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Design of Building-Installed PV system, with consideration given to the size and position of the building

Author

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  • Shao, Chuanyong
  • Migan-Dubois, Anne
  • Diallo, Demba

Abstract

Building-installed Photovoltaic (BPV) provides solutions to improving energy flexibility in urban areas. There are two problems: 1) the simple view factor is inapplicable to cities; 2) Partial Shading Conditions (PSCs) are the main cause of power loss in urban areas. Moreover, the PSCs increase the difficulties in estimating performance and designing a BPV system with higher efficiency. This work implements the Building Environment Analysis Tool (BEA) to help design BPV configurations and proposes installation strategies under dynamic PSCs. This work designs a dual-building model to study the performance of the BPV system under PSCs. In addition, the paper considers the series-parallel (SP) and the total-cross-tied (TCT) for the PV system’s configuration to mitigate the PSCs’ effect. Based on the PV configuration and the double building model, this article studies the impact of the height and shape of the neighboring building on the performance of the BPV system. Based on the simulation results, this article provides guidelines for designing more efficient BPV systems, considering their environment.

Suggested Citation

  • Shao, Chuanyong & Migan-Dubois, Anne & Diallo, Demba, 2025. "Design of Building-Installed PV system, with consideration given to the size and position of the building," Renewable Energy, Elsevier, vol. 250(C).
    • Handle: RePEc:eee:renene:v:250:y:2025:i:c:s0960148125008055
    DOI: 10.1016/j.renene.2025.123143
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    References listed on IDEAS

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    1. Bamisile, Olusola & Acen, Caroline & Cai, Dongsheng & Huang, Qi & Staffell, Iain, 2025. "The environmental factors affecting solar photovoltaic output," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    2. Jin-Hee Kim & Ha-Ryeon Kim & Jun-Tae Kim, 2015. "Analysis of Photovoltaic Applications in Zero Energy Building Cases of IEA SHC/EBC Task 40/Annex 52," Sustainability, MDPI, vol. 7(7), pages 1-19, July.
    3. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
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    Cited by:

    1. Xi Chen & Hai Long, 2025. "Optimal Placement of Distributed Solar PV Adapting to Electricity Real-Time Market Operation," Sustainability, MDPI, vol. 17(15), pages 1-19, July.

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