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Optimisation for large-scale photovoltaic arrays’ placement based on Light Detection And Ranging data

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  • Lukač, Niko
  • Špelič, Denis
  • Štumberger, Gorazd
  • Žalik, Borut

Abstract

The availability of high-resolution LiDAR (Light Detection And Ranging) geospatial data has increased immensely, providing new opportunities to solve challenges in the field of spatial energy planning. This paper presents a new method for large-scale placement of photovoltaic arrays over buildings’ rooftops in an optimal manner by using the global optimisation approach. The position, aspect and slope are the ey geometrical parameters being optimised for each photovoltaic array. The predicted energy generation (i.e. photovoltaic potential) is simulated by using state-of-the-art hourly shadowing estimation from the surroundings, anisotropic diffuse, reflected, and direct irradiances that are based on a Typical Meteorological Year, and non-linear efficiency characteristics of a considered photovoltaic system configuration. The optimisation performs multiple simulation scenarios throughout an entire year for each photovoltaic array, in order to maximise its photovoltaic potential. The method was tested over three LiDAR datasets with different landscape topographies and urban densities. In comparison to the methods for photovoltaic arrays’ fixed optimal slope estimation, the proposed method is substantially more suitable for application in urban environments.

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  • 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).
    • Handle: RePEc:eee:appene:v:263:y:2020:i:c:s0306261920301045
    DOI: 10.1016/j.apenergy.2020.114592
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    1. 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.
    2. Amaro e Silva, R. & Brito, M.C., 2019. "Spatio-temporal PV forecasting sensitivity to modules’ tilt and orientation," Applied Energy, Elsevier, vol. 255(C).
    3. Gao, Hao & Koch, Christian & Wu, Yupeng, 2019. "Building information modelling based building energy modelling: A review," Applied Energy, Elsevier, vol. 238(C), pages 320-343.
    4. Zhang, Ji & Xu, Le & Shabunko, Veronika & Tay, Stephen En Rong & Sun, Huixuan & Lau, Stephen Siu Yu & Reindl, Thomas, 2019. "Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city," Applied Energy, Elsevier, vol. 240(C), pages 513-533.
    5. Huang, Zhaojian & Mendis, Thushini & Xu, Shen, 2019. "Urban solar utilization potential mapping via deep learning technology: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 250(C), pages 283-291.
    6. Ziar, Hesan & Sönmez, Furkan Fatih & Isabella, Olindo & Zeman, Miro, 2019. "A comprehensive albedo model for solar energy applications: Geometric spectral albedo," Applied Energy, Elsevier, vol. 255(C).
    7. Toroghi, Shahaboddin H. & Oliver, Matthew E., 2019. "Framework for estimation of the direct rebound effect for residential photovoltaic systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    8. Lee, Minhyun & Hong, Taehoon & Jeong, Kwangbok & Kim, Jimin, 2018. "A bottom-up approach for estimating the economic potential of the rooftop solar photovoltaic system considering the spatial and temporal diversity," Applied Energy, Elsevier, vol. 232(C), pages 640-656.
    9. Lukač, Niko & Seme, Sebastijan & Žlaus, Danijel & Štumberger, Gorazd & Žalik, Borut, 2014. "Buildings roofs photovoltaic potential assessment based on LiDAR (Light Detection And Ranging) data," Energy, Elsevier, vol. 66(C), pages 598-609.
    10. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    11. Kovač, Marko & Stegnar, Gašper & Al-Mansour, Fouad & Merše, Stane & Pečjak, Andrej, 2019. "Assessing solar potential and battery instalment for self-sufficient buildings with simplified model," Energy, Elsevier, vol. 173(C), pages 1182-1195.
    12. Mohajeri, N. & Gudmundsson, A. & Kunckler, T. & Upadhyay, G. & Assouline, D. & Kämpf, J.H & Scartezzini, J.L., 2019. "A solar-based sustainable urban design: The effects of city-scale street-canyon geometry on solar access in Geneva, Switzerland," Applied Energy, Elsevier, vol. 240(C), pages 173-190.
    13. 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.
    14. Felix Creutzig & Peter Agoston & Jan Christoph Goldschmidt & Gunnar Luderer & Gregory Nemet & Robert C. Pietzcker, 2017. "The underestimated potential of solar energy to mitigate climate change," Nature Energy, Nature, vol. 2(9), pages 1-9, September.
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    4. 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).
    5. Bala Bhavya Kausika & Wilfried G. J. H. M. van Sark, 2021. "Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands," Energies, MDPI, vol. 14(7), pages 1-16, March.
    6. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).
    7. Chen, Yixing & Deng, Zhang & Hong, Tianzhen, 2020. "Automatic and rapid calibration of urban building energy models by learning from energy performance database," Applied Energy, Elsevier, vol. 277(C).
    8. Ren, Haoshan & Sun, Yongjun & Norman Tse, Chung Fai & Fan, Cheng, 2023. "Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities," Energy, Elsevier, vol. 274(C).
    9. 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).
    10. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    11. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

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