Effect of dust and methods of cleaning on the performance of solar PV module for different climate regions: Comprehensive review

Salamah, Tareq; Ramahi, Ahmad; Alamara, Kadhim; Juaidi, Adel; Abdallah, Ramez; Abdelkareem, Mohammad Ali; Amer, El-Cheikh; Olabi, Abdul Ghani

doi:10.1016/j.scitotenv.2022.154050

Cited by 113 publications

(55 citation statements)

References 155 publications

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“…The renewable energy industry is improving continuously and there is a big trend all over the world to use renewable energy to generate electricity and eliminate the need for burning oil and fossil fuel which adversely impacts the environment and, ultimately, human living conditions [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…The solar energy that hits the earth in one hour is nearly sufficient for one year of the Earth's requirements for energy but, unfortunately, some of this energy is lost when it is absorbed by some solid particles or reflected by water vapor. Additionally, for the solar panels, some of the radiation is absorbed by deposits and dust accumulation, which reduces the efficiency of the PV panels and it is from this perspective, that the idea of cleaning the PV panels originated [4,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Renewable energy created by PV panels is an efficient solution to the ever-increasing need for energy while also lowering hazardous emissions and the environmental implications that come with it [4,11]. According to REN21, the total worldwide solar PV capacity reached 627 GW in 2019, up from less than 23 GW only ten years ago, and an increase of 115 GW within one year from 2018, as seen in Figure 1 [12].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there is a trend toward the PV plants industry as a source of electricity since it is friendly to nature [9,10]. Solar energy as an investment is very efficient but it is not possible everywhere since the productivity of energy depends on the number of sunshine hours and the number of rainy days in a particular location, and because of this, the Middle East and North Africa have high productivity in the sector of solar energy [4,13].…”

Section: Introductionmentioning

confidence: 99%

“…The impact of dust is significantly dependent on the geographic location and local circumstances. As a result, determining a numerical figure for its negative effect is challenging [4,[27][28][29]. Main factors that affect the performance of PV modules include temperature, dust deposition, relative humidity, solar irradiance, wind movement, tilt angle, and shading [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine

et al. 2022

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The performance of photovoltaic (PV) solar panels is dependent on certain factors, such as dust effects. Even though Palestine’s energy issues are well-known, no research has been undertaken on the soiling effect on solar energy generation in Palestine’s climatic circumstances. The study’s findings can aid Palestine’s efforts to achieve long-term energy sustainability and solar energy use. Outdoor research was conducted in Tulkarm, Palestine, to explore the impact of dust on PV systems. The current study examined the impact of dust accumulation based on the Mediterranean climate. To accomplish this, a one-year experiment was conducted from 1 January to 31 December 2021. An 85-kW PV power plant at Tulkarm was utilized in the study. Knowing the efficiency reduction over time will aid in minimizing cleaning expenses by selecting the most appropriate cleaning interval. The results concluded that in January, February, November, and December, there will be a two-month cleaning period, monthly cleaning in March and October, as well as two weeks of cleaning in April and May. It may also be concluded that the plant should be cleaned weekly throughout the months of June, July, August, and September. This recommendation is necessary to maintain the PV panel plant operating at peak efficiency.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine

et al. 2022

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Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models

et al. 2022

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and clouds create fluctuations in the power generation, which result in voltage unbalance, voltage rise and voltage flickers in networks with high PV presence. [3] Forecasting technologies can help grid operators with the scheduling and dispatching of this renewable energy source more effectively. Due to the stochastic nature of PV power generation, machine learning (ML) approaches have gained popularity in forecasting tasks. [4] The main characteristic of ML algorithms is that the coefficients of the model are obtained automatically by using training data. [5] There is a huge amount of algorithms inside this family, whose complexity ranges from linear regression techniques to deep learning neural networks.Despite abundant literature, most studies consider <5 PV systems that are generally located in the same areas. [6] Most researchers study systems in Europe, the US, Australia, or China, which are population-dense areas, but receive relatively small solar intensity (except for Australia). Few studies have considered PV systems subjected to diverse meteorological conditions in order to study the effect that climate has on the performance of ML models.Pasion et al. forecasted the PV power of 12 northern-hemisphere locations subjected to seven different climate regions. [7] The selected algorithm best predicted the data in a hot-dry climate region, and a mixed-humid climate region had the secondbest model performance, with coefficient of determination (R 2 ) scores of 0.968 and 0.962, respectively. In contrast, the model performance for the tropical rainforest site was the poorest with an R 2 score of 0.908. Do et al. forecasted two PV systems, one in Guadeloupe (North America, tropical climate) and the other in Lille (Europe, mild temperate climate). [8] They discovered that the system in Lille required a longer training duration but achieved a lower normalized root mean squared error of 10.69 % compared to the one in Guadeloupe (error of 11.97 %). In ref. [9], two systems located in tropical (Singapore) and mild temperate (Australia) climates were also considered for a probabilistic forecasting model. As opposed to the previous study, they reported worse metrics for the system located in the mild temperate climate, due to the highly variable Australian weather and thunderstorms in the summer season. Zhang et al. considered three distant PV systems located in the USA, Denmark, and Italy. [10] Because of the unique weather and climate characteristics of each location, the modeling parameters and best features of the optimum ML model differed between sites. Finally,

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Self‐Healable Sandfish Scale‐Inspired Scalable Triboelectric Layer for Hybrid Energy Harvesting in Desert Environment

Chen,

Parashar,

Sharma

et al. 2024

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In deserts, sedimentation from frequent dust activities on solar cells poses a substantial technical challenge, reducing efficiency and necessitating advanced cost‐inefficient cleaning mechanisms. Herein, a novel sandfish scale‐inspired self‐healing fluorinated copolymer‐based triboelectric layer is directly incorporated on top of the polysilicon solar cell for sustained hybrid energy harvesting. The transparent biomimetic layer, with distinctive saw‐tooth microstructured morphology, exhibits ultra‐low sand adhesion and high abrasion‐resistant properties, inhibits sedimentation deposition on solar cells, and concurrently harvests kinetic energy from wind‐driven sand particles through triboelectric nanogenerator (TENG). The film exhibits a low friction coefficient (0.149), minimal sand adhesion force (27 nN), and a small wear area (327 µm2). In addition, over 2 months, a solar cell with the sandfish scale‐inspired structure demonstrates only a 16% decline in maximum power output compared to the bare solar cell, which experiences a 60% decline. Further, the sandfish scale‐based TENG device's electrical output is fully restored to its original value after a 6‐h self‐healing cycle and maintains consistent stable outputs. These results highlight the exceptional advantages of employing biomimetic self‐healing materials as robust triboelectric layers, showcasing sustained device stability and durability for prolonged use in harsh desert environments, ultimately contributing to a low cost‐of‐electricity generation paradigm.

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Effect of dust and methods of cleaning on the performance of solar PV module for different climate regions: Comprehensive review

Cited by 113 publications

References 155 publications

The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine

The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine

Effect of Climate on Photovoltaic Yield Prediction Using Machine Learning Models

Self‐Healable Sandfish Scale‐Inspired Scalable Triboelectric Layer for Hybrid Energy Harvesting in Desert Environment

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