Computational Modeling of the Electrical Performance and Degradation of Third-generation Photovoltaic Modules, Under Accelerated and Real Operating Conditions

Abstract

ABSTRACT: This work evaluated the perovskite solar technology’s outdoor performance. An emerging technology that was not commercial at the beginning of this work (Velilla et al., 2017). Therefore, minimodules with an inverted mesoporous MAPbI3 structure (NiOx/Al2O3/MAPbI3/PCMB/rhodamine/Au), were fabricated in a drybox by spin coating (Ramirez et al., 2019). The devices formed for 4 cells interconnected in series and 8.0 cm2 of an active area were fabricated on ITO substrates of 5 x 5 cm and manually encapsulated with ethylene-vinyl acetate (EVA). These were analyzed by impedance frequency response and ideality factor following the procedure shown in (Yoo et al., 2021, 2020), providing physical insight into the recombination mechanism dominating the performance and fully characterize the devices under indoor conditions. Accordingly, a methodology based on the international standard IEC 61853-1 to evaluate the perovskite technology’s outdoor performance was proposed (Velilla et al., 2019b) because no international standards have been fully established, and most published works have focused on laboratory-scale cells (i.e., 1 cm2 or smaller in size). This methodology was implemented as Python’s functions (scripts) in a remote server to estimate the photovoltaic device’s outdoor performance. Hence, the developed I-V curve tracers (Cano et al., 2015) were synchronized with a weather station (to record the irradiance levels and ambient/device temperatures). This procedure allowed validated the power rating conditions for commercial modules of different technologies such as silicon, HIT, and CIGS according to the manufacturers’ reported values in its datasheets (Velilla et al., 2019a). Then, the procedure was extended to evaluate perovskite minimodules performance under outdoor conditions. The perovskite minimodules outdoor evaluation under natural sunlight without a tracker in the Solar Cell Outdoor Performance Laboratory (OPSUA, University of Antioquia, Medellín-Colombia) allowed observation of three maximum power (Pmax) evolution patterns, named convex, linear, and concave patterns because of the exhibited shapes. In this sense, all the analyzed minimodules can be statistically associated with one of these three patterns, commonly described for degradation processes in the literature to study possible degradation paths and estimate the failure time. Therefore, to analyze these degradation behaviors, well-known statistical models such as linear regression models were used to estimate the degradation rate and lifetime (T80). Relating to ideality factor also called quality factor or shape curve factor, which is the most reported parameter for different solar cell technologies. This parameter has been used to define the electrical behavior of solar devices due to its relationship with conduction, transport, recombination, and behavior at interface junctions, providing physical insight into the recombination mechanism dominating the performance. Consequently, the changes in nID could be correlated with the recombination mechanisms or degradation processes occurring in the device, highlighting the importance of this parameter to complete the device’s characterization. Therefore, the nID values were estimated from the relationship between the open-circuit voltage and light intensity, from the impedance frequency response (IFR) under different light intensities calculating the recombination resistance (Rrec) (Yoo et al., 2021, 2020), and fitting the I-V curve to one-diode model to extract this parameter. In these cases, an agreement has been shown between the nID value estimated from the recombination resistance extracted through IFR analysis and the value calculated from Voc at different light intensities (Almora et al., 2018; Yoo et al., 2020). Therefore, an Autolab’s procedure was implemented to record the device’s Voc and IFR as a light function. Besides, to estimate the Rrec fitting the IFR to a circuit model or extract the one-diode model’s parameters fitting the I-V curve to this model, a global optimization process involving a genetic algorithm (GA) and the simplex method was implemented, following the previous work’s methodology (Velilla et al., 2018). Moreover, due to the day-night cycles, including dawn and noon conditions, which can naturally provide a broad range of illumination conditions, it was proposed to estimate nID from the open-circuit voltage (Voc) dependence on irradiance and ambient temperature (outdoor data). Consequently, the changes in nID could be correlated with the recombination mechanisms or degradation processes occurring in the device. In this context, it was observed that the three different degradation patterns identified for Pmax can also be identified by nID. Hence, these three representative power loss tendencies were compared with their corresponding ideality factor (nID). To this end, we defined TnID2 as the time at which nID first reaches a value of 2, with a physical meaning related to the transition point between bulk SRH recombination through a single level to recombination through multiple levels because of device degradation. Thus, based on the linear relationship between T80 and the time to reach nID=2 (TnID2) is demonstrated that nID analysis could offer important complementary information with important implications for this technology’s outdoor development. Finally, we must admit that the photovoltaic industry has invested efforts in developing diagnostic tools intended to improve the energy production’s reliability and the installations’ safety. In this sense, although nID has not been employed to monitor device evolution to see how the relevant processes evolve, for example, in degradation, this work proposed a methodology to characterize the technology’s outdoor performance evolution and improve the conventional Pmax analyses, using the nID as a figure of merit (Velilla et al., 2021). This methodology could be quickly adapted by research groups to estimate the status and evaluate the device’s performance evolution and by the industrial sector to develop equipment or tools to perform diagnostic devices.