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in date 14/06/2013

Uncertainty Estimations of PV Outdoor Measurements

The webinar offers comprehensive information on different aspects on Uncertainty Estimations of PV Outdoor Measurements to properly estimate energy yield.


Useful information

Not only the measurement equipment itself contribute to uncertainty but also site-specific aspects, calibration of the applied equipment, the models and the uncertainty estimation method have an impact on the overall result. Starting from uncertainties of measurement devices like irradiance sensors and I-V-curve measurement systems, different calculation methods are presented and discussed.

The importance of the experimental design is addressed in the final discussion. In fact specific energy yield in kWh/kWp is also the most interesting parameter for investments in photovoltaics


Agenda


Details

Topic 1: Uncertainty of Pmax calibration of PV modules

Speakers : MÜLLEJANS Harald

The measurement uncertainties for the determination of maximum power of PV devices at a reference laboratory are presented based on the accreditation of the European Solar Test Installation (ESTI) for the calibration of photovoltaic devices. The various contributions to the final uncertainty are listed and discussed in detail, evaluating their contribution. Typical numeric values for measurement uncertainty in the calibration of PV devices with solar simulators and under natural sunlight are given

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Topic 2: Uncertainty Estimation of I-V-Curve Measurement Devices

Speakers : KIRCHHOF Jörg

Using the ISET-mpp meter as example, the sources of uncertainty and its estimation will be presented. The information given by the calibration report and by the documentation of the ISET-mpp meter will be used and a comparison between basic uncertainty of a not calibrated meter and the uncertainty of a calibrated ISET-mpp meter at a specific module will be done.

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Topic 3:PV Energy Yield Uncertainty Estimations using the propagation of uncertainty method

Speakers :

The estimation of uncertainties for PV module energy yield measurements under outdoor conditions is suggested by applying the propagation of uncertainty method. This method allows estimating the uncertainty of the applied measurement equipment. The energy yield of a certain PV module is normalized to its performance at STC allowing to compare the energy yield of different PV modules site specifically. The overall uncertainty of long-term PV outdoor energy yield measurements is always limited to the quality of the test site and its maintenance. Effects like soiling or MPPT tracking uncertainties are not considered within this method.

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Topic 4: Calculation of the uncertainty of a PV electricity yield simulation

Speakers : SPRENGER Wendelin

The presentation summarizes the most common statistical measures applied for the quantiative analysis of the uncertainty of an electricity yield simulation. The measures are compared, and their advantages and disadvantages are listed. Random and systematic errors are distinguished. For the case of the annual simulation of the time-dependence of a physical quantity, it is shown that measures stressing the correlation between simulation and measurement is the most useful and applicable option. Statistical results gained from measurements are presented for several models for simulating different physical quantities

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Topic 5: Uncertainties in the outdoor performance assessment of PV modules – the importance of the experimental design

Speakers : GOTTSCHALG Ralph

The paper investigates when variations in the specific yield (kWh/kWp) in the inter-comparisons of photovoltaic modules are meaningful. A model for the estimation of uncertainties is developed and used to quantify uncertainties in the comparability of devices. It is shown that the absolute measurement uncertainty is not the critical determinant in the comparability in the measurements. The major contributions are in the selection of modules, the value chosen for the normalisation (i.e. kWp value) and the naturally occurring environmental variations across the sites. It appears to be very difficult to get a comparability better than 4.5% (k=2), which means that differences in the specific yield below this are actually not statistically meaningful. The only way to overcome this is to measure statistical number (n>2) to get a better comparability

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