Purdue University Purdue e-Pubs Open Access Theses Theses and Dissertations January 2015 A Numerical Tool for Evaluating and Optimizing Multijunction PV Systems Kevon Clint Charles Purdue University Follow this and additional works at: https://docs.edu/open_access_theses Recommended Citation Charles, Kevon Clint, "A Numerical Tool for Evaluating and Optimizing Multijunction PV Systems" (2015). Open Access Theses.edu/open_access_theses/1099 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact epubs@purdue.edu for additional information. A NUMERICAL TOOL FOR EVALUATING AND OPTIMIZING MULTIJUNCTION PV SYSTEMS A Thesis Submitted to the Faculty of Purdue University by Kevon C.
Charles In Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical and Computer Engineering August 2015 Purdue University West Lafayette, Indiana ii ACKNOWLEDGEMENTS I would like to thank Professor Jeffery Gray for his remarkable guidance and instruction. He has truly been an exceptional mentor to me. In addition, I would like to thank Professors Mark Lundstrom, and Peter Bermel for serving on my thesis committee. I would also like to thank my colleagues John Wilcox, Raghu Chavali and Xufeng Wang for their advice and assistance throughout grad school.
I appreciate the stellar learning opportunities available at Purdue University. Lastly, I am grateful for the facilities and computer resources provided to me by Network for Computational Nanotechnology, Engineering Computer Network, and the School of Electrical and Computer Engineering. iii TABLE OF CONTENTS Page LIST OF TABLES .v LIST OF FIGURES .1 Why Solar Energy? .2 Solar Cell Operation .4 Importance of Optimizing Solar Cells .2 Physics of the Tool .2 Reverse Saturation Current density (𝐽𝐽0) .3 Short-Circuit Current .4 Temperature Dependence on Bandgap .3 PV System Operation .1 Spectrum Absorption by Junctions .2 𝐽𝐽𝐽𝐽𝐽𝐽 For each Junction in a System .3 PV Systems that are Electrically in Series .4 PV Systems that are both Electrically Independent and in Series .5 PV Systems with an Optical Split .1 Tool Capabilities and Features.2 Interface and Parameters .1 Shockley-Queisser Limit in Single Junction Solar Cells .2 Electrically in Series Junctions .3 Spectral Splitting and Electrically Independent Junctions.4 Absorption Coefficient Comparison. 35 3 EXAMPLE RESULTS AND ANALYSIS.1 Examples of Series Connected PV Systems .2 Series Resistance, J0 Model, and Spectrum Files .3 Finite/infinite Junction Thickness .5 External Quantum Efficiency (EQE) .2 Electrically Independent Junctions in a PV System .3 Optically Independent PV Systems.
54 LIST OF REFERENCES. RAW DATA FROM TOOL .80 v LIST OF TABLES Table Page 2. 1 Tool input parameters and descriptions. 2 Output parameters and descriptions.
3 Showing results of series connected simulation (Fig 2. 4 Showing results of electrically and optically independent simulations (Fig 2. 5 Showing results of a 4 junction system with the second junction having the highest bandgap EG (Fig 2. 1 Showing results of varying the position of GaAs in a System.
2 Showing results of varying key parameters. 3 Showing a comparison between finite and infinite junction thickness. 4 Showing the effect of temperature on the performance of a PV system. 5 Showing the effect of Sp, Sn, and xB on the overall efficiency.
6 Showing the effect of having electrically independent junctions in a system. 7 Showing optical splitting characteristics. 1 Showing physical constants. 1 Showing raw data of configurations varying from 1 junction to 8 junctions for 1 sun.
2 Showing raw data of configurations varying from 1 junction to 8 junctions for 100 suns. 3 Showing raw data of configurations varying from 1 junction to 8 junctions for 100 suns. 64 vi Table Page B. 4 Showing raw data of varying the position of GaAs in PV systems.
5 Showing raw data of varying the solar spectrum for a PV system. 6 Showing raw data of varying the temperature of a PV system. 7 Showing raw data of finite/infinite thickness simulations. 8 Showing raw data for varying series resistance on PV system.
9 Showing raw data using SOTA as the J0 model. 10 Showing raw data of electrically independent junctions in a PV system. 11 Showing raw data of an optically independent PV system. 77 vii LIST OF FIGURES Figure Page 1.
1 Diagram showing the cross-section of a solar cell and the conversion of photons from the light to electron and hole charge carriers. 1 PV junctions that are (a) electrically in series. (b) Top junction electrically independent from the rest but optically in series. and (c) Top junction optically independent from the remaining junctions.
2 Typical solar cell circuit model. 3 Solar cell circuit model for tool. 4 Saturation current density for the Shockley-Queisser detailed balance limit (blue) and "state-of-the-art" limit (red) as a function of bandgap. 5 Plot showing parts of the AM1.5 direct spectrum that is absorbed by each junction.
6 Plot showing the general characteristics of the transmission of a split. 7 Introductory page of the tool interface. 8 Interface showing Global parameters. 9 Interface showing Device parameters.
10 Efficiency of each individual band gap in a 5-junction PV system. 11 Plot showing am1. 12 Plot showing the spectral absorption of each bandgap in a 5-junction system. 13 Plot showing the transmission of a split.
14 Plot showing the external Quantum efficiency for a particular junction. 15 Plots showing Bandgap vs Max. Efficiency for different concentrations. 16 Configuration showing 4 junctions electrically in series.
32 viii Figure Page 2. 17 (a) showing configuration of a 4 junction system with the top cell electrically independent from the rest, (b) showing configuration of a 4 junction system with the top cell optically independent from the rest. 18 Showing a 4 junction system with the top 2 junctions electrically independent. 19 Plots showing the comparison of the actual absorption coefficient to that of the fitting equation for an indirect material (Si) and a direct material (GaAs).
1 Diagram showing configurations varying from 8 junctions to 1 junction. 2 Plots showing the efficiency of varying configurations at different geometric concentrations (series connected in one stack). 3 Properties of individual cells in (a) 2 junction, (b) 3 junction, and (c) 4 junction PV systems. 4 (a) 4 junction system with GaAs next to top cell, (b) 4 junction system with GaAs next to the bottom cell, (c) 3 junction system with GaAs in the middle, and (d) 2 junction system with GaAs at the bottom.
5 Diagram showing the configuration from Wang's paper .5dc spectrum, (b) AM1.5g spectrum, (c) AM0 spectrum, and (d) blackbody spectrum. 7 Showing how the spectrum is absorbed for finite/infinite thickness in (a) GaInP, and (b) GaAs. 8 Plots showing the effect of Sp on the external quantum efficiency. 9 Plots showing the effect of Sn and xB on the external quantum efficiency.
10 Plots showing the effect of Sp, Sn, and xB on the overall efficiency. 11 Configurations showing (a) all junctions, (b) bottom junction, and (c) top junction electrically independent. 12 Configuration showing the optically split PV system simulated. 13 Plots showing ideal transmission characteristics (left) and transmission characteristics with a transition width of 0.2 µm super imposed on a normalized spectrum (right).
51 ix ABSTRACT Charles, Kevon C., Purdue University, August 2015. Numerical Tool for Evaluating and Optimizing Multijunction PV Systems. Major Professor: Jeffery Gray. Solar energy is one of the most abundant sources of clean renewable energy and is also an important source of electrical energy.
Solar energy has the potential of meeting all of the world's energy needs, and has seen substantial growth and development in recent years. Solar cells can convert sunlight directly into electrical energy, and the solar industry has made a great deal of progress in making them less costly and more efficient. The conversion efficiency of solar cells, however, is one of the main factors that limits the solar industry from competing with fossil fuels. Once the efficiency of solar cells is improved, solar energy will have a greater impact on the worlds energy consumption, and hence more clean energy will be consumed.
It is known that in order to take full advantage of the solar spectrum, a multijunction PV system has to be implemented in order to absorb more photons. The design of this system is very important in improving the overall conversion efficiency. Choosing the right bandgap energies in a PV system is an important design characteristic that helps improve the performance of solar cells. In this thesis, a numerical tool is designed to determine the bandgap energies that yield the highest possible system power efficiency for a given number of PV junctions.
The tool has the ability to simulate PV systems with combinations of junctions that are optically split or in series, as well as electrically independent or in series.1 Why Solar Energy? Renewable Energy has been a big topic of interest for researchers in recent years, as the world tries to decrease its dependence on fossil fuels, in an effort to reduce pollution in the atmosphere. According to REN21’s 2014 report, 22% of electricity generated were as a result of renewables in 2013 [1]. Renewable energies such as wind and solar energy, have experienced large growth over recent years [2]. Wind generated electricity has increased by 44% from 2013 to 2014 and 32% of new electric generating capacity came from solar in 2014 [3] Solar energy is the beaming light and heat that is generated by the sun.
There are many advantages of solar energy. The main benefit of solar energy is that it can be easily utilized by home and business users since it is easy to install unlike wind and geothermal. Solar energy is also a non-polluting source of electricity, there is no pollution in the air by harmful gases like CO2 which is a byproduct of fossil fuels. Another advantage is that solar cells are long lasting and require very little maintenance.
Solar panels do have initial cost in the beginning, however, there are no repeated costs. The technology in solar power has been improving rapidly over the years and as non-renewable sources such as fossil fuels decline, it is vital that the world move towards renewable sources of energy [4].2 Solar Cell Operation A solar cell is an electronic device that converts light energy into electricity. It is also considered to be photovoltaic, regardless of whether the source is an artificial light or sunlight. When the light shines on the solar cell, it produces both a current and a voltage, which generates power.
In order for a solar cell to produce electric energy, basic fundamental functions have to be met. These functions are as follows: 2 1. The conversion of photons from light source into light generated carriers, which is a process called photogeneration. Quick separation of the light generated carriers to prevent electron/hole recombination 3.
The collection of these separated carriers to generate a current. The generation of a voltage across the solar cell 5. And finally, the extraction of the collected charged carriers to an external circuit as shown in Fig 1. 1 Diagram showing the cross-section of a solar cell and the conversion of photons from the light to electron and hole charge carriers 1.3 Analytical Modeling Analytical modeling can be described as a mathematical technique used for simulating and making valid predictions about mechanisms that are involved in complex physical processes.
Analytical models are built for a number of reasons. Some are constructed in order to gain a better understanding of how a complex system works, as well as measure the performance and analyze different behaviors. Others build analytical models for the 3 purpose of predicting patterns and behavior of certain parameters with in a system [6]. As a result, having an analytical model for solar cells is important since it has the ability to speed up development time, as well as reduce the number of experimental devices needed.