Presented at the IEEE Photovoltaic Specialists Conference (PVSC) in June 2015. The software PVSAT is introduced and the impacts of parameter true-up are discussed. This version of the slides was produced at the same time Amplify Energy was spun off from Yingli Americas (just prior to public announcement of the spin-off).

1. Kenneth J. Sauer, Ian C. Tse, and Ryan A. Desharnais
42nd IEEE Photovoltaic Specialists Conference | June 17, 2015
Revisiting the model parameters of an existing
system using the Photovoltaic System Analysis
Toolbox (PVSAT)
Area 9. #579

2. 2
Pro Forma
Weather
Data Energy Simulation
Software (PVsyst,
PVWatts, SAM)
Pro Forma
Energy
Production
ForecastPro Forma
Model
Parameters
Procurement Phase
Pro forma Energy Production Forecasts
Pro forma parameters come from:
• Original design plans / drawings
• Manufacturer datasheets
• 3rd party test reports
• Industry rules of thumb
• Default parameters from PVsyst, etc.
Q: How well do the pro forma
parameters represent a PV
system as built?

3. 3
• Photovoltaic System Analysis Toolbox (PVSAT)
• Precision of PVSAT validated with PVsyst
• Accuracy of PVSAT checked against data from test array
• Execute a modern performance guarantee
• Three levels of model parameter true-up
• Evaluate impact of true-up on forecast accuracy
Outline

4. 4
Photovoltaic System Analysis Toolbox (PVSAT)
Performance
Metrics
Calculation
Energy Simulation
Model
Validation
Data Import
& Filtration
PVSAT
Sandia
PV_LIB v1.2
Configured to
closely match
PVsyst
SUBMODELS
• Irradiance transposition
• Heat transfer (U values)
• Diode circuitry (.PAN)
• Various power loss mechanisms (IAM)
ADVANTAGES
• Subhourly
• Longer than 1 year
• Can model degradation rates

5. 5
Measurement Data: Test Array at PVTL
Measurements collected on-site:
• 1-min data
• Sept. 2013 – Apr. 2015
• Global horizontal irradiance
• Diffuse horizontal irradiance
• Ambient temperature
• Wind speed
• DC current & voltage
• Filtered for shading
PV system specifications:
• Roof-mounted
• mc-Si
• 11 modules in series
• 1 inverter

6. 6
measured weather +
high-loss parameters
PVsyst PVSAT
simulation
results
simulation
results
solar position algorithm error
compare residuals
Validation Test I: PVsyst vs. PVSAT

7. 7
PVsyst PVSAT
RMSD 1.94%
>
1.92%
MBD -0.47% -0.44%
Energy
Production
Deviation
-0.72% -0.67%
simulation
results
simulation
results
PVsyst PVSAT
measured weather +
Ext. AB parameters
measured energy
production
 Energy forecasts from
PVSAT are as accurate
as those from PVsyst
compare residuals
Validation Test II: Model vs. Measurement

8. 8
Power Capacity Test
subset for
regression
GpoaEff_RC
Month GpoaEff_RC Tcell_RC PmpDC_RC
Jan 519 26 1312
Feb 610 30 1518
Mar 762 37 1839
Apr 870 38 2084
May 895 38 2139
Jun 924 40 2186
Jul 918 39 2181
Aug 891 41 2105
Sept 860 40 2044
Oct 703 37 1700
Nov 534 31 1324
Dec 534 28 1342
Monthly Guarantee Table (Ext. AB)
PPI > 100% : PASS

9. 9
from the Guarantee Table execution
GpoaEff_RC Tcell_RC PmpDC_RC PmpDC’
Sept 860 40 2044 2040
Oct 703 37 1700 1686
Nov 534 31 1324 1340
Dec 534 28 1342 1321
Jan 519 26 1312 1305
Feb 610 30 1518 1523
Mar 762 37 1839 1828
Apr 870 38 2084 2072
May 895 38 2139 2127
Jun 924 40 2186 2170
Jul 918 39 2181 2166
Aug 891 41 2105 2084
TimeinOperation
Degradation Test
Power Capacity Test (1st month)
PPI
time
RDEG < 0.7 %/yr : PASS

10. 10
Energy Yield Test
EPI > 100% : PASS
 Modeled RDEG = 0.7 %/yr
 Typically run over one year period;
here all 20 months is used
weather-adjusted

11. 11
Model Sources and Attributes
Original Pro Forma • Original system design plans
 Tilt: 12°
 Azimuth: 190°
• Datasheet
 Power tolerances
 γPmp
• Default .PAN, IAM, and U values
from PVsyst v6.38
Typical As-Built • On-site survey
 Tilt: 13.87°
 Azimuth: 190.8°
• Manufacturer flash test data
 Power tolerances
Extended As-Built • Custom .PAN, IAM, U values
Levelofparametertrue-up
Three Models

12. 12
Model Parameter True-up
One-diode model optimization method from: Sauer et al., IEEE J. Photovoltaics, 2015.

13. 13
Model Parameter True-up (continued)
Ex-post derivation of thermal parameters from:
Faiman, Prog. Photovolt: Res. Appl., 2008.
BOM-specific analytical modeling of IAM:
Fatehi & Sauer, Proc. 40th IEEE PVSC, 2014.
Orig. PF Ext. AB
UC [W/m2/°C] 20 19.9
UV [W/m2/°C/m/s] 0 2.4

14. 14
Results
Orig. PF Typ. AB Ext. AB
PPI [%] 107.50 105.82 99.81
Power Capacity Test for 1st month (Target = 100%):
Degradation Test over 20 months (Target = 0.7 %/year):
Orig. PF Typ. AB Ext. AB
Rdeg [%/year] 1.27 1.14 1.15
Energy Production Test over 20 months (Target = 100%):
Orig. PF Typ. AB Ext. AB
EPI [%] 106.07 104.12 99.43
RMSD [% of nameplate] 4.53 3.57 1.10
MBD [% of nameplate] 4.15 2.87 -0.41

15. 15
Results

16. 16
Summary
• Fidelity of model parameters influences test results
• Degradation Test can detect long-term durability issues
• Seasonal errors introduced or masked by inaccurate parameters
• Possible to de-risk with efforts to true-up model parameters
• Setting the right bar for performance is also useful for O&M

17. Thank you for your attention.
kenneth.sauer@yingliamericas.com