Snow loss

Overview

Snowfall significantly affects photovoltaic systems by blocking solar irradiation, leading to power production losses. This phenomenon is particularly challenging in regions with frequent snowfall, such as parts of Europe, North America, and Asia. The impact of snow on PV systems is difficult to mitigate, as manual or robotic cleaning is often impractical, especially when snow freezes onto the modules. Effective forecasting and planning are crucial for managing these impacts, such as reporting to grid operators or purchasing balancing power.

Modeling snow losses involves using global meteorological models like ERA5 and Solargis satellite data. These models provide valuable insights into snowfall patterns and their effects on PV systems. However, challenges arise from data resolution, validation, and the integration of meteorological data with PV system models. This document discusses these challenges and presents a methodology for estimating snow losses, including the categorization of snow events and the extrapolation of models to global scales.

Modeling snow losses in PV systems

Introduction to snow losses

Snow on PV modules causes significant power production losses due to blocked solar irradiation. This is an undesirable phenomenon, especially in regions where snowfall is frequent and prolonged. Manual or robotic cleaning is often impractical, and in cases where the snow has frozen onto the modules, it becomes virtually impossible. The only effective way to mitigate these impacts is through accurate forecasting and planning, which can include reporting to grid operators, purchasing balancing power, or even investing in PV power in snow-prone areas.

The snow losses model

The model uses data from the ERA5 global meteorological model and Solargis satellite-model solar radiation data.

• ERA5 provides hourly meteorological data, including fresh snow depth, air temperature, and global tilted irradiation.

• Solargis data offers solar radiation information, which is crucial for estimating PV power production under normal conditions.

Data Sources and Resolution

Snow Event Categorization

Snow events are categorized into three types based on their impact on PV power production:

• Partial Losses: Some solar energy is absorbed by the snow, but not all. This typically occurs when a thin layer of snow covers the modules, allowing some irradiation to pass through.

• Total Losses: All solar energy is absorbed or reflected by the snow, resulting in no power production. This occurs when the snow cover is thick enough to block all sunlight.

• Snow Slide Events: Snow slides off the PV modules rapidly, often triggered by rising temperatures. This results in a sudden increase in power production as the modules are cleared of snow.

Figure 1: Illustrates typical daily patterns representing partial loss, total loss, and snow slide events.

Model Simplification and Parameters

A simplified variant of the dynamic snow loss model is used, adjusting input parameters to effectively utilize ERA5 outputs. The model incorporates meteorological parameters such as fresh snow depth water equivalent, air temperature, and global tilted irradiation. These parameters are crucial for accurately simulating snow coverage and its impact on PV systems.

Challenges and Limitations

Snow losses calculation time during the simulation depends on the number of segments in the energy system. More segments result in longer estimation computations.

Global Extrapolation

The model is extrapolated globally using ERA5 data, allowing for the creation of a global map illustrating expected long-term average monthly snow pollution losses. However, this approach carries risks due to local variations in climate and topography. A comprehensive approach involving diverse geographical data and regional models is necessary for accurate predictions.

Figure 2: Shows a global monthly map of expected monthly snow losses (long-term average).

Year-to-year variability

Long-term average maps alone are insufficient for predicting snow losses due to significant year-to-year variability. To highlight areas at risk of substantial losses, Figure 3 presents a map showing the minimum (P10), maximum (P90), and standard deviation of projected snow losses in January from 1994 to 2022. This approach provides a clearer picture of potential snow impacts.

Figure 3: Global maps depicting fundamental statistical parameters of projected monthly snow losses for January (1994–2022).

Tilt angle effect

The impact of snow on photovoltaic systems also varies significantly based on the tilt angle of PV modules. Modules with steeper tilts accumulate and shed snow differently compared to those with shallower tilts, affecting how snow interacts with the panels and impacting energy production. Figure 4 illustrates how different tilt angles influence the expected average monthly snow losses in December from 1994 to 2022. Extrapolating the model to various tilt angles can lead to larger errors, similar to geographical extrapolations, highlighting the need for precise modeling to account for these variations.

Figure 4: Global maps of average expected monthly snow losses in Decembers 1994-2022 in PV systems with different tilts: 10, 30, 50, and 60 degrees.

Conclusion

This document provides a comprehensive overview of modeling snow-related energy losses in photovoltaic systems used by Solargis. It highlights the use of global meteorological models, such as ERA5, to estimate snow losses at specific locations. The approach involves categorizing snow events into partial, total, and snow slide losses, which are crucial for understanding the impact of snow on PV power production.

Key findings emphasize that while the model demonstrates satisfactory performance, further refinement is necessary. High accuracy from global models based on limited sites and low-resolution data may be unrealistic. Incorporating data from a wider range of locations worldwide is essential for improving model performance.

The document also underscores the complexities of snow loss modeling, including the intricate physics of snow melting and sliding, influenced by factors like heat transfer and surface properties. Additionally, real-world PV system layouts introduce significant variability, complicating accurate predictions and necessitating site-specific calibration and validation.

By acknowledging these challenges and continually refining our models, we can better support the planning and operation of PV systems in snowy regions, ensuring more reliable energy production and grid stability.

More details about the snow loss model development and the results of its validation were presented at the 40th EU PVSEC 2023, Lisbon, Portugal.

Further reading

• Instrumentation for Evaluating PV System Performance Losses from Snow. B. Marion, J. Rodriguez, and J. Pruett.

• Photovoltaics and snow: An update from two winters of measurements in the SIERRA. T. Townsend and L. Powers.

• Dynamic Snow Loss Model in PVSim: Modeling Impact of Snow on PV Production. D. Gun, M. Anderson, G. Kimball, and B. Bourne.