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Modeling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study

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  • Hammad, Bashar
  • Al–Abed, Mohammad
  • Al–Ghandoor, Ahmed
  • Al–Sardeah, Ali
  • Al–Bashir, Adnan

Abstract

This paper provides a regionally focused review of work conducted in the Middle East and North Africa (MENA) region related to the effects of dust accumulation and ambient temperature on PV performance. It proposes models to simulate these effects, and suggests a financial methodology to determine cleaning frequency for a case study in Jordan. Two models have been developed; the first utilizing Multivariate Linear Regression (MLR), and the second utilizing Artificial Neural Network (ANN), to estimate PV system conversion efficiency based on experimental data of exposure time to natural dust and ambient temperature. The methodology of building the two models is demonstrated and a comparison between them is made to discuss their validity and accuracy. In addition, estimating the losses due to dust accumulation and, consequently, optimizing cleaning frequency are presented. It is found that both models can predict conversion efficiency closely, with R2 values close to 90%. The two models are employed in calculating losses (represented in losses in system efficiency, η, and the monetary value of these losses) due to dust accumulation only; thus, finding an optimal cleaning frequency of the systems. During the 192 days spanning the duration of the study, the average efficiency reductions due to dust are 0.768%/day and 0.607%/day using MLR and ANN models, respectively. Consequently, energy losses for the duration of the study are 10.282 kWh/m2 and 8.140 kWh/m2, and economic losses are 3.76 US$/m2 and 2.98 US$/m2 using MLR and ANN models, respectively. The optimal cleaning frequency is calculated to be 12–15 days depending on the model and length of exposure time adopted in analysis. This is the first reported optimal cleaning frequency in Jordan, and agrees with most recommendations of other already published works by other researchers and techniques in the MENA region.

Suggested Citation

  • Hammad, Bashar & Al–Abed, Mohammad & Al–Ghandoor, Ahmed & Al–Sardeah, Ali & Al–Bashir, Adnan, 2018. "Modeling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2218-2234.
    • Handle: RePEc:eee:rensus:v:82:y:2018:i:p3:p:2218-2234
    DOI: 10.1016/j.rser.2017.08.070
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    5. Chiteka, Kudzanayi & Arora, Rajesh & Sridhara, S.N. & Enweremadu, C.C., 2021. "Optimizing wind barrier and photovoltaic array configuration in soiling mitigation," Renewable Energy, Elsevier, vol. 163(C), pages 225-236.
    6. Heinrich, Matthias & Meunier, Simon & Samé, Allou & Quéval, Loïc & Darga, Arouna & Oukhellou, Latifa & Multon, Bernard, 2020. "Detection of cleaning interventions on photovoltaic modules with machine learning," Applied Energy, Elsevier, vol. 263(C).
    7. Shing-Lih Wu & Hung-Cheng Chen & Kai-Jun Peng, 2023. "Quantification of Dust Accumulation on Solar Panels Using the Contact-Characteristics-Based Discrete Element Method," Energies, MDPI, vol. 16(6), pages 1-15, March.
    8. Ullah, Asad & Amin, Amir & Haider, Turab & Saleem, Murtaza & Butt, Nauman Zafar, 2020. "Investigation of soiling effects, dust chemistry and optimum cleaning schedule for PV modules in Lahore, Pakistan," Renewable Energy, Elsevier, vol. 150(C), pages 456-468.
    9. Dida, Mustapha & Boughali, Slimane & Bechki, Djamel & Bouguettaia, Hamza, 2020. "Output power loss of crystalline silicon photovoltaic modules due to dust accumulation in Saharan environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    10. Adak, Deepanjana & Bhattacharyya, Raghunath & Barshilia, Harish C., 2022. "A state-of-the-art review on the multifunctional self-cleaning nanostructured coatings for PV panels, CSP mirrors and related solar devices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
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