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Investigating the impact of weather variables on the energy yield and cost of energy of grid-connected solar concentrator systems

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  • Fernández, Eduardo F.
  • Talavera, D.L.
  • Almonacid, Florencia M.
  • Smestad, Greg P.

Abstract

This work connects the electrical performance and economics of High Concentrator Photovoltaic technology beyond the cell and module levels. It analyses the impact of fundamental variables on the calculated energy output and economics of a typical system for real-world solar power plants in five locations with diverse climatic conditions. It was found that there exists a nearly linear relationship between the Final Energy Yield and the average direct normal irradiance, while the cell temperature and spectral AC energy losses ranged from 4.6% to 1.8% and 5.0%–2.4%. The LCOE (Levelised Cost of Electricity) calculations used these insights, together with the specific economic values for each location. The results show that the locations with the higher annual energy yield tend to have the lower LCOE values. In particular, the LCOE ranged from 5.5 c€/kWh to 22.2 c€/kWh for a conservative scenario. However, the sites with the highest final yield do not necessarily present the lowest values of LCOE. The results emphasize the interrelationship between the instantaneous effects of cell temperature and spectrum on the performance of the system, as well as the importance of considering the specific economic parameters to estimate the LCOE at each location.

Suggested Citation

  • Fernández, Eduardo F. & Talavera, D.L. & Almonacid, Florencia M. & Smestad, Greg P., 2016. "Investigating the impact of weather variables on the energy yield and cost of energy of grid-connected solar concentrator systems," Energy, Elsevier, vol. 106(C), pages 790-801.
    • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:790-801
    DOI: 10.1016/j.energy.2016.03.060
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    References listed on IDEAS

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    1. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    2. Mouhib, Elmehdi & Fernández-Solas, Álvaro & Pérez-Higueras, Pedro J. & Fernández-Ocaña, Ana M. & Micheli, Leonardo & Almonacid, Florencia & Fernández, Eduardo F., 2024. "Enhancing land use: Integrating bifacial PV and olive trees in agrivoltaic systems," Applied Energy, Elsevier, vol. 359(C).
    3. Saleheen, Mohammed Zeehan & Salema, Arshad Adam & Mominul Islam, Shah Mohammad & Sarimuthu, Charles R. & Hasan, Md Zobaer, 2021. "A target-oriented performance assessment and model development of a grid-connected solar PV (GCPV) system for a commercial building in Malaysia," Renewable Energy, Elsevier, vol. 171(C), pages 371-382.
    4. Fernandez, Eduardo F. & Chemisana, Daniel & Micheli, Leonardo & Almonacid, Florencia, 2019. "Spectral nature of soiling and its impact on multi-junction based concentrator systems," MPRA Paper 106251, University Library of Munich, Germany.
    5. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
    6. Fernández, Eduardo F. & Villar-Fernández, Antonio & Montes-Romero, Jesús & Ruiz-Torres, Laura & Rodrigo, Pedro M. & Manzaneda, Antonio J. & Almonacid, Florencia, 2022. "Global energy assessment of the potential of photovoltaics for greenhouse farming," Applied Energy, Elsevier, vol. 309(C).
    7. 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.
    8. Guobin Cao & Hua Qin & Rajan Ramachandran & Bo Liu, 2019. "Solar Concentrator Consisting of Multiple Aspheric Reflectors," Energies, MDPI, vol. 12(21), pages 1-14, October.

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