Quantification of PV Power and Economic Losses Due to Soiling in Qatar

Amr Zeedan,Farid Touati,Khaled Al-Fakhroo,Abdulaziz Barakeh,Antonio S P Gonzales

doi:10.3390/su13063364

Abstract

Soiling losses of photovoltaic (PV) panels due to dust lead to a significant decrease in solar energy yield and result in economic losses; this hence poses critical challenges to the viability of PV in smart grid systems. In this paper, these losses are quantified under Qatar’s harsh environment. This quantification is based on experimental data from long-term measurements of various climatic parameters and the output power of PV panels located in Qatar University’s Solar facility in Doha, Qatar, using a customized measurement and monitoring setup. A data processing algorithm was deliberately developed and applied, which aimed to correlate output power to ambient dust density in the vicinity of PV panels. It was found that, without cleaning, soiling reduced the output power by 43% after six months of exposure to an average ambient dust density of 0.7 mg/m3. The power and economic loss that would result from this power reduction for Qatar’s ongoing solar PV projects has also been estimated. For example, for the Al-Kharasaah project power plant, similar soiling loss would result in about a 10% power decrease after six months for typical ranges of dust density in Qatar’s environment; this, in turn, would result in an 11,000 QAR/h financial loss. This would pose a pressing need to mitigate soiling effects in PV power plants.

Highlights

In this age of rapid technological advancement, conventional energy sources are being replaced by renewable energy resources for various economic and environmental reasons.Solar energy is among the most widespread sources of renewable energy
The study results show that soiling removal is a manageable cost element for PV panel systems, and it should not form a major financial obstacle to widespread deployment of PV cleaning solutions in Saudi Arabia or other countries with similar soiling characteristics [12]
This linear fit is far from being an accurate representation of the data, because this linear fit is far from being an accurate representation of the data, because the R2 value the R value is 0.117, while ideally it should be 1. This large error is because this data not is 0.117, while ideally it should be 1. This large error is because this data reflects only reflects the effect of dust on output power and the effect of all other environmenthe effect of dust on output power and the effect of all other environmental parameters taland parameters andbethus cannotbybethis modeled by this single variable function.the

Summary

Introduction

Solar energy is among the most widespread sources of renewable energy This energy is harnessed using solar photovoltaic (PV) cells. Solar power is one of the cleanest renewable energy sources It is highly dependent on local climate conditions, which can vastly change a solar panel’s efficiency due to various processes, such as soiling losses [1]. Dust accumulation is standard in areas with large amounts of soil, dust, or sandstorms, such as the Middle East [1]. In such locations, this site-specific phenomenon can cause a power generation loss exceeding one percent per day [1]. Dust from agricultural emissions and industry emissions as well as engine exhaust, pollen, plant debris, fungi, mosses, algae, bacteria biofilms, bird droppings, and dust deposits of minerals are some of the many location-dependent contamination types that can reduce and scatter sunlight, affecting the overall performance of solar photovoltaic cells [1]

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