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A scalable method for estimating rooftop solar irradiation potential over large regions

Author

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  • Buffat, René
  • Grassi, Stefano
  • Raubal, Martin

Abstract

An estimate of solar irradiation potential over large regions requires the knowledge of the long-term spatio-temporal distribution of the solar radiation as well as the identification of the suitable surfaces where the photovoltaic (PV) installations can be built. These main components can be modelled in different ways and are thus affected by different sources of uncertainty. Thus, when estimating the exploitable potential over large regions, it is important to measure the accuracy of the entire process. In this work, we provide a generic method to estimate the solar irradiation potential of rooftops over large regions and an estimate of the corresponding uncertainties when calculating the long-term electricity generation of PV plants. This method uses satellite based solar radiation data covering a period of 22 years, with a temporal resolution of 30 min and a spatial resolution of 3.8–5.6 km. Suitable surfaces on rooftops are identified using Digital Surface Models combined with building footprints. This allows to determine the geometry of rooftops, such as slope, and orientation with a spatial resolution of 0.5 m. Finally, we calculated the electricity generation based on models which take into account all characteristics of PV system components. In order to estimate the accuracy of the model for electricity production, we compared the monthly generation of 500 PV plants in Switzerland consisting of different PV technologies (mono-crystalline, poly-crystalline and thin film) with the estimates. The validation results show a correlation coefficient (R2) of 0.9 and a median monthly relative error between 0.28% (August) and 28.08% (December). The monthly estimates are more accurate during summer time, while spatially and technology-wise no significant differences are found.

Suggested Citation

  • Buffat, René & Grassi, Stefano & Raubal, Martin, 2018. "A scalable method for estimating rooftop solar irradiation potential over large regions," Applied Energy, Elsevier, vol. 216(C), pages 389-401.
    • Handle: RePEc:eee:appene:v:216:y:2018:i:c:p:389-401
    DOI: 10.1016/j.apenergy.2018.02.008
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    References listed on IDEAS

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    1. Yadav, Amit Kumar & Chandel, S.S., 2013. "Tilt angle optimization to maximize incident solar radiation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 503-513.
    2. Suomalainen, Kiti & Wang, Vincent & Sharp, Basil, 2017. "Rooftop solar potential based on LiDAR data: Bottom-up assessment at neighbourhood level," Renewable Energy, Elsevier, vol. 111(C), pages 463-475.
    3. Myers, Daryl R., 2005. "Solar radiation modeling and measurements for renewable energy applications: data and model quality," Energy, Elsevier, vol. 30(9), pages 1517-1531.
    4. Lingfors, D. & Bright, J.M. & Engerer, N.A. & Ahlberg, J. & Killinger, S. & Widén, J., 2017. "Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis," Applied Energy, Elsevier, vol. 205(C), pages 1216-1230.
    5. Arnette, Andrew N., 2013. "Integrating rooftop solar into a multi-source energy planning optimization model," Applied Energy, Elsevier, vol. 111(C), pages 456-467.
    6. Gooding, James & Crook, Rolf & Tomlin, Alison S., 2015. "Modelling of roof geometries from low-resolution LiDAR data for city-scale solar energy applications using a neighbouring buildings method," Applied Energy, Elsevier, vol. 148(C), pages 93-104.
    7. Jacques, David A. & Gooding, James & Giesekam, Jannik J. & Tomlin, Alison S. & Crook, Rolf, 2014. "Methodology for the assessment of PV capacity over a city region using low-resolution LiDAR data and application to the City of Leeds (UK)," Applied Energy, Elsevier, vol. 124(C), pages 28-34.
    8. Lu, Ning & Qin, Jun & Yang, Kun & Sun, Jiulin, 2011. "A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data," Energy, Elsevier, vol. 36(5), pages 3179-3188.
    9. Cole, Wesley & Lewis, Haley & Sigrin, Ben & Margolis, Robert, 2016. "Interactions of rooftop PV deployment with the capacity expansion of the bulk power system," Applied Energy, Elsevier, vol. 168(C), pages 473-481.
    10. Peng, Jinqing & Lu, Lin, 2013. "Investigation on the development potential of rooftop PV system in Hong Kong and its environmental benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 149-162.
    11. Sun, Yan-wei & Hof, Angela & Wang, Run & Liu, Jian & Lin, Yan-jie & Yang, De-wei, 2013. "GIS-based approach for potential analysis of solar PV generation at the regional scale: A case study of Fujian Province," Energy Policy, Elsevier, vol. 58(C), pages 248-259.
    12. Hong, Taehoon & Lee, Minhyun & Koo, Choongwan & Jeong, Kwangbok & Kim, Jimin, 2017. "Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis," Applied Energy, Elsevier, vol. 194(C), pages 320-332.
    13. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    14. 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.
    15. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    16. Demain, Colienne & Journée, Michel & Bertrand, Cédric, 2013. "Evaluation of different models to estimate the global solar radiation on inclined surfaces," Renewable Energy, Elsevier, vol. 50(C), pages 710-721.
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