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An experimental comparative analysis of different PV technologies performance including the influence of hot-arid climatic parameters: Toward a realistic yield assessment for desert locations

Author

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  • Mehdi, Maryam
  • Ammari, Nabil
  • Alami Merrouni, Ahmed
  • El Gallassi, Hicham
  • Dahmani, Mohamed
  • Ghennioui, Abdellatif

Abstract

In this study, a one-year experiment has been conducted to evaluate the performances of two different photovoltaic technologies: Polycrystalline (pc-Si) and cadmium telluride (CdTe) under a hot desert climate in Mid-south Morocco. For this reason, two PV modules (from each technology) have been exposed and all electrical parameters were monitored for performance comparison. Also, for a realistic comparison, the impact of the climatic parameters, mainly soiling and temperature were taken into consideration. Results show that both technologies have a good performance with a daily Performance Ratio (PR) close to 1 for the pc-Si and around 0.9 for CdTe, but with superiority of 3.5% to the pc-Si technology. The peak daily energy yields are of 7.51 kWh/kWp and 6.99 kWh/kWp for pc-Si and CdTe respectively, with 7% high production for the pc-Si. Nevertheless, the CdTe is less affected by temperature, where the average PR deviation (between the technologies) is 4.37% for temperature values less than 25 °C and 2.35% for temperatures above 25 °C in favor of the CdTe. Regarding soiling, the CdTe shows high performance in comparison to the pc-Si where the daily Soiling Rate reaches 24.6% and 26.9% for both technologies respectively, which causes a drop in the electricity production of 25.5% for the pc-Si and 23.2% for CdTe technology.

Suggested Citation

  • Mehdi, Maryam & Ammari, Nabil & Alami Merrouni, Ahmed & El Gallassi, Hicham & Dahmani, Mohamed & Ghennioui, Abdellatif, 2023. "An experimental comparative analysis of different PV technologies performance including the influence of hot-arid climatic parameters: Toward a realistic yield assessment for desert locations," Renewable Energy, Elsevier, vol. 205(C), pages 695-716.
    • Handle: RePEc:eee:renene:v:205:y:2023:i:c:p:695-716
    DOI: 10.1016/j.renene.2023.01.082
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    2. Tan, Hongjun & Guo, Zhiling & Chen, Yuntian & Zhang, Haoran & Song, Chenchen & Jiang, Mingkun & Yan, Jinyue, 2025. "PV potential analysis through deep learning and remote sensing-based urban land classification," Applied Energy, Elsevier, vol. 387(C).
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