SolarAnywhere® Support for Snow-loss ModelingReduce PV performance uncertainty with specific, data-driven snow loss estimates using time-series data
Accumulated snow can reduce the energy output of a solar PV system by obstructing the sunlight available for energy conversion. PV energy losses due to snow—commonly referred to as “snow losses”—can vary widely between locations and time periods.
Estimating snow losses accurately can help PV system owners and financiers reduce financial and operational risk to their solar projects. However, several factors can impact the magnitude of snow losses. The local climate, as well as the PV system’s configuration (such as the tilt and tracking type) can determine the rate of snow accumulation and shedding. Additionally, since the local weather is always in flux—changing from season to season and year to year—snow losses can exhibit high seasonal and inter-annual variability.
By incorporating the full-time series weather data at a project location with a snow loss model, customers can more accurately predict the impact of snow losses on PV energy output.
Using time-series weather data to improve the accuracy of snow loss estimates
With SolarAnywhere Sites , customers can access full historical time-series weather data at their project site. Detailed information about daily weather patterns such as snow depth and ambient temperature can be leveraged by energy simulation tools to generate site-specific loss estimates.
To understand how snow loss estimates can be made more accurate, we compared two approaches:
- A “Generic Loss Approach” that uses loss estimates provided in an NREL study of the PV snow loss model in the System Advisor Model (SAM)
- A “Specific Loss Approach” that incorporates SolarAnywhere time-series data with the snow loss model in SAM
Key differences between the two approaches are listed in Figure 1.
Figure 1: Comparison of Generic Loss Approach with Specific Loss Approach
|Generic Loss Approach
|Specific Loss Approach
(SolarAnywhere Time-series Data SAM)
|Snow Loss Model||Marion||Marion|
|Configurations estimated||20-degree and latitude fixed-tilt||Any|
|Spatial resolution of snow data||Interpolated from 239 locations||4 km|
|Weather period||1961-1990||2004 – present|
Annual Snow Losses
We modeled annual snow losses for an arbitrary utility-scale PV system (50 MWdc, DC:AC ratio 1.3) with a 20-degree fixed-tilt. The system was placed across five different locations in the U.S. Figure 2 shows that the Generic Loss Approach can result in both over- or under-estimation of snow losses at an energy site.
Figure 2: Annual Snow Losses Using Generic and Specific Loss Approaches for the Year 2020
So what does this mean for PV energy estimates? As shown in Figure 3, the difference in annual energy yield using the Generic and Specific Loss Approaches can easily reach 7-8%, and this can further vary from year to year and location to location.
Figure 3: Difference in Annual Energy Yield Using Generic and Specific Loss Approaches for the Year 2020
|Location||Annual Energy Yield (kWh/kW)
Generic Loss Approach
|Annual Energy Yield (kWh/kW)
Specific Loss Approach
|Absolute difference in energy yield (%)|
Fort Peck, MT
The system configuration (such as tracking type and tilt) can further affect the magnitude of energy losses. To demonstrate this, NREL performed a study comparing five different system designs at an energy site in Colorado. As shown in Figure 4, energy losses can easily exceed 9% for a low-tilt, fixed PV system.
Figure 4: Comparison of Annual Energy Output for Different System Configurations
Calculating P50/P90 snow loss estimates
Using multiple years of time-series weather data can improve the accuracy and reliability of average snow loss estimates by accounting for the effect of inter-annual variability. To demonstrate this, we calculated P50/P90 snow loss estimates in SAM using SolarAnyhwere historical time-series weather data from 2004-2020 and compared them with generic estimates. An arbitrary utility-scale PV system was placed across five different locations in the U.S. Two energy site configurations were considered: a 20-degree fixed-tilt and a single-axis tracking PV system.
Figure 5 shows how the P50/P90 estimates can be made more data driven and site specific with time-series modeling compared to generic estimates derived from typical meteorological year (TMY) weather data. Often, financing terms may be set on downside annual energy production (e.g., a P90 or P99 estimate). Therefore, it’s imperative to consider how the power plant performs in that scenario. Improving the accuracy of P90 loss estimates with time-series modeling can reduce the uncertainty in reported energy estimates. This helps project owners minimize the financial and operational risk of their solar project.
Figure 5: Snow Losses for a 20-degree Fixed-tilt and Single-axis Tracking PV System Using Generic and Specific Loss Approaches
Using SolarAnywhere Sites data with a snow loss model
To learn how SolarAnywhere Sites data can be used with a snow loss model, watch the video.
On July 16, 2021, this page was updated using corrected snow depth data.