Energy-Efficient Operation Optimization of High-Speed Trains Based on Network-Train-Line Coupling Under Carbon Perspective
Li Xin1, Zhu Chengkun2,3
1. School of New Energy and Power Engineering Lanzhou Jiaotong University Lanzhou 730070 China; 2. School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou 730070 China; 3. Xi’an Railway Vocational & Technical Institute Xi’an 710026 China
Abstract：The increasing operational mileage of high-speed railways brings significant energy consumption challenges. High-speed train operation energy consumption is the primary contributor to overall energy consumption in high-speed railway operations. During high-speed train operation, the traction power supply network (network), traction drive system (train), and line operating conditions (line) comprehensively affect train energy consumption through the external power grid’s energy supply, the conversion of electrical/mechanical energy driving train motion, and changes in additional resistance. Reducing high-speed train operational energy consumption will promote energy efficiency, green, low-carbon development, and high-quality development of high-speed railways. First, starting from the architecture of the high-speed railway traction system, the coupling relationship among the network, train, and line during energy transfer, conversion, and consumption processes is clarified. Next, combining the bi-directional system carbon emission measurement concept, the coupled carbon emission characteristics of the network-train-line system are qualitatively analyzed under two scenarios: “accounting for regenerative braking energy feedback” and “non-accounting for regenerative braking energy feedback.” Using the carbon emission factor method, a quantitative analysis method is proposed for calculating high-speed train operational carbon emissions, integrating factors such as the power grid, fuel, and passenger volume. Considering energy savings and carbon reduction, two key objectives for optimizing energy-saving train operation are defined: reducing traction energy consumption and utilizing regenerative braking energy. A single-train operation strategy is presented based on the composition of energy-saving operating conditions. In contrast, a multi-train regenerative braking energy utilization strategy is designed, incorporating an elastic buffer time utilization method. Finally, a hierarchical optimization model for energy-efficient train operation is established. Its upper layer focuses on utilizing multi-train elastic buffer time, and the lower layer addresses optimizing single-train energy-saving operation. The sine-cosine algorithm (SCA) is used to improve the deep Q network (DQN), and the SCA-DQN algorithm is proposed. Simulation analysis is conducted using data from real-world high-speed railway systems. Simulation results show that compared with the DP, Q-learning, and DQN algorithms, the SCA-DQN algorithm improves efficiency and energy savings, achieving energy-saving and carbon reduction rates of 19.9%, 9.3%, and 1.6%, respectively. For a train running on the Xi’anbei-Huyi section of the Xi’an-Chengdu high-speed railway, the optimized energy-saving and carbon reduction rate reaches 6.36%. For trains running on the Lanzhouxi-Tianshuinan section of the Baoji-Lanzhou high-speed railway, the energy-saving and carbon reduction rates of two trains supplemented by regenerative braking energy reach 8.82% and 7.65%, respectively.
李欣, 朱成琨. 碳视角下基于网-车-线耦合的高速列车节能运行优化[J]. 电工技术学报, 2025, 40(22): 7431-7448.
Li Xin, Zhu Chengkun. Energy-Efficient Operation Optimization of High-Speed Trains Based on Network-Train-Line Coupling Under Carbon Perspective. Transactions of China Electrotechnical Society, 2025, 40(22): 7431-7448.
[1] 陈冲, 贾利民, 赵天宇, 等. 去碳化导向的轨道交通与新能源融合发展: 形态模式、解决方案和使/赋能技术[J]. 电工技术学报, 2023, 38(12): 3321-3337. Chen Chong, Jia Limin, Zhao Tianyu, et al.Decarbonization-oriented rail transportation and renewable energy integration development: con- figurations, solutions, and enabling/empowering technologies[J]. Transactions of China Electro- technical Society, 2023, 38(12): 3321-3337. [2] Li Xin, Zhu Chengkun, Liu Yingzhi.Traction power supply system of China high-speed railway under low-carbon target: form evolution and operation control[J]. Electric Power Systems Research, 2023, 223: 109682. [3] 张雷, 李明, 司志强, 等. 高速列车多目标均衡综合节能技术[M]. 北京: 中国铁道出版社, 2022. [4] 李昱. 京沪高速铁路牵引供电系统能耗分析[J]. 电气化铁道, 2020, 31(2): 14-17. Li Yu.Analysis of energy consumption of traction power supply system for Beijing-Shanghai high speed railway[J]. Electric Railway, 2020, 31(2): 14-17. [5] 魏波, 胡海涛, 王科, 等. 基于实测数据和行车运行图的高铁牵引变电站负荷预测方法[J]. 电工技术学报, 2020, 35(1): 179-188. Wei Bo, Hu Haitao, Wang Ke, et al.Research on traction load forecasting method for high-speed railway traction substation based on measured data and train timetable[J]. Transactions of China Elec- trotechnical Society, 2020, 35(1): 179-188. [6] 章叶心, 邓云川, 刘志刚, 等. 一种长大下坡路段高速铁路再生制动能量的分析方法[J]. 电气传动, 2022, 52(17): 18-26. Zhang Yexin, Deng Yunchuan, Liu Zhigang, et al.An analysis method for regenerative braking energy of high-speed railway under long downslope route[J]. Electric Drive, 2022, 52(17): 18-26. [7] Wang Ke, Hu Haitao, Chen Junyu, et al.System-level dynamic energy consumption evaluation for high- speed railway[J]. IEEE Transactions on Trans- portation Electrification, 2019, 5(3): 745-757. [8] 冯晓云, 黄德青, 王青元, 等. 轨道交通列车综合节能研究综述[J]. 铁道学报, 2023, 45(2): 22-34. Feng Xiaoyun, Huang Deqing, Wang Qingyuan, et al.Overview on comprehensive energy saving schemes for rail transit train operation system[J]. Journal of the China Railway Society, 2023, 45(2): 22-34. [9] 陈进杰, 吕希奎. 高速铁路区间节能坡设计方法研究[J]. 铁道学报, 2013, 35(12): 83-89. Chen Jinjie, Lü Xikui.Research on method of energy saving gradient design for high-speed railway sections[J]. Journal of the China Railway Society, 2013, 35(12): 83-89. [10] 张磊, 许帅康, 陈洁, 等. 列车车体轻量化设计研究进展[J]. 机械工程学报, 2023, 59(24): 177-196. Zhang Lei, Xu Shuaikang, Chen Jie, et al.Research progress in lightweight design of train body[J]. Journal of Mechanical Engineering, 2023, 59(24): 177-196. [11] 聂泉存. 基于改进粒子群算法的牵引变电所的数量及位置优化研究[D]. 兰州: 兰州交通大学, 2022. Nie Quancun.Optimization of the number and location of traction substations based on improved particle swarm algorithm[D]. Lanzhou: Lanzhou Jiaotong University, 2022. [12] 程卫军. 基于变压器新技术的高速铁路牵引变电所节能与标准化布置[J]. 电气技术, 2021, 22(1): 58-62. Cheng Weijun.Energy saving and standardized layout of high-speed railway traction substation based on new technology of transformer[J]. Electrical Engin- eering, 2021, 22(1): 58-62. [13] 胡海涛, 陈俊宇, 葛银波, 等. 高速铁路再生制动能量储存与利用技术研究[J]. 中国电机工程学报, 2020, 40(1): 246-256, 391. Hu Haitao, Chen Junyu, Ge Yinbo, et al.Research on regenerative braking energy storage and utilization technology for high-speed railways[J]. Proceedings of the CSEE, 2020, 40(1): 246-256, 391. [14] 袁佳歆, 曲锴, 郑先锋, 等. 高速铁路混合储能系统容量优化研究[J]. 电工技术学报, 2021, 36(19): 4161-4169, 4182. Yuan Jiaxin, Qu Kai, Zheng Xianfeng, et al.Optimizing research on hybrid energy storage system of high speed railway[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4161-4169, 4182. [15] 李欣, 卢景涛, 肖林润, 等. 高速铁路列车长大坡道混合储能系统容量优化配置[J]. 电工技术学报, 2023, 38(20): 5645-5660. Li Xin, Lu Jingtao, Xiao Linrun, et al.Capacity optimization configuration of hybrid energy storage system for long steep slope of high-speed railway[J]. Transactions of China Electrotechnical Society, 2023, 38(20): 5645-5660. [16] 李欣, 卢景涛, 黄文勋. 绿电牵引场景下高速铁路长大坡道混合储能系统容量优化配置[J/OL]. 中国电机工程学报, 2024, https://doi.org/10.13334/j. 0258-8013.pcsee.232070. Li Xin, Lu Jingtao, Huang Wenxun. Capacity optimization of hybrid energy storage system for long steep slope of high-speed railway under green electric traction scenarios[J/OL]. Proceedings of the CSEE, 2024, https://doi.org/10.13334/j.0258-8013.pcsee. 232070. [17] 李俊豪, 涂春鸣, 王鑫, 等. 基于“规则+优化”的电气化铁路站点实时能量管控策略[J]. 电工技术学报, 2024, 39(11): 3339-3352. Li Junhao, Tu Chunming, Wang Xin, et al.Real-time energy management strategy for electrified railroad stations based on “rules+optimization”[J]. Transa- ctions of China Electrotechnical Society, 2024, 39(11): 3339-3352. [18] 高锋阳, 宋志翔, 高建宁, 等. 计及光伏和储能接入的牵引供电系统能量管理策略[J]. 电工技术学报, 2024, 39(3): 745-757. Gao Fengyang, Song Zhixiang, Gaojianning, et al. Energy management strategies for traction power systems with PV and energy storage access[J]. Transactions of China Electrotechnical Society, 2024, 39(3): 745-757. [19] 陈冲, 贾利民, 赵天宇, 等. 光伏和储能植入铁路牵引供电系统的拓扑架构与控制策略研究综述[J]. 电工技术学报, 2024, 39(24): 7874-7901. Chen Chong, Jia Limin, Zhao Tianyu, et al.Research review on topology and control strategy of PV and energy storage connected to railway traction power supply systems[J]. Transactions of China Electro- technical Society, 2024, 39(24): 7874-7901. [20] 革新, 张玉召, 黄志鹏. 基于再生制动能利用的高铁列车运行图优化[J]. 深圳大学学报(理工版), 2023, 40(5): 539-545. Ge Xin, Zhang Yuzhao, Huang Zhipeng.Optimization of high-speed train timetable based on regenerative braking energy utilization[J]. Journal of Shenzhen University (Science and Engineering), 2023, 40(5): 539-545. [21] 章鹏. 超长大坡道条件下西成高铁节能优化研究[D]. 成都: 西南交通大学, 2020. Zhang Peng.Study on energy saving optimization of Xi’an-Chengdu high speed railway under the con- dition of super long ramp[D]. Chengdu: Southwest Jiaotong University, 2020. [22] Zhu Chengkun, Lu Jingtao, Li Xin.Review of studies on energy-efficient train operation in high-speed railways[J]. IEEJ Transactions on Electrical and Electronic Engineering, 2023, 18(3): 451-462. [23] Chen Mo, Feng Xiaoyun, Wang Qingyuan, et al.Cooperative eco-driving of multi-train under DC traction net-work[J]. IEEE Transactions on Trans- portation Electrification, 2021, 7(3): 1805-1821. [24] 肖壮, 冯晓云, 王青元, 等. 考虑电力牵引链路非线性损耗的列车节能驾驶方法[J]. 铁道学报, 2022, 44(10): 33-39. Xiao Zhuang, Feng Xiaoyun, Wang Qingyuan, et al.Eco-driving for train considering nonlinear power loss of electric traction chain[J]. Journal of the China Railway Society, 2022, 44(10): 33-39. [25] Sun Zheng, He Deqiang, He Yan, et al.A bi-objective optimization model of metro trains considering energy conservation and passenger waiting time[J]. Journal of Cleaner Production, 2024, 437: 140427. [26] 盛昭. 考虑电分相的高速列车节能优化方法研究[D]. 北京: 北京交通大学, 2022. Sheng Zhao.Energy-saving optimization method for high-speed trains considering neutral sections[D]. Beijing: Beijing Jiaotong University, 2022. [27] 上官伟, 敦旖晨, 蔡伯根, 等. 基于群体合作演化的高速列车编队运行多目标优化[J]. 铁道学报, 2023, 45(8): 99-109. Shangguan Wei, Dun Yichen, Cai Baigen, et al.Multi-objective optimization for high-speed train operation in platoon based on swarm cooperation evolution[J]. Journal of the China Railway Society, 2023, 45(8): 99-109. [28] 康重庆, 杜尔顺, 李姚旺, 等. 新型电力系统的“碳视角”: 科学问题与研究框架[J]. 电网技术, 2022, 46(3): 821-833. Kang Chongqing, Du Ershun, Li Yaowang, et al.Key sci-entific problems and research framework for carbon perspective research of new power systems[J]. Power System Technology, 2022, 46(3): 821-833. [29] 孙菁阳, 孔祥玉, 陈一, 等. 电力系统全环节碳排放核算方法综述[J/OL]. 电力系统自动化, 2024, http://kns.cnki.net/kcms/detail/32.1180.TP.20240410.1325.002.html. Sun Jingyang, Kong Xiangyu, Chen Yi, et al. Review of carbon emission accounting methods for power system[J/OL]. Automation of Electric Power Systems, 2024, http://kns.cnki.net/kcms/detail/32.1180.TP. 20240410.1325.002.html. [30] Yang Chao, Liu Jinjie, Liao Huanxin, et al.An improved carbon emission flow method for the power grid with prosumers[J]. Energy Reports, 2023, 9: 114-121. [31] 包维瀚, 李姚旺, 季节, 等. 储能系统与双向电力负荷的碳排放核算方法[J]. 电网技术, 2023, 47(8): 3049-3058. Bao Weihan, Li Yaowang, Ji Jie, et al.Carbon emission accounting method for energy storage system and bidirectional power load[J]. Power System Technology, 2023, 47(8): 3049-3058. [32] 陈进杰, 王兴举, 王祥琴, 等. 高速铁路全生命周期碳排放计算[J]. 铁道学报, 2016, 38(12): 47-55. Chen Jinjie, Wang Xingju, Wang Xiangqin, et al.Calculation of carbon dioxide emissions in the life cycle of high-speed railway[J]. Journal of the China Railway Society, 2016, 38(12): 47-55. [33] 曹猛, 袁振洲, 杨洋, 等. 京雄城际铁路全生命周期能耗及碳排放研究[J]. 交通运输系统工程与信息, 2024, 24(5): 37-44, 55. Cao Meng, Yuan Zhenzhou, Yang Yang, et al.Research on energy consumption and carbon emissions in the whole life cycle of Beijing-Xiong’an intercity rail-way[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(5): 37-44, 55. [34] 毛保华, 赵义馨, 李宁海, 等. 我国高速铁路与民航客运碳排放因子及其影响因素研究[J]. 北京交通大学学报, 2024, 48(4): 11-21. Mao Baohua, Zhao Yixin, Li Ninghai, et al.Study on carbon emission factors and their influencing factors in high-speed railway and civil aviation passenger transport in China[J]. Journal of Beijing Jiaotong University, 2024, 48(4): 11-21. [35] Mirjalili S.SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based Systems, 2016, 96: 120-133. [36] 张淼, 张琦, 张梓轩. 基于Q学习算法的高速铁路列车节能优化研究[J]. 铁道运输与经济, 2019, 41(12): 111-117. Zhang Miao, Zhang Qi, Zhang Zixuan.A study on energy-saving optimization for high-speed railways train based on Q-learning algorithm[J]. Railway Transport and Economy, 2019, 41(12): 111-117. [37] 李旭阳. 面向节能的高速列车运行分析与优化研究[D]. 兰州: 兰州交通大学, 2020. Li Xuyang.Research on operation analysis and optimization for energy-saving operations of high- speed train[D]. Lanzhou: Lanzhou Jiaotong University, 2020.
2025, Vol. 40 
