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24 mavromatakis vignola_spectral_corrections_for_pv_performance_modelling

Spectral corrections for PV performance modelling

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24 mavromatakis vignola_spectral_corrections_for_pv_performance_modelling

  1. 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. 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. 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. 4. SPECTRAL RESPONSIVITY
  5. 5. GHI SPECTRAL DISTRIBUTION
  6. 6. SPECTRAL EFFECTS UPON ISC
  7. 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. 8. CHANGE IN AVERAGE CLEAR SKY DNI RESPONSE
  9. 9. NORMALIZED TO 45
  10. 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. 11. CHANGE IN AVERAGE CLEAR SKY DfHI RESPONSE
  12. 12. CHANGE IN AVERAGE CLEAR SKY GHI RESPONSE
  13. 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. 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. 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

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