Smart Solar Field Instrumentation for Development of Site-Specific Irradiance to Power Models

1. U.A. Whitaker College of Engineering
Smart Solar Field Instrumentation for
Development of Site-Specific Irradiance to
Power Models
Joseph Cuiffi, Ph.D.
J. Simmons (FGCU), C. Bokrand (FGCU), B. Potter (U of A),
H. Hamann (IBM), S. Lu, (IBM)
EPRI-Sandia PV Systems Symposium, May 9th, 2016

2. Cautionary Tale
Modeling a clear sky day
on a single axis tracking
system in Florida.
J. Cuiffi EPRI-Sandia PV Systems Symposium 2
Post-Inverter AC Power Output
Measured Direct Normal Irradiance
Power(W)
IrradianceW/m2
Looks like a reasonably good fit!

3. Deeper Look - Clear Sky
Uh oh - The clear sky model, using Linke
turbidity tables, is not quite right…
• The look-up turbidity factor is 3.5,
but it appears to be ~2.6.
• The daily turbidity factor over a
month varied from 2.2-4.5 (fitted
data), and showed a strong
dependence on relative humidity.
• Sensitivity: A 10% error in turbidity
leads to a 1% error in GHI.
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DNI, DHI, Measured and Model
IrradianceW/m2

4. Use Measured POA Irradiance
Using measured plane
of array irradiance, the
error in the power
model increases!
We need data to help
tune the model.
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Post-Inverter AC Power Output
IrradianceW/m2

5. Watt-Sun and SOFIE
• DoE SunShot program team lead by
IBM
• Watt-Sun: A localized, machine
learning technology for weather
and solar power forecasting.
(technology transfer to NREL in
progress)
• Smart Solar Field (SOFIE) mobile
units developed by FGCU to
support localized weather forecast
machine learning and irradiance to
power model development.
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6. Irradiance to Power Modeling
SOFIE provides data for optimization of the
components of the Irradiance-to-power calculation:
• Clear sky calculations
• Plane of array irradiance calculations (with tracking):
model selection, directionality of diffuse radiation, surface
albedo
• Module temperature: model selection, temperature
model coefficients
• Diode/power/inverter models: optimize maximum power,
Pmp0, including various losses
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7. SOFIE - FGCU
Monitors a single-axis tracking
system at FGCU (SW Florida):
• DNI, DHI, GHI
• Ambient Temperature, wind
speed & direction
• Post inverter AC power
• POA directly mounted on
tracker
• Module temperature, surface
mounted probe on back of
panel
• 1min data acquisition
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8. Plane of Array Irradiance Models
Various models tested
from the PVLIB tool kit:
• Isotropic
• King – includes a
circumsolar component
• Perez – various
coefficient sets
For each model the
albedo was optimized
through curve fitting.
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Model
NRMSE
(normalized to
1000 W/m2)
Default Albedo Isotropic 3.6%
Optimized Albedo Isotropic 2.5%
Optimized Albedo King 2.4%
Optimized Albedo Perez
(composite 1990)
2.3%
Optimized Albedo Perez (ABQ) 2.0%

9. POA Model Results
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IrradianceW/m2
Example Plane of Array Irradiance – 3 Days
(below is typical SW Florida)

10. POA Sensitivity to Albedo
• Albedo has a relatively
weak impact on overall
POA irradiance (RMSE).
• For single-axis tracking,
the albedo dictates the
“shoulders” shape of the
Irradiance curve.
• Maximum error is
reduced with proper
albedo optimization.
• Reflections and shadows
can also be seen in POA
data.
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POA NRMSE (Percent Error) vs. Albedo Percent Error
Albedo Error (%)
POANRMSE

11. Module Temperature Models
• Sandia Model
• FGCU Model
Where c1 captures the heat capacity of the module.
Note: Add one coefficient to include wind direction.
WS = WS*(C3a cos2θ + C3b sin2θ)
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𝑇 𝑚𝑜𝑑𝑢𝑙𝑒 = 𝑇𝑎𝑚𝑏𝑖𝑒𝑛𝑡 + 𝐸 𝑃𝑂𝐴 𝑒(𝑎+𝑏 𝑊𝑆)
𝑇 𝑚𝑜𝑑𝑢𝑙𝑒 = (𝑐1(𝑇 𝑚𝑜𝑑−1 − 𝑇𝑎𝑚𝑏𝑖𝑒𝑛𝑡) + 𝑐2 𝐸 𝑃𝑂𝐴)𝑒(𝑐3 𝑊𝑆)

12. Sandia Model Site Optimization
Optimization study using data from the Tucson
Electric Power test yard at University of Arizona.
2 years (learning and prediction) of 15min data on
fixed panels.
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0
50
100
150
200
250
300
-30 -20 -10 0 10 20 30
Error Bins (oC)
Error Histograms
Sandia Baseline
-0.14
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
-4
-3.9
-3.8
-3.7
-3.6
-3.5
-3.4
-3.3
-3.2
-3.1
-3
1 2 3 4 5 6 7 8 9 10 11 12
Sandia - Optimized Coefficients by
Month
a
b (right scale)

13. Module Temp. Results at FGCU
• At 15min and shorter intervals, the FGCU model
provides significant improvement, especially in
maximum absolute error (MaxAE).
• Sensitivity of power to temperature error is linear
with gamma ~.44%/oC for poly-Si.
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1min Intervals 15min Intervals 1hr intervals
Model RMSE MaxAE RMSE MaxAE RMSE MaxAE
Optimized Sandia 3.9 23.2 3.8 20.9 3.8 18.0
Optimized FGCU 1.8 10.5 3.0 17.0 3.5 14.9
Errors in oC

14. Module Temp. Results at FGCU
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Example Back of Panel Module Temperature Results
1min Intervals
Temperature(C)

15. Power Conversion
Three models tested
• PVWatts (basic equation): ignores the inverter characteristics
and angular reflection losses based on a given Pmp0
• DeSoto: uses module back plate data, runs through a single
diode and then an inverter model
• SAPM: uses Sandia database parameters, implements an
angle of incidence model, runs through the inverter model
Key is to fit Pmp0 (or number of series and parallel cells)!
Using plate Pmp0 can easily lead to 5-10% error.
Output power is directly proportional to Pmp0.
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16. Fully Modeled Power Results
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All three models fit well. SAPM had the lowest NRMSE
(normalized to plate Pmp0) of 2.3%.
Power(W)

17. SOFIE FRV Site
• No back panel module
temperature – cannot interfere
with installation
• No POA sensor attached to the
field tracker (waiting for POA data
from SunEdison)
• Using provided power output
(15 min intervals)
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18. Strategy for FRV Model
• POA model is difficult to tune without data, so we use an
isotropic model with an albedo of 0.35.
• Optimize the temp coefficients through the entire power
calculation instead of through a temperature measurement.
• Use PVWatts as a simple model to optimize Pmp0.
• Optimized parameters: albedo, Pmp0, temp coefficients
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19. Example Power Model Results
Power modeling at 1hr intervals.
NRMSE Power (normalized to plate Pmp0) = 5.4%
Synchronizing data from multiple sources is critical.
Power(MW)
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20. Overall Summary
• Site specific learning is key for accurate modeling!
• On-site irradiance and weather data allows tuning
of the various irradiance to power modeling
components.
• Including a time (heat capacity) component in the
developed FGCU temperature models increases
accuracy and reduces maximum error at 15min and
shorter intervals.
• Many of the model parameters take ~1 month of
data to optimize, however yearly variations will
require a year of data collection.
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21. Going Forward
• Continue a full year study at both SOFIE sites.
• Develop parameter extraction algorithms for better
fitting.
• Complete a Watt-Sun study comparing the use of
machine learning for weather forecasting and
combined weather-power forecasting.
• Hopefully - Using SOFIE units to improve site
specific models at various locations.
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22. Thank you!
DoE SunShot (#6017): Watt-Sun: A Multi-scale, Multi-
Model, Machine-Learning Solar Forecasting
Technology
J. Simmons (FGCU), C. Bokrand (FGCU)
B. Potter (U of A)
H. Hamann (IBM), S. Lu, (IBM)
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