improved prediction of site spectral impact · 4th stpvpmc workshop – cologne – 21 october 2015...

Improved prediction of site spectral impact Dr Benjamin Duck, Dr Chris Fell

16 June 2015

ENERGY FLAGSHIP

CSIRO PV Outdoor Research Facility

4th PVPMC Workshop – Cologne – 21st October 2015

The problem of spectrum


• 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

Impact of changes to spectrum


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

• Directly measured mismatch (spectroradiometer)

• In-directly measured mismatch (short circuit current)

Measuring spectral mismatch


• 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


CIGS

c-Si

CdTe

• 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


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)

Comparing measurements to predictions


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

Offset in measured Pmp data

• Data corrected to 1000 W/m2 and 25 °C shows an offset

PVSC 42 – 16th June 2015

Spectrum at air mass 1.5 ≠ AM1.5 reference

• Data at 1000 W/m2, 25 °C and air mass = 1.5 shows an offset


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

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.


𝑓 𝐴𝑀, 𝑘𝑡∗ =𝑊𝑈𝐹𝑚𝑒𝑎𝑠𝑊𝑈𝐹𝑟𝑒𝑓= 𝑨 ∶ 𝑷 𝑘𝑡

∗ 𝑸 𝐴𝑀

𝑸 𝐴𝑀 = 𝐴𝑀𝑚𝑚

0

𝑷 𝑘𝑡∗ = 𝑘𝑡

∗𝑛𝑛

0

• Captures variations due to cloudy skies as well as seasonal changes.

Modified CREST model predictions


c-Si measured

c-Si modelled (CREST)

Modified CREST model predictions


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?

Comparison of impact


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

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.


Unmodified CREST Adjusted Sandia Modified CREST

• Surface shape is different from direct measurements

• Cloudy data not as consistently captured. Diffuse contribution?

Modified CREST using Isc data


CIGS measured

CIGS modelled

Modified CREST using Isc data


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.

Clear sky model dependence


• Surface result is dependent upon modelling of kt*

• Requires consistent GHI model to be adopted

Frequency of air mass and kt* data


• 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

Other variations


• 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

• 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


Acknowledgements

• This work was conducted with support from • The CSIRO Energy Flagship program

• The Australian Renewable Energy Agency (ARENA)


improved prediction of site spectral impact · 4th stpvpmc workshop – cologne – 21 october 2015...

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