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Soiling Modelling in Large Grid-Connected PV Plants for Cleaning Optimization

Author

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  • Marta Redondo

    (Department of Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain
    Department of Operation & Maintenance Improvement, Enel Green Power Iberia, 28042 Madrid, Spain)

  • Carlos A. Platero

    (Department of Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial, Universidad Politécnica de Madrid, 28006 Madrid, Spain)

  • Antonio Moset

    (Department of Operation & Maintenance Solar Iberia, Enel Green Power Iberia, 28042 Madrid, Spain)

  • Fernando Rodríguez

    (Department of Operation & Maintenance Improvement, Enel Green Power Iberia, 28042 Madrid, Spain)

  • Vicente Donate

    (Department of Operation & Maintenance Solar Iberia, Enel Green Power Iberia, 28042 Madrid, Spain)

Abstract

Soiling of PV modules is an issue causing non-negligible losses on PV power plants, between 3 and 4% of the total energy production. Cleaning is the most common way to mitigate soiling. The impact of the cleaning activity can be significant, both in terms of cost and resources consumption. For these reasons, it is important to monitor and predict soiling profiles and establish an optimal cleaning schedule. Especially in locations where raining is irregular or where desert winds carry a high concentration of particles, it is also important to know how precipitation and dust events affect the soiling ratio. This paper presents a new model based on environmental conditions that helps the decision-making process of the cleaning schedule. The model was validated by the analysis of five large grid-connected PV plants in Spain over two years of operation, with a total power of 200 MW. The comparison between the model and soiling sensors at the five locations was included. Excellent results were achieved, the mean difference between sensors and model being 0.71%.

Suggested Citation

  • Marta Redondo & Carlos A. Platero & Antonio Moset & Fernando Rodríguez & Vicente Donate, 2023. "Soiling Modelling in Large Grid-Connected PV Plants for Cleaning Optimization," Energies, MDPI, vol. 16(2), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:904-:d:1034261
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    References listed on IDEAS

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    1. Saheli Sengupta & Aritra Ghosh & Tapas K. Mallick & Chandan Kumar Chanda & Hiranmay Saha & Indrajit Bose & Joydip Jana & Samarjit Sengupta, 2021. "Model Based Generation Prediction of SPV Power Plant Due to Weather Stressed Soiling," Energies, MDPI, vol. 14(17), pages 1-16, August.
    2. Lisa B. Bosman & Walter D. Leon-Salas & William Hutzel & Esteban A. Soto, 2020. "PV System Predictive Maintenance: Challenges, Current Approaches, and Opportunities," Energies, MDPI, vol. 13(6), pages 1-16, March.
    3. Mithhu, Md. Mahamudul Hasan & Rima, Tahmina Ahmed & Khan, M. Ryyan, 2021. "Global analysis of optimal cleaning cycle and profit of soiling affected solar panels," Applied Energy, Elsevier, vol. 285(C).
    4. You, Siming & Lim, Yu Jie & Dai, Yanjun & Wang, Chi-Hwa, 2018. "On the temporal modelling of solar photovoltaic soiling: Energy and economic impacts in seven cities," Applied Energy, Elsevier, vol. 228(C), pages 1136-1146.
    5. Ramez Abdallah & Adel Juaidi & Salameh Abdel-Fattah & Mahmoud Qadi & Montaser Shadid & Aiman Albatayneh & Hüseyin Çamur & Amos García-Cruz & Francisco Manzano-Agugliaro, 2022. "The Effects of Soiling and Frequency of Optimal Cleaning of PV Panels in Palestine," Energies, MDPI, vol. 15(12), pages 1-18, June.
    6. Abdulsalam S. Alghamdi & AbuBakr S. Bahaj & Luke S. Blunden & Yue Wu, 2019. "Dust Removal from Solar PV Modules by Automated Cleaning Systems," Energies, MDPI, vol. 12(15), pages 1-21, July.
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    Cited by:

    1. Fabian Zuñiga-Cortes & Juan D. Garcia-Racines & Eduardo Caicedo-Bravo & Hernan Moncada-Vega, 2023. "Minimization of Economic Losses in Photovoltaic System Cleaning Schedules Based on a Novel Methodological Framework for Performance Ratio Forecast and Cost Analysis," Energies, MDPI, vol. 16(16), pages 1-18, August.
    2. Boris I. Evstatiev & Dimitar T. Trifonov & Katerina G. Gabrovska-Evstatieva & Nikolay P. Valov & Nicola P. Mihailov, 2024. "PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning," Energies, MDPI, vol. 17(20), pages 1-20, October.
    3. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).

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