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Ian Marius Peters, Haohui Liu, Thomas Reindl, Tonio Buonassisi  Joule 

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Presentation on theme: "Ian Marius Peters, Haohui Liu, Thomas Reindl, Tonio Buonassisi  Joule "— Presentation transcript:

1 Global Prediction of Photovoltaic Field Performance Differences Using Open-Source Satellite Data 
Ian Marius Peters, Haohui Liu, Thomas Reindl, Tonio Buonassisi  Joule  Volume 2, Issue 2, Pages (February 2018) DOI: /j.joule Copyright © 2017 Elsevier Inc. Terms and Conditions

2 Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions

3 Figure 1 Effect of Temperature and Precipitable Water on PV Performance (A) Top: radiative efficiency limit as a function of band gap and solar cell temperature at 1.416 cm total precipitable water. Also shown in the absorption coefficient of water (log scale). Bottom: solar cell temperature versus performance ratio for CdTe and Si. Data for CdTe were provided by First Solar, and taken from Refs.27,28 for silicon. (B) Effect of precipitable water on PV performance. Top: radiative efficiency limit as a function of band gap and precipitable water. Bottom: precipitable water versus performance ratio for CdTe and Si. Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions

4 Figure 2 Schematic of PV Performance-Prediction Model
Satellite-based meteorological data are used to calculate solar spectra for different dates and locations using the SMARTS calculator.15,16 Ambient temperature data are used to predict module temperature. Module temperature is used to generate temperature-corrected IV data, based on existing solar cell data.35,36 Spectrum- and temperature-dependent IV, QE, and satellite-based irradiance data are then used to calculate the energy output of the solar cell. Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions

5 Figure 3 Comparison of Ground-Based Measurement and Satellite-Based Simulation Results (A–D) Ground-based measurements (A and C) and satellite-based simulation results (B and D). Shown are differences in performance ratio between the respective CdTe and Si modules (upper row, dark blue circles), measured ambient temperature (upper middle row, red circles), total precipitable water (lower middle row, light blue circles), and irradiation (lower row, black diamonds). The added lines are guides to the eye. Orange lines in right-hand figures are performance ratios calculated by PVsyst. Satellite insolation data were only available from January to August 2016. Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions

6 Figure 4 Comparison between Two Different Reference Modules with Different Efficiencies Upper: SunPower SPR-E with a nominal efficiency of 20.1%. Lower: Trina PDG5-255W with a nominal efficiency of 15.2%. Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions

7 Figure 5 ΔPR Plotted as a Function of Temperature (Red) and Precipitable Water (Blue) for Simulated and Measured System Performance in Perrysburg (A and B) Simulated (A) and measured (B) system performance in Perrysburg. The data are the same as used for Figure 3. Lines in the temperature plot are guides to the eye. Correlation with temperature is approximately linear for temperatures above freezing, whereas correlation with precipitable water shows a characteristic described by an exponentially decaying function, which can be explained by the Beer-Lambert law. Also included are the Pearson correlation factors calculated for each curve. Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions

8 Figure 6 Worldwide Projected Performance Ratios
(A and B) Calculated annual average performance ratio of (A) CdTe and (B) Si. Both plots use the same scale to allow a direct comparison. Values for Si vary more strongly due to the greater sensitivity to water and temperature. (C) The difference between the two performance ratios is shown. Positive values, indicated by red and yellow tones, mark a performance advantage for CdTe, and blue values one for Si. Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions

9 Figure 7 Location-Specific Harvesting Efficiency Predictions for PV Materials For a Figure360 author presentation of Figure 7, see the figure legend at (A–E) Variation in the radiative harvesting efficiency limit for solar cell absorbers with different band gaps, considering variations in temperature, spectrum, and band gap narrowing. Each figure is given with three scales: on the right the radiative limit absolute, in the middle the relative value compared with the STC radiative efficiency, and on the left relating the values to highest currently achieved device efficiencies. In (A), values in parentheses correspond to CIGS. Figure360: An Author Presentation of Figure 7 Joule 2018 2, DOI: ( /j.joule ) Copyright © 2017 Elsevier Inc. Terms and Conditions


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