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Numerical investigation of coupled optical-electrical-thermal processes for plasmonic solar cells at various angles of incident irradiance

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  • Zhang, J.J.
  • Qu, Z.G.
  • Maharjan, A.

Abstract

Plasmonic solar cells include metal nanoparticles deposited on the top surface of substrate, whose output performance is constrained by self-heating, light induced heating, and environmental conditions. In this study, a numerical model for coupled optical-thermal-electrical processes was developed for GaAs based plasmonic solar cells with nanoparticles (Ag, Au). Temperature fluctuation due to hotspots and thermalization was observed. Light absorption, thermalization power, and electric performance of the solar cells at various angles of incident irradiation were analyzed. Results show that the hot spot makes a significant uneven temperature distribution in the layers of plasmonic solar cells. Varied angles of incident irradiance can cause more fluctuation in thermalization power and electrical performance for plasmonic solar cells than for non-plasmonic ones. Ag nanoparticles can enhance the broad-spectrum light absorption (0.3–0.87 μm), and Au nanoparticles can enhance the light absorption near the cutoff wavelength of GaAs (0.7–0.87 μm). It was concluded that plasmonic solar cells with Ag nanoparticles have higher efficiency of optical-electrical conversion at wide angles (of incidence), whereas plasmonic solar cells with Au nanoparticles provide higher efficiency of optical-electrical conversion at a specific angle. Thus, by properly designing the nanoparticle properties, the thermalization loss can be reduced, and the electrical performance can be improved.

Suggested Citation

  • Zhang, J.J. & Qu, Z.G. & Maharjan, A., 2019. "Numerical investigation of coupled optical-electrical-thermal processes for plasmonic solar cells at various angles of incident irradiance," Energy, Elsevier, vol. 174(C), pages 110-121.
    • Handle: RePEc:eee:energy:v:174:y:2019:i:c:p:110-121
    DOI: 10.1016/j.energy.2019.02.131
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    References listed on IDEAS

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    1. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    2. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    3. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
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    Cited by:

    1. Jiang, Yi & Lv, Mingyun & Wang, Chuanzhi & Meng, Xiangrui & Ouyang, Siyue & Wang, Guodong, 2021. "Layout optimization of stratospheric balloon solar array based on energy production," Energy, Elsevier, vol. 229(C).
    2. Zhang, J.J. & Qu, Z.G. & Zhang, J.F., 2022. "Diode model of nonuniform irradiation treatment to predict multiscale solar-electrical conversion for the concentrating plasmonic photovoltaic system," Applied Energy, Elsevier, vol. 324(C).
    3. Liu, Yang & Du, Huafei & Xu, Ziyuan & Sun, Kangwen & Lv, Mingyun, 2022. "Mission-based optimization of insulation layer for the solar array on the stratospheric airship," Renewable Energy, Elsevier, vol. 191(C), pages 318-329.
    4. Nižetić, Sandro & Jurčević, Mišo & Arıcı, Müslüm & Arasu, A. Valan & Xie, Gongnan, 2020. "Nano-enhanced phase change materials and fluids in energy applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 129(C).

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