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A novel stochastic dynamic modeling for photovoltaic systems considering dust and cleaning

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  • Cheema, Armaghan
  • Shaaban, M.F.
  • Ismail, Mahmoud H.

Abstract

Stochastic photovoltaic (PV) modeling that can be used for long-term planning of power systems is essential for future renewable power generation. One of the most prevalent problems that PV systems face is the accumulation of dust on the PV panel surface that negatively impacts the output power. Wind speed along with other weather variables including relative humidity, temperature, and precipitation are some of the major factors that contribute to dust accumulation. This paper presents a novel dynamic model of the PV output power profile including the dust accumulation using a Markov chain model. The proposed model incorporates the seasonal variations in ambient temperature, solar irradiance, dust accumulation, and rate of dust accumulation as well as the desired cleaning frequency, which affect the overall energy yield of the PV system. The outcome of the model is virtually generated scenarios that can be used by the investors to decide on the optimal size of the PV system and the optimal cleaning frequency for each season. The model outcome shows an error of less than 5% when compared to actual data collected from the field without cleaning. This error can be reduced by increasing the number of states, which affects the computational time. Various case studies are presented to show the effectiveness of the proposed model and its benefits.

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  • Cheema, Armaghan & Shaaban, M.F. & Ismail, Mahmoud H., 2021. "A novel stochastic dynamic modeling for photovoltaic systems considering dust and cleaning," Applied Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:appene:v:300:y:2021:i:c:s030626192100800x
    DOI: 10.1016/j.apenergy.2021.117399
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    References listed on IDEAS

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    1. Yarhands Dissou Arthur. & Kwasi Baah Gyamfi & S.K Appiah, 2013. "Probability Distributional Analysis of Hourly Solar Irradiation in Kumasi-Ghana," International Journal of Business and Social Research, LAR Center Press, vol. 3(3), pages 63-75, March.
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    3. Yarhands Dissou Arthur. & Kwasi Baah Gyamfi & S.K Appiah, 2013. "Probability Distributional Analysis of Hourly Solar Irradiation in Kumasi-Ghana," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 3(3), pages 63-75, March.
    4. Antonio Bracale & Pierluigi Caramia & Guido Carpinelli & Anna Rita Di Fazio & Gabriella Ferruzzi, 2013. "A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control," Energies, MDPI, vol. 6(2), pages 1-15, February.
    5. Maghami, Mohammad Reza & Hizam, Hashim & Gomes, Chandima & Radzi, Mohd Amran & Rezadad, Mohammad Ismael & Hajighorbani, Shahrooz, 2016. "Power loss due to soiling on solar panel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1307-1316.
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    Cited by:

    1. Yao, Wanxiang & Kong, Xiangru & Xu, Ai & Xu, Puyan & Wang, Yan & Gao, Weijun, 2023. "New models for the influence of rainwater on the performance of photovoltaic modules under different rainfall conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    2. Anna Manowska & Artur Dylong & Bogdan Tkaczyk & Jarosław Manowski, 2023. "Analysis and Monitoring of Maximum Solar Potential for Energy Production Optimization Using Photovoltaic Panels," Energies, MDPI, vol. 17(1), pages 1-16, December.

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