EXPLORING THE POTENTIAL OF LOW-COST PEROVSKITE CELLS AND IMPROVED MODULE RELIABILITY TO REDUCE LEVELIZED COST OF ELECTRICITY
thesisposted on 2020-12-16, 16:12 authored by Reza AsadpourReza Asadpour
The manufacturing cost of solar cells along with their efficiency and reliability define the levelized cost of electricity (LCOE). One needs to reduce LCOE to make solar cells cost competitive compared to other sources of electricity. After a sustained decrease since 2001 the manufacturing cost of the dominant photovoltaic technology based on c-Si solar cells has recently reached a plateau. Further reduction in LCOE is only possible by increasing the efficiency and/or reliability of c-Si cells. Among alternate technologies, organic photovoltaics (OPV) has reduced manufacturing cost, but they do not offer any LCOE gain because their lifetime and efficiency are significantly lower than c-Si. Recently, perovskite solar cells have showed promising results in terms of both cost and efficiency, but their reliability/stability is still a concern and the physical origin of the efficiency gain is not fully understood.
In this work, we have collaborated with scientists industry and academia to explain the origin of the increased cell efficiency of bulk solution-processed perovskite cells. We also explored the possibility of enhancing the efficiency of the c-Si and perovskite cells by using them in a tandem configuration. To improve the intrinsic reliability, we have investigated 2D-perovskite cells with slightly lower efficiency but longer lifetime. We interpreted the behavior of the 2D-perovskite cells using randomly stacked quantum wells in the absorber region. We studied the reliability issues of c-Si modules and correlated series resistance of the modules directly to the solder bond failure. We also found out that finger thinning of the contacts at cell level manifests as a fake shunt resistance but is distinguishable from real shunt resistance by exploring the reverse bias or efficiency vs. irradiance. Then we proposed a physics-based model to predict the energy yield and lifetime of a module that suffers from solder bond failure using real field data by considering the statistical nature of the failure at module level. This model is part of a more comprehensive model that can predict the lifetime of a module that suffers from more degradation mechanisms such as yellowing, potential induced degradation, corrosion, soiling, delamination, etc. simultaneously. This method is called forward modeling since we start from environmental data and initial information of the module, and then predict the lifetime and time-dependent energy yield of a solar cell technology. As the future work, we will use our experience in forward modeling to deconvolve the reliability issues of a module that is fielded since each mechanism has a different electrical signature. Then by calibrating the forward model, we can predict the remaining lifetime of the fielded module. This work opens new pathways to achieve 2030 Sunshot goals of LCOE below 3c/kWh by predicting the lifetime that the product can be guaranteed, helping financial institutions regarding the risk of their investment, or national laboratories to redefine the qualification and reliability protocols.
U.S. Department of Energy under DOE Cooperative Agreement No. DE-EE0004946
National Science Foundation under Grant No. #1724728
- Doctor of Philosophy
- Electrical and Computer Engineering
- West Lafayette
Advisor/Supervisor/Committee ChairProf. Muhammad A. Alam
Additional Committee Member 2Prof. Mark Lundstrom
Additional Committee Member 3Prof. Peter Bermel
Additional Committee Member 4Prof. Peide Ye
Solar CellSolar ModuleReliabilityLevelized Cost of ElectricityLCOEPerovskite Solar CellsRuddlesden-Popper PerovskiteTandem Solar CellBifaicial Solar CellContact CorrosionContact DelaminationSolder Bond FailureDark Lock-in ThermographyDLITMarkov Chain MethodElectrical and Electronic Engineering not elsewhere classifiedRenewable Power and Energy Systems Engineering (excl. Solar Cells)Photodetectors, Optical Sensors and Solar Cells