Soiling Assessment in Large-Scale PV Arrays: Page 3 of 5
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The best way to estimate the impact of soiling is to compare operational data to plant performance under clean conditions, which we refer to as the plant baseline. Obtaining a performance baseline is a process of characterizing the electrical performance of source circuits, combiners, inverters or an entire plant and isolating these data for frequent comparison. The goal of establishing a baseline is to understand how the system or subsystem performs under known operating conditions when the array is free of faults and unsoiled. Generally speaking, a rough plant baseline is good enough.
Establishing a clean plant baseline is more of a process than an event. The logical opportunity to obtain a baseline for an entire plant is at the time of initial back-feed, testing and commissioning. If you want to get two detailed answers at once, you can perform a full-plant baseline characterization in parallel with performance testing, which is ideal. However, you can establish a baseline at any system level, over any duration of time and under any operating conditions. Nothing is lost if you are unable to characterize some parts and pieces at commissioning. You can always revisit and recalibrate these parts later and make sure that they fit the general performance trend once they are up and running. As long as you restore malfunctioning blocks to operation and characterize their performance using the same measurement methods, the baseline will be accurate and useful despite its piecemeal assembly.
There are various means of applying the baseline. The simplest form—comparing dirty versus clean performance—is effective for both long- and short-term analyses. By characterizing the plant according to its big pieces, such as inverters, skids or ac collection circuits, you can compare these results to one another, normalize dirty results against the clean baseline and make informed decisions about soil abatement. You can express the baseline in whatever terms best suit your goals, such as specific yield (kWh/kW) or energy output in relation to POA irradiance. The latter is useful if you need to tie actual performance back to expected performance based on an energy model.
Since assumptions, data resolution and as-built conditions constrain energy models, we strongly recommend that you use operational data rather than modeled plant behavior as the basis of comparison. Whereas an energy model describes how the plant is supposed to behave, measured data describe how the plant actually behaves. In broad terms, energy modeling software applies soiling assumptions as an effective monthly reduction in POA irradiance and essentially stops there. One-month averages for soiling levels can shore up production and revenue models, but they have little to say about soiling events, differential energy impacts or soiling rates in general. As a result, the input/output resolution for an energy model is far less precise than it is for most operational datasets.
End use and accuracy drive the baseline characterization method. Production losses can be very subtle, typically only a few percentage points, before they become noticeable, so accuracy is vitally important to tying production losses specifically to soiling.
The simplest characterization method is to catalog plant production at the meter as well as measured irradiance in the plane of array. Since this obviously ignores thermal differences within the array, for increased accuracy you may need to apply a temperature compensation to account for deviations from weather station conditions. You also need to remove or ignore performance issues that are not related to soiling, such as module degradation, equipment failures and configuration differences. Soiling analysis has to quantify or transcend these factors to reach a reasonable conclusion.
To illustrate the challenge: A POA irradiance sensor might have an accuracy of ±1.0%; ac power measurement transducers are typically accurate within ±0.2%; dc transducers are rarely better than ±1.0% accurate; secondary measurements, such as temperature and wind speed, have ±2% accuracies at best. These measurement errors typically compound rather than cancel one other. Compounded, these uncertainties suggest that isolating a few percentage points of performance loss using gear with measurement errors of a few percent can produce dubious results.
The net result is that a thorough soiling analysis could very well estimate that modules are 4.5% soiled, plus or minus 2%. Given these uncertainties, module washing may or may not be cost effective. While no one likes this type of answer, it is often the case that soiling analysis results have a high degree of uncertainty.