Standard Test Conditions Research Articles

Evaluation of the bifaciality coefficient of bifacial photovoltaic modules under real operating conditions

Among the parameters that define a bifacial photovoltaic module, the bifaciality coefficients indicate the rear and front side ratio of the most representative IV curve points of a photovoltaic panel, that is, Isc, Voc and Pm. However, these parameters are defined under the ideal Standard Test Conditions (STC). Therefore, to provide a realistic insight regarding the performance of bifacial modules, it is necessary to evaluate these coefficients under real operating conditions. For such purpose, an outdoor campaign was performed to experimentally measure the maximum power bifaciality coefficient of two modified bifacial modules that resemble a rear and a front monofacial panel respectively. As a first result, if the measurements are translated to STC, using a linear approximation, the bifaciality matches the value indicated by the manufacturer. Additionally, the operating bifaciality coefficient shows a linear decrease trend, proportional to the irradiance level decrease. This result implies that on cloudy days, the average bifaciality factor is below the corresponding one from sunny days. Finally, for irradiances below 200 W/m2, there is a non-linear increase in the bifaciality, with greater values than the corresponding to the ideal STC conditions, which presumably are caused by the non-linearity performance of photovoltaic modules at low irradiances.

Parameter identification and generality analysis of photovoltaic module dual-diode model based on artificial hummingbird algorithm

Abstract The aim of this study is to propose a photovoltaic (PV) module simulation model with high accuracy under practical working conditions and strong applicability in the engineering field to meet various PV system simulation needs. Unlike previous model-building methods, this study combines the advantages of analytical and metaheuristic algorithms. First, the applicability of various metaheuristic algorithms is comprehensively compared and the seven parameters of the PV cell under standard test conditions are extracted using the double diode model, which verifies that the artificial hummingbird algorithm has higher accuracy than other algorithms. Then, the seven parameters under different conditions are corrected using the analytical method. In terms of the correction method, the ideal factor correction is added on the basis of previous methods to solve the deviation between simulated data and measured data in the non-linear section. Finally, the root mean squared error between the simulated current data and the measured current data of the proposed model under three different temperatures and irradiance is 0.0697, 0.0570 and 0.0289 A, respectively.

Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability.

Measurement and analysis of annual solar spectra at different installation angles in central Europe

The solar spectra in outdoor installations is very seldom equal to that under standard test conditions. This points to the importance of measuring, analyzing and understanding the implications of solar spectra variations with respect to the performance of solar cells. In this work, we present and analyze one year of sun spectra measured at two different angles in central Europe, where the spectrometers were installed at the optimum inclination angle and in vertical orientation, the latter being relevant for building integrated photovoltaics. We report for the first time the differences between the two inclination angles in terms of key performance indicators, such as average photon energy, blue fraction and spectral factor. Moreover, we show the impact of these spectral changes on the maximum current density of ideal single-junction and tandem devices in tilted and vertical orientations. Red-shifted solar spectra were more often found at the vertical installation in comparison to the optimum installation angle, which translated into up to 30% lower current-mismatch losses in idealized tandem devices throughout the year.

A New MPPT-Based Extended Grey Wolf Optimizer for Stand-Alone PV System: A Performance Evaluation versus Four Smart MPPT Techniques in Diverse Scenarios

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications.

A simplified three-diode model for photovoltaic module: cell modeling and performance analysis

Purpose Estimation of solar cell parameters, mathematical modeling and the actual performance analysis of photovoltaic (PV) cells at various ecological conditions are very important in the design and analysis of maximum power point trackers and power converters. This study aims to propose the analysis and modeling of a simplified three-diode model based on the manufacturer’s performance data. Design/methodology/approach A novel technique is presented to evaluate the PV cell constraints and simplify the existing equation using analytical and iterative methods. To examine the current equation, this study focuses on three crucial operational points: open circuit, short circuit and maximum operating points. The number of parameters needed to estimate these built-in models is decreased from nine to five by an effective iteration method, considerably reducing computational requirements. Findings The proposed model, in contrast to the previous complex nine-parameter three-diode model, simplifies the modeling and analysis process by requiring only five parameters. To ensure the reliability and accuracy of this proposed model, its results were carefully compared with datasheet values under standard test conditions (STC). This model was implemented using MATLAB/Simulink and validated using a polycrystalline solar cell under STC conditions. Originality/value The proposed three-diode model clearly outperforms the earlier existing two-diode model in terms of accuracy and performance, especially in lower irradiance settings, according to the results and comparison analysis.

Indoor and outdoor photovoltaic modules Performances based on thin films solar cells

This paper summarizes the electrical and thermal characterizations of thin film PV modules based on amorphous triple junctions (3J: a-Si) and Copper Indium Selenide (CIS) thin film solar cells. Tests are operated in outdoor exposure and under natural sunlight of URAER located in Saharan region of Ghardaïa (Algeria) as specific desert climate environment, characterized by high irradiation and temperature levels as well as ENEA-Portici located in Naples (Italy) as Mediterranean site. PV modules performance evaluation was performed according to international standard as diagnostic test of data manufacturer. We used the thermal infrared analysis to detect the homogeneity mapping temperature of solar cells in the PV module. Data acquired from Environmental Operating Conditions (EOC) was converted into solar module output characteristics at Standard Test Conditions (STC) by using three method suggested by A.J. Anderson and G. Blaesser as well as the equations already standardized as IEC 60891. Then, based on the investigation results of the conversion equations, these methods of translation are distinguished by the type of solar cell technology and the application range. A difference between the tests in situ and in natural environment exists, attributed to various factors but mainly to the mismatch between the spectral responses of PV module and the reference solar cell.

Prediction of band gap and optimum electrical parameters of a thin homojunction perovskite solar cell based on FA1−xCsxSnyPb1−yI3 through a combination of SCAPS-1D and machine learning based modelling

This paper presents a machine learning (ML) model developed to predict the band gap and optimum electrical parameters of a thin homojunction perovskite solar cell (PSC) based on FA1−xCsxSnyPb1−yI3. For different cesium (x) and tin (y) compositions, band gaps are tabulated, and electrical parameters such as open circuit voltage, short circuit current, and power conversion efficiency are generated in standard test condition (STC) using the SCAPS-1D platform. The data obtained are used to build two sub-models wherein first Model 1 uses the composition to predict the band gap and second Model 2 uses the predicted band gap to predict the electrical parameters using a polynomial regression algorithm. The sub-models are then connected in series. Train and test scores, correlation (r), and root mean square error (RMSE) are calculated to investigate the accuracy of the models while the cross-validation score checks the stability and the overfitting of the models. The final, best performance model’s metrics–R2, r, and RMSE, are 0.99, 0.99, and 0.09 respectively for Model 1 and 0.97, 0.99, and 0.28 for Model 2. These are achieved for a fourth order polynomial. In addition, we realize that the homojunction PSC based on FACsSnPbI3 offers a high efficiency of 17.17% for a band gap of around 1.44 eV. We also reveal that the presence of tin induces lattice contraction, affects the stability, and changes the configuration of the crystal structure from cubic to octahedral tilting when tin composition increases.

Improved spectral mismatch and performance of a phosphor-converted light-emitting diode solar simulator

<span lang="EN-US">A phosphor-converted light-emitting diode (LED) solar simulator is an illuminance device that produced irradiance intensity and spectral close to the sunlight. It is determined as spectral mismatch, non-uniformity of irradiance, and temporal instability. This paper has improved the LED solar simulator (LSS) system to have a spectral distribution consistent with the AM1.5G spectrum at 100%. It was developed as a new prototype to have the AAA class spectral characteristics, time instability, and inconsistency according to IEC 60904-9. The results showed that an optimal approach was to use phosphor-converted natural white LED (pc-nWLED), combining a monochromatic near-infrared (NIR) (730, 800, 850, 940, and 1,000 nm) as well as the proposed LSS system capable of generating 1,000 W/m<sup>2</sup> irradiation over the test plane of 125×125 mm and operated continuously in a constant temperature LED state for at least 2 hours, therefore suitable for demonstration of solar cell features under standard test condition (STC) in the laboratory.</span>

Biodegradable Nanofiber/Metal-Organic Framework/Cotton Air Filtration Membranes Enabling Simultaneous Removal of Toxic Gases and Particulate Matter.

The typical filters that protect us from harmful components, such as toxic gases and particulate matter (PM), are made from petroleum-based materials, which need to be replaced with other environmentally friendly materials. Herein, we demonstrate a route to fabricate biodegradable and dual-functional filtration membranes that effectively remove PM and toxic gases. The membrane was integrated using two layers: (i) cellulose-based nanofibers for PM filtration and (ii) metal-organic framework (MOF)-coated cotton fabric for removal of toxic gases. Zeolitic imidazolate framework (ZIF-8) was grown from the surface of the cotton fabric by the treatment of cotton fabric with an organic precursor solution and subsequent immersion in an inorganic precursor solution. Cellulose acetate nanofibers (NFs) were deposited on the MOF-coated cotton fabric via electrospinning. At the optimal thickness of the NF layer, the quality factor of 18.8 × 10-2 Pa-1 was achieved with a filtration efficiency of 93.1%, air permeability of 19.0 cm3/cm2/s, and pressure drop of 14.2 Pa. The membrane exhibits outstanding gas adsorption efficiencies (>99%) for H2S, formaldehyde, and NH3. The resulting membrane was highly biodegradable, with a weight loss of 62.5% after 45 days under standard test conditions. The proposed strategy should provide highly sustainable material platforms for practical multifunctional membranes in personal protective equipment.

Characterization, traceability, and uncertainty estimation of reference solar panel module measurements using pulsed solar simulators and reference solar cells

This paper presents the design, characterization, and traceability of reference solar panel modules for determining the performance of photovoltaic (PV) modules at standard test conditions (STC). The research introduces an advanced experimental system based on a class AAA pulsed solar simulator to measure the radiometric, electrical performances, and efficiency of PV modules. I-V/P characteristics of three PV modules at different STCs and the associated uncertainty budget of the system were estimated. I-V characteristic and associated parameters including Isc, Voc, Pmax, FF, and efficiency were measured. The radiometric and electrical traceability were discussed, and the relative expanded combined uncertainties were concluded to be 1.62 % (Isc), 0.42 % (Voc), 2.05 % (Pmax), and 2.5% (η), with a coverage factor k = 2. Reference solar panel modules were also used on-site to test the performance of large PV panels, and the results are reported.

How Does Stacking Pressure Affect the Performance of Solid Electrolytes and All‐Solid‐State Lithium Metal Batteries?

All‐solid‐state lithium metal batteries (ASSLMBs) with solid electrolytes (SEs) have emerged as a promising alternative to liquid electrolyte‐based Li‐ion batteries due to their higher energy density and safety. However, since ASSLMBs lack the wetting properties of liquid electrolytes, they require stacking pressure to prevent contact loss between electrodes and SEs. Though previous studies showed that stacking pressure could impact certain performance aspects, a comprehensive investigation into the effects of stacking pressure has not been conducted. To address this gap, we utilized the Li6PS5Cl solid electrolyte as a reference and investigated the effects of stacking pressures on the performance of SEs and ASSLMBs. We also developed models to explain the underlying origin of these effects and predict battery performance, such as ionic conductivity and critical current density. Our results demonstrated that an appropriate stacking pressure is necessary to achieve optimal performance, and each step of applying pressure requires a specific pressure value. These findings can help explain discrepancies in the literature and provide guidance to establish standardized testing conditions and reporting benchmarks for ASSLMBs. Overall, this study contributes to the understanding of the impact of stacking pressure on the performance of ASSLMBs and highlights the importance of careful pressure optimization for optimal battery performance.

Carbonation depth assessment in shotcrete with various initial damage degrees and accelerator dosages: Experimental study

Underground structures widely use shotcrete and operate in elevated CO2 concentration environments. Accelerators significantly affect the hydration process and structure of concrete, while continuous tunnel construction leads to initial damage (cracks) in the hardened concrete, both of which have a significant impact on the carbonation resistance of shotcrete. The effects of accelerator dosage and the initial damage degrees on the carbonation depth of shotcrete were analyzed. The results show that initial damage accelerated shotcrete’s carbonation process. The carbonation depth of shotcrete samples with initial damage degrees of 0.06, 0.12, 0.18, 0.24, and 0.30 at 56 days increased by 35.3, 60.7, 74.8, 164.2, and 246.5%, respectively, compared to the reference group. Sodium aluminate accelerator increases the carbonation depth of shotcrete, and this influence becomes more evident at higher dosages. Microscopic analysis indicates that the accelerator reduces the content of Ca(OH)2 in the system, accelerates the formation of AFt hydrates in early hydration, and the aluminosilicate ions in the accelerator will also further facilitate the transformation of AFt into plate-like AFm, deteriorating the gel structure. After considering the carbonation depth trend and the requirements for setting time, the optimal dosage of the rapid-setting agent is determined to be 5% and try to avoid disturbing the early shotcrete. By fitting the experimental results, a carbonation depth prediction model was derived for concrete with consideration of initial damage degrees under standard testing conditions, based on Fick's second law. These findings are instrumental in the durability evaluation and residual life prediction of corroded structures with various damage degrees.

Green synthesis, characterization and adsorption of chromium and cadmium from wastewater using cerium oxide nanoparticles; reaction kinetics study

Chromium and cadmium are two hazardous heavy metals that are known carcinogens and can cause a range of health problems as well as changes in the regular functioning of the environment. Green nanotechnology has sparked a lot of attention in recent years as a potential solution to heavy metal concerns. Because of their unique chemical, physical, and biological properties that make them favorable for a wide range of uses, application of cerium oxide nanoparticles (CeO2 NPs) has gained in popularity. CeO2 NPs have a larger surface area than larger particles, leading in improved biochemical reactivity, catalytic activity, and environmental effectiveness. In this study, CeO2 NPs were made from green procedure by the use of plant extract from Azadirachta indica. The stat of the art techniques were utilized to study the physical characteristics of the synthesized CeO2 NPs. These nanoparticles were utilized as bio-sorbents to extract chromium (Cr) and cadmium (Cd) ions from wastewater. In standard testing conditions, the CeO2 nanoparticles exhibited high removal efficiency, removing 93% of Cr within approximately 15 min of contact time, and achieving 89% removal of Cd. To acquire the best results for industrial-scale applications and to simplicity the reuse of these heavy metals, exact experimental conditions that improve the absorbance efficiency must be identified. Despite the fact that nanoparticles have demonstrated great efficiency and selectivity in extracting and recovering chromium from wastewater, further study is needed to optimize their production and performance for industrial-scale applications.

PERFORMANCE EVALUATION OF THE IMPACTS OF METROLOGICAL PARAMETERS ON CRYSTALLINE AND AMORPHOUS MODULES AT MINNA, NIGERIA

Photovoltaic (PV) module performance is rated under standard test conditions (STC) i.e. irradiance of 1000 W/m², solar spectrum of Air Mass 1.5 and module temperature at 25°C. Manufacturers of photovoltaic modules typically provide the ratings at only one operating condition i.e. STC. However, PV module operates over a large range of environmental conditions at the field. So the manufacturer’s information is not sufficient to determine the actual performance of the module at field. Optimization of solar energy is affected by so many factors ranging from conversion efficiency of PV module to local metrological conditions. The research work therefore, evaluates the performance of three PV technologies using performance ratio. Metrological parameters such as solar radiation intensity, wind speed, relative humidity, and air temperature were measured simultaneously with the output electrical parameters from the three modules exposed to field test using metrological sensors and a CR1000 software-based data logging system with computer interface attached to the modules. Four years consecutives metrological and modules output data’s were collected from the modules and analyzed. The findings indicates that metrological parameters fluctuate non-linear with the modules output, under this conditions the trends as measured by the output power revealed that polycrystalline module has a better performance than amorphous module followed by mono-crystalline module in this experiment. The paper recommends the need to mitigate substandard modules entering our market through appropriate monitoring agencies and the setting of solar module laboratory for locally production of solar modules that would captures our local metrological parameters towards greater efficiency.

Dynamic Performance Improvement Using Model Reference Adaptive Control of Photovoltaic Systems under Fast-Changing Atmospheric Conditions

The effectiveness of a photovoltaic (PV) system can be increased by using maximum power point tracking (MPPT). The literature has suggested a number of methods for tracking the maximum power point (MPP). However, this number of methods most often presents a high convergence speed in reaching the MPP, complexity under their implementation, power fluctuations, overshoots, and great difficulty in reaching the MPP under fast-changing atmospheric conditions, thus influencing the efficiency of PV systems. Intending to improve the performance of PV systems under rapid changes in the atmosphere, this paper proposes model reference adaptive control (MRAC) as a technique for tracking the MPP based on the employ of reference models such as optimal voltage and current at the MPP ( V MPP and I MPP ). The MATLAB/Simulink environment is used to produce the simulation results; the Kyocera Solar KC 130 GT module is used here as a photovoltaic power plant, connected to a boost converter, supplying a resistive load. The Lyapunov theory was used to demonstrate the stability of the system. The simulation outcomes obtained using the suggested method are compared with those obtained by techniques such as perturb and observe (P&O), incremental conductance (INC), variable step incremental conductance (VSINC), particle swarm optimization (PSO), and grey wolf optimization (GWO), thus showing a very large improvement under standard test and fast-changing atmospheric conditions of the technique proposed on the other techniques in terms of convergence speed and tracking efficiency. The simulation results prove that the suggested method has great tracking effectiveness (>99.88%), less time for convergence (<0.01 s), and simple implementation complexity under fast-changing atmospheric conditions without both transient and steady-state power oscillations, overshoots, and chattering effects, thus causing a great minimization of energy losses, and the proposed technique reaches exactly the MPP under fast-changing atmospheric conditions.

Computational modelling and improvement of heterojunction perovskite solar cell based on CsPbI3/MAPbX3(X = I1−xBrx)

This paper presents a computational modelling and improvement of the heterojunction perovskite solar cell (PSC) based on CsPbI3/MAPb(I1−xBrx)3. The device is composed of two absorber layers named CsPbI3 and MAPb(I1−xBrx)3), Cd1−xZnxS as electron transport layer (ETL) and poly(3-hexylthiophene) (P3HT) as hole transport layer (HTL). As part of the investigations, the contribution of thickness, doping, defect density of each absorber layer and different metals (rear contact electrode) is checked on the quantum efficiency (QE) and electrical parameters (efficiency, short circuit current, open circuit voltage, and fill factor). The investigations are conducted in standard test condition using the SCAPS-1D platform. The results obtained show that to improve the performances of this PSC device, thickness of CsPbI3 must be much lower than that of MAPb(I1−xBrx)3. Regarding the doping, only the MAPb(I1−xBrx)3 (donor) layer significantly affects the dynamics of the PSC compared to CsPbI3 (acceptor) which has almost no effect. Defects densities greatly reduce QE and electrical parameters and should be as low as possible. The new prototype of PSC offers an efficiency of 31.97 % when the back contact electrode is gold (Au). In addition, this efficiency is achieved when thickness, doping, defect density of CsPbI3 and MAPb(I1−xBrx)3 are 100 nm and 1000 nm, 1015 cm−3 and 1019 cm−3, 1012 cm−3 and 2 × 1012 cm−3 respectively. The results of these simulations provide valuable insights for the experimental fabrication of this heterojunction PSC.

Simulation of Lead-Free Heterojunction CsGeI2Br/CsGeI3-Based Perovskite Solar Cell Using SCAPS-1D

This paper presents the simulation of the novel prototype of a heterojunction perovskite solar cell (PSC) based on CSGeI2Br/CSGeI3. The device consists of two absorber layers (CSGeI2Br, CSGeI3), an electron transport layer (ETL) chosen as TiO2 and a hole transport layer (HTL) given as poly(3-hexylthiophene) (P3HT). Within the simulation, the effects of thickness, doping and defect density in each absorber layer and different back contact metal electrodes on electrical parameters (efficiency, short circuit current, open circuit voltage, and fill factor) are evaluated. In addition, the contribution of the HTL (doping density and thickness), temperature, shunt and series resistance were also checked on the same electrical parameters. The simulations are conducted in standard test conditions with the irradiation normalized as 0.1 W/cm2 using the SCAPS-1D platform. The maximum efficiency obtained within the simulation of this device was about 31.86%. For this device, the thickness of the CSGeI3 layer should be around 900 nm, while that of the CsGeI2Br should be around 100 nm to facilitate optimal absorption of the incident photons. The doping density in the absorber layer is such that in CsGeI3 should be around 1018 cm−3 and around 1016 cm−3 in the CsGeI2Brlayer. The defects densities in both layers of the perovskite materials should be around 1014 cm−3. Concerning the HTL, the thickness and the doping density of the P3HT should be around 50 nm and 1018 cm−3, respectively. In terms of the back contact electrode, the work function of the metal should be at least equal to 5 eV, corresponding to gold (Au) metal. The series resistance due to the connection of the cell to the external load should be very small, while the shunt resistance due to the leakage current in the solar cell should be high. Furthermore, the operating temperature of the new PSC should be maintained at an ambient level of around 25 °C in order to deliver high efficiency.

A Detailed Analysis of the Modified Economic Method for Assessing the Performance of Photovoltaic Module Enhancing Techniques

This paper presents a detailed analysis of the modified economic method (FMCE) for evaluating the performance of photovoltaic module (PV)-enhancing techniques, aiming to address existing research gaps. The impact of influential parameters on the FMCE is examined through illustrative examples. These parameters include the output power of a single solar cell without an enhancer, output power of a PV with an enhancer, manufacturing cost of the PV enhancer, one-watt cost of PV power, and maximum output power of a solar cell with an enhancer equivalent to maximum output power at standard test conditions (STC). The results of this study reveal that the output power of a single solar cell without an enhancer, number of solar cells with an enhancer in the PV, and manufacturing cost of the PV enhancer have a proportional relationship with the FMCE. As these parameters increase, the FMCE also increases, which negatively affects the cost-effectiveness of the PV enhancer, leading to lower performance. So, it is advisable to maintain the values of these parameters at lower levels. Conversely, the output power of a PV with an enhancer and the one-watt cost of PV power exhibit an inverse proportional relationship with the FMCE. As the output power of a PV with an enhancer and the one-watt cost of PV power increase, the FMCE decreases, which positively affects the cost-effectiveness of the PV enhancer, leading to higher performance. Hence, it is recommended to keep these two parameters high for optimal performance. In conclusion, the FMCE may have potential for application by designers and manufacturers of PV enhancers.

Effect of test conditions on the efficiency and hysteresis of Sn-Pb mixed perovskite solar cells

With the continuous improvement of the efficiency of perovskite solar cells (PSCs), the possible pollution of lead to the environment has become a significant. The appearance of tin–lead mixed PSCs reduces its toxicity. Still, tin-based perovskite materials are easy to oxidize and decompose in the air, which poses a challenge to evaluate its photoelectric performance in the test process correctly. Our work compared and analyzed the photoelectric performance of tin–lead mixed PSCs from the test environment and setting parameters. We discussed the effects of temperature, humidity, measurement delay time, measurement step, and bias voltage on the efficiency and hysteresis of tin–lead mixed PSCs. We summarized the standard test conditions applicable to tin–lead mixed PSCs. The reference of this test method will help to get accurate test results of tin–lead mixed PSCs and promote its further development.