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Applying advanced ray tracingto predict the energy yield
of bifacial systems with reduced uncertainty
PV ModuleTech 2018
Keith McIntosh, Malcolm Abbott & Ben Sudbury, PV Lighthouse
Jenya Meydbray, Cypress Creek Renewables
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Perennial questions:• How will different system configurations compare?
– Bifacial vs monofacial?
– 2D tracking vs 1D tracking vs static?
– One-high vs two-high configurations?
– One location vs another?
– Good sunny years vs bad cloudy years?
– Good days vs bad days? One albedo vs another? Etc… Etc…
• How would different modules perform in a particular system?
– Conventional vs PERC vs HIT vs CdTe?
– Binned cells vs non-binned cells?
– Black silicon vs random pyramids vs isotexture?
– Textured ribbons vs planar ribbons vs smart wires? Etc… etc…
How accurately can we predict answers to those questions?
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Today’s question:• When predicting a system’s energy yield, is it worth accounting for
– spectral variability in the
▪ incident spectrum
▪ albedo
▪ angular spectral response of module
– mismatch in a module due to non-uniform illumination?
• The answer depends on
– how much the PV system is influenced by those effects; and
– how rapidly, easily, and accurately the effects can be simulated.
Today’s talk:
A major advance in simulation that enables us to answer this questions.
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Systems investigated
• Bifacial.
• 1D tracking, NS axis
• One-high & two-high
Image from https://www.nextracker.com Image from https://www.pv-magazine.com
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Challenge 1: Rear illumination
• Differs for direct and diffuse light.
• Direct light reflected more onto bottom of the module,depends on the time of day.
• Torque-tube shading.
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Challenge 2: Spectral variability
• Solar spectrum changes throughout day & year
Data for direct illumination at Golden, CO, on 14-Mar-2018. Taken from NREL databases; A. Andreas, T. Stoffel; (1981). NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS); Golden, Colorado (Data); NREL Report No. DA-5500-56488. http://dx.doi.org/10.5439/1052221
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Challenge 2: Spectral variability
• Solar spectrum changes throughout day & year.
• Solar spectrum differs for direct and diffuse light.
Data for direct illumination at Golden, CO, on 14-Mar-2018. Taken from NREL databases; A. Andreas, T. Stoffel; (1981). NREL Solar Radiation Research Laboratory (SRRL): Baseline Measurement System (BMS); Golden, Colorado (Data); NREL Report No. DA-5500-56488. http://dx.doi.org/10.5439/1052221
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Challenge 2: Spectral variability
• Solar spectrum changes throughout day & year.
• Solar spectrum differs for direct and diffuse light.
• Reflectance of ground and torque-tube depend on wavelength.
Data from NASA databases: https://speclib.jpl.nasa.gov/.
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Challenge 2: Spectral variability
• Solar spectrum changes throughout day & year.
• Solar spectrum differs for direct and diffuse light.
• Reflectance of ground and torque-tube depend on wavelength.
• Module’s response depends on wavelength and incident angle.
Plag et al., “Angular‐dependent spectral responsivity—Traceable measurements on optical losses in PV devices,” PIP, 2017.
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Challenge 2: Spectral variability
• Solar spectrum changes throughout day & year.
• Solar spectrum differs for direct and diffuse light.
• Reflectance of ground and torque-tube depend on wavelength.
• Module’s response depends on wavelength and incident angle.
• Module’s rear response differs to front response.
As simulated by PV Lighthouse
for contemporary bifacial module
under normal incidence.
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Challenge 3: Mismatch4
3
2
1
South
θsun = 90˚ φsun = 91˚
θinc = 90˚ θmod = 0˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 84.3˚ φsun = 102˚
θinc = 60.4˚θmod = 24.5˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 73.3˚ φsun = 112˚
θinc = 24.3˚θmod = 60.0˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 63.2˚ φsun = 124˚
θinc = 30.0˚θmod = 58.6˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 54.5˚ φsun = 138˚
θinc = 37.4˚θmod = 43.0˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 48.2˚ φsun = 156˚
θinc = 42.7˚θmod = 24.7˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 45.3˚ φsun = 176˚
θinc = 45.1˚θmod = 4.2˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 46.5˚ φsun = 197˚
θinc = 44.1˚θmod = 16.8˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 51.6˚ φsun = 215˚
θinc = 39.8˚θmod = 36.1˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 59.4˚ φsun = 231˚
θinc = 33.0˚θmod = 52.7˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 69.1˚ φsun = 243˚
θinc = 25.6˚θmod = 60.0˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 79.8˚ φsun = 254˚
θinc = 28.5˚θmod = 55.2˚
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Challenge 3: Mismatch4
3
2
1
South
θsun = 90˚ φsun = 268˚
θinc = 90˚θmod = 0˚
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• Inputs are material properties and geometries.
• Optics solved by ray tracing:– cloud-based (≤ 1000 parallel cores)
– optimized physics solver
– extremely fast.
• Widely used by– tier 1 module manufacturers
– materials companies
– leading research institutes.
• Expanded for PV systems– Ground, torque-tube, system configuration, backtracking
– SPICE to solve module circuit
– Temperature model
– Backtracking
SunSolve™
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12-months at NREL, Colorado
Data from NREL databases
• Ambient temperature.
• Wind velocity.
• Cloud fraction.
• Incident global intensity.
1-Sep-2017 31-Aug-2018
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12-months at NREL, Colorado
Data from NREL databases for 14-Mar-2018.
• Integrated direct intensity
• Integrated diffuse intensity
• Global spectra
• Direct spectra
→ Diffuse spectra
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• 20 million rays per incident angle.
• 4400 solutions per year (hourly in daylight hours).
• ~45 mins to solve the annual yield per system configuration(ray tracing + temperature solving + SPICE solving).
• Ways to reduce solutions to <5 mins have been identified.
Solving annual yield
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• For baseline cases– 0.23% for one-high system;
– 0.1% for two-high system.
• Greater loss on diffuse days.
Mismatch loss (due to non-uniform illumination in module)
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Evaluate different system configurations
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Accuracy of predicting daily yield
Global
intensity
Non-uniformity
in module
Tamb
& wind
Direct
intensity
Spectral
dependencies
Uncertainty
✓ ✓ ✓ ✓ ✓ 0%
✓ ✓ ✓ ✓ ±11.4%
✓ ±18.4%
• For our location, albedo, module & system configuration, we define accuracy of daily yield prediction.
• Spectral dependencies have the greatest influence.
Uncertainty to 95% confidence.
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• It can be done: Annual yield solved by ray tracing to micron-level, accounting for– Spectral variability of direct and diffuse light,
– Spectral and angular dependencies of ground, torque-tube and module,
– Mismatch within a module due to non-uniform illumination.
• Solutions currently ~45 mins per configuration. Future: < 5mins.
• Results allow us to quantify advantages– system configurations,
– module features,
– simulation assumptions.
• Is it worth accounting for spectral variability & mismatch due to non-uniformity? Early days, but yes, it looks that way.
Summary
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