As the global transition to renewable energy accelerates, high-efficiency solar photovoltaic (PV) technologies such as TOPCon (Tunnel-Oxide Passivated Contact), heterojunction (HJT), and perovskite tandem solar cells are becoming central to the design of future solar farms. However, their energy yield and reliability are highly sensitive to local atmospheric conditions. While standard energy yield models often rely on broadband irradiance assumptions, recent studies reveal that variations in solar spectral distribution, especially due to clouds, aerosols, water vapour, and air mass, significantly influence the performance of advanced solar cells.
This research project aims to develop and apply spectral modelling frameworks to better understand how high-efficiency solar cells respond to dynamic atmospheric conditions, with a focus on cloud-induced spectral shifts. Using a combination of radiative transfer simulations (e.g., SMARTS, libRadtran), ground-based spectral measurements, and high-resolution weather and satellite data, we will quantify the spectral content of sunlight under a range of sky conditions clear, overcast, partly cloudy, and polluted.
School
Photovoltaic and Renewable Energy Engineering
Research Area
Renewable energy | Atmospheric science | Computing science | Software engineering
Suitable for recognition of Work Integrated Learning (industrial training)?
No
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
The student will work as part of the modelling group at SPREE. They will be supported by a team of postdocs and HDR students. The group works with data from commercial partners, as well as publicly available datasets. The work will consist of data analysis, programming and physical modelling, with support provided for area's outside the student's core competence.
Characterise the spectral response of advanced PV technologies under real-world, time-resolved atmospheric conditions. Model the impact of cloud optical thickness, aerosol loading, and water vapour on incident spectra and resulting solar cell performance.
- Ripalda, J.M., Buencuerpo, J. & García, I. Solar cell designs by maximizing energy production based on machine learning clustering of spectral variations. Nat Commun 9, 5126 (2018). https://doi.org/10.1038/s41467-018-07431-3
- Kouklaki, D.; Kazadzis, S.; Raptis, I.-P.; Papachristopoulou, K.; Fountoulakis, I.; Eleftheratos, K. Photovoltaic Spectral Responsivity and Efficiency under Different Aerosol Conditions. Energies 2023, 16, 6644. https://doi.org/10.3390/en16186644