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Planning the installation of building-integrated photovoltaic shading devices: A GIS-based spatiotemporal analysis and optimization approach

Author

Listed:
  • Ye, Yuxuan
  • Zhu, Rui
  • Yan, Jinyue
  • Lu, Lin
  • Wong, Man Sing
  • Luo, Wei
  • Chen, Min
  • Zhang, Fan
  • You, Linlin
  • Wang, Yafei
  • Qin, Zheng

Abstract

Building-integrated photovoltaics (BIPV) can produce power while occupying little urban space. Photovoltaic-integrated shading devices (PVSDs) are a key component of BIPV that can generate electricity while blocking excess daylight. However, previous studies have lacked a systematic design of PVSDs that accurately estimates the trade-offs between indoor sunshade duration and electricity generation. This study proposes a multi-objective optimization framework for maximizing PV potential, minimizing PV area, and enabling proper sunshade duration in complex urban surfaces. A GIS-based spatiotemporal analysis and optimization approach was applied to three PVSD planning scenarios: (i) parallel to the horizontal land surface, (ii) inclined at an angle equal to the local latitude, and (iii) rotated in real-time to keep the PV surface perpendicular to the solar radiation. Different PV widths are determined under different scenarios considering power generation and solar shading duration. In the real-time rotating scenario, the optimized 0.7-m-wide PVSDs can generate 0.861 GWh of electricity annually, with a competitive average power generation efficiency of 0.811 kWh/m2/day and a solar shading duration of 6.61 h/day. Flexible installation scenarios are suggested to account for shading from other upper PVSDs and surrounding buildings. This study can facilitate solar farming in global cities and contribute to renewable energy penetration.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:renene:v:216:y:2023:i:c:s0960148123009989
    DOI: 10.1016/j.renene.2023.119084
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