Determining the causes and rates of PV degradation using the Loss Factors Model (LFM) with high quality IV measurements

Transcription

1 Determining the causes and rates of PV degradation using the Loss Factors Model (LFM) with high quality IV measurements Steve Ransome 1 & Juergen Sutterlueti 2 1 Steve Ransome Consulting Limited, London UK 2 Gantner Instruments, Germany PVPMC #6 Freiburg Germany 25 th Oct

2 Introduction to degradation analysis Most reported PV degradation are STC corrected efficiency only (1kW/m 2, 25C, AM1.5, AOI=0, direct only) I SC variability dominates performance uncertainty (due to soiling, spectral effects, irradiance sensor calibration ) Can we analyse other parameters independently of I SC? The cause of degradation (e.g. R SHUNT, R SERIES, V OC ) gives site dependent energy yield degradation rates (due to differing proportions of insolation vs. irradiance, T MOD etc.) 2

3 Smooth IV curves are needed for good R SC and R OC calculations R n = Apparent resistance between adjacent data points Typical GI measured IV curve (CdTe) GI raw measured (smooth data) vs. synthesised poor data truncated accuracy and added noise V OC, R OC R n = V I = V n V n 1 I n I n 1 I SC, R SC 3 Worse R SC accuracy from synthesised data (e.g. truncated or noisy)

4 Checking IV data quality with Log Resistance-Voltage (RV) curves GI data much smoother than NREL s Daystar and therefore easier to fit. GI CdTe NREL CdTe 4 Can ignore a few bad end points with V~0 or V > V OC

5 SRCL/Gantner Loss Factors Model [LFM] GI data Curvature for better understanding Measure raw IV curves = f(g,t) Fit lines to R SC and R OC Normalise data to datasheet 6 normalised losses LFM Cell mismatch, shading Cell rollover 5 PR DC = ni SC *nr SC *ni MP * nv MP *nr OC *nv OC

6 Comparing Loss Factors Model with standard models Fit to IV curves Normalised values for module variability Independent Parameters? L F M Exact values for 8 parameters around every IV curve Yes. e.g. nrsc = 98.0 ± 2.0% Almost independent (nv OC depends a little on nr SC and ni SC ) 1-diode (and similar models) Best fit to whole curve depends on data point distribution/weighting imperfect traces e.g. cell mismatch, roll over No. Specific module data only e.g. R SHUNT = 1234Ohms. No. Parameters are often interdependent e.g. nf and Io Dependence on Irradiance and temperature Simple optimum fits give exact coefficient behaviour for low light, temp coeffs etc. each module Try to fit pre-defined equations (even if they don t fit data) e.g. R SHUNT (G I ), I 0 (T CELL ) etc. low light and temp coeffs. may be wrong. Separation of all inputs e.g. ISC ~ AOI, SR Fault finding and quantification of loss Not needed. Can just measure outdoor params (for I SC separate clear from cloudy skies) Yes. Can easily identify quantify Cell mismatch, shading, R and V OC changes etc. Need to separate all parameters I SC = I SC0 * f(aoi) * f(sr) Some are possible (e.g. R SHUNT, R SERIES ) but not mismatch, rollover etc. 6

7 Yearly IV traces by irradiance Sept GI data For each module and Irradiance (e.g. ~0.8kW/m²) I SC variability e.g. soiling, sensor calibration etc. Changes in LFM parameters ΔnI SC ΔnR SC ΔPRDC ΔnR OC ΔnV OC Discrepancies seen at very low light levels? 7

8 Yearly IV traces by irradiance Sept GI data Year Deg %/y If module is degrading it s worse at low light kw/m 2 Discrepancies in I SC seen at very low light levels 0.04kW/m² Why? 8

9 GI Tempe OTF from North to South East Low horizon shading for morning sun GI hut position red Power lines green Sensors Cyan Modules Magenta 9 Google Street view from south east

10 Shading from powerlines affect the sensors and modules at different times of morning (5 distinct dips) Efficiency Isc / Gi Approx. shade times modules (07:35-08:05) sensors (07:10-07:40) Sensors higher than modules so are shaded earlier in morning (Late afternoons are affected by 2D tracker) Low light performance measurements vs. irradiance must be properly corrected for shading 10

11 SRCL/Gantner Loss Factors Model vs. Irradiance detailed information at GI data PR DC = ni SC *nr SC *ni MP * nv MP *nr OC *nv OC A drop in any LFM parameter limits overall PR DC Any LFM parameter changing over time affects PR DC Low light limiting High light limiting 11

12 Analysis method for frequent IV curves outdoors GI Data PR DC from 6 years of hourly measurements modules chosen to analyse differing behaviour Stable performance module Steady decline module Sudden change damaged or failed module 12 PR DC at High Irradiance tends not to be seasonally dependent PR DC at Low light may be seasonally dependent (longer day length, sun behind module)

13 LFM vs. irradiance GI data It s hard to see any changes in ni SC unless corrected for shading, soiling, aoi, sr and direct:diffuse 13

14 LFM vs. irradiance GI data Irradiance dependent degradation dnr SC Irradiance independent degradation dnr OC 14

15 nrsc vs. DateTime and Log(Irradiance) Low light levels performance degrades much faster than high light levels High light levels ( kW/m²) dnr SC -0.5%/y Low light levels ( kW/m²) dnr SC -2.0%/y Very Low light levels ( kW/m²) dnr SC -5%/y 15

16 Measurement Conclusions NOTES: Atypical devices analysed vs. a stable module Smooth IV curves needed for degradation analysis (check if Rn = V/ I is good on your measurement system) GANTNER INSTRUMENTS dataset in AZ (6 years) - SRCL/GI Loss Factors Model LFM separates degradation components from ni SC Good Gantner Instruments IV trace quality allows study of R SC and R OC Modules may degrade differently at high or low light levels LFM allows a fast independent check of degradation rates 16

17 Predictions : Site Dependent Energy Yield Degradation Energy Yield Gi,Tmod [Insolation(Gi,Tmod) * Efficiency(Gi,Tmod)] Irradiance distribution is site dependent (cumulative Hi kwh/m² % > Gi kw/m²) nr SC (related to R SHUNT ) degradation/y vs. Irradiance * -2.0%/year low light -0.5%/year high light 17

18 Predictions : Energy yield degradation rate at sites (from measured dnr SC ) High Insolation site = Lower Energy Yield degradation -0.7%/y Lower Insolation site = Higher Energy Yield degradation -1.3%/y 18

19 Predictions : Conclusions LFM gives Degradation rates for various parameters vs. irradiance etc. Predicted Energy Yield (kwh/y) degradation vs. site Low light drops in nrsc (~ R SHUNT ) cause worse falls at low than high insolation sites Analysis methodology is being integrated into (see separate poster) Thank you for your attention! 19