Modeling bifacial PV performance under variable Albedo using Python: A case study of the Himalayan foothills, Kathmandu

Authors

  • Debendra Bahadur Raut Department of Automobile and Mechanical Engineering, Thapathali Campus, Institute of Engineering, Tribhuvan University, Thapathali, Kathmandu, Nepal
  • Bishal Kumar Department of Mechanical and Aerospace Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Pulchowk, Lalitpur, Nepal

DOI:

https://doi.org/10.3126/jiee.v8i1.86583

Keywords:

Bifacial PV, Bifacial radiance, Python, Albedo, Agrivoltaics, Ray-tracing, Specific yield

Abstract

The ground reflectance known as albedo has a significant impact on the performance of bifacial photovoltaic modules, which generate power from both their front and rear surfaces. Utilizing a Python-based workflow and NREL’s bifacial radiance tool, this study aims to assess the energy yield of bifacial PV under variations in ground reflectance and albedo for the case of Kathmandu utilizing site-specific Typical Meteorological Year (TMY) data. An array of a 590 Wp bifacial module with a bifaciality factor of 0.80 is simulated at 4.0 m row pitch, 1.0 m clearance height, and 30° tilt. This study uses ray tracing simulations for a range of albedo values, from 0.18 to 0.75, taking temperature and loss correction factors into account. The findings show that bifacial gain increases almost linearly with albedo, from around 10% at 0.18 to 36% at 0.75. The results suggest that the performance of bifacial PV, particularly in agrivoltaic systems, can be significantly improved by reflecting ground cover and optimized array design. The Python-based approach offers a framework that may be modified for different locations and systems of a similar nature.

 

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Published

2025-12-31

How to Cite

Raut, D. B., & Kumar, B. (2025). Modeling bifacial PV performance under variable Albedo using Python: A case study of the Himalayan foothills, Kathmandu. Journal of Innovations in Engineering Education, 8(1), 117–126. https://doi.org/10.3126/jiee.v8i1.86583

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Articles