Optimal Control of Metro Energy Conservation Based on Regenerative Braking: A Complex Model Study of Trajectory and Overlap Time

Due to increasing environmental concerns regarding urban transit systems, the specific operating characteristics of metro trains and the rules of regenerative braking energy recycling were studied in this paper to relieve environmental stress. Based on the integrated research on trajectory and operation time, we considered complex operation routes mixed with ramps and detours, which also caused a complicated situation of overlap time, to better fit the actual and more efficient situation. A complex new model combined with a matrix control algorithm was proposed in this study. This model overcomes the lack of matching opportunity for overlap time as well as the precocity and instability of the genetic algorithm. In addition, the search ability of the model in the solution space is more comprehensive. In the interest of achieving minimum energy consumption, the objective function was set to minimize the total energy. Taking the Chongqing Metro Line 1 as a numerical example, the energy consumption results show that the energy saving method is effective and has a practical advantage. Then, a comparison of different models using the index of renewable utilization ratio as the indicator shows that the proposed model has a superior potential for energy conservation.

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

[2]  Daniel Tuyttens,et al.  Simulation-Based Genetic Algorithm towards an Energy-Efficient Railway Traffic Control , 2013 .

[3]  Kitae Kim,et al.  Optimal Train Operation for Minimum Energy Consumption Considering Track Alignment, Speed Limit, and Schedule Adherence , 2011 .

[4]  Phil Howlett,et al.  Optimal strategies for the control of a train , 1996, Autom..

[5]  Antonio Fernández-Cardador,et al.  Fuzzy optimal schedule of high speed train operation to minimize energy consumption with uncertain delays and driver's behavioral response , 2012, Eng. Appl. Artif. Intell..

[6]  Lacra Pavel,et al.  A two-step linear programming model for energy-efficient timetables in metro railway networks , 2015, 1506.08243.

[7]  Lajos Hanzo,et al.  Artificial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT , 2018, IEEE Journal on Selected Areas in Communications.

[8]  Youneng Huang,et al.  An integrated approach for the energy-efficient driving strategy optimization of multiple trains by considering regenerative braking , 2018, Comput. Ind. Eng..

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

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

[11]  Kemal Keskin,et al.  Energy-Efficient Train Operation Using Nature-Inspired Algorithms , 2017 .

[12]  Paul Batty,et al.  Optimal energy management of urban rail systems: Key performance indicators , 2015 .

[13]  Ziyou Gao,et al.  Joint train scheduling optimization with service quality and energy efficiency in urban rail transit networks , 2017 .

[14]  Eduardo Mario Dias,et al.  Improvement of the Energy Efficiency of Subway Traction Systems Through the Use of Genetic Algorithm in Traffic Control , 2019 .

[15]  Dario Pacciarelli,et al.  Integrating train scheduling and delay management in real-time railway traffic control , 2017 .

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

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

[18]  Michela Longo,et al.  Application of Genetic Algorithms for Driverless Subway Train Energy Optimization , 2016 .

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

[20]  Mohammad Ali Sandidzadeh,et al.  Optimal speed control of a multiple-mass train for minimum energy consumption using ant colony and genetic algorithms , 2017 .

[21]  Huijun Sun,et al.  Multiperiod-based timetable optimization for metro transit networks , 2017 .

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

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

[24]  Rob M. P. Goverde,et al.  Multi-train trajectory optimization for energy-efficient timetabling , 2019, Eur. J. Oper. Res..

[25]  Gilbert Laporte,et al.  Single-line rail rapid transit timetabling under dynamic passenger demand , 2014 .

[26]  Paola Pellegrini,et al.  Energy saving in railway timetabling: A bi-objective evolutionary approach for computing alternative running times , 2013 .

[27]  Ziyou Gao,et al.  Train speed profile optimization with on-board energy storage devices: A dynamic programming based approach , 2018, Comput. Ind. Eng..

[28]  Haidong Liu,et al.  A Two-level Optimization Model and Algorithm for Energy-Efficient Urban Train Operation , 2011 .

[29]  Rose Qingyang Hu,et al.  Computation Rate Maximization in UAV-Enabled Wireless-Powered Mobile-Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.

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

[31]  Xiang Li,et al.  A Subway Train Timetable Optimization Approach Based on Energy-Efficient Operation Strategy , 2012 .

[32]  Ziyou Gao,et al.  Metro timetable optimisation for minimising carbon emission and passenger time: a bi-objective integer programming approach , 2018 .

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

[34]  Fuhui Zhou,et al.  Robust AN-Aided Beamforming and Power Splitting Design for Secure MISO Cognitive Radio With SWIPT , 2016, IEEE Transactions on Wireless Communications.