Simulating Atmospheric Dust Impact on Photovoltaic Performance: A sensitivity analysis to guide modelling choices in a data scarce region

As solar photovoltaic (PV) systems are deployed globally to decarbonize energy production systems, atmospheric dust has emerged as a critical challenge due to its potential to drastically reduce production efficiency in many regions. Dust particles both attenuate incoming solar radiation and accumulate on photovoltaic module surfaces thereby reducing light transmission and power output. Soiling losses (defined as power production losses due to dust accumulation on PV panels) vary at daily, monthly and interannual timescales, as dust accumulation and removal processes depend on time-varying factors such as particulate matter concentration, wind, relative humidity, precipitation and cleaning operations. Capturing these dynamics thus requires assessments spanning several years.

Numerous studies have examined dust impacts on solar power generation, most relying on observations from solar farms or experimental sites. However, such observations remain scarce and often cover short time periods, particularly in data-scarce regions thus preventing comprehensive dust impact assessments. Dust simulation models offer an alternative approach: they enable the reconstruction of dust accumulation dynamics and their impacts on power production from meteorological data over extended periods.

This simulation approach was applied by Isaacs et al. (2023) for West Africa with atmospheric reanalysis (MERRA-2) and satellite-derived data. However, the extent to which input data and modelling choices may influence the conclusions of simulated estimates remains unclear. Reanalysis products are subject to substantial uncertainties and errors, especially in regions where ground-based observations used for their development are scarce. Dust models also typically rely on simplified process representations and poorly constrained parametrizations.

In this study, we introduce PVWAT, a simple dust simulation model developed for dust impact assessment as part of the ANR-funded NETWAT project, which examines water-energy nexus challenges in West Africa. Linking different sub-models from literature, it uses meteorological inputs from on-site observations or atmospheric reanalysis to simulate time series of dust deposition fluxes, deposited dust amounts and the resulting soiling losses.

We then use PVWAT to demonstrate how simulated dust impacts depend on input data and modeling choices. For this, we consider West Africa, a hot spot for dust-related PV production losses. The region's high solar potential and unmet energy demand are expected to drive large PV expansion in the coming years (10+ GW of solar capacity by 2030; IRENA, 2023) but the region borders the Sahara and Bodélé depression, the world's most prolific dust source. Our analysis considers three sites along a north-south transect, representing contrasting dust conditions, climates (arid to humid), and land covers (savanna to tropical forest), in order to draw recommendations for diverse solar production contexts.

Through systematic sensitivity analysis, we perturb model parameters up to 8× and meteorological variables up to 2× to quantify their effects on long-term soiling ratios. This reveals the dominant sources of uncertainty and assesses how the model responds to parametric versus variable perturbations across contrasting sites.

References
International Renewable Energy Agency. (2023). Scaling up renewable energy investments in West Africa. https://www.irena.org
Isaacs et al., 2023. Dust soiling effects on decentralized solar in West Africa. Applied Energy, 340, 120993. https://doi.org/10.1016/j.apenergy.2023.120993