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Framework for Mapping and Optimizing the Solar Rooftop Potential of Buildings in Urban Systems

Author

Listed:
  • Nima Narjabadifam

    (Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Mohammed Al-Saffar

    (Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Yongquan Zhang

    (Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Joseph Nofech

    (Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Asdrubal Cheng Cen

    (Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Hadia Awad

    (National Research Council Canada, Ottawa, ON K1V 1J8, Canada)

  • Michael Versteege

    (Energy Management & Sustainable Operations, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Mustafa Gül

    (Department of Civil & Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

The accurate prediction of the solar energy that can be generated using the rooftops of buildings is an essential tool for many researchers, decision makers, and investors for creating sustainable cities and societies. This study is focused on the development of an automated method to extract the useable areas of rooftops and optimize the solar PV panel layout based on the given electricity loading of a building. In this context, the authors of this article developed two crucial methods. First, a special pixel-based rooftop recognition methodology was developed to analyze detailed and complex rooftop types while avoiding the challenges associated with the nature of the particular building rooftops. Second, a multi-objective enveloped min–max optimization algorithm was developed to maximize solar energy generation and minimize energy cost in terms of payback based on the marginal price signals. This optimization algorithm facilitates the optimal integration of three controlled variables—tilt angle, azimuth angle, and inter-row spacing—under a non-linear optimization space. The performance of proposed algorithms is demonstrated using three campus buildings at the University of Alberta, Edmonton, Alberta, Canada as case studies. It is shown that the proposed algorithms can be used to optimize PV panel distribution while effectively maintaining system constraints.

Suggested Citation

  • Nima Narjabadifam & Mohammed Al-Saffar & Yongquan Zhang & Joseph Nofech & Asdrubal Cheng Cen & Hadia Awad & Michael Versteege & Mustafa Gül, 2022. "Framework for Mapping and Optimizing the Solar Rooftop Potential of Buildings in Urban Systems," Energies, MDPI, vol. 15(5), pages 1-32, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1738-:d:758893
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    References listed on IDEAS

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    4. Dirk Johan van Vuuren & Annlizé L. Marnewick & Jan Harm C. Pretorius, 2021. "A Financial Evaluation of a Multiple Inclination, Rooftop-Mounted, Photovoltaic System Where Structured Tariffs Apply: A Case Study of a South African Shopping Centre," Energies, MDPI, vol. 14(6), pages 1-26, March.
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    Cited by:

    1. Özdemir, Samed & Yavuzdoğan, Ahmet & Bilgilioğlu, Burhan Baha & Akbulut, Zeynep, 2023. "SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data," Renewable Energy, Elsevier, vol. 216(C).
    2. Formolli, M. & Kleiven, T. & Lobaccaro, G., 2023. "Assessing solar energy accessibility at high latitudes: A systematic review of urban spatial domains, metrics, and parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 177(C).
    3. Alharbi, Abdulaziz & Awwad, Zeyad & Habib, Abdulelah & de Weck, Olivier, 2023. "Economical sizing and multi-azimuth layout optimization of grid-connected rooftop photovoltaic systems using Mixed-Integer Programming," Applied Energy, Elsevier, vol. 335(C).
    4. Zhou, Y. & Wilmink, D. & Zeman, M. & Isabella, O. & Ziar, H., 2023. "A geographic information system-based large scale visibility assessment tool for multi-criteria photovoltaic planning on urban building roofs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

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