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Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations

Author

Listed:
  • Francisco J. Gómez-Gil

    (Department of Electromechanical Engineering, University of Burgos, Burgos 09006, Spain)

  • Xiaoting Wang

    (Department of Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA)

  • Allen Barnett

    (School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Sydney 2052, Australia)

Abstract

A method for the prediction of Energy Production (EP) in Concentrating Photovoltaic (CPV) installations is examined in this study. It presents a new method that predicts EP by using Global Horizontal Irradiation (GHI) and the Photovoltaic Geographical Information System (PVGIS) database, instead of Direct Normal Irradiation (DNI) data, which are rarely recorded at most locations. EP at four Spanish CPV installations is analyzed: two are based on silicon solar cells and the other two on multi-junction III-V solar cells. The real EP is compared with the predicted EP. Two methods for EP prediction are presented. In the first preliminary method, a monthly Performance Ratio (PR) is used as an arbitrary constant value (75%) and an estimation of the DNI. The DNI estimation is obtained from GHI measurements and the PVGIS database. In the second method, a lineal model is proposed for the first time in this paper to obtain the predicted EP from the estimated DNI. This lineal model is the regression line that correlates the real monthly EP and the estimated DNI in 2009. This new method implies that the monthly PR is variable. Using the new method, the difference between the predicted and the real EP values is less than 2% for the annual EP and is in the range of 5.6%–16.1% for the monthly EP. The method that uses the variable monthly PR allows the prediction of the EP with reasonable accuracy. It is therefore possible to predict the CPV EP for any location, using only widely available GHI data and the PVGIS database.

Suggested Citation

  • Francisco J. Gómez-Gil & Xiaoting Wang & Allen Barnett, 2012. "Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV) Installations," Energies, MDPI, vol. 5(3), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:3:p:770-789:d:16705
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    Citations

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    Cited by:

    1. 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.
    2. García-Domingo, B. & Aguilera, J. & de la Casa, J. & Fuentes, M., 2014. "Modelling the influence of atmospheric conditions on the outdoor real performance of a CPV (Concentrated Photovoltaic) module," Energy, Elsevier, vol. 70(C), pages 239-250.
    3. Carlo Renno & Michele De Giacomo, 2014. "Dynamic Simulation of a CPV/T System Using the Finite Element Method," Energies, MDPI, vol. 7(11), pages 1-20, November.
    4. Zhe Mi & Jikun Chen & Nuofu Chen & Yiming Bai & Wenwang Wu & Rui Fu & Hu Liu, 2016. "Performance Analysis of a Grid-connected High Concentrating Photovoltaic System under Practical Operation Conditions," Energies, MDPI, vol. 9(2), pages 1-12, February.
    5. João Perdigão & Paulo Canhoto & Rui Salgado & Maria João Costa, 2020. "Assessment of Direct Normal Irradiance Forecasts Based on IFS/ECMWF Data and Observations in the South of Portugal," Forecasting, MDPI, vol. 2(2), pages 1-21, May.

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