25 ben duck_improved_prediction_of_site_spectral_impact
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This document discusses improved methods for predicting the spectral impact on PV systems based on commonly available data. It presents a modified CREST model that uses the true spectral response rather than a useful fraction to better account for variations due to cloudy conditions. Evaluation shows this modified CREST-WUF model significantly outperforms existing models in predicting daily spectral impact factors for different module types. While this approach captures more variation than previous models, further work is still needed to fully characterize the relationship between site properties and spectral offset to enable general predictions of spectral impact.
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25 ben duck_improved_prediction_of_site_spectral_impact
- 1. Improved prediction of site spectral impact Dr Benjamin Duck, Dr Chris Fell 16 June 2015 ENERGY FLAGSHIP
- 2. CSIRO PV Outdoor Research Facility 4th PVPMC Workshop – Cologne – 21st October 2015
- 3. The problem of spectrum 4th PVPMC Workshop – Cologne – 21st October 2015 • OBJECTIVE – Method for determining the impact that changes in the spectral irradiance distribution has on PV for both validation (historical) and forecasting (predictive) at arbitrary locations • METRICS – Spectral mismatch factor – Spectral impact factor • PROBLEM – Detailed spectral information is typically unavailable – Existing models have difficulty making accurate short timescale predictions • QUESTION – Can we improve on existing methods for predicting spectral impact based on commonly available data
- 4. Impact of changes to spectrum 4th PVPMC Workshop – Cologne – 21st October 2015 c-Si measured daily SIF c-Si measured mismatch • Impact depends on timescale – Instantaneous > 25% – Affected by instantaneous cloud cover – Daily > 10% – Days with constant clouds are rare – High air mass compensation – Yearly = 1% – Averaging reduces impact significantly
- 5. • Directly measured mismatch (spectroradiometer) • In-directly measured mismatch (short circuit current) Measuring spectral mismatch 4th PVPMC Workshop – Cologne – 21st October 2015
- 6. • Directly measured results agree reasonably with indirect results from module Isc values. • Variation in data is well matched • Seasonal changes are duplicated • Small differences may be due to • Low light level performance • Unacounted for seasonal changes Measuring spectral mismatch II 4th PVPMC Workshop – Cologne – 21st October 2015 CIGS c-Si CdTe
- 7. • Without direct measurements spectral mismatch must be estimated • Commonly used methods are: – Sandia array performance model (SAM) – CREST ‘utilization factor’ (PVsyst) • Underlying assumption: Spectrum at air mass 1.5 = AM1.5 Linking spectral mismatch to air mass 4th PVPMC Workshop – Cologne – 21st October 2015 King, Boyson and Kratochvil, Photovoltaic Array Performance Model, Sandia National Laboratories report SAND2004-3535, Albuquerque, NM (2004) Betts, Gottschalg and Infield, Spectral irradiance correction for PV system yield calculations,19 th European Photovoltaic Solar Energy Conference, Paris (2004)
- 8. Comparing measurements to predictions 4th PVPMC Workshop – Cologne – 21st October 2015 CdTe CIGS • Season independent offset • Daily SIF variation is not matched • Small average season independent offset • Large daily SIF variation not matched •Out of season behaviour not explained
- 9. Offset in measured Pmp data • Data corrected to 1000 W/m2 and 25 °C shows an offset PVSC 42 – 16th June 2015
- 10. Spectrum at air mass 1.5 ≠ AM1.5 reference • Data at 1000 W/m2, 25 °C and air mass = 1.5 shows an offset 4th PVPMC Workshop – Cologne – 21st October 2015
- 11. Clear skies vs Cloudy skies • Data when skies are not clear does not follow simple air mass model • CREST model attempts to capture this using clearness index • Original form uses bandgap based windowing of spectrum data. • Apply results from modified model using true spectral response. PVSC 42 – 16th June 2015
- 12. Spectral mismatch estimation – CREST • CREST uses a functional relationship between Air Mass, a clearness index (kt *) and the useful fraction of incident irradiance. • Useful fraction is calculated using a spectral windowing technique • Improvement is found by calculating spectral mismatch using the true spectral response (WUF) rather than the useful fraction. • Adds ability to account for cloud cover • Coefficients are found by fitting a surface to the spectral mismatch data. 4th PVPMC Workshop – Cologne – 21st October 2015 𝑓 𝐴𝑀, 𝑘 𝑡 ∗ = 𝑊𝑈𝐹𝑚𝑒𝑎𝑠 𝑊𝑈𝐹𝑟𝑒𝑓 = 𝑨 ∶ 𝑷 𝑘 𝑡 ∗ 𝑸 𝐴𝑀 𝑸 𝐴𝑀 = 𝐴𝑀 𝑚 𝑚 0 𝑷 𝑘 𝑡 ∗ = 𝑘 𝑡 ∗ 𝑛 𝑛 0
- 13. • Captures variations due to cloudy skies as well as seasonal changes. Modified CREST model predictions 4th PVPMC Workshop – Cologne – 21st October 2015 c-Si measured c-Si modelled (CREST)
- 14. Modified CREST model predictions 4th PVPMC Workshop – Cologne – 21st October 2015 CdTe CIGS • Site spectral offset is accounted for. • Variation in data due to cloudy conditions is replicated. • Site dependence is implicit due to fits to specific site data. • Possible to use the site spectral offset as a scaling factor?
- 15. Comparison of impact 4th PVPMC Workshop – Cologne – 21st October 2015 Module Type Model MBE daily SIF MAE daily SIF RMSE daily SIF Standard Deviation CdTe SANDIA 0.030 0.031 0.044 0.032 CREST -0.010 0.014 0.019 0.016 CREST-WUF 0.001 0.009 0.012 0.012 c-Si SANDIA -0.003 0.021 0.027 0.026 CREST -0.014 0.016 0.021 0.016 CREST-WUF -0.002 0.007 0.011 0.010 CIGS SANDIA -0.017 0.024 0.026 0.020 CREST -0.015 0.017 0.021 0.015 CREST-WUF -0.001 0.007 0.010 0.010
- 16. Comparison of impact • Hourly absolute resource estimate error statistics • Adjusted Sandia has had site spectral offset applied. • Improvement of using the modified CREST is clear. 4th PVPMC Workshop – Cologne – 21st October 2015 Unmodified CREST Adjusted Sandia Modified CREST
- 17. • Surface shape is different from direct measurements • Cloudy data not as consistently captured. Diffuse contribution? Modified CREST using Isc data 4th PVPMC Workshop – Cologne – 21st October 2015 CIGS measured CIGS modelled
- 18. Modified CREST using Isc data 4th PVPMC Workshop – Cologne – 21st October 2015 CdTe CIGS • Equivalent results to direct spectral measurement observed. • Out of season differences are still present. • Results are stable with subsampling of dataset and within module type.
- 19. Clear sky model dependence 4th PVPMC Workshop – Cologne – 21st October 2015 • Surface result is dependent upon modelling of kt * • Requires consistent GHI model to be adopted
- 20. Frequency of air mass and kt * data 4th PVPMC Workshop – Cologne – 21st October 2015 • Most data is below air mass = 3.0 • Highest concentration of data for clear sky days • Lots of cloudy days but surface data is scattered • kt * is not continuous • kt * > 1 suggests limitations of GHI model
- 21. Other variations 4th PVPMC Workshop – Cologne – 21st October 2015 • Out of season variation is not captured • Changes to air mass dependence for clear skies c-Si measured mismatch for a clear sky day in each season
- 22. • Propose combining the modified CREST surface and site spectral offset to lead to superior predictions • The relationship between site location and climate and the site spectral offset is unclear • Added measurements are also needed to determine clearness surface dependence on site and offset properties • Standardisation of measurement and fitting methods. Is general characterisation possible 4th PVPMC Workshop – Cologne – 21st October 2015
- 23. Acknowledgements • This work was conducted with support from • The CSIRO Energy Flagship program • The Australian Renewable Energy Agency (ARENA) 4th PVPMC Workshop – Cologne – 21st October 2015