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Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method

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  • Sredenšek, Klemen
  • Štumberger, Bojan
  • Hadžiselimović, Miralem
  • Mavsar, Primož
  • Seme, Sebastijan

Abstract

The main objective of this paper is to present a novel approach for determining PV potential through an optimization method. The novel approach considers the importance of technical and economic potential simultaneously for determining the optimal configuration of PV systems using a digital surface model. The integration of photovoltaic systems is conditioned mainly by the location and type of installation - configuration. Thus, more and more photovoltaic systems are being integrated into urban areas. For the further successful integration of photovoltaic systems into networks and the successful establishment of appropriate policies and directives, it is essential to properly assess the photovoltaic potential. Based on the described methodology, the potential determination is made for a completed area of 75,537 m2 using a digital surface model of the observed area. The annual values of physical, geographical, technical, and economic potentials were 19.57 GWh, 7.54 GWh, 875.50 MWh, and 19.64 kWh, respectively. The methodologies presented in the paper are based on detailed meteorological data for 20 years, a digital surface model of the observed area, a novel optimization approach, and multi-year data of electricity prices on markets. The presented results can be an excellent basis for further analyzes of determining the photovoltaic potential.

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  • Sredenšek, Klemen & Štumberger, Bojan & Hadžiselimović, Miralem & Mavsar, Primož & Seme, Sebastijan, 2022. "Physical, geographical, technical, and economic potential for the optimal configuration of photovoltaic systems using a digital surface model and optimization method," Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221032205
    DOI: 10.1016/j.energy.2021.122971
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    as
    1. Lazzeroni, Paolo & Moretti, Francesco & Stirano, Federico, 2020. "Economic potential of PV for Italian residential end-users," Energy, Elsevier, vol. 200(C).
    2. 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.
    3. Lukač, Niko & Seme, Sebastijan & Dežan, Katarina & Žalik, Borut & Štumberger, Gorazd, 2016. "Economic and environmental assessment of rooftops regarding suitability for photovoltaic systems installation based on remote sensing data," Energy, Elsevier, vol. 107(C), pages 854-865.
    4. Kurdgelashvili, Lado & Li, Junli & Shih, Cheng-Hao & Attia, Benjamin, 2016. "Estimating technical potential for rooftop photovoltaics in California, Arizona and New Jersey," Renewable Energy, Elsevier, vol. 95(C), pages 286-302.
    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. Mendis, Thushini & Huang, Zhaojian & Xu, Shen & Zhang, Weirong, 2020. "Economic potential analysis of photovoltaic integrated shading strategies on commercial building facades in urban blocks: A case study of Colombo, Sri Lanka," Energy, Elsevier, vol. 194(C).
    7. 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.
    8. Khan, Jibran & Arsalan, Mudassar Hassan, 2016. "Estimation of rooftop solar photovoltaic potential using geo-spatial techniques: A perspective from planned neighborhood of Karachi – Pakistan," Renewable Energy, Elsevier, vol. 90(C), pages 188-203.
    9. Xiaoyang Song & Yaohuan Huang & Chuanpeng Zhao & Yuxin Liu & Yanguo Lu & Yongguo Chang & Jie Yang, 2018. "An Approach for Estimating Solar Photovoltaic Potential Based on Rooftop Retrieval from Remote Sensing Images," Energies, MDPI, vol. 11(11), pages 1-14, November.
    10. 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.
    11. Lee, Minhyun & Hong, Taehoon & Jeong, Jaewook & Jeong, Kwangbok, 2018. "Development of a rooftop solar photovoltaic rating system considering the technical and economic suitability criteria at the building level," Energy, Elsevier, vol. 160(C), pages 213-224.
    12. Phuong Minh Khuong & Russell McKenna & Wolf Fichtner, 2020. "A Cost-Effective and Transferable Methodology for Rooftop PV Potential Assessment in Developing Countries," Energies, MDPI, vol. 13(10), pages 1-46, May.
    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. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    15. Ermolenko, Boris V. & Ermolenko, Georgy V. & Fetisova, Yulia A. & Proskuryakova, Liliana N., 2017. "Wind and solar PV technical potentials: Measurement methodology and assessments for Russia," Energy, Elsevier, vol. 137(C), pages 1001-1012.
    16. Wong, Man Sing & Zhu, Rui & Liu, Zhizhao & Lu, Lin & Peng, Jinqing & Tang, Zhaoqin & Lo, Chung Ho & Chan, Wai Ki, 2016. "Estimation of Hong Kong’s solar energy potential using GIS and remote sensing technologies," Renewable Energy, Elsevier, vol. 99(C), pages 325-335.
    17. 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.
    18. Srećković, Nevena & Lukač, Niko & Žalik, Borut & Štumberger, Gorazd, 2016. "Determining roof surfaces suitable for the installation of PV (photovoltaic) systems, based on LiDAR (Light Detection And Ranging) data, pyranometer measurements, and distribution network configuratio," Energy, Elsevier, vol. 96(C), pages 404-414.
    19. Xin-gang, Zhao & Yi-min, Xie, 2019. "The economic performance of industrial and commercial rooftop photovoltaic in China," Energy, Elsevier, vol. 187(C).
    20. 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.
    21. 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.
    22. Ko, Li & Wang, Jen-Chun & Chen, Chia-Yon & Tsai, Hsing-Yeh, 2015. "Evaluation of the development potential of rooftop solar photovoltaic in Taiwan," Renewable Energy, Elsevier, vol. 76(C), pages 582-595.
    23. 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.
    24. Primož Mavsar & Klemen Sredenšek & Bojan Štumberger & Miralem Hadžiselimović & Sebastijan Seme, 2019. "Simplified Method for Analyzing the Availability of Rooftop Photovoltaic Potential," Energies, MDPI, vol. 12(22), pages 1-17, November.
    25. Miranda, Raul F.C. & Szklo, Alexandre & Schaeffer, Roberto, 2015. "Technical-economic potential of PV systems on Brazilian rooftops," Renewable Energy, Elsevier, vol. 75(C), pages 694-713.
    26. Cheng, Liang & Zhang, Fangli & Li, Shuyi & Mao, Junya & Xu, Hao & Ju, Weimin & Liu, Xiaoqiang & Wu, Jie & Min, Kaifu & Zhang, Xuedong & Li, Manchun, 2020. "Solar energy potential of urban buildings in 10 cities of China," Energy, Elsevier, vol. 196(C).
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