AzRISE PV and BOS Reliability Research

Transcription

1 AzRISE PV and BOS Reliability Research K. Simmons-Potter: UA ECE, MSE, OSC B.G. Potter, Jr.: UA MSE, OSC Students T. Lai M. Dzurick W. Bennett W-J. Huang T. Elwood C. Ramirez

2 PV Degradation Two test environments available: Outdoor test yard. Full-scale accelerated degradation chamber. Each platform has its advantages: Test yard provides in-situ environments for real-time testing of PV modules as well as BOS components, real-time data acquisition. Test chamber enables complete control of environmental conditions, accelerated exposure times, ability to perform stress tests, fully instrumented for in-situ monitoring of device performance.

3 TEP/AzRISE Test Yard 600 grid-tied PV modules from 20 different manufacturers enabling field testing of ~90 kw peak of PV systems. N Comprehensive upgrade of data collection instrumentation and storage capability ongoing.

4 Module Performance Data collected in 1 min intervals. Voltage and temperature data depicted over several days of monitoring. Drops in PV module string voltage mid-day correspond with peak module backside temperature due to temperature derating effects.

5 Irradiance (kw / m 2 ) Weather Station 1.3 POA Irradiance /19 04/21 04/23 04/25 Date (mo/day) Weather station monitors: Amb Temp, RH, Wind Speed, Precip, Irrad (POA, GHI).

6 Thermal Profile Model of PV Module Strings in Test Yard Model-based position-dependent simulated thermal profile across PV string using local environmental conditions as inputs to model. Input local weather station data Output thermal profile of location-specific points in PV array

7 Parametric 3D Model of PV Volume Input: weather station wind speed and local air temperature. Points Control volume

8 Thermal Transient Model

9 Temperature (C) Test Yard Data vs. Model 80 Test Yard TC Data vs. Model Data TC 17 air 30 ANSYS Time (s)

10 Solar Forecasting UA-AzRISE research contributes to enhanced irradiance-to-power modeling and solar forecasting. Incidence-angle dependent EQE cell performance to augment empirical parameter set determination for PV output prediction. Autonomous, on-site irradiance/weather condition monitoring with remote data access to facilitate improved forecast model optimization (irradiance model validation, irradianceto-power) SOFIE located at UA-TEP PV Test Yard: DNI, DHI, GHI, POA Wind velocity, wind direction, ambient temperature measured. Back of panel temperature measured on fixed horizontal panel. Irradiance measurement station at TEP/SunEdison Picture Rocks Solar Array (20 MW)

11 SOFIE Data SOFIE data (UA-TEP test yard) comparison to NREL OASIS site data (UA Gould-Simpson Building) DNI Clear days Cloudy days GHI

12 Chamber-based PV Degradation

13 Chamber-based PV Degradation Internal workspace of 99 L x 85 W x H. Temperature range of -30 C to +85 C ± 1.1 C. Relative humidity range of 20% to 95% ± 5% via integrated steamer and/or atomizer. Front-side and back-side temp. monitoring. Testing Methodologies: Cyclic stress test to failure Accelerated lifecycle testing

14 Irradiance (W/m 2 /nm) Vertical (in) Vertical (in) Lamp Irradiance Chamber Ave ASTM % Wavelength (nm) Spectrum compared with AM Horizontal (in) (in) Irradiance Distribution: ~ 98% uniformity 100%

15 / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /4 Relative Humidity (%) Temperature (C) Chamber Temperature and Humidity Environment Day Temp Night Temp One year equivalent degradation test Annual weather profile setpoints obtained from test yard weather station for Tucson, AZ Oct. Jan. Apr. July Time (Month) Twelve month time period from October to September, weekly average set-points Day RH Night RH Oct Jan Mar Jun Sept Time (Month)

16 STC Efficiency Degradation Polycrystalline Si Temperature corrections for month-to-month variation in module temperature enable extraction of time-dependent module degradation. Fast initial decay within first hours, followed by slower, sustained, long-term, environmentally-induced long time loss. Double exponential decay fit: η = η 0 + A 1 e t τ 1 + A 2 e t τ A B C Equivalent Solar Exposure Time (hours)

17 PV Module Efficiency Comparisons One-year actual efficiency degradation losses: 0.28% (A) Max efficiency: 13.5% Rated: 14.9% 0.37% (B) Max efficiency: 15.3% Rated: 15.3% 0.92% (C) Max efficiency: 16.5% Rated: 16.4% One-year relative (normalized) efficiency degradation losses: 2.1% (A) 2.4% (B) 5.6% (C) Extrapolated 5-year loss estimates ~0.6%/yr (A) ~0.48%/yr (B) ~1.2%/yr (C)

18 EL Imaging of Aged PV Modules Initial noise removal through acquisition of background image with no energizing source Optical barrel distortion (seen as bend towards each corner of the image) correction Unprocessed EL Image

19 Histogram Profiles Intensity histograms of individual cells enabled degradation analysis using statistical parameters (mean, standard deviation). Smoother decline in mean and more consistent standard deviation indicated a slowly degrading cell. Sudden drop in pixel mean and rapid increase in standard deviation suggest potential cracks or significant defects.

20 SOC (%) Voltage (V) Energy Storage Testing Through recent investment by the UA s Institute for Energy Solutions (IES), UA-AzRISE degradation facility is now instrumented for qualification and lifecycle testing of batteries and battery systems under environmental stressors VRLA (AGM) battery Elapsed time (hr) 0