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A spatial optimization approach to increase the accuracy of rooftop solar energy assessments

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  • Zhong, Qing
  • Nelson, Jake R.
  • Tong, Daoqin
  • Grubesic, Tony H.

Abstract

Recent years have seen a substantial increase in energy produced by renewable sources. The International Energy Agency (IEA) expects a large portion of future growth in renewable energy to come from solar, especially rooftop photovoltaic (PV) systems. Studies focused on estimating rooftop solar energy potential generally use the total area available for PV installation as determined by solar irradiance availability. This process can lead to substantial over- or under-estimation of energy estimates. Only a few studies have incorporated the spatial layout of PV panels in the solar energy generation estimates, and none have simultaneously considered PV panel size, orientation, and rooftop structure. We address this limitation with a new spatially explicit optimization framework to enhance the accuracy of rooftop solar energy assessments. We consider both the roof's structural configuration and the shape and size of the panels in a novel maximum cover spatial optimization model. After applying the framework to three different types of rooftops (flat roof, pitched roof, and complex roof), we find that conventional methods can lead to a nearly 60% overestimation of energy potential compared to the optimized panel layout. Our work illustrates the importance of considering panel size and rooftop characteristics and offers a mechanism for designing more efficient rooftop PV systems.

Suggested Citation

  • Zhong, Qing & Nelson, Jake R. & Tong, Daoqin & Grubesic, Tony H., 2022. "A spatial optimization approach to increase the accuracy of rooftop solar energy assessments," Applied Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922005062
    DOI: 10.1016/j.apenergy.2022.119128
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    1. Diane Palmer & Elena Koumpli & Ian Cole & Ralph Gottschalg & Thomas Betts, 2018. "A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry," Energies, MDPI, vol. 11(12), pages 1-22, December.
    2. Narayanan, Arun & Mets, Kevin & Strobbe, Matthias & Develder, Chris, 2019. "Feasibility of 100% renewable energy-based electricity production for cities with storage and flexibility," Renewable Energy, Elsevier, vol. 134(C), pages 698-709.
    3. Freitas, S. & Catita, C. & Redweik, P. & Brito, M.C., 2015. "Modelling solar potential in the urban environment: State-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 915-931.
    4. Regnier, Eva, 2007. "Oil and energy price volatility," Energy Economics, Elsevier, vol. 29(3), pages 405-427, May.
    5. 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.
    6. Kavlak, Goksin & McNerney, James & Trancik, Jessika E., 2018. "Evaluating the causes of cost reduction in photovoltaic modules," Energy Policy, Elsevier, vol. 123(C), pages 700-710.
    7. Elham Fakhraian & Marc Alier & Francesc Valls Dalmau & Alireza Nameni & Maria José Casañ Guerrero, 2021. "The Urban Rooftop Photovoltaic Potential Determination," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    8. Lukač, Niko & Špelič, Denis & Štumberger, Gorazd & Žalik, Borut, 2020. "Optimisation for large-scale photovoltaic arrays’ placement based on Light Detection And Ranging data," Applied Energy, Elsevier, vol. 263(C).
    9. Jaehyung An & Fe Amor Parel Gudmundsson & Natalia Sokolinskaya & Sergey Prosekov & Artur Meynkhard & Au?unn Arn rsson & Lernui Simonyan, 2020. "Efficiency of Renewable Energy Plants in Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 81-88.
    10. ., 2020. "Renewables and CO2 emissions," Chapters, in: Energy Transitions in Mediterranean Countries, chapter 5, pages 84-112, Edward Elgar Publishing.
    11. 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.
    12. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    13. Byrne, John & Taminiau, Job & Kurdgelashvili, Lado & Kim, Kyung Nam, 2015. "A review of the solar city concept and methods to assess rooftop solar electric potential, with an illustrative application to the city of Seoul," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 830-844.
    14. Zhong, Qing & Tong, Daoqin, 2020. "Spatial layout optimization for solar photovoltaic (PV) panel installation," Renewable Energy, Elsevier, vol. 150(C), pages 1-11.
    15. Kucuksari, Sadik & Khaleghi, Amirreza M. & Hamidi, Maryam & Zhang, Ye & Szidarovszky, Ferenc & Bayraksan, Guzin & Son, Young-Jun, 2014. "An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments," Applied Energy, Elsevier, vol. 113(C), pages 1601-1613.
    16. Aslani, Mohammad & Seipel, Stefan, 2022. "Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment," Applied Energy, Elsevier, vol. 306(PA).
    17. Bódis, Katalin & Kougias, Ioannis & Jäger-Waldau, Arnulf & Taylor, Nigel & Szabó, Sándor, 2019. "A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    18. 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.
    19. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    20. Hafez, A.Z. & Soliman, A. & El-Metwally, K.A. & Ismail, I.M., 2017. "Tilt and azimuth angles in solar energy applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 147-168.
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    5. Tan, Hongjun & Guo, Zhiling & Zhang, Haoran & Chen, Qi & Lin, Zhenjia & Chen, Yuntian & Yan, Jinyue, 2023. "Enhancing PV panel segmentation in remote sensing images with constraint refinement modules," Applied Energy, Elsevier, vol. 350(C).
    6. Nikolaos Nagkoulis & Eva Loukogeorgaki & Michela Ghislanzoni, 2022. "Genetic Algorithms-Based Optimum PV Site Selection Minimizing Visual Disturbance," Sustainability, MDPI, vol. 14(19), pages 1-19, October.

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