Composition-Resolved Spectral Absorption and Thermal Heating Effects of Dust Layers on Crystalline Silicon Photovoltaic Modules
DOI:
https://doi.org/10.70917/jcc-2026-007Keywords:
Spectral soiling, Dust composition, Optical-thermal modelling, Crystalline silicon photovoltaics, Performance degradation, Dust absorptanceAbstract
Dust deposition on photovoltaic modules induces losses through wavelength-selective attenuation and dust-driven heating, but most soiling models quantify only optical transmission. The present study developed a composition-resolved optical-thermal framework that links measured dust mineralogy to coupled spectral and temperature-dependent power degradation in crystalline silicon modules. Dust was collected from operating PV modules in Harare (Zimbabwe) and Roodepoort (South Africa) and characterized for particle size, mass loading, and oxide composition. Oxide fractions were converted to effective complex refractive indices using an effective-medium formulation, and Mie-based calculations were used to compute spectral absorptance in the range 400-900 nm. The absorbed radiative flux was then introduced as a heat-input term in a surface energy-balance model to predict dust-induced cell-temperature rise, and the modified spectrum and temperature were propagated to electrical metrics. At a representative loading of 2.0 g/m2, the model predicted a temperature rise of 4.8 °C for Roodepoort dust and 4.6 °C for Harare dust. The corresponding total power losses were 14.2% and 13.8%, respectively. Solar simulator validation showed measured temperature rises of 4.52 °C and 4.29 °C for Roodepoort and Harare dust, giving deviations of 5.73% and 6.81% from the model predictions. Using the measured temperature rises and a crystalline silicon temperature coefficient of 0.45%/°C, the temperature-aligned total power losses were estimated as 14.08% and 13.66% for Roodepoort and Harare dust, respectively. These results confirm that dust induced heating amplifies optical soiling losses and should be included when forecasting yield and defining cleaning triggers in dusty, high irradiance environments.
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