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Analysis of dust deposition law at the micro level and its impact on the annual performance of photovoltaic modules

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  • Yao, Wanxiang
  • Xu, Ai
  • Kong, Xiangru
  • Wang, Yan
  • Li, Xianli
  • Gao, Weijun

Abstract

Solar photovoltaic (PV) power generation is a promising clean energy technology, but dust affects its performance. This study, conducted in nine Chinese cities with varying climates and geographies, analyzed the physicochemical properties of dust and its deposition patterns at different inclination angles using microanalysis. Inclination angles were classified into three categories, and a dust density prediction (DDP) model was developed for each. The study also quantified the effect of different dust densities on PV module temperature in a controlled environment and created a PV module output power prediction (POPP) model to assess dust accumulation impacts on PV efficiency. It was found that dust insulation could increase PV module output power by 1.64 %–1.79 % at dust densities of 5–10 g/m2 and radiation levels of 400–800 W/m2. Combining the DDP and POPP models, a PV efficiency loss rate model was proposed to evaluate annual average PV efficiency and power generation losses at different tilting angles. Results showed an annual average PV efficiency loss of over 7 % at a 15° inclination angle and about 4.5 % at a 90° inclination angle. This study provides a framework for evaluating PV module mounting angles and cleaning intervals, optimizing solar energy systems for improved efficiency and returns.

Suggested Citation

  • Yao, Wanxiang & Xu, Ai & Kong, Xiangru & Wang, Yan & Li, Xianli & Gao, Weijun, 2024. "Analysis of dust deposition law at the micro level and its impact on the annual performance of photovoltaic modules," Energy, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:energy:v:306:y:2024:i:c:s0360544224022229
    DOI: 10.1016/j.energy.2024.132448
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    2. Hui, Tan Kah & Hwee, Ah Khoon & QingHua, Liu & Jing, Khoo Terh & Ohueri, Chukwuka Christian, 2026. "From state-of-the-art to next-generation solutions: A review for Gen-AI integration in photovoltaic maintenance," Applied Energy, Elsevier, vol. 402(PB).
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