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Distributionally robust optimization configuration of integrated photovoltaic and energy storage in rail transit green energy system

Author

Listed:
  • Tian, Haoxin
  • Chen, Yanbo
  • Zhang, Zhi
  • Zhang, Ning
  • Deng, Hanyu
  • Qiang, Tuben
  • Zhang, Runzhao

Abstract

In the context of carbon neutrality goals, the integration of distributed photovoltaics (DPV) and energy storage systems into high-speed railway traction substations contributes to improved energy self-sufficiency and emission reductions in rail transit. However, the inherent intermittency of DPV generation and the impulsive nature of traction loads present significant challenges to the safe and stable operation of the traction power supply system. To address these issues, this study proposes a novel planning framework for the co-deployment of DPV and hybrid energy storage systems (HESS) within an integrated rail transit green energy system, aiming to achieve synergistic coordination among the grid, generation, storage, and rolling stock. First, a grid-side three-phase voltage unbalance constraint is formulated based on power quality indices and linearized via a compensation strategy using the back-to-back converter. Second, a sliding window event detection algorithm is applied to extract high-speed train load profiles from field measurements, enabling minute-level dynamic traction load prediction through train diagram data. A two-stage distributionally robust optimization model is then developed to minimize the system's average daily total cost, accounting for uncertainties in DPV output and the cycle life degradation of lithium-ion batteries, while incorporating 1-min resolution safety constraints. The model is efficiently solved using an inexact column-and-constraint generation algorithm. Finally, a case study based on a high-speed railway substation in China validates the proposed approach, demonstrating a 22.74 % energy self-sufficiency rate, a 24.91 % reduction in costs, and a payback period of 3.26 years. The results confirm that the integration of DPV and HESS not only enhances energy efficiency and mitigates power quality issues, but also yields substantial economic benefits, offering a viable pathway toward low-carbon, high-quality, and cost-effective energy supply solutions for rail transit systems.

Suggested Citation

  • Tian, Haoxin & Chen, Yanbo & Zhang, Zhi & Zhang, Ning & Deng, Hanyu & Qiang, Tuben & Zhang, Runzhao, 2026. "Distributionally robust optimization configuration of integrated photovoltaic and energy storage in rail transit green energy system," Applied Energy, Elsevier, vol. 404(C).
    • Handle: RePEc:eee:appene:v:404:y:2026:i:c:s0306261925018045
    DOI: 10.1016/j.apenergy.2025.127074
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    References listed on IDEAS

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    1. Flavio Ciccarelli & Luigi Pio Di Noia & Renato Rizzo, 2018. "Integration of Photovoltaic Plants and Supercapacitors in Tramway Power Systems," Energies, MDPI, vol. 11(2), pages 1-14, February.
    2. Li Ji & Zhenwei Yu & Jing Ma & Limin Jia & Fuwei Ning, 2020. "The Potential of Photovoltaics to Power the Railway System in China," Energies, MDPI, vol. 13(15), pages 1-17, July.
    3. Li, Zhanhe & Li, Xiaoqian & Lu, Chao & Ma, Kechun & Bao, Weihan, 2024. "Carbon emission responsibility accounting in renewable energy-integrated DC traction power systems," Applied Energy, Elsevier, vol. 355(C).
    4. Shen, Xiaojun & Wei, Hongyang & Wei, Li, 2020. "Study of trackside photovoltaic power integration into the traction power system of suburban elevated urban rail transit line," Applied Energy, Elsevier, vol. 260(C).
    5. Pu, Yuchen & Li, Qi & Huo, Shasha & Breaz, Elena & Chen, Weirong & Gao, Fei, 2024. "Optimal configuration for shared electric-hydrogen energy storage for multiple integrated energy systems with mobile hydrogen transportation," Renewable Energy, Elsevier, vol. 237(PC).
    6. Yuanli Liu & Minwu Chen & Shaofeng Lu & Yinyu Chen & Qunzhan Li, 2018. "Optimized Sizing and Scheduling of Hybrid Energy Storage Systems for High-Speed Railway Traction Substations," Energies, MDPI, vol. 11(9), pages 1-29, August.
    7. Xing, Zongyi & Zhang, Zhenyu & Guo, Jian & Qin, Yong & Jia, Limin, 2023. "Rail train operation energy-saving optimization based on improved brute-force search," Applied Energy, Elsevier, vol. 330(PA).
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