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Performance analysis and modelling of a 50 MW grid-connected photovoltaic plant in Spain after 12 years of operation

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  • Fuster-Palop, Enrique
  • Vargas-Salgado, Carlos
  • Ferri-Revert, Juan Carlos
  • Payá, Jorge

Abstract

This study aims to estimate the performance and losses of a 50 MW photovoltaic (PV) utility-scale after 12 years of operation. The PV plant has monocrystalline and polycrystalline silicon modules and is located in the central region of Spain with an annual insolation of 1976 kWh/m2. Monitoring data over the entire year 2020 has been analyzed and filtered to assess the performance results following the IEC 61724 standard guidelines. The annual average reference yield, final yield, performance ratio and capacity utilization factor are of 5.44 h/d, 4.28 h/d, 79.24%, and 19.77%, respectively. Besides the experimental analysis, this work improves the estimation of the daily performance ratio, especially in days with low insolation. Two different modelling approaches have been assessed and compared. In first place, a physical model has been adopted, based on the most common losses, and including an exponential expression to account for low irradiance losses. In second place, statistical models have been used, with either multiple linear regressions or random forest algorithms. In contrast with other published models which require many inputs, the best accuracy has been reached with the random forest model using only the ambient temperature and solar irradiance as predictors, obtaining a RMSE of 1% for the PR and for the energy production.

Suggested Citation

  • Fuster-Palop, Enrique & Vargas-Salgado, Carlos & Ferri-Revert, Juan Carlos & Payá, Jorge, 2022. "Performance analysis and modelling of a 50 MW grid-connected photovoltaic plant in Spain after 12 years of operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
  • Handle: RePEc:eee:rensus:v:170:y:2022:i:c:s1364032122008498
    DOI: 10.1016/j.rser.2022.112968
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    References listed on IDEAS

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    1. Sascha Lindig & Atse Louwen & David Moser & Marko Topic, 2020. "Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches," Energies, MDPI, vol. 13(19), pages 1-18, September.
    2. Silvano Vergura, 2018. "A Statistical Tool to Detect and Locate Abnormal Operating Conditions in Photovoltaic Systems," Sustainability, MDPI, vol. 10(3), pages 1-15, February.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Savvakis, Nikolaos & Tsoutsos, Theocharis, 2015. "Performance assessment of a thin film photovoltaic system under actual Mediterranean climate conditions in the island of Crete," Energy, Elsevier, vol. 90(P2), pages 1435-1455.
    5. Hashemi, Behzad & Taheri, Shamsodin & Cretu, Ana-Maria & Pouresmaeil, Edris, 2021. "Systematic photovoltaic system power losses calculation and modeling using computational intelligence techniques," Applied Energy, Elsevier, vol. 284(C).
    6. AL-Rasheedi, Majed & Gueymard, Christian A. & Al-Khayat, Mohammad & Ismail, Alaa & Lee, Jared A. & Al-Duaj, Hamad, 2020. "Performance evaluation of a utility-scale dual-technology photovoltaic power plant at the Shagaya Renewable Energy Park in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Jaeun Kim & Matheus Rabelo & Siva Parvathi Padi & Hasnain Yousuf & Eun-Chel Cho & Junsin Yi, 2021. "A Review of the Degradation of Photovoltaic Modules for Life Expectancy," Energies, MDPI, vol. 14(14), pages 1-21, July.
    8. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    9. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
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    1. Rediske, Graciele & Michels, Leandro & Siluk, Julio Cezar Mairesse & Rigo, Paula Donaduzzi & Rosa, Carmen Brum & Lima, Andrei Cunha, 2024. "A proposed set of indicators for evaluating the performance of the operation and maintenance of photovoltaic plants," Applied Energy, Elsevier, vol. 354(PA).

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