PlantPredict Soiling & Cleaning Extension
Powered by PVRADAR
- Import site-specific soiling loss factors right into your prediction
- Optimize cleaning schedules to minimize cost
- Daily or monthly soiling loss factors based on 20 years of historic data
Include accurate soiling loss factors in your predictions
- Predict site-specific monthly soiling loss factors (P50, P90, and P99) from historic meteorological data
- The model accounts for seasonal and year-on-year variations through a combination of satellite data and local meteorological measurements and site specific model parameters
- Daily soiling loss factors can be included in your prediction with a single click
Optimize cleaning cost and reduce soiling losses
- Cleaning is an investment. Find the right balance between soiling reduction and OPEX budgets
- Decide how often and when to clean based on site-specific historic precipitation patterns
- Compare all possible options: tractor plus brush, semi- and fully-autonomous cleaning robots, dry and wet
From your prediction to an accurate soiling loss estimation in under 5 minutes
- Within Terabase PlantPredict, click on the Soiling & Cleaning Extension (with Pro Tools)
- You are directed to PVRADAR. Select the prediction you are currently working on from the list
- Review soiling modeling results
- Optimize cleaning based on your preferences
- Once you are done, add daily soiling loss factors to your prediction’s weather file in PlantPredict
- Print a PDF report including a summary of all data-sources, model parameters and results
PVRADAR is trusted by leading renewable energy companies!
Esther Gomez Ruiz
Head of Studies and Modeling, Global Engineering
Types of Soiling
Soiling refers to the accumulation of substances or materials blocking part of the incoming irradiance and thereby reducing the electrical output of PV modules. Soiling losses vary by location, influenced by climatic conditions, vegetational cycles, human activity, and other factors, and usually result from the combined effect of various pollutants.
Climatic effects
Dust
Dust is the primary source of soiling in dry and arid environments, such as deserts. Originating from mineral sources, dust can be effectively cleaned by rain. However, after extended dry periods, losses can be significant.
Snow
Snow soiling occurs when snowfall covers PV modules. Although this type of soiling can be severe, it is typically short-lived because the snow tends to slide off, melt, or be blown away by the wind. The impact of snow soiling is highly dependent on the orientation and mounting structure of the modules. While cleaning machines can remove snow, such an approach is usually not cost-effective.
Pollen
Pollen soiling, caused by nearby vegetation, is highly seasonal. Although rain can wash away some of the pollen, a significant portion may adhere to the modules in the presence of high humidity, forming persistent layers that require wet cleaning. Initially, the impact of pollen soiling may be minimal, but it can accumulate over the years, leading to a substantial reduction in production efficiency.
Agriculture
Industrial and agricultural activities can create super-localized soiling effects, which change over time as land use evolves. These effects are challenging to predict and must be factored in by engineers. Other examples of localized soiling sources include livestock, mines, and unpaved roads.
Birds
Bird droppings is a form of highly localized soiling that is very difficult to predict. Bird droppings can create hot spots on PV modules, leading to potential damage and efficiency loss. In areas with large bird populations, regular maintenance and cleaning schedules are essential to mitigate these effects.
PVRADAR Soiling Model
The PVRADAR soiling model allows predicting lifetime soiling losses for utiltiy-scale PV power plants:
- Combination of Satellite Data and Local Meteorological Measurements: Our model uses data describing the concentration of dust and other particle in the atmosphere (e.g., PM2.5 and PM10), precipitation, and other meteorological factors. By combining satellite data with measurements from the nearest weather stations, we account for historical patterns and seasonal impacts.
- Location-Specific Model Parameters: We estimate model parameters, such as the rain cleaning threshold, using soiling measurements from various geographies and climatic regions. This approach tailors the model to the specific conditions of each location.
- Consideration of Plant Technical Parameters: To ensure accurate assessments our model factors in essential plant technical parameters, such as mounting structure type, maximum tilt angle, and night stow angle.
35% lower error compared to Pvlib HSU
Since 2017, our engineers have been actively involved in measuring, predicting, and preventing soiling losses. The PVRADAR team is constantly working to improve the performance of its soiling models by building an extensive database of soiling measurements from locations worldwide.
Here are 10 examples from locations in the USA, showing a comparison of the PVRADAR model with the HSU and KIMBER models available in pvlib. The data for this example has been gathered by NREL [1] and is available in the Duramat Datahub. Results for pvlib HSU and pvlib Kimber were calculated using their default inputs.
Model example is not loaded yet.
[1] Micheli, L., Ruth, D., Deceglie, M. C., & Muller, M. (2017). Time Series Analysis of Photovoltaic Soiling Station Data (NREL/TP-5J00-69131). NREL.
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