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DC energy yield prediction in large monocrystalline and polycrystalline PV plants: Time-domain integration of Osterwald's model

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  • Muñoz, J.V.
  • Nofuentes, G.
  • Fuentes, M.
  • de la Casa, J.
  • Aguilera, J.

Abstract

The energy produced by a large PV plant is a paramount parameter for predicting the profitability of the PV system. This prediction generally consists of first estimating the DC energy and then estimating the AC energy. At present, the well-known behavior and reliability of the inverters available on the market make the estimation of the DC energy the most important source of uncertainty in the prediction of the energy produced by a PV installation. This paper presents an experimental validation of a method based on a time-domain integration of Osterwald's model for predicting the DC energy produced by a large PV system. The statistical error indicators RMSEE and MBEE, as well as a study based on scatter plots and best-fit lines, were used to validate the method. Ten large PV systems under operation in Spain were tested. Some of the PV generators exhibited hot spots, snail tracks, blown fuses and, as a result, remarkable drops in their nominal power. Despite such remarkable power decreases, the validated method was demonstrated to perform remarkably well, particularly when the systems operate under high irradiances, displaying values of RMSEE, MBEE and R2 of up to 0.56 %, 0.30 % and 0.999974, respectively.

Suggested Citation

  • Muñoz, J.V. & Nofuentes, G. & Fuentes, M. & de la Casa, J. & Aguilera, J., 2016. "DC energy yield prediction in large monocrystalline and polycrystalline PV plants: Time-domain integration of Osterwald's model," Energy, Elsevier, vol. 114(C), pages 951-960.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:951-960
    DOI: 10.1016/j.energy.2016.07.064
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    Cited by:

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    2. Singh, Rashmi & Sharma, Madhu & Rawat, Rahul & Banerjee, Chandan, 2020. "Field Analysis of three different silicon-based Technologies in Composite Climate Condition – Part II – Seasonal assessment and performance degradation rates using statistical tools," Renewable Energy, Elsevier, vol. 147(P1), pages 2102-2117.
    3. Javed Sayyad & Paresh Nasikkar & Abhaya Pal Singh & Stepan Ozana, 2021. "Capacitive Load-Based Smart OTF for High Power Rated SPV Module," Energies, MDPI, vol. 14(3), pages 1-18, February.
    4. Nofuentes, Gustavo & de la Casa, Juan & Solís-Alemán, Ernesto M. & Fernández, Eduardo F., 2017. "Spectral impact on PV performance in mid-latitude sunny inland sites: Experimental vs. modelled results," Energy, Elsevier, vol. 141(C), pages 1857-1868.
    5. Manuel Cáceres & Andrés Firman & Jesús Montes-Romero & Alexis Raúl González Mayans & Luis Horacio Vera & Eduardo F. Fernández & Juan de la Casa Higueras, 2020. "Low-Cost I–V Tracer for PV Modules under Real Operating Conditions," Energies, MDPI, vol. 13(17), pages 1-17, August.
    6. Singh, Rashmi & Sharma, Madhu & Yadav, Kamlesh, 2022. "Degradation and reliability analysis of photovoltaic modules after operating for 12 years: A case study with comparisons," Renewable Energy, Elsevier, vol. 196(C), pages 1170-1186.

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