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A novel model to determine the relationship between dust concentration and energy conversion efficiency of photovoltaic (PV) panels

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  • Fan, Siyuan
  • Wang, Xiao
  • Cao, Shengxian
  • Wang, Yu
  • Zhang, Yanhui
  • Liu, Bingzheng

Abstract

Dust accumulation reduces the energy conversion efficiency of photovoltaic (PV) panels and their safe operation. In this paper, a novel dust concentration and energy conversion efficiency (DC-ECE) model is proposed to estimate the effect of dust accumulation on PV generation performance. The effects of wind, particle flow, and dust deposition on the PV panels are investigated by computational fluid dynamics (CFD). The realizable k-ϵ model and discrete phase model (DPM) are used to predict the turbulent flow and dust deposition, respectively. An on-site wind tunnel experiment is designed to verify the effectiveness of the CFD simulation. Moreover, an expression of the deposition rate is developed to analyze the effects of wind speed and particle diameter on dust concentration. The results indicate that the spiral vortex of the dust particles becomes more pronounced and gradually diffuses as the installation angle increases. The length and angle of the vortex at the rear of the PV panel reach the maximum at a wind speed of 3.9 m/s and an installation angle of 45°. When the dust particle diameter is less than 120 μm, the wind speed has the largest impact on the conversion efficiency of the PV panels, and the conversion efficiency has a linear relationship with the deposition time. The conversion efficiency loss increases with an increase in the dust particle diameter and the wind speed. The maximum conversion efficiency loss of the PV panels is 72.9%.

Suggested Citation

  • Fan, Siyuan & Wang, Xiao & Cao, Shengxian & Wang, Yu & Zhang, Yanhui & Liu, Bingzheng, 2022. "A novel model to determine the relationship between dust concentration and energy conversion efficiency of photovoltaic (PV) panels," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222008301
    DOI: 10.1016/j.energy.2022.123927
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    References listed on IDEAS

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    Cited by:

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    5. Zhao, Weiping & Lv, Yukun & Dong, Zhiguang & Zhao, Fang & Lv, Fengyong & Yan, Weiping, 2024. "Effect of self-cleaning superhydrophobic coating on dust deposition and performance of PV modules," Renewable Energy, Elsevier, vol. 227(C).
    6. Tang, Wuqin & Yang, Qiang & Dai, Zhou & Yan, Wenjun, 2024. "Module defect detection and diagnosis for intelligent maintenance of solar photovoltaic plants: Techniques, systems and perspectives," Energy, Elsevier, vol. 297(C).
    7. Gowtham Vedulla & Anbazhagan Geetha & Ramalingam Senthil, 2022. "Review of Strategies to Mitigate Dust Deposition on Solar Photovoltaic Systems," Energies, MDPI, vol. 16(1), pages 1-28, December.
    8. Iman El-Mahallawi & Engy Elshazly & Mohamed Ramadan & Reem Nasser & Moaaz Yasser & Seif El-Badry & Mahmoud Elthakaby & Olugbenga Timo Oladinrin & Muhammad Qasim Rana, 2022. "Solar PV Panels-Self-Cleaning Coating Material for Egyptian Climatic Conditions," Sustainability, MDPI, vol. 14(17), pages 1-13, September.
    9. Tehseen Mazhar & Hafiz Muhammad Irfan & Sunawar Khan & Inayatul Haq & Inam Ullah & Muhammad Iqbal & Habib Hamam, 2023. "Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods," Future Internet, MDPI, vol. 15(2), pages 1-37, February.

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