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A review of research on traction load models and modeling methods for electrified railways

Author

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  • Che, Yulong
  • Wang, Xiaoru
  • Ge, Leijiao
  • Lin, Hongjian
  • Lyu, Xiaoqin
  • Su, Hongsheng
  • Wang, Hao

Abstract

As the electrified railways, especially high-speed railways, continue to expand, the proportion of traction load in overall power load is increasing significantly. Establishing a traction load model is crucial for the design and operation of traction power supply system (TPSS) and the evaluation of impact on power systems. The paper provides an overview of key issues related to traction loads in electrified railways. It systematically summarizes research trends and achievements on traction load characteristics, models, and modeling methods, as well as their comparability and practicality. Firstly, we analyze the electrical and spatiotemporal characteristics of traction loads. Secondly, based on the modeling requirements and application scenarios of traction loads, this paper classifies and compares existing traction load models. Then, we review the current research status of traction load modeling methods, with a focus on deterministic and uncertainty modeling methods for traction loads. The advantages and disadvantages of various traction load modeling methods are analyzed and compared. Finally, it highlights trends in traction load modeling, including traction load forecasting, multi random variable modeling, data-driven approaches, and integration of AI and IoT. This paper presents a comprehensive survey on traction load modeling, current practices, and future outlook, while providing guidance for selecting appropriate traction load models.

Suggested Citation

  • Che, Yulong & Wang, Xiaoru & Ge, Leijiao & Lin, Hongjian & Lyu, Xiaoqin & Su, Hongsheng & Wang, Hao, 2025. "A review of research on traction load models and modeling methods for electrified railways," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:rensus:v:219:y:2025:i:c:s1364032125005428
    DOI: 10.1016/j.rser.2025.115869
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    1. Liu, Yaru & Wang, Lei & Ng, Bing Feng, 2024. "A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved Kalman filter algorithm," Applied Energy, Elsevier, vol. 359(C).
    2. Zhengyou He & Zheng Zheng & Haitao Hu, 2016. "Power quality in high-speed railway systems," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 4(2), pages 71-97, June.
    3. Nils-Bastian Heidenreich & Anja Schindler & Stefan Sperlich, 2013. "Bandwidth selection for kernel density estimation: a review of fully automatic selectors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 403-433, October.
    4. Zhensheng Wu & Deling Fan & Fan Zou, 2022. "Traction Load Modeling and Parameter Identification Based on Improved Sparrow Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-18, July.
    5. Antonio García-Olivares & Jordi Solé & Roger Samsó & Joaquim Ballabrera-Poy, 2020. "Sustainable European Transport System in a 100% Renewable Economy," Sustainability, MDPI, vol. 12(12), pages 1-21, June.
    6. Chen, Shuang & Hu, Minghui & Lei, Yanlei & Kong, Linghao, 2023. "Novel hybrid power system and energy management strategy for locomotives," Applied Energy, Elsevier, vol. 348(C).
    7. Yu, Jianxi & Petersen, Nils & Liu, Pei & Li, Zheng & Wirsum, Manfred, 2022. "Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development," Energy, Elsevier, vol. 260(C).
    8. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    9. Domínguez, M. & Fernández-Cardador, A. & Fernández-Rodríguez, A. & Cucala, A.P. & Pecharromán, R.R. & Urosa Sánchez, P. & Vadillo Cortázar, I., 2025. "Review on the use of energy storage systems in railway applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    10. Ha Che-Ngoc & Thao Nguyen-Trang & Hieu Huynh-Van & Tai Vo-Van, 2024. "Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique," Annals of Data Science, Springer, vol. 11(2), pages 709-732, April.
    11. Han, Qinkai & Ma, Sai & Wang, Tianyang & Chu, Fulei, 2019. "Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    12. Zhao, Chenyu & Wang, Chaoyang & Fan, Jianlin & Liu, Ming & Liu, Jiping & Yan, Junjie, 2024. "Enhancing the load ramp-up capability of the subcritical CFB power unit by considering the evolution of internal stored energy," Energy, Elsevier, vol. 307(C).
    13. Che, Jinxing & Yuan, Fang & Zhu, Suling & Yang, Youlong, 2022. "An adaptive ensemble framework with representative subset based weight correction for short-term forecast of peak power load," Applied Energy, Elsevier, vol. 328(C).
    14. Sun, Xiaojun & Feng, Ding & Zhang, Qiang & Lin, Sheng, 2024. "Optimal siting of substations of traction power supply systems considering seismic risk," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    15. Gensler, André & Sick, Bernhard & Vogt, Stephan, 2018. "A review of uncertainty representations and metaverification of uncertainty assessment techniques for renewable energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 352-379.
    16. Claeys, Robbert & Cleenwerck, Rémy & Knockaert, Jos & Desmet, Jan, 2024. "Capturing multiscale temporal dynamics in synthetic residential load profiles through Generative Adversarial Networks (GANs)," Applied Energy, Elsevier, vol. 360(C).
    17. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    18. Junqi Zhang & Mingli Wu & Qiujiang Liu, 2018. "A Novel Power Flow Algorithm for Traction Power Supply Systems Based on the Thévenin Equivalent," Energies, MDPI, vol. 11(1), pages 1-17, January.
    19. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    20. Zhensheng Wu & Xinyi Zhao & Deling Fan, 2023. "Research on the Influence of Traction Load on Transient Stability of Power Grid Based on Parameter Identification," Energies, MDPI, vol. 16(22), pages 1-19, November.
    21. Mansour, Shaza H. & Azzam, Sarah M. & Hasanien, Hany M. & Tostado-Veliz, Marcos & Alkuhayli, Abdulaziz & Jurado, Francisco, 2024. "Wasserstein generative adversarial networks-based photovoltaic uncertainty in a smart home energy management system including battery storage devices," Energy, Elsevier, vol. 306(C).
    22. Ke Wang & Haitao Hu & Cai Chen & Zhengyou He & Lihua Chen, 2018. "A simulation platform to assess comprehensive power quality issues in electrified railways," International Journal of Rail Transportation, Taylor & Francis Journals, vol. 6(4), pages 233-254, October.
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