Dust impact on solar PV performance: A critical review of optimal cleaning techniques for yield enhancement across varied environmental conditions

Solar Photovoltaic (PV) systems' installations are growing from pico-solar to large grid-connected systems continuously all over the world. The growth is aided by increasing environmental concerns and favorable governments' policies. However, the power loss from these systems is threatened by several environmental factors, with dust effect being the most significant. Solar PV cleaning technique aims to boost the energy yield of the system and its performance. In this article, promising dust cleaning techniques based on performance parameters across varied climatic conditions and environmental factors are investigated. Particular emphasis was placed on mathematical model correlations to evaluate dust deposition and their effects. It turns out that the energy losses per day due to this impact are up to 2.8-50 % depending to soiling density by applying the methodology of mathematical correlations in the weather conditions of Middle East, Asia and Australia. It noted that the estimated economic losses per PV cleaning system in these regions have costed from 0.0011 to 22.43 US $/m2. This review considered all these performance indicators to propose optimal PV cleaning techniques based on mathematical models to find the best cleaning frequency. The optimization would definitely decrease the operation and maintenance (O&M) cost in relation to the life cycle of the PV system installation. The major challenges, limitations and strengths of each PV cleaning approaches are discussed, with the review establishing that dust accumulation significantly influences the PV power loss, efficiency and lifespan of the PV system. Before implementing any possible cleaning system, it is advisable to know the dust properties and their characteristic parameters of the localized study area. Further, PV panels cleaning system based on water is inappropriate where water is scarce or even unavailable, while robotic based cleaning presents difficulties in terms of handling and control. In this regard, electrostatic dust removal technique and self-cleaning system are recommended. It is hoped that this work will help researchers and experts to spot future directions in decision support.