find a good solution. Finally, we present numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line in Beijing, China. The results show that the proposed scheduling approach can reduce energy consumption by 6.97% and save about 1,054,388 CNY (or 169,223 USD) each year in comparison with the current timetable. Compared to the cooperative scheduling (CS) approach, the proposed scheduling approach can improve the utilization of regenerative energy by 36.16% and reduce the total energy consumption by 4.28%.

[1]  W. Gunselmann,et al.  Technologies for increased energy efficiency in railway systems , 2005, 2005 European Conference on Power Electronics and Applications.

[2]  Anthony Chen,et al.  A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem , 2006 .

[3]  Flavio Ciccarelli,et al.  Wayside Ultracapacitors Storage Design for Light Transportation Systems: a Multiobjective Optimization Approach , 2013 .

[4]  Xiang Li,et al.  A Two-Objective Timetable Optimization Model in Subway Systems , 2014, IEEE Transactions on Intelligent Transportation Systems.

[5]  Philippe Delarue,et al.  Energy Storage System With Supercapacitor for an Innovative Subway , 2010, IEEE Transactions on Industrial Electronics.

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Masafumi Miyatake,et al.  Energy Saving Speed and Charge/discharge Control of a Railway Vehicle with On-board Energy Storage by Means of an Optimization Model , 2009 .

[8]  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.

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

[10]  Andres Ramos,et al.  MATHEMATICAL PROGRAMMING APPROACH TO UNDERGROUND TIMETABLING PROBLEM FOR MAXIMIZING TIME SYNCHRONIZATION , 2007 .

[11]  Ion Etxeberria-Otadui,et al.  A supercapacitor based light rail vehicle: system design and operations modes , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[12]  Henry X. Liu,et al.  SMART-Signal Phase II: Arterial Offset Optimization Using Archived High-Resolution Traffic Signal Data , 2013 .

[13]  Feng Shi,et al.  Optimization method of alternate traffic restriction scheme based on elastic demand and mode choice behavior , 2014 .

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

[15]  Zhenhong Lin,et al.  Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data , 2014 .

[16]  F. Foiadelli,et al.  Ultracapacitors application for energy saving in subway transportation systems , 2007, 2007 International Conference on Clean Electrical Power.

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

[18]  D. Iannuzzi,et al.  Speed-Based State-of-Charge Tracking Control for Metro Trains With Onboard Supercapacitors , 2012, IEEE Transactions on Power Electronics.

[19]  Shing Chung Josh Wong,et al.  A reliability-based land use and transportation optimization model , 2011 .

[20]  Xin Yang,et al.  An optimisation method for train scheduling with minimum energy consumption and travel time in metro rail systems , 2015 .

[21]  Xiang Li,et al.  A STOCHASTIC TIMETABLE OPTIMIZATION MODEL IN SUBWAY SYSTEMS , 2013 .

[22]  Keping Li,et al.  Balanced train timetabling on a single-line railway with optimized velocity , 2014 .

[23]  Xiang Li,et al.  Optimizing trains movement on a railway network , 2012 .

[24]  Hossein Iman-Eini,et al.  Stationary super-capacitor energy storage system to save regenerative braking energy in a metro line , 2012 .

[25]  Flavio Ciccarelli,et al.  Stationary ultracapacitors storage device for improving energy saving and voltage profile of light transportation networks , 2012 .

[26]  Wuhua Li,et al.  Comparison of supercapacitor and lithium-ion capacitor technologies for power electronics applications , 2010 .

[27]  Indra Narayan Kar,et al.  Design of Model-Based Optimizing Control Scheme for an Air-Conditioning System , 2010 .

[28]  Seungjae Lee,et al.  Stochastic multi-objective models for network design problem , 2010, Expert Syst. Appl..

[29]  Jj Carruthers,et al.  The application of a systematic approach to material selection for the lightweighting of metro vehicles , 2009 .

[30]  A Nasri,et al.  Timetable optimization for maximum usage of regenerative energy of braking in electrical railway systems , 2010, SPEEDAM 2010.

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

[32]  Flavio Ciccarelli,et al.  Control of metro-trains equipped with onboard supercapacitors for energy saving and reduction of power peak demand , 2012 .

[33]  R. Barrero,et al.  Energy savings in public transport , 2008, IEEE Vehicular Technology Magazine.

[34]  Hanmin Lee,et al.  Capacity optimization of the supercapacitor energy storages on DC railway system using a railway powerflow algorithm , 2011 .

[35]  Anthony Chen,et al.  Analysis of regulation and policy of private toll roads in a build-operate-transfer scheme under demand uncertainty , 2007 .

[36]  D. Iannuzzi,et al.  Generalized approach to design supercapacitor-based storage devices integrated into urban mass transit systems , 2011, 2011 International Conference on Clean Electrical Power (ICCEP).