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<p>The performance of Photovoltaic (PV) modules is significantly degraded by dust accumulation, leading to reduced energy output and increased operational costs. This study presents a novel, data-driven model to determine the optimal cleaning frequency and associated costs by analyzing the impact of dust deposition on PV output power and cleaning event data. While the model is applied to a PV power plant in central Egypt, its methodology is adaptable to other regions with similar soiling conditions. The proposed approach integrates financial and technical considerations to maximize profitability while minimizing energy losses. Using a focused three-month period of site-specific data—capturing the season with the highest wind speeds and solar irradiance—the model employs the Stochastic Rate and Recovery (SRR) method to evaluate soiling loss and cleaning strategies, revealing a median soiling ratio of 0.978. The Monte Carlo method is applied to generate probabilistic soiling profiles, enabling precise estimation of soiling impacts. Validation based on the IEC 61724-1 standard and comparison with actual cleaning events demonstrates high accuracy, with an average error of 0.04% and a Root Mean Square Error (RMSE) of 0.0096, confirming the reliability of the model. Furthermore, the integration of MATLAB Simulink with field soiling data provides a robust platform for optimizing cleaning frequency, effectively balancing technical performance and economic feasibility. The results indicate that implementing a three-day cleaning cycle at the study site reduces annual energy losses by 10% and decreases soiling-related expenses by 43%. This study addresses a critical research gap by providing a data-driven framework for optimizing cleaning strategies in regions with high soiling rates, such as arid and semi-arid environments. The novelty of this work lies in its combined use of statistical modeling, Monte Carlo simulations, and MATLAB Simulink integration to deliver actionable insights for PV plant operators. By bridging the gap between theoretical models and real-world applications, this research contributes to improving PV system performance and operational efficiency in soiling-prone regions.</p>

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A novel approach for optimal cleaning scenario of MW scale photovoltaic farm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Abdelrahman H. El Morshedy,
  • Amr Y. Elbanhawy

摘要

The performance of Photovoltaic (PV) modules is significantly degraded by dust accumulation, leading to reduced energy output and increased operational costs. This study presents a novel, data-driven model to determine the optimal cleaning frequency and associated costs by analyzing the impact of dust deposition on PV output power and cleaning event data. While the model is applied to a PV power plant in central Egypt, its methodology is adaptable to other regions with similar soiling conditions. The proposed approach integrates financial and technical considerations to maximize profitability while minimizing energy losses. Using a focused three-month period of site-specific data—capturing the season with the highest wind speeds and solar irradiance—the model employs the Stochastic Rate and Recovery (SRR) method to evaluate soiling loss and cleaning strategies, revealing a median soiling ratio of 0.978. The Monte Carlo method is applied to generate probabilistic soiling profiles, enabling precise estimation of soiling impacts. Validation based on the IEC 61724-1 standard and comparison with actual cleaning events demonstrates high accuracy, with an average error of 0.04% and a Root Mean Square Error (RMSE) of 0.0096, confirming the reliability of the model. Furthermore, the integration of MATLAB Simulink with field soiling data provides a robust platform for optimizing cleaning frequency, effectively balancing technical performance and economic feasibility. The results indicate that implementing a three-day cleaning cycle at the study site reduces annual energy losses by 10% and decreases soiling-related expenses by 43%. This study addresses a critical research gap by providing a data-driven framework for optimizing cleaning strategies in regions with high soiling rates, such as arid and semi-arid environments. The novelty of this work lies in its combined use of statistical modeling, Monte Carlo simulations, and MATLAB Simulink integration to deliver actionable insights for PV plant operators. By bridging the gap between theoretical models and real-world applications, this research contributes to improving PV system performance and operational efficiency in soiling-prone regions.