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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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    Cited by:

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    5. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    6. Lodhi, Muhammad Kamran & Tan, Yumin & Wang, Xiaolu & Masum, Syed Muhammad & Nouman, Khan Muhammad & Ullah, Nasim, 2024. "Harnessing rooftop solar photovoltaic potential in Islamabad, Pakistan: A remote sensing and deep learning approach," Energy, Elsevier, vol. 304(C).
    7. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).

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