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Solar photovoltaic generation for charging shared electric scooters

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  • Zhu, Rui
  • Kondor, Dániel
  • Cheng, Cheng
  • Zhang, Xiaohu
  • Santi, Paolo
  • Wong, Man Sing
  • Ratti, Carlo

Abstract

Scooter-sharing has been introduced as a new transportation mode. However, e-scooters have a limited battery capacity and require frequent charging, which causes the operational cost significantly high and hinders the viability of the service. To tackle this problem, this study proposes a solar charging solution with the creation of a real-time shareability network that maximizes the scooter-sharing capability and minimizes the total trip distance, constrained by e-scooters with real-time battery levels. Specifically, hourly solar potential is simulated based on a three-dimensional solar irradiation model so that photovoltaic (PV) electricity generation can be estimated when PV modules are installed at the parking stations, which enables solar charging when the origin–destination matrix of scooter-sharing trips is clustered and associated to the charging stations. As a case study in Singapore, the proposed solar charging system only needs 1–3 m2 PV modules at each station and 24%–67% of the total number of e-scooters to support almost all the real trips with a 98% reduction of trips used for charging. The system also supports 90% of on-demand mobility for at least three consecutive days without solar charging, which suggests the resilience of the system and inspires us to promote the proposed solar charging in the other global cities.

Suggested Citation

  • Zhu, Rui & Kondor, Dániel & Cheng, Cheng & Zhang, Xiaohu & Santi, Paolo & Wong, Man Sing & Ratti, Carlo, 2022. "Solar photovoltaic generation for charging shared electric scooters," Applied Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261922001854
    DOI: 10.1016/j.apenergy.2022.118728
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    Cited by:

    1. Le Trong Hieu & Ock Taeck Lim, 2024. "A Deep Learning Approach to Optimize the Performance and Power Demand of Electric Scooters under the Effect of Operating and Structure Parameters," Energies, MDPI, vol. 17(2), pages 1-19, January.
    2. Li, Yue & Luo, Hao & Cai, Hua, 2023. "Photovoltaic-battery powered bike share stations are not necessarily energy self-sufficient," Applied Energy, Elsevier, vol. 348(C).
    3. Elżbieta Macioszek & Maria Cieśla & Anna Granà, 2023. "Future Development of an Energy-Efficient Electric Scooter Sharing System Based on a Stakeholder Analysis Method," Energies, MDPI, vol. 16(1), pages 1-24, January.
    4. Ye, Yuxuan & Zhu, Rui & Yan, Jinyue & Lu, Lin & Wong, Man Sing & Luo, Wei & Chen, Min & Zhang, Fan & You, Linlin & Wang, Yafei & Qin, Zheng, 2023. "Planning the installation of building-integrated photovoltaic shading devices: A GIS-based spatiotemporal analysis and optimization approach," Renewable Energy, Elsevier, vol. 216(C).
    5. Yin, Rumeng & He, Jiang, 2023. "Design of a photovoltaic electric bike battery-sharing system in public transit stations," Applied Energy, Elsevier, vol. 332(C).
    6. Zhang, Kai & Chen, Min & Yang, Yue & Zhong, Teng & Zhu, Rui & Zhang, Fan & Qian, Zhen & Lü, Guonian & Yan, Jinyue, 2022. "Quantifying the photovoltaic potential of highways in China," Applied Energy, Elsevier, vol. 324(C).
    7. Zhu, Rui & Cheng, Cheng & Santi, Paolo & Chen, Min & Zhang, Xiaohu & Mazzarello, Martina & Wong, Man Sing & Ratti, Carlo, 2022. "Optimization of photovoltaic provision in a three-dimensional city using real-time electricity demand," Applied Energy, Elsevier, vol. 316(C).
    8. Arias-Rosales, Andrés & LeDuc, Philip R., 2023. "Urban solar harvesting: The importance of diffuse shadows in complex environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).

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