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A new GIS-based algorithm to estimate photovoltaic potential of solar train: Case study in Gyeongbu line, Korea

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  • Kim, Hanjin
  • Ku, Jiyoon
  • Kim, Sung-Min
  • Park, Hyeong-Dong

Abstract

Photovoltaic (PV) power generation is considered a forward-looking industry. Nevertheless, solar energy is yet to become a direct source of electric power for mobile vehicles. Recently, there have been cases where solar panels were attached to the roof of trains to generate electricity. In this study, a method was devised to estimate the power generated by a solar train with panels. The solar irradiance on the roof of a moving train was calculated with respect to the location and time of the train, as well as the shadow effects of obstacles. The preprocessing of the input data required for calculating solar irradiation was executed through Geographic Information Systems, and finally, an algorithm for calculating solar irradiation and power generation was developed. With the algorithm-embodied Graphical User Interface, when spatial information of various routes is provided, the PV potential for each route can be calculated. Experimental calculations were conducted on the Gyeongbu line in Korea. During train operation, 122.15 MWh of power can be generated per year, with a reduction of 56 tons of CO2. The results of this preliminary evaluation are expected to accelerate the growth of the solar train industry.

Suggested Citation

  • Kim, Hanjin & Ku, Jiyoon & Kim, Sung-Min & Park, Hyeong-Dong, 2022. "A new GIS-based algorithm to estimate photovoltaic potential of solar train: Case study in Gyeongbu line, Korea," Renewable Energy, Elsevier, vol. 190(C), pages 713-729.
    • Handle: RePEc:eee:renene:v:190:y:2022:i:c:p:713-729
    DOI: 10.1016/j.renene.2022.03.130
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    References listed on IDEAS

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    Cited by:

    1. Baek, Jieun & Choi, Yosoon, 2023. "Optimal installation and operation planning of parking spaces for solar-powered electric vehicles using hemispherical images," Renewable Energy, Elsevier, vol. 219(P1).
    2. Ku, Jiyoon & Kim, Sung-Min & Park, Hyeong-Dong, 2024. "Energy-saving path planning navigation for solar-powered vehicles considering shadows," Renewable Energy, Elsevier, vol. 236(C).
    3. Suh, Jangwon, 2025. "Economic analysis of a solar roof as an optional extra to electric vehicles in Korea: A case study," Renewable Energy, Elsevier, vol. 239(C).
    4. Sari, Fatih & Rusen, Selmin Ener, 2025. "Assessment of the criteria importance for determining solar panel site potential via machine learning algorithms, a case study Central Anatolia region, Turkey," Renewable Energy, Elsevier, vol. 239(C).
    5. Jieun Baek & Yosoon Choi, 2022. "Comparative Study on Shading Database Construction for Urban Roads Using 3D Models and Fisheye Images for Efficient Operation of Solar-Powered Electric Vehicles," Energies, MDPI, vol. 15(21), pages 1-24, November.
    6. Žalik, Mitja & Mongus, Domen & Lukač, Niko, 2024. "High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning," Renewable Energy, Elsevier, vol. 222(C).

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