A review of self-cleaning technology to reduce dust and ice accumulation in photovoltaic power generation using superhydrophobic coating

Yubo Wu, Jianqiang Du, Guangxin Liu, Danzhu Ma, Fengrui Jia, Jiří Jaromír Klemeš and Jin Wang

Renewable Energy, 2022, vol. 185, issue C, 1034-1061

Abstract: Photovoltaic (PV) power has become one of the most important methods of electricity generation using renewable sources to progress towards carbon emissions neutrality. However, the accumulation of dust seriously affects the visible light transmittance of glass, which significantly decreases the power generation efficiency of PV modules. As promising passive cleaning solutions, a superhydrophobic coating can be used to effectively reduce the surface adhesion rate of dust due to special micro-nano structures and low surface energy. The superhydrophobic coating on the glass surface shows great application values for material requirements of high light transmission and mechanical stability. This paper summarises problems of dust and ice accumulation and its cleaning technologies for PV modules, and the basic principle and development of superhydrophobicity are introduced. Preparation methods and design features of rough structures in the past decade are emphatically discussed. The superhydrophobic coating generally has a good light transmission up to 98.7%, which is higher than that of the original glass. However, their application is limited by the shape of their applicable object and by their durability. Most coatings can only be applied to planar modules, and their durability needs to be further improved, with coatings capable of withstanding a maximum of 6H hardness appearing. The preparation methods and their applications of the superhydrophobic coating are summarized and prospected.

Keywords: PV power generation; PV superhydrophobic coating; PV dust and ice accumulation; PV self-cleaning; Transparency and stability (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:185:y:2022:i:c:p:1034-1061

DOI: 10.1016/j.renene.2021.12.123

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