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Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)

Author

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  • Sarawut Ninsawat

    (Remote Sensing and Geographic Information Systems (RS&GIS) FoS, Asian Institute of Technology (AIT), P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

  • Mohammad Dalower Hossain

    (Environmental Engineering and Management (EEM) FoS, Asian Institute of Technology (AIT), P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand)

Abstract

In an urban area, the roof is the only available surface that can be utilized for installing solar photovoltaics (PV), and the active surface area depends on the type of roof. Shadows on a solar panel can be caused by nearby tall buildings, construction materials such as water tanks, or the roof configuration itself. The azimuth angle of the sun varies, based on the season and the time of day. Therefore, the simulation of shadow for one or two days or using the rule of thumb may not be sufficient to evaluate shadow effects on solar panels throughout the year. In this paper, a methodology for estimating the solar potential of solar PV on rooftops is presented, which is particularly applicable to urban areas. The objective of this method is to assess how roof type and shadow play a role in potentiality and financial benefit. The method starts with roof type extraction from high-resolution satellite imagery, using Object Base Image Analysis (OBIA), the generation of a 3D structure from height data and roof type, the simulation of shadow throughout the year, and the identification of potential and financial prospects. Based on the results obtained, the system seems to be adequate for calculating the financial benefits of solar PV to a very fine scale. The payback period varied from 7–13 years depending on the roof type, direction, and shadow impact. Based on the potentiality, a homeowner can make a profit of up to 200%. This method could help homeowners to identify potential roof area and economic interest.

Suggested Citation

  • Sarawut Ninsawat & Mohammad Dalower Hossain, 2016. "Identifying Potential Area and Financial Prospects of Rooftop Solar Photovoltaics (PV)," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:8:y:2016:i:10:p:1068-:d:81114
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    References listed on IDEAS

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    1. Jo, J.H. & Otanicar, T.P., 2011. "A hierarchical methodology for the mesoscale assessment of building integrated roof solar energy systems," Renewable Energy, Elsevier, vol. 36(11), pages 2992-3000.
    2. Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.
    3. Shafiullah, G.M. & Amanullah, M.T.O. & Shawkat Ali, A.B.M. & Jarvis, Dennis & Wolfs, Peter, 2012. "Prospects of renewable energy – a feasibility study in the Australian context," Renewable Energy, Elsevier, vol. 39(1), pages 183-197.
    4. Hofierka, Jaroslav & Kaňuk, Ján, 2009. "Assessment of photovoltaic potential in urban areas using open-source solar radiation tools," Renewable Energy, Elsevier, vol. 34(10), pages 2206-2214.
    5. Stevanović, Sanja, 2013. "Optimization of passive solar design strategies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 177-196.
    6. Ordóñez, J. & Jadraque, E. & Alegre, J. & Martínez, G., 2010. "Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 2122-2130, September.
    7. Muhammad-Sukki, Firdaus & Ramirez-Iniguez, Roberto & Munir, Abu Bakar & Mohd Yasin, Siti Hajar & Abu-Bakar, Siti Hawa & McMeekin, Scott G. & Stewart, Brian G., 2013. "Revised feed-in tariff for solar photovoltaic in the United Kingdom: A cloudy future ahead?," Energy Policy, Elsevier, vol. 52(C), pages 832-838.
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    2. Wenyang Han & Meng Han & Menglong Zhang & Ying Zhao & Kai Xie & Yin Zhang, 2024. "Historic Building Renovation with Solar System towards Zero-Energy Consumption: Feasibility Analysis and Case Optimization Practice in China," Sustainability, MDPI, vol. 16(3), pages 1-16, February.
    3. Dalibor Dobrilovic & Jasmina Pekez & Eleonora Desnica & Ljiljana Radovanovic & Ivan Palinkas & Milica Mazalica & Luka Djordjević & Sinisa Mihajlovic, 2023. "Data Acquisition for Estimating Energy-Efficient Solar-Powered Sensor Node Performance for Usage in Industrial IoT," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    4. Nima Monghasemi & Amir Vadiee & Konstantinos Kyprianidis & Elaheh Jalilzadehazhari, 2023. "Rank-Based Assessment of Grid-Connected Rooftop Solar Panel Deployments Considering Scenarios for a Postponed Installation," Energies, MDPI, vol. 16(21), pages 1-16, October.

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