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Comprehensive method for analyzing the power conversion efficiency of organic solar cells under different spectral irradiances considering both photonic and electrical characteristics

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  • Chong, Kok-Keong
  • Khlyabich, Petr P.
  • Hong, Kai-Jeat
  • Reyes-Martinez, Marcos
  • Rand, Barry P.
  • Loo, Yueh-Lin

Abstract

The solar spectral irradiance varies significantly for different locations and time due to latitude, humidity, cosine effect of incident sunlight, etc. For convenience, the power-conversion efficiency of a solar cell is referenced to the international standard of AM1.5G spectral irradiance, which inevitably leads to varying performance of deployed solar cells under the specific local climate and insolation conditions. To predict the actual performance of solar cells under local climate conditions, we propose a methodology to compute the power-conversion efficiency of organic photovoltaic cells based upon indoor measurement with a solar simulator, the measured local solar spectrum, and making use of both optical and electrical factors. From our study, the annual average energy density yield of poly(3-hexylthiophene):phenyl-C61-butyric acid methyl ester (P3HT:PCBM) bulk-heterojunction organic solar cells under the local spectral irradiance of Malaysia is estimated to be 31.89kWh/m2 and the power-conversion efficiency is increased by 5.4% compared to that measured under AM1.5G conditions. In addition, diffuse solar irradiance (cloudy condition) was found to be in favor of P3HT:PCBM solar cells, with gain of 23.7–27.9% relative to AM1.5G conditions.

Suggested Citation

  • Chong, Kok-Keong & Khlyabich, Petr P. & Hong, Kai-Jeat & Reyes-Martinez, Marcos & Rand, Barry P. & Loo, Yueh-Lin, 2016. "Comprehensive method for analyzing the power conversion efficiency of organic solar cells under different spectral irradiances considering both photonic and electrical characteristics," Applied Energy, Elsevier, vol. 180(C), pages 516-523.
    • Handle: RePEc:eee:appene:v:180:y:2016:i:c:p:516-523
    DOI: 10.1016/j.apenergy.2016.08.002
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    References listed on IDEAS

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    Cited by:

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    3. Wilson Castillo-Rojas & Fernando Medina Quispe & César Hernández, 2023. "Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks," Energies, MDPI, vol. 16(13), pages 1-25, July.
    4. Tran, Thomas T.D. & Smith, Amanda D., 2017. "fEvaluation of renewable energy technologies and their potential for technical integration and cost-effective use within the U.S. energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1372-1388.
    5. Seera, Manjeevan & Tan, Choo Jun & Chong, Kok-Keong & Lim, Chee Peng, 2021. "Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm," Energy, Elsevier, vol. 223(C).
    6. Hong, Kai Jeat & Tan, Sin Tee & Chong, Kok-Keong & Yap, Chi Chin & Hj Jumali, Mohammad Hafizuddin & Loo, Yueh-Lin, 2019. "Numerical analysis with experimental verification to predict outdoor power conversion efficiency of inverted organic solar devices," Renewable Energy, Elsevier, vol. 135(C), pages 589-596.

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