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Experimental validation of a probabilistic model for estimating the double axis PV tracking energy production

Author

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  • Tina, Giuseppe Marco
  • Gagliano, Salvina
  • Graditi, Giorgio
  • Merola, Angelo

Abstract

This work aims to study the impact of different models for the evaluation of the efficiency of a double axis PV tracking system on the monthly probability distribution function of the AC power. Two components of the global efficiency are analysed, that is: the effect of PV cells temperature on the module efficiency and the DC/AC converter efficiency. In particular, the temperature efficiency model combines basic parameters characterizing the array, with the local monthly average temperature and the monthly clearness index to yield a monthly average efficiency. The simulation results are compared with experimental data related to a 9.6kWp PV plant installed in ENEA research centre located in Portici, Naples (Italy). The tuning of the model is performed by both system measurements and environmental data.

Suggested Citation

  • Tina, Giuseppe Marco & Gagliano, Salvina & Graditi, Giorgio & Merola, Angelo, 2012. "Experimental validation of a probabilistic model for estimating the double axis PV tracking energy production," Applied Energy, Elsevier, vol. 97(C), pages 990-998.
    • Handle: RePEc:eee:appene:v:97:y:2012:i:c:p:990-998
    DOI: 10.1016/j.apenergy.2012.01.054
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    References listed on IDEAS

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    1. Zhou, Wei & Lou, Chengzhi & Li, Zhongshi & Lu, Lin & Yang, Hongxing, 2010. "Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems," Applied Energy, Elsevier, vol. 87(2), pages 380-389, February.
    2. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
    3. Tina, Giuseppe Marco & Gagliano, Salvina, 2011. "Probabilistic modelling of hybrid solar/wind power system with solar tracking system," Renewable Energy, Elsevier, vol. 36(6), pages 1719-1727.
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    Citations

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    Cited by:

    1. Singh, G.K., 2013. "Solar power generation by PV (photovoltaic) technology: A review," Energy, Elsevier, vol. 53(C), pages 1-13.
    2. Song, Zhiying & Zhang, Yuzhe & Ji, Jie & He, Wei & Hu, Zhongting & Xuan, Qingdong, 2024. "Yearly photoelectric/thermal and economic performance comparison between CPV and FPV dual-source heat pump systems in different regions," Energy, Elsevier, vol. 289(C).
    3. Fathabadi, Hassan, 2016. "Novel high efficient offline sensorless dual-axis solar tracker for using in photovoltaic systems and solar concentrators," Renewable Energy, Elsevier, vol. 95(C), pages 485-494.
    4. Federico Minelli & Diana D’Agostino & Maria Migliozzi & Francesco Minichiello & Pierpaolo D’Agostino, 2023. "PhloVer: A Modular and Integrated Tracking Photovoltaic Shading Device for Sustainable Large Urban Spaces—Preliminary Study and Prototyping," Energies, MDPI, vol. 16(15), pages 1-35, August.
    5. Nižetić, S. & Duić, N. & Papadopulos, A.M. & Tina, G.M. & Grubišić-Čabo, F., 2015. "Energy efficiency evaluation of a hybrid energy system for building applications in a Mediterranean climate and its feasibility aspect," Energy, Elsevier, vol. 90(P1), pages 1171-1179.
    6. D'Agostino, D. & Minelli, F. & D'Urso, M. & Minichiello, F., 2022. "Fixed and tracking PV systems for Net Zero Energy Buildings: Comparison between yearly and monthly energy balance," Renewable Energy, Elsevier, vol. 195(C), pages 809-824.
    7. Graditi, G. & Adinolfi, G. & Tina, G.M., 2014. "Photovoltaic optimizer boost converters: Temperature influence and electro-thermal design," Applied Energy, Elsevier, vol. 115(C), pages 140-150.
    8. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.

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