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Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm

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  • Seera, Manjeevan
  • Tan, Choo Jun
  • Chong, Kok-Keong
  • Lim, Chee Peng

Abstract

A novel comprehensive methodology integrated with genetic algorithm (GA) has been formulated by accounting for local spectral irradiance and the specifications of commercial photovoltaic (PV) module. Despite having the same solar irradiance, variation in power conversion efficiency (PCE) of PV module can be significant in different site locations by referencing to that of standard AM1.5 spectrum. We have carried out a case study using GA for a combination of three site locations in Peninsular Malaysia and twelve types of commercial PV modules. Type-9 PV module operating in Bangi has recorded the lowest gain with 0.1%, while type-10 PV module operating in Bandar Sungai Long has shown the best gain of up to 27%. For solving multi-objective problems, MmGA and NSGAII have been applied to optimize three objectives concurrently including PCE, PV weight and PV panel area. From the simulation, type-1, type-6, and type-12 PV modules show the best in at least two objectives for the categories of m-Si, p-Si, and thin film, respectively.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s0360544221002589
    DOI: 10.1016/j.energy.2021.120009
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    3. Sammy J. Martínez-Deusa & Carlos A. Gómez-García & Jaime Velasco-Medina, 2023. "A Platform for Outdoor Real-Time Characterization of Photovoltaic Technologies," Energies, MDPI, vol. 16(6), pages 1-16, March.
    4. Siyu, Liu & Kangwen, Sun & Jian, Gao & Haoquan, Liang, 2023. "Receiving energy analysis and optimal design of crystalline silicon solar cell array on solar airship," Energy, Elsevier, vol. 282(C).
    5. 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.
    6. Belsky, A.A. & Glukhanich, D.Y. & Carrizosa, M.J. & Starshaia, V.V., 2022. "Analysis of specifications of solar photovoltaic panels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    7. Md. Imamul Islam & Mohd Shawal Jadin & Ahmed Al Mansur & Nor Azwan Mohamed Kamari & Taskin Jamal & Molla Shahadat Hossain Lipu & Mohd Nurulakla Mohd Azlan & Mahidur R. Sarker & A. S. M. Shihavuddin, 2023. "Techno-Economic and Carbon Emission Assessment of a Large-Scale Floating Solar PV System for Sustainable Energy Generation in Support of Malaysia’s Renewable Energy Roadmap," Energies, MDPI, vol. 16(10), pages 1-32, May.

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