An Energy-Efficient Train Operation Approach by Integrating the Metro Timetabling and Eco-Driving

被引:244
|
作者
Su, Shuai [1 ]
Wang, Xuekai [1 ]
Cao, Yuan [2 ]
Yin, Jiateng [1 ]
机构
[1] Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China
[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
Energy consumption; Optimization; Acceleration; Switches; Dynamic programming; Rails; Mathematical model; Energy conservation; timetable; scheduling; train control; regenerative energy; TRAJECTORY OPTIMIZATION; REGENERATIVE ENERGY; OPTIMAL STRATEGIES; FUEL CONSUMPTION; MINIMIZATION; SPEED;
D O I
10.1109/TITS.2019.2939358
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Energy-efficient train operation is regarded as an effective way to reduce the operational cost and carbon emissions in metro systems. Reduction of the traction energy and increasing of the regenerative energy are two important ways for saving energy, which is closely related to the train timetable and driving strategy. To minimize the systematic net energy consumption, i.e., the difference between the traction energy consumption and the reused regenerative energy, this paper proposes an integrated train operation approach by jointly optimizing the train timetable and driving strategy. A precise train driving strategy is presented and the timetable model considers the headway between successive trains, the distribution of the trip time, and passenger demand in this paper. In addition, a distributed regenerative braking energy model is proposed, based on which the integrated optimization model is formulated. Then, a two-level approach is proposed to solve the problem. At the driving strategy level, the train control problem is transferred into a multi-step decision problem and the Dynamic Programming method is introduced to calculate the energy-efficient driving strategy with the given trip time. As for the timetable level, the trip times and headway of trains are optimized by using the Simulated Annealing algorithm based on the results of dynamic programming method. The timetable optimization level balances the mechanical traction energy of multi-interstations and the amount of the reused regenerative energy such that the net mechanical energy consumption of the metro system is minimized. Furthermore, two numerical examples are conducted for train operations in the peak and off-peak hours separately based on the real-world data of a metro line. The simulation results illustrate that the proposed approach can produce a good performance on energy-saving.
引用
收藏
页码:4252 / 4268
页数:17
相关论文
共 50 条
  • [21] A Simulation Approach for Optimising Energy-Efficient Driving Speed Profiles in Metro Lines †
    Gallo, Mariano
    Botte, Marilisa
    Ruggiero, Antonio
    D'Acierno, Luca
    [J]. ENERGIES, 2020, 13 (22)
  • [22] Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility
    Hesami, Simin
    Vafaeipour, Majid
    De Cauwer, Cedric
    Rombaut, Evy
    Vanhaverbeke, Lieselot
    Coosemans, Thierry
    [J]. ENERGIES, 2023, 16 (18)
  • [23] Energy-Efficient Train Operation Optimization in Istanbul Metro Network Considering Variable Passenger Numbers
    Yildiz, Ahmet
    Arikan, Oktay
    Keskin, Kemal
    [J]. TRANSPORTATION RESEARCH RECORD, 2024, 2678 (07) : 224 - 244
  • [24] Optimization of Energy-efficient Train Operation Based on Dynamic Programming Approach
    Gao H.
    Zhang Y.
    Guo J.
    Zha Z.
    [J]. Zhang, Yadong (ydzhang@home.swjtu.edu.cn), 1600, Science Press (42): : 76 - 84
  • [25] Incentivizing energy-efficient behavior at work: An empirical investigation using a natural field experiment on eco-driving
    Schall, Dominik L.
    Mohnen, Alwine
    [J]. APPLIED ENERGY, 2017, 185 : 1757 - 1768
  • [26] Energy-Efficient Automatic Train Driving by Learning Driving Patterns
    Huang, Jin
    Gao, Yue
    Lu, Sha
    Zhao, Xibin
    Deng, Yangdong
    Gu, Ming
    [J]. THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, : 2265 - 2272
  • [27] Energy-efficient approach combining train speed profile and timetable optimisations for metro operations
    Ran, Xin-Chen
    Chen, Shao-Kuan
    Liu, Ge-Hui
    Bai, Yun
    [J]. IET INTELLIGENT TRANSPORT SYSTEMS, 2020, 14 (14) : 1967 - 1977
  • [28] Eco-Driving for Energy Efficient Cornering of Electric Vehicles in Urban Scenarios
    Padilla, G. P.
    Pelosi, C.
    Beckers, C. J. J.
    Donkers, M. C. F.
    [J]. IFAC PAPERSONLINE, 2020, 53 (02): : 13816 - 13821
  • [29] Realistic Optimal Policies For Energy-Efficient Train Driving
    Grabocka, Josif
    Dalkalitsis, Alexandros
    Lois, Athanasios
    Katsaros, Evangelos
    Schmidt-Thieme, Lars
    [J]. 2014 IEEE 17TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2014, : 629 - 634
  • [30] A SUBWAY TRAIN TIMETABLE OPTIMIZATION APPROACH BASED ON ENERGY-EFFICIENT OPERATION STRATEGY
    Su, Shuai
    Li, Xiang
    Tang, Tao
    [J]. PROCEEDINGS OF THE ASME JOINT RAIL CONFERENCE 2012, 2012, : 717 - 727