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The Upper Limit of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study

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  • Amro M Elshurafa

    (King Abdullah Petroleum Studies and Research Center (KAPSARC), Riyadh 11672, Saudi Arabia)

  • Abdel Rahman Muhsen

    (King Abdullah Petroleum Studies and Research Center (KAPSARC), Riyadh 11672, Saudi Arabia)

Abstract

Rooftop solar photovoltaic (PV) systems, commonly referred to as distributed generation (DG) solar systems, are deemed important contenders in future sustainable cities. Because deploying DG systems is associated with technical, financial, policy, and market implications that impact utilities, governments, and businesses, quantifying the potential of DG systems that could be deployed in a certain jurisdiction ex ante helps inform the decision-making process for all stakeholders. To that end, the upper limit of rooftop PV systems that could be deployed in Riyadh, the capital of Saudi Arabia, was assessed with the aid of geographic information systems (GIS). By relying on urban land lot data for different categories, i.e., zones, and the maximum allowable area that could be built within a certain lot using prevailing building codes and regulations, the rooftop area suitable for PV deployment within Riyadh Metro was quantified. The analysis was restricted to rooftops in residential, mosque, shopping mall, and health care buildings only. Following the quantification of the rooftop area, the upper limit of rooftop solar PV capacity that can be deployed in the city of Riyadh was found to be 4.34 GW. This capacity represents nearly 22% of the peak load and can satisfy approximately 9% of the energy requirement in the central region, the region in which Riyadh resides.

Suggested Citation

  • Amro M Elshurafa & Abdel Rahman Muhsen, 2019. "The Upper Limit of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4301-:d:256038
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    3. Elshurafa, Amro M. & Alatawi, Hatem & Hasanov, Fakhri J. & Algahtani, Goblan J. & Felder, Frank A., 2022. "Cost, emission, and macroeconomic implications of diesel displacement in the Saudi agricultural sector: Options and policy insights," Energy Policy, Elsevier, vol. 168(C).
    4. Abdulaziz Alhammad & Qian (Chayn) Sun & Yaguang Tao, 2022. "Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study," Energies, MDPI, vol. 15(1), pages 1-21, January.
    5. 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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