Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm

被引:26
|
作者
Lin, Fei [1 ]
Liu, Shihui [1 ]
Yang, Zhihong [1 ]
Zhao, Yingying [1 ]
Yang, Zhongping [1 ]
Sun, Hu [1 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect Engn, 3 Shangyuancun, Beijing 100044, Peoples R China
来源
ENERGIES | 2016年 / 9卷 / 03期
基金
中国国家自然科学基金;
关键词
dwell time; urban rail transit; braking energy; genetic algorithm; multi-train; energy saving; FLYWHEEL ENERGY; TRACTION ENERGY; STORAGE; POWER; SPEED; BATTERIES; VEHICLE; DESIGN;
D O I
10.3390/en9030208
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
With its large capacity, the total urban rail transit energy consumption is very high; thus, energy saving operations are quite meaningful. The effective use of regenerative braking energy is the mainstream method for improving the efficiency of energy saving. This paper examines the optimization of train dwell time and builds a multiple train operation model for energy conservation of a power supply system. By changing the dwell time, the braking energy can be absorbed and utilized by other traction trains as efficiently as possible. The application of genetic algorithms is proposed for the optimization, based on the current schedule. Next, to validate the correctness and effectiveness of the optimization, a real case is studied. Actual data from the Beijing subway Yizhuang Line are employed to perform the simulation, and the results indicate that the optimization method of the dwell time is effective.
引用
收藏
页数:21
相关论文
共 50 条
  • [1] Collaborative Optimization Method for Multi-Train Energy-Saving Control with Urban Rail Transit Based on DRLDA Algorithm
    Dong, Luxi
    Qin, Linan
    Xie, Xiaolan
    Zhang, Lieping
    Qin, Xianhao
    [J]. APPLIED SCIENCES-BASEL, 2023, 13 (04):
  • [2] Intelligent scheduling method for energy saving operation of multi-train based on genetic algorithm and regenerative kinetic energy
    Zou, Bingqian
    Gong, Lixiong
    Yu, Ning
    Chen, Jin
    [J]. JOURNAL OF ENGINEERING-JOE, 2018, (16): : 1550 - 1554
  • [3] Study on Energy-Saving Optimization of Urban Rail Transit Train Timetable under Regenerative Braking
    Zheng, Yajing
    Ma, Zihan
    Liu, Naiyu
    Jin, Wenzhou
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [4] Energy Saving Operation Optimization of Urban Rail Transit Trains Through the Use of Regenerative Braking Energy
    Feng, Yu
    Chen, Shaokuan
    Ran, Xinchen
    Bai, Yun
    Jia, Wenzheng
    [J]. Tiedao Xuebao/Journal of the China Railway Society, 2018, 40 (02): : 15 - 22
  • [5] Energy-efficient Train Control in Urban Rail Transit: Multi-train Dynamic Cooperation based on Train-to-Train Communication
    Jin, Bo
    Fang, Qian
    Wang, Qingyuan
    Sun, Pengfei
    Feng, Xiaoyun
    [J]. 2021 32ND IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV), 2021, : 309 - 314
  • [6] Research on multi-train energy saving optimization based on cooperative multi-objective particle swarm optimization algorithm
    Zhang, Yong
    Zuo, Tingting
    Zhu, Muhan
    Huang, Cheng
    Li, Jun
    Xu, Zhiliang
    [J]. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2021, 45 (02) : 2644 - 2667
  • [7] Real-time energy saving optimization method for urban rail transit train timetable under delay condition
    Zhang, Lang
    He, Deqiang
    He, Yan
    Liu, Bin
    Chen, Yanjun
    Shan, Sheng
    [J]. ENERGY, 2022, 258
  • [8] Train Operation Traction Energy Calculation and Saving in Urban Rail Transit System
    Hu, Peng
    Chen, Rongwu
    Li, Haoyu
    Liang, Yi
    [J]. PROCEEDINGS OF THE 2012 SECOND INTERNATIONAL CONFERENCE ON INSTRUMENTATION & MEASUREMENT, COMPUTER, COMMUNICATION AND CONTROL (IMCCC 2012), 2012, : 505 - 507
  • [9] Energy Saving Train Control for Urban Railway Train with Multi-population Genetic Algorithm
    Liu Wei
    Li Qunzhan
    Tang Bing
    [J]. 2009 INTERNATIONAL FORUM ON INFORMATION TECHNOLOGY AND APPLICATIONS, VOL 2, PROCEEDINGS, 2009, : 58 - +
  • [10] Optimization of Train Headway and Traction Energy Consumption in Urban Rail Transit
    Gao, Hao
    Guo, Jin
    Zhang, Ya-Dong
    [J]. Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 2020, 20 (06): : 170 - 177