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Pro Tips for Passing a PV System Capacity Test
The fundamental objective of **ASTM E2848**, "Standard Test Method for Reporting Photovoltaic Non-Concentrator System Performance," is to evaluate the actual performance of a photovoltaic (PV) power plant against its expected performance based on a system model. For those new to PV capacity testing, understanding the model and schedule is essential. However, there are several nuanced factors that can significantly impact the accuracy and success of the test.
In this article, I'll explore some key considerations—such as weather files and shade models, commissioning and instrumentation, seasonality and location, and technology and design. By focusing on these areas, you can improve your chances of passing the capacity test more efficiently.
### Weather File & Shade Model
While many inputs influence a PVsyst model, the **weather file** and **shade model** have the most significant impact on long-term energy yield. Since the capacity test is normalized for weather conditions, the shade model must be carefully considered. A shade model is not just important—it's a requirement. Omitting it can lead to overestimations in the modeled results, setting unrealistic expectations.
Shade models also help identify data points that should be excluded during testing, ensuring only optimal conditions are used. Additionally, 3D terrain affects the average plane-of-array (POA) irradiance, which directly impacts the accuracy of capacity tests. If field performance doesn’t match the model, it’s often due to an improperly configured shade model.
### Commissioning & Instrumentation
Proper **commissioning** and **instrumentation** are critical for accurate capacity testing. Before testing, the site must be fully operational and free of outages. It’s recommended to conduct at least a 48-hour pre-qualification period to ensure the system is stable.
Instrumentation is often overlooked, but sensors must be correctly aligned and calibrated before testing begins. Issues like misalignment or incorrect calibration can skew results. Key checks include verifying POA sensor placement, ensuring temperature sensors are not affected by direct sunlight or cold spots from racking, and confirming proper correlation between ambient and module temperatures.
For sites with complex topography, the POA sensor should reflect the actual irradiance across the entire site—not just the design specification. This ensures the model accurately represents real-world performance.
### Seasonality & Location
The time of year and geographic location play a major role in the success of a capacity test. ASTM E2848 outlines acceptable conditions and disqualifiers, such as low or high irradiance, shading, or inverter power limiting. These factors are often interconnected; for example, low irradiance may coincide with shading, while high irradiance can lead to inverter clipping.
In winter, daylight hours are limited, reducing the window for testing. If two hours in the morning and two in the evening are excluded due to low irradiance, only four hours remain. Add in clipping during peak hours, and you’re left with just two hours—or eight 15-minute intervals—for testing. Given that 50 valid data points are needed, a week of ideal weather might be required. This makes winter testing impractical in certain regions, so planning ahead is crucial.
### Technology & Design
Finally, the choice of technology and system design can affect how you conduct the test. For instance, **bifacial modules** require additional sensors to account for rear-side irradiance. The total POA for testing is the sum of front and rear irradiance, which must be included in the regression equation.
For systems with a **high DC-to-AC ratio**, adjustments may be necessary. If inverters limit power for more than half the day, consider temporarily disabling trackers or reducing DC capacity. Whatever approach you take, make sure the model reflects the modified system behavior.
By addressing these key areas, you can increase the likelihood of a successful capacity test and avoid costly delays.