| Preface |
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xxiii | |
| Acknowledgments |
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xxv | |
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1 Overview: Sun, Earth, and Solar Cell |
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1 | (26) |
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1.1 Introduction: A self-driven nuclear reactor fueling the solar system |
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2 | (3) |
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1.1.1 Extraterrestrial solar intensity is easily calculated |
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2 | (1) |
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1.1.2 A planet's temperature is defined by the light transmitted through its atmosphere |
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3 | (2) |
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1.2 Intensity of sunlight depends on the geographical location, the season, and the hour of the day |
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5 | (7) |
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1.2.1 Seasonal variation depends on the distance from the sun |
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6 | (1) |
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1.2.2 Extraterrestrial intensity is reduced as sunlight travels through the atmosphere |
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7 | (2) |
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1.2.3 Nonzero insolation even when you cannot see the sun: Contributions from diffused light |
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9 | (2) |
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1.2.4 Irradiance and insolation are slightly different concepts |
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11 | (1) |
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1.2.5 There are many different ways to harvest solar energy, but we will focus on solar cells |
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11 | (1) |
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1.3 A solar cell can use only part of the solar spectrum |
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12 | (4) |
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1.3.1 The extraterrestrial radiation carries the imprint of the solar emission |
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12 | (3) |
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1.3.2 The spectrum changes significantly as it passes through the atmosphere |
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15 | (1) |
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1.3.3 Standardized spectrum is used to compare solar cell technologies |
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15 | (1) |
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1.4 Technology, energy yield, and cost of solar cells |
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16 | (5) |
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1.4.1 A technology spanning over magnitudes of length scales |
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16 | (1) |
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1.4.2 How much energy output should we expect? |
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17 | (1) |
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1.4.3 How much does a unit of solar energy cost? |
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18 | (3) |
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21 | (6) |
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PART I THERMODYNAMICS OF SOLAR CELLS |
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27 | (19) |
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2.1 Introduction: How efficient can a solar cell be? |
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27 | (1) |
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2.2 Thermodynamic performance limit of an isolated two-level system |
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28 | (4) |
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28 | (1) |
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2.2.2 A two-level system illuminated by a monochromatic sun |
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29 | (2) |
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2.2.3 Two-level atoms with different energy gaps |
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31 | (1) |
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2.3 Thermalization energy loss in a two-level "molecular" solar cell |
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32 | (6) |
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2.3.1 Thermalization loss in a "bilayer" solar cell |
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34 | (1) |
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2.3.2 Thermalization loss in series-connected atoms |
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35 | (1) |
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2.3.3 Thermalization loss in a series-connected cell with many atoms |
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36 | (1) |
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2.3.4 Reducing the thermalization loss |
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37 | (1) |
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2.4 Efficiency loss due to angular anisotropy of sunlight |
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38 | (4) |
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2.4.1 Discussion: Isotropic vs. direct sunlight |
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40 | (1) |
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2.4.2 Recovery of angle entropy loss |
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41 | (1) |
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2.5 Energy loss due to below-bandgap light transmission |
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42 | (1) |
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2.5.1 Recovery of below-bandgap loss |
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43 | (1) |
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2.6 Conclusions: Insights from a two-level model of a solar cell |
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43 | (3) |
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3 Thermodynamic Limits of 3D Solar Cells |
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46 | (20) |
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3.1 Introduction: Real solar cells have more than two levels |
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46 | (1) |
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3.2 The J--V characteristics of a solar cell |
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47 | (2) |
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3.3 Power output and conversion efficiency of a solar cell |
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49 | (1) |
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3.4 Power budget: How did we lose 70% of the incident sunlight? |
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49 | (4) |
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3.5 Key features of the J--V characteristics can be calculated analytically |
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53 | (2) |
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55 | (11) |
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4 Thermodynamic Limits of Tandem, Bifacial, and Concentrator Solar Cells |
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66 | (29) |
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66 | (2) |
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4.1.1 PV efficiency is improved by reducing energy and entropy losses |
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67 | (1) |
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4.1.2 It is not easy to optimize next-generation solar cells |
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68 | (1) |
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4.2 The Shockley--Queisser triangle |
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68 | (3) |
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4.3 Application of a model to a variety of PV concepts |
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71 | (6) |
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4.3.1 Efficiency of a single-junction PV with c = 1 |
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71 | (1) |
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4.3.2 Thermodynamic efficiency of an N-junction tandem cell |
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71 | (2) |
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4.3.3 Non-optimum Eg in tandem PV |
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73 | (4) |
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4.4 Concentrator solar cells reduce entropy loss of a solar cell |
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77 | (3) |
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4.4.1 Efficiency of single-junction concentrated solar cells |
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77 | (2) |
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4.4.2 Multi-junction concentrator tandem cells address both energy and entropy losses |
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79 | (1) |
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4.5 Bifacial tandem solar cells: An emerging solar cell technology |
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80 | (3) |
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4.6 Thermodynamic limits of non-ideal solar cells |
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83 | (2) |
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4.6.1 Imperfect EQE and ERE in a tandem solar cell |
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84 | (1) |
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4.7 Third-generation solar cells |
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85 | (6) |
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4.7.1 Intermediate-band solar cell as a split-spectrum tandem cell |
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86 | (2) |
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4.7.2 Multiple-exciton generation solar cells (MEG-PV) compared to a double-junction tandem cell |
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88 | (1) |
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4.7.3 A hot-electron solar cell converts the thermalization energy of a solar cell |
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89 | (1) |
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4.7.4 Flat-plate luminescent concentrator solar cell |
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90 | (1) |
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91 | (4) |
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5 Self-heating of Solar Cells |
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95 | (17) |
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95 | (1) |
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5.2 Self-heating is defined by a complex balance of multiple fluxes |
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96 | (5) |
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5.2.1 Self-heating: The absorbed photon flux heats the solar cell |
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98 | (1) |
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5.2.2 Self-cooling: A module can be cooled by convection |
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98 | (2) |
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5.2.3 Self-cooling: A solar cell is also cooled by radiation |
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100 | (1) |
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5.2.4 Device temperature must be computed self-consistently |
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100 | (1) |
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5.2.5 Importance of wavelength-dependent radiation and absorption |
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101 | (1) |
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5.3 Temperature-dependent efficiency of solar cells |
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101 | (3) |
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5.3.1 Numerical calculation of η(TD) |
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102 | (1) |
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5.3.2 Analytical calculation of η(TD): Thermodynamic limit of temperature coefficient, β |
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102 | (2) |
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5.4 Determination of TD by the temperature sensitivity of Voc |
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104 | (2) |
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5.5 How to cool a solar cell |
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106 | (3) |
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5.5.1 Active and passive convective cooling |
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107 | (1) |
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5.5.2 Radiative cooling makes heat dissipation more effective |
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107 | (1) |
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5.5.3 Spectrally selective cooling rejects sub-bandgap photons |
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107 | (1) |
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5.5.4 Integrated spectral and radiative cooling |
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107 | (1) |
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5.5.5 Cooling by adding an energy harvester in series |
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108 | (1) |
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109 | (3) |
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6 Limits of Light Absorption |
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112 | (27) |
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6.1 Introduction: A solar cell cannot absorb all incident photons |
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112 | (1) |
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6.2 Absorption in a PV material: Overview |
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112 | (2) |
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6.3 Finite absorption in a finite solar cell |
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114 | (1) |
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6.3.1 Reflection from the air-cell interface |
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114 | (1) |
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6.3.2 Entry does not guarantee absorption |
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114 | (1) |
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6.4 The Yablonovitch limit suggests strategies to improve absorption |
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115 | (9) |
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6.4.1 Statistics of light rays within a solar cell |
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116 | (1) |
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6.4.2 A dielectric slab confined by two parallel surfaces |
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117 | (1) |
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6.4.3 Bottom mirror with 1D randomness |
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118 | (2) |
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6.4.4 Two-dimensional random reflecting surface |
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120 | (3) |
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6.4.5 Planar surfaces and photon recycling |
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123 | (1) |
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6.5 Intensity enhancement: exceeding the Yablonovitch limit |
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124 | (7) |
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6.5.1 Reducing the escape cone |
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124 | (2) |
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6.5.2 Anisotropic scattering by metasurfaces |
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126 | (3) |
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6.5.3 Anisotropic scattering into guided modes |
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129 | (2) |
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6.5.4 Perfect absorption does not imply zero emission |
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131 | (1) |
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6.6 A general model of absorption |
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131 | (1) |
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6.7 Conclusions: absorption limit in PV |
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132 | (7) |
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PART II TRANSPORT PHYSICS OF THREE TYPES OF CELLS |
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7 Physics of Typical Solar Cells |
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139 | (30) |
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139 | (1) |
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7.2 Generation, recombination, and the J -- V relationship |
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140 | (4) |
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7.3 A versatile flux-based approach to calculate dark and r photocurrents |
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144 | (1) |
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7.4 The current-voltage characteristics of p-i-n solar cells |
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145 | (7) |
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7.4.1 Dark current: A p-i-n solar cell |
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146 | (3) |
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7.4.2 Photocurrent: p-i-n cells |
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149 | (3) |
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7.5 Current-voltage characteristics of p-n-junction solar cells |
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152 | (5) |
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7.5.1 Dark current: p-n-junction solar cells |
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152 | (2) |
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7.5.2 Photocurrent: p-n-junction solar cells |
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154 | (1) |
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7.5.3 Device design for improved photocurrent collection |
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155 | (2) |
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7.6 Current--voltage characteristics of heterojunction solar cells |
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157 | (3) |
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7.6.1 Dark current: Heterojunction solar cells |
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158 | (1) |
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7.6.2 Photocurrent: Heterojunction solar cells |
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159 | (1) |
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7.7 Principle of (current) superposition |
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160 | (2) |
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7.8 Performance parameters of a practical solar Cell |
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162 | (1) |
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7.9 The puzzle of the reverse saturation current |
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163 | (3) |
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7.9.1 Radiative photon flux inside the solar cell |
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164 | (1) |
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7.9.2 Radiative photon flux outside the solar cell |
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164 | (1) |
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7.9.3 Internal and external fluxes compared |
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165 | (1) |
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166 | (3) |
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169 | (14) |
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8.1 Introduction: Bulk-heterojunction solar cells |
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169 | (1) |
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8.2 Exciton involves an electron--hole diffusing together |
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170 | (1) |
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8.3 Physics of a planar heterojunction cell |
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171 | (6) |
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8.3.1 Photocurrent can be calculated by flux balance argument |
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173 | (1) |
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8.3.2 Dark current is small and is calculated by elementary arguments |
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174 | (1) |
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8.3.3 The puzzle of very low fill factor |
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175 | (2) |
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8.3.4 Organic solar cells may not obey superposition |
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177 | (1) |
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8.4 Physics of a vertical-heterojunction cell |
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177 | (3) |
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8.4.1 Exciton harvesting in a VHJ solar cell |
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177 | (2) |
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8.4.2 Photocurrent from harvested excitons |
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179 | (1) |
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8.4.3 Dark current of a VHJ solar cell |
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180 | (1) |
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8.5 Physics of bulk-heterojunction cells |
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180 | (1) |
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181 | (2) |
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9 Physics and Universality of Shunt Distribution |
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183 | (19) |
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9.1 Introduction: Parasitic shunt current reduces solar cell efficiency |
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183 | (1) |
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9.2 Unique voltage, temperature, and thickness dependence of Ish(V, T, L) |
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184 | (3) |
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9.2.1 Four features of the shunt current |
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184 | (1) |
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9.2.2 Interpreting shunt current by the space charge-limited theory |
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185 | (2) |
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9.3 Understanding the space charge-limited current |
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187 | (6) |
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9.3.1 Ideal, trap-free semiconductor: Mott--Gurney law |
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188 | (2) |
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9.3.2 SCL current in a semiconductor with shallow traps |
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190 | (1) |
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9.3.3 A scaling theory for SCL conduction |
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191 | (2) |
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9.4 Experimental validation of the universal non-ohmic shunt conduction |
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193 | (2) |
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9.4.1 SCL current involves either electrons or holes |
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193 | (1) |
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9.4.2 Shunt conduction has an inverse power law thickness dependence |
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194 | (1) |
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9.4.3 Shunt current is a universal feature of solar cells |
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194 | (1) |
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9.5 Shunt magnitude distribution is log-normal, but its spatial distribution is random |
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195 | (5) |
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9.5.1 Statistical analysis of shunt current magnitude |
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196 | (1) |
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9.5.2 Spatial size and position distribution of shunts |
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196 | (4) |
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200 | (2) |
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10 Physics of Series Resistance of Solar Cells and Modules |
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202 | (23) |
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202 | (1) |
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10.2 You cannot avoid the losses due to series resistance |
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203 | (2) |
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10.3 For typical subcells, a rectangular grid minimizes power dissipation |
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205 | (1) |
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10.4 Determining the number of subcells in an optimization problem |
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206 | (1) |
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10.5 Rectangular cells are typical, but non-rectangular cells can reduce module power dissipation |
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207 | (4) |
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10.6 Crystalline Si solar cells are gridded differently than thin-film solar cells |
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211 | (5) |
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10.6.1 A simple constant-dissipation approach specifies the ratio of metal grids |
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214 | (1) |
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10.6.2 An optimized grid design must balance module-wide shading and power dissipation |
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215 | (1) |
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10.7 c-Si mimics a thin-film cell: The Q-cell strategy |
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216 | (1) |
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10.8 More complex grids must be designed by computer simulation |
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217 | (1) |
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10.9 A summary of gridding principles of c-Si solar cells |
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218 | (1) |
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10.10 Emerging trends and the future of gridding |
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219 | (2) |
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221 | (4) |
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PART III DESIGN OF A PV SYSTEM: PANELS, FARMS, AND STORAGE |
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11 System Integration of Solar Modules |
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225 | (10) |
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225 | (1) |
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11.2 A PV system consists of a variety of electronic components |
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226 | (2) |
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228 | (2) |
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11.3.1 Standalone PV system |
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228 | (1) |
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11.3.2 Grid-connected solar modules |
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228 | (1) |
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229 | (1) |
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230 | (1) |
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11.4 Inverter connection topologies |
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230 | (4) |
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231 | (1) |
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231 | (1) |
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231 | (2) |
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11.4.4 Inverter with power optimizer |
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233 | (1) |
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234 | (1) |
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235 | (12) |
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235 | (1) |
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12.2 The sun path depends on the geographic location of the farm |
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236 | (1) |
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12.2.1 Location of the sun at the peak of the sun path |
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237 | (1) |
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12.3 Energy yield is determined by the panel tilt |
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237 | (1) |
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12.3.1 An empirical rule for the tilt of a stand-alone panel |
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237 | (1) |
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12.3.2 No-shadowing constraint determines the row spacing of a solar farm |
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238 | (1) |
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12.4 Calculation of the energy yield of a panel |
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238 | (7) |
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12.4.1 Components of the sun's illumination |
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239 | (2) |
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12.4.2 Light collection by solar panels |
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241 | (4) |
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245 | (2) |
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13 Design of a Vertical Solar Farm |
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247 | (16) |
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247 | (1) |
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13.2 Basics of a solar farm output: A simplified case study |
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248 | (13) |
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13.2.1 Vertical bifacial panel: Stand-alone |
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248 | (4) |
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13.2.2 Vertical bifacial panels: Array |
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252 | (7) |
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13.2.3 Energy output of the panel and farm |
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259 | (2) |
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261 | (2) |
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14 Solar Farms: Practical Perspectives |
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263 | (21) |
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263 | (2) |
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265 | (1) |
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14.3 Monofacial panel farm: Optimized for minimum shading |
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266 | (3) |
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14.4 Monofacial panel farm: Maximized farm output |
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269 | (2) |
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14.5 Bifacial panel farm: Vertically aligned |
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271 | (2) |
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14.6 Bifacial panel farms: South-facing tilted panel |
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273 | (1) |
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14.7 Bifacial panel farms: Landscaping |
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274 | (2) |
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14.8 Bifacial panel farms: Solar tracking |
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276 | (1) |
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14.9 Emerging solar farm technologies |
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277 | (1) |
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14.9.1 Floating solar farms are being deployed across the world |
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277 | (1) |
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14.10 Agrophotovoltaic solar farms are being tested on a smaller scale |
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278 | (3) |
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281 | (3) |
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15 Storing Energy from Solar Cells |
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284 | (29) |
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284 | (1) |
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15.2 Electrical energy can be stored mechanically |
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285 | (4) |
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15.3 Electrical energy can be stored in electrostatic capacitors |
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289 | (1) |
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15.4 There are a variety of electrochemical energy (EC) storage schemes |
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290 | (3) |
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15.4.1 Battery technologies |
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291 | (2) |
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15.4.2 Hydrolysis and flow batteries store energy in separate reservoirs |
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293 | (1) |
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15.5 A deeper look into electrochemical storage: water hydrolysis |
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293 | (1) |
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15.6 Redox reactions can be represented by a single diode |
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294 | (2) |
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15.7 Optimum energy storage for solar cell |
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296 | (2) |
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15.7.1 I-V Characteristics of an N-cell tandem |
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297 | (1) |
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15.7.2 I-V characteristics of a series-connected M-cell module |
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298 | (1) |
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15.8 Charging an EC system with PV |
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298 | (2) |
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15.8.1 Operating point of the PV-EC system |
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298 | (1) |
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15.8.2 PV-EC operation: An intuitive picture |
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299 | (1) |
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15.9 How efficiently can we store solar energy? |
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300 | (7) |
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15.9.1 PV-EC system design rule |
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300 | (1) |
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15.9.2 PV-EC system efficiency limit |
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301 | (2) |
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303 | (4) |
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307 | (6) |
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PART IV RELIABILITY AND CHARACTERIZATION OF SOLAR CELLS |
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16 Levelized Cost of Electricity Highlights the Importance of Efficiency and Reliability of Solar Modules |
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313 | (18) |
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16.1 Introduction: COE is a simple but an important concept |
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313 | (3) |
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16.1.1 A solar farm requires significant investment: An analysis of C(Y) |
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314 | (1) |
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16.1.2 A solar farm cannot produce energy forever: The physics of E(Y) |
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315 | (1) |
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16.2 LCOE is a similar but slightly more complicated concept |
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316 | (2) |
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16.3 Two learning curves can be used to project "future" LCOE |
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318 | (5) |
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16.3.1 Anticipated future growth of the PV industry |
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319 | (1) |
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16.3.2 Swanson's law for reduced module price |
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319 | (1) |
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16.3.3 Goetzberger's law of improved efficiency |
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320 | (3) |
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16.4 LCOE* decouples local vs. universal components of LCOE |
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323 | (4) |
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16.4.1 Solar farm topologies are determined by LCOE and LCOE* |
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324 | (1) |
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16.4.2 An example calculation involving LCOE and LCOE* |
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325 | (2) |
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16.5 Smart recycling increases the "residual value" of a solar module |
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327 | (1) |
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16.6 Conclusions: LCOE is an important but imperfect measure of cost-effectiveness of solar cells |
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327 | (4) |
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17 Soiling vs. Cleaning: An Optimization Problem |
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331 | (14) |
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17.1 Introduction: How does soiling affect PV energy output? |
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331 | (1) |
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17.2 What is the cleaning cost to produce an extra watt of power? |
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332 | (1) |
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17.3 Optimized cleaning is defined by a cost-benefit analysis |
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333 | (2) |
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17.4 The soiling parameter a depends on a number of variables |
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335 | (5) |
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17.4.1 Soiling depends on the tilt angle |
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336 | (2) |
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17.4.2 Photocurrent reduction also depends on the geometry and type of soiling particles |
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338 | (2) |
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17.5 A number of technologies have been developed to clean solar farms |
|
|
340 | (2) |
|
17.6 Conclusions: Optimized cleaning maximizes cost-effective energy output of a solar cell |
|
|
342 | (3) |
|
18 A Transient Partial Shadow May Cause Permanent Damage |
|
|
345 | (15) |
|
18.1 Introduction: The danger of a partial shadow |
|
|
345 | (1) |
|
18.2 A module is optimized for shadow-free operation |
|
|
346 | (2) |
|
18.3 A shadow decreases the power output dramatically |
|
|
348 | (2) |
|
18.4 Semitransparent shadows produce complex I-V characteristics |
|
|
350 | (2) |
|
18.5 Partial shadows cause irreversible damage |
|
|
352 | (1) |
|
18.6 Strategies to mitigate the effect of partial shadows |
|
|
353 | (5) |
|
18.6.1 Bypass diodes reduce partial shadow degradation in a c-Si module |
|
|
353 | (2) |
|
18.6.2 Bypass diodes cannot protect thin-film solar modules |
|
|
355 | (1) |
|
18.6.3 Spiral-shaped subcells improve shadow performance |
|
|
356 | (2) |
|
18.7 Conclusions: A module is designed/installed for shadow tolerance |
|
|
358 | (2) |
|
19 Dangerous Hotspots are Caused by Weak Diodes and Strong Shunts |
|
|
360 | (10) |
|
19.1 Introduction: The origin of hotspots in solar modules |
|
|
360 | (1) |
|
19.2 Process non-uniformity creates weak diodes |
|
|
361 | (1) |
|
19.3 Light I-V characteristics can be expressed in a diode-like form |
|
|
361 | (1) |
|
19.4 Low-Voc diodes sink the photocurrent generated in the neighboring region |
|
|
362 | (3) |
|
19.5 Strong shunts and weak diodes have similar effects on module performance |
|
|
365 | (1) |
|
19.6 Hotspots are acerbated by the proximity to the electrodes |
|
|
366 | (1) |
|
19.7 Solution strategies: There are different ways to reduce hotspots |
|
|
366 | (2) |
|
19.7.1 Process improvement solves the root cause of hotspot formation |
|
|
367 | (1) |
|
19.7.2 Striping suppresses the effects of hotspots formed |
|
|
367 | (1) |
|
19.8 Conclusions: Hotspots and partial shadowing must be reduced |
|
|
368 | (2) |
|
20 Photodegradation of Solar Cells Due to UV Exposure |
|
|
370 | (15) |
|
20.1 Introduction: Photodegradation of solar absorber, polymer encapsulant, and backsheets are intrinsic reliability concerns |
|
|
370 | (1) |
|
20.2 Yellowing of polymer encapsulant |
|
|
371 | (2) |
|
20.2.1 Why do we need an encapsulant |
|
|
371 | (1) |
|
20.2.2 Chemical composition of encapsulants |
|
|
371 | (2) |
|
20.3 A phenomenological model for UV degradation |
|
|
373 | (2) |
|
20.4 A physical theory of UV degradation |
|
|
375 | (5) |
|
20.4.1 Number of high-energy photons in a blackbody spectra |
|
|
375 | (1) |
|
20.4.2 Rate of polymer degradation |
|
|
376 | (2) |
|
20.4.3 Photodegradation is accelerated by UV intensity, but not by module temperature |
|
|
378 | (2) |
|
20.5 Solution strategies: Techniques to reduce encapsulant yellowing |
|
|
380 | (2) |
|
20.5.1 Filter high-energy photons before it reaches the encapsulant |
|
|
380 | (1) |
|
20.5.2 Use stronger polymers |
|
|
380 | (1) |
|
20.5.3 Add photo-stabilizer molecules |
|
|
381 | (1) |
|
20.6 The backsheet polymer is also damaged by UV radiation |
|
|
382 | (1) |
|
20.7 Conclusions: UV degradation affects absorber materials as well |
|
|
383 | (2) |
|
21 Light-induced Degradation in Solar Cells |
|
|
385 | (9) |
|
21.1 Light-induced degradation has been known since the 1970s |
|
|
385 | (1) |
|
21.2 Physics and mathematics of LID in a-Si solar cells |
|
|
386 | (4) |
|
21.2.1 A-Si: H is passivated by Si-H bonds |
|
|
386 | (1) |
|
21.2.2 A model for light-induced degradation |
|
|
387 | (3) |
|
21.3 Crystalline silicon cells suffer from LID too |
|
|
390 | (1) |
|
21.3.1 LID of bulk silicon |
|
|
390 | (1) |
|
21.3.2 LID of the rear-side passivation |
|
|
391 | (1) |
|
21.4 Conclusions: Photodegradation affects all the components of a solar module |
|
|
391 | (3) |
|
22 Potential-induced Degradation is a Serious Reliability Issue |
|
|
394 | (16) |
|
22.1 Introduction: PID is a reliability problem with a long history |
|
|
394 | (1) |
|
22.2 An empirical formula summarizes the experimental observations |
|
|
395 | (2) |
|
22.3 PID occurs when the modules are connected in series and the frame is grounded |
|
|
397 | (1) |
|
22.4 PID leakage involves complex pathways |
|
|
398 | (2) |
|
22.5 The voltage at glass-polymer interface is sufficient to pull Na out of the glass and push them toward the negative electrode of the solar cell |
|
|
400 | (1) |
|
22.6 Multiple processes occur once Na+ ions reach the cell electrode |
|
|
400 | (3) |
|
22.6.1 As a mid-gap state, Na+ increases shunt and diode leakage |
|
|
400 | (3) |
|
22.6.2 Positive Na+ ions attract negative charges leading to surface polarization |
|
|
403 | (1) |
|
22.7 Solution strategies: How to reduce potential-induced degradation |
|
|
403 | (1) |
|
22.8 A semi-quantitative model for PID |
|
|
404 | (5) |
|
22.8.1 Na transport through EVA controls PID-s |
|
|
404 | (1) |
|
22.8.2 Physics of t1: Delayed onset of PID-s |
|
|
404 | (1) |
|
22.8.3 Case 1: Linear voltage dependence of PID-s |
|
|
405 | (1) |
|
22.8.4 Case 2: Nonlinear voltage dependence of PID-s |
|
|
405 | (2) |
|
22.8.5 The physics of PID saturation |
|
|
407 | (1) |
|
22.8.6 Na+ ions also increase the diode dark current |
|
|
408 | (1) |
|
22.8.7 Recovery of PID-s due to Na out-diffusion |
|
|
408 | (1) |
|
22.9 Conclusions: PID is a system-level reliability issue |
|
|
409 | (1) |
|
23 Humid Environment Causes Electrode Corrosion |
|
|
410 | (23) |
|
23.1 Introduction: The corrosion of electrodes reduces power output |
|
|
410 | (3) |
|
23.2 Corrosion involves a sequential diffusion-reaction process |
|
|
413 | (2) |
|
23.2.1 Diffusion barriers and breakthrough time |
|
|
414 | (1) |
|
23.2.2 Solar cells need special types of metal electrodes |
|
|
414 | (1) |
|
23.2.3 Metal corrosion and charge collection |
|
|
415 | (1) |
|
23.3 Physics of dark corrosion that persists during daytime |
|
|
415 | (2) |
|
23.3.1 Dark corrosion involves acids produced by moisture-EVA reaction |
|
|
415 | (1) |
|
23.3.2 A phenomenological model for dark corrosion |
|
|
415 | (2) |
|
23.3.3 Strategies to suppress dark corrosion |
|
|
417 | (1) |
|
23.4 Physics of light-induced corrosion |
|
|
417 | (3) |
|
23.4.1 Light corrosion involves moisture hydrolysis by the electrodes |
|
|
417 | (1) |
|
23.4.2 A phenomenological model for light corrosion: Water hydrolysis |
|
|
418 | (1) |
|
23.4.3 A phenomenological model for light corrosion: PID current |
|
|
419 | (1) |
|
23.4.4 Strategies to suppress light corrosion |
|
|
420 | (1) |
|
23.5 Corrosion does not increase the series resistance |
|
|
420 | (10) |
|
23.5.1 The puzzle defined |
|
|
420 | (1) |
|
23.5.2 Finger thinning reflected in an increasing (fake) shunt resistance |
|
|
421 | (3) |
|
23.5.3 Corrosion-induced delamination is reflected in the loss of photocurrent |
|
|
424 | (1) |
|
23.5.4 Solder bond failure is reflected in the module series resistance |
|
|
424 | (2) |
|
23.5.5 The combination of corroded and uncorroded cells determine module performance |
|
|
426 | (1) |
|
23.5.6 Is it possible to determine the degradation mechanisms from the terminal I-V characteristics alone? |
|
|
426 | (4) |
|
23.6 Conclusions: corrosion is an important PV degradation mechanism |
|
|
430 | (3) |
|
24 Physics of Glass, Cell, and Backsheet Cracking: Mechanical Reliability of Solar Modules |
|
|
433 | (18) |
|
24.1 Mechanical integrity is essential for module operation |
|
|
433 | (1) |
|
24.2 Cracking and delamination must be avoided |
|
|
434 | (1) |
|
24.2.1 Interracial delamination |
|
|
434 | (1) |
|
24.2.2 Cracking of glass and backsheet |
|
|
435 | (1) |
|
24.3 Cracking of a single layer of glass, backsheet, or solar cell |
|
|
435 | (7) |
|
24.3.1 Stress is uniform in a defect-free thin film |
|
|
435 | (2) |
|
24.3.2 Even a microcrack reduces material strength dramatically |
|
|
437 | (1) |
|
24.3.3 An actual crack has even higher stress concentration |
|
|
438 | (1) |
|
24.3.4 Fatigue failure: Cracks begin to grow under repeated cycling |
|
|
439 | (1) |
|
24.3.5 Each material is defined by its Nt -- S relationship |
|
|
440 | (1) |
|
24.3.6 Miner's law predicts lifetime under variable loading |
|
|
441 | (1) |
|
24.3.7 Distribution of failure cycles: Weibull distribution |
|
|
441 | (1) |
|
24.4 Delamination is a form of cracking |
|
|
442 | (3) |
|
24.4.1 Delamination of two interfaces |
|
|
442 | (1) |
|
24.4.2 Calculating the lateral stress involves a few simple steps |
|
|
442 | (2) |
|
24.4.3 Shear stress intensity factors and interracial Paris law |
|
|
444 | (1) |
|
24.5 Stress-induced delamination of the metal grid lines |
|
|
445 | (2) |
|
24.6 Stress accumulation leads to solder bond failure |
|
|
447 | (2) |
|
24.7 Numerical modeling is essential for predictive modeling and quantitative insights |
|
|
449 | (1) |
|
24.8 Conclusions: Stress-induced delamination is an important PV reliability issue |
|
|
449 | (2) |
|
25 Qualification of Module Reliability |
|
|
451 | (13) |
|
25.1 Extensive characterization is necessary to ensure module reliability |
|
|
451 | (1) |
|
25.2 Insulation resistance ensures safe operation in dry/wet conditions |
|
|
452 | (1) |
|
25.3 Thermal cycling ensures modules are resistant to cracking and delamination |
|
|
453 | (2) |
|
25.4 Damp heat test identifies if a module is susceptible to excessive corrosion |
|
|
455 | (2) |
|
25.5 Humidity freeze test provides integrated testing for corrosion and cracking |
|
|
457 | (1) |
|
25.6 UV measurements determines the rate of EVA yellowing |
|
|
458 | (1) |
|
25.7 Mechanical loading test ensures that the module will survive typical wind load |
|
|
459 | (1) |
|
25.8 Integrated stress sequence replicate actual environmental conditions |
|
|
460 | (2) |
|
25.9 Conclusions: Rigorous module qualification is an essential prerequisite for reliable field operation |
|
|
462 | (2) |
|
26 Predicting the Lifetime of Solar Farms |
|
|
464 | (15) |
|
26.1 PV Lifetime depends on local climate and module technology |
|
|
464 | (1) |
|
26.2 Local climate information (C) can be obtained from public databases |
|
|
465 | (3) |
|
26.3 Cell, module, and farm information (G) are available from Research Labs, Solar Cell Manufacturers, and System Installers |
|
|
468 | (1) |
|
26.4 There are different ways to predict PV lifetime |
|
|
469 | (7) |
|
26.4.1 Module Lifetime can be predicted by empirical degradation models |
|
|
469 | (1) |
|
26.4.2 Lifetime prediction is improved by physics-based compact models |
|
|
470 | (5) |
|
26.4.3 Physics-based numerical modeling of PV reliability |
|
|
475 | (1) |
|
26.5 Conclusions: Predictive models do not account for extrinsic failures |
|
|
476 | (3) |
|
27 Inverse Modeling and Monitoring the Health of a Solar Farm |
|
|
479 | (16) |
|
27.1 Why forward/predictive modeling is insufficient: The need for inverse/diagnostic modeling |
|
|
479 | (1) |
|
27.2 Solar farm on Planet X defined by two weather variables, T and RH |
|
|
480 | (5) |
|
27.2.1 Statistical approach: Linear regression for a single farm |
|
|
481 | (1) |
|
27.2.2 Statistical Approach: Log-log regression for a single farm |
|
|
482 | (1) |
|
27.2.3 Statistical approach to data-based farm modeling |
|
|
482 | (3) |
|
27.3 Physics-based inverse modeling of solar farms |
|
|
485 | (1) |
|
27.4 There are two ways to calibrate the model coefficients |
|
|
486 | (5) |
|
27.4.1 Physics-based forecasting: Full I -- V -- T method |
|
|
486 | (2) |
|
27.4.2 Physics-based forecasting: The Vmp -- Imp method |
|
|
488 | (3) |
|
27.5 There are several ways to determine the power degradation |
|
|
491 | (2) |
|
27.5.1 Performance ratio method |
|
|
491 | (1) |
|
27.5.2 A statistical machine learning-based approach obviates the need for temperature/irradiance sensors |
|
|
492 | (1) |
|
27.6 Conclusions: The future of machine learning for PV technologies |
|
|
493 | (2) |
|
|
|
495 | (16) |
| Index |
|
511 | |
