This document discusses using spectral corrections to better model PV performance. It presents an overview of how the spectral distribution of sunlight changes over the day and affects PV module performance. Models that account for the spectral response of modules and changing spectral irradiance can provide more accurate estimates of performance metrics like current and power output. The document shares results of analyzing spectral data that demonstrate variations in average module response under direct normal, diffuse horizontal, and global irradiance over the course of a day due to these spectral effects. Future work aims to refine estimates of air mass effects and integrate spectral modeling into broader PV modeling.

1. SPECTRAL CORRECTIONS FOR
PV PERFORMANCE MODELLING
Fotis Mavromatakis – T.E.I. of Crete
Frank Vignola – University of Oregon
4th PV Performance Modelling and Monitoring Workshop – Cologne, Germany
October 2015

2. INTRODUCTION
• Overview
• Spectral responsivity of PV modules
• Changing spectral distribution over the day
• Effects of spectral changes on module performance
• DNI
• DfHI
• GHI
• Utility of understanding the influence of changing spectral irradiance
• Next steps

3. OVERVIEW
• Energy of the photon (wavelength) affects the performance of solar cells
• Modeling and testing efforts by King et. al. in the late 1990’s showed that PV
module performance is dependent upon
• Incident irradiance
• Module Temperature
• Angle of Incidence
• Air Mass Effect/Spectral Correction
• This presentation is on the effects of the systematic changes in the spectral
distribution over the day and the affect on module performance estimates.
• Goal is to better understand and refine PV module performance
estimates

4. SPECTRAL RESPONSIVITY

5. GHI SPECTRAL DISTRIBUTION

6. SPECTRAL EFFECTS UPON ISC

7. CALCULATING AVERAGE PV
MODULE RESPONSE
The average PV module response is =
𝑃𝑉𝐷𝑁𝐼 =
𝑃𝑉(λ) × 𝐷𝑁𝐼(λ)λ=280
λ=4000
𝐷𝑁𝐼(λ)λ=280
λ=4000
Where PV(λ) = module’s spectral response and
DNI(λ) is the spectral radiation
Note this can be DNI, DfHI, or GHI

8. CHANGE IN AVERAGE CLEAR SKY
DNI RESPONSE

9. NORMALIZED TO 45

10. EXAMPLE OF A PHOTODIODE’S DNI RESPONSE
USING MEASURE DNI SPECTRAL DATA
Five months of measured DNI spectral
data from Payerne, Switzerland under
all weather conditions
Results from a previous study
The photodiode spectral response is
similar to a mono-crystalline spectral
response

11. CHANGE IN AVERAGE CLEAR SKY
DfHI RESPONSE

12. CHANGE IN AVERAGE CLEAR SKY
GHI RESPONSE

13. SPECTRAL DEPENDENCE OF THE
“AIR MASS” EFFECT
• The “Air Mass” effect is caused by the changing spectral distribution of the
incident solar radiation
• The different DNI and DfHI spectral compositions lead to different air mass
effects
• Each solar cell technology reacts differently to the changing spectral
distributions in the incident solar radiation
• The largest effects are in the early morning and late afternoons when the
sunlight traverses the largest air masses
• Changes in atmospheric conditions can affect the magnitude of the air
mass effect
• With SZA less than 45, the solar technologies examined have similar DNI air
mass effects

14. VALUE OF USING THE SPECTRAL
MODEL TO DETERMINE THE
AIR MASS EFFECT
• Using spectral models and data on the PV module spectral response
enables an estimate of the modules change in responsivity over the day
• This methodology enables estimates of PV module performance in location
(sites) with different atmospheric compositions
• Performance of different solar cell technologies can be compared at a
given site by just changing the module’s spectral responsivity in the modeling
process
• Improved estimates of PV module performance can be obtained if the
atmospheric conditions at a site can be specified
• The effect of various atmospheric conditions on the performance of PV
modules can be studied

15. FUTURE EFFORTS
• Determine the air mass effect for DfHI and GHI components under all
weather conditions
• Evaluate the variability of the spectral model performance estimates
• Integrate spectral model in with other PV model components