Author(s)

Mamadou Tamboura, Ekaterina Alexandrovna Gosteva

Institute of New Materials, National University of Science and Technology MISIS, Moscow, Russia.

Dust deposition on the surface of photovoltaic (PV) cells poses a significant challenge to their efficiency, especially in arid regions characterized by desert and semi-desert conditions. Despite the pronounced impact of dust accumulation, these regions offer optimal solar radiation and minimal cloud cover, making them ideal candidates for widespread PV cell deployment. Various surface cleaning methods exist, each employing distinct approaches. Choosing an appropriate cleaning method requires a comprehensive understanding of the mechanisms involved in both dust deposition on module surfaces and dust adhesion to PV cell surfaces. The mechanisms governing dust deposition and adhesion are complex and multifaceted, influenced by factors such as the nature and properties of the dust particles, environmental climatic conditions, characteristics of protective coatings, and the specific location of the PV installation. These factors exhibit regional variations, necessitating the implementation of diverse cleaning approaches tailored to the unique conditions of each location. The first part of this article explores the factors influencing dust deposition on PV cell surfaces, delving into the intricate interplay of environmental variables and particle characteristics. Subsequently, the second part addresses various cleaning methods, offering an analysis of their respective advantages and disadvantages. By comprehensively examining the factors influencing dust accumulation and evaluating the effectiveness of different cleaning strategies, this article aims to contribute valuable insights to the ongoing efforts to optimize the performance and longevity of photovoltaic systems in diverse geographical contexts.

Solar Energy, Dust Deposition, Cleaning Methods

Tamboura, M. and Gosteva, E. (2024) Overview of Factors Affecting Dust Deposition on Photovoltaic Cells and Cleaning Methods. Smart Grid and Renewable Energy, 15, 49-78. doi: 10.4236/sgre.2024.151004.