1 4 epri sandia cuiffi 050916 43

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

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

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Looks like a reasonably good fit!

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.


DNI, DHI, Measured and Model

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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.


Post-Inverter AC Power Output

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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.


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


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


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.


ModelNRMSE

(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%

POA Model Results


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Example Plane of Array Irradiance – 3 Days(below is typical SW Florida)

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.


POA NRMSE (Percent Error) vs. Albedo Percent Error

Albedo Error (%)

PO

A N

RM

SE

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)


𝑇𝑚𝑜𝑑𝑢𝑙𝑒 = 𝑇𝑎𝑚𝑏𝑖𝑒𝑛𝑡 + 𝐸𝑃𝑂𝐴𝑒(𝑎+𝑏𝑊𝑆)

𝑇𝑚𝑜𝑑𝑢𝑙𝑒 = (𝑐1(𝑇𝑚𝑜𝑑−1 − 𝑇𝑎𝑚𝑏𝑖𝑒𝑛𝑡) + 𝑐2𝐸𝑃𝑂𝐴)𝑒(𝑐3𝑊𝑆)

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.


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

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-3.4

-3.3

-3.2

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1 2 3 4 5 6 7 8 9 10 11 12

Sandia - Optimized Coefficients by Month

a

b (right scale)

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.


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

Module Temp. Results at FGCU


Example Back of Panel Module Temperature Results1min Intervals

Tem

per

atu

re (

C)

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.


Fully Modeled Power Results


All three models fit well. SAPM had the lowest NRMSE (normalized to plate Pmp0) of 2.3%.

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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)


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


Example Power Model Results

Power modeling at 1hr intervals.NRMSE Power (normalized to plate Pmp0) = 5.4%Synchronizing data from multiple sources is critical.

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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.


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.


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)
