Photovoltaic Cell Parameters Research Articles

Growth and reaction mechanism of solution-processed Cu2ZnSnSe4 thin films for realising efficient photovoltaic applications

This article reports on Cu2ZnSnSe4 (CZTSe) thin film preparation via a nonhydrazine, nonpyridine and environmentally friendly low-cost solution process method. CZTSe fabrication through a solution-based process has not yet been suitably demonstrated given the impediments to addressing the presence of selenium in solutions. In this study, we introduced a two-step CZTSe fabrication method that used monoethanolamine as the chelating agent/co-solvent and ethanol as the main solvent. Selenization was then conducted. In this process, we successfully avoided the use of hydrazine to synthesise CZTSe thin films. Material characterisations (e.g. UV–VIS–NIR, scanning electron microscopy, electron dispersive spectroscopy, X-ray diffractometry and Fourier transform infrared spectroscopy) confirmed the high quality of the deposited thin films. The deposited CZTSe thin film showed high crystallinity without carbon residues, indicating its potential application as a photovoltaic absorber. Hence, we investigated the photovoltaic parameters of the CZTSe-based solar cells on the basis of the deposited thin film’s optoelectronic properties. We utilised Solar Cell Capacitance Simulator to examine the electrical effects of CZTSe solar cells and used three-dimensional finite-difference time-domain optical simulations to investigate the optics of the solar cells. We estimated that the realistic power conversion efficiency of the CZTSe solar cells could reach 18.5% with a short-circuit current density of 30 mA/cm2.

A comprehensive analysis of PV cell parameters with varying halides stoichiometry in mixed halide perovskite solar cells

The optimized halide stoichiometry in mixed-halide perovskites offers the enhanced photophysical properties of perovskites films for efficient and stable perovskites solar cells (PSCs). Herein, a comprehensive analysis of the impact of the variation of cationic bromide (CH3NH3Br or MABr) concentration on the performance parameters of mixed halide (MAPbI3-xBrx) PSCs have been presented. It was noted that with an increase of MABr concentration from 10 to 70 mg/ml, open-circuit voltage (Voc) increases from 942 mV to 992 mV, short circuit current density (Jsc) decreases from 19.83 mA/cm2 to 12.36 mA/cm2. Device having 20 mg/ml MABr exhibits best performance with an efficiency η = 12.18%, Voc = 0.96 V, Jsc = 18.99 mA/cm2 and fill factor (FF) = 0.67. Furthermore, photovoltaic (PV) cell parameters, i.e., Jph, Rsh, Rs, n, and J0 were analytically extracted and analyzed to recognize the influence of MABr. The best performing devices exhibits a higher photocurrent density: Jph = 19.16 mA/cm2, optimum device resistances: Rsh=869.57 Ω-cm2, Rs = 7.91 Ω-cm2, minimum value of ideality factor n = 1.52 and negligible leakage current density: J0 = 3.95 × 10−13 mA/cm2. The predicted PV cell parameters suggest that the best performing device possesses optimum morphology, lowest leakage current, and reduced recombination of charge carriers.

ON THE CAPACITY OF SOLAR CELLS UNDER PARTIAL SHADING CONDITIONS

Abstract
 A photovoltaic system under uniform illumination conditions has only one global maximum power point, but this same panel in the case of partial shading conditions can have more maxima on the power-voltage curve. The motivation of this work are two-fold: to show nonlinear capacitance of solar cell and to propose a determination technique for partial shading condition. The author shows the results of measurements on a photovoltaic system under different lighting conditions. Shows the effect of capacitance on the shape of the response to excitation in the form of a load. The paper also shows which photovoltaic cell parameters influence the shape of the C-V curve.

Parameter Identification of Photovoltaic Cell Model Using Modified Elephant Herding Optimization-Based Algorithms

The use of metaheuristics in estimating the exact parameters of solar cell systems contributes greatly to performance improvement. The nonlinear electrical model of the solar cell has some parameters whose values are necessary to design photovoltaic (PV) systems accurately. The metaheuristic algorithms used to determine solar cell parameters have achieved remarkable success; however, most of these algorithms still produce local optimum solutions. In any case, changing to more suitable candidates through elephant herd optimization (EHO) equations is not guaranteed; in addition, instead of making parameter α adaptive throughout the evolution of the EHO, making them adaptive during the evolution of the EHO might be a preferable choice. The EHO technique is used in this work to estimate the optimum values of unknown parameters in single-, double-, and three-diode solar cell models. Models for five, seven, and ten unknown PV cell parameters are presented in these PV cell models. Applications are employed on two types of PV solar cells: the 57 mm diameter RTC Company of France commercial silicon for single- and double-diode models and multi-crystalline PV solar module CS6P-240P for the three-diode model. The total deviations between the actual and estimated result are used in this study as the objective function. The performance measures used in comparisons are the RMSE and relative error. The performance of EHO and the proposed three improved EHO algorithms are evaluated against the well-known optimization algorithms presented in the literature. The experimental results of EHO and the three improved EHO algorithms go as planned and proved to be comparable to recent metaheuristic algorithms. The three EHO-based variants outperform all competitors for the single-diode model, and in particular, the culture-based EHO (CEHO) outperforms others in the double/three-diode model. According the studied cases, the EHO variants have low levels of relative errors and therefore high accuracy compared with other optimization algorithms in the literature.

Parameter extraction of photovoltaic models using a comprehensive learning Rao-1 algorithm

The environmental impact and scarcity of fossil fuels have led to a tremendous increase in the demand and use of renewable energy resources. The use of photovoltaic (PV) energy is due to several benefits, such as low maintenance and operating costs. The modeling of the PV systems process requires the identification of PV cells parameters, which can be formulated in an optimization problem. This problem is a very challenging task as it is nonlinear and multimodal. Therefore, metaheuristic algorithms suggested recently may present unsatisfactory results at the level of solution quality and convergence speed. To ensure the effectiveness of any algorithm, the equilibrium between exploration and exploitation must be guaranteed. To achieve an accurate model of the photovoltaic cells, a comprehensive learning Rao-1 (CLRao-1) optimization algorithm is proposed in this work. Rao-1 is chosen due to its simplicity, easy comprehension, and implementation. The suggested modifications based on three mutually exclusive search equations do not affect its easiness. The effectiveness of our algorithms is assessed through PV cells and three modules. The results of the experimental and simulated data were in high agreement. Moreover, our algorithm achieved the best results in terms of reliability and accuracy.

Parameter extraction of solarPVcell models using novel metaheuristic chaotic tunicate swarm algorithm

The photovoltaic(PV) system's efficiency can be improved with the aid of effective solar PV cell modeling. However, flawed solar cell parameters affect PV cell modeling incorrectly. The manufacturers are usually not providing all the necessary data for the exact modeling of PV cells. It is therefore necessary to efficiently predict the parameters of the PV cell. The literature discusses various optimization algorithms, but the suboptimal outcomes are obtained by most of the algorithms due to their convergence toward local minima. This paper introduces a new stochastic optimization algorithm for the estimation of solar PV cell parameters. Therefore, for the evaluation of the solar cell, a new novel chaotic algorithm is introduced in this paper called the chaotic tunicate swarm algorithm (CTSA). The proposed algorithm has a new function called an outstanding mathematical model of adaptive weights to stimulate negative and positive inputs from the spreading wave in order to find the best way to connect food with an excellent exploitation tendency and exploratory ability. For the double-diode model, the computation time of the chaotic variants (CTSA1-1.1885, CTSA2-1.2502, CTSA3-1.2962, CTSA4-1.1204, and CTSA5-1.2214) is better than the rest of the compared algorithms (particle swarm optimization [PSO]-17.4532, tunicate swarm algorithm (TSA)-2.5278, Harris hawks optimization [HHO]-10.8942, ant lion optimizer [ALO]-15.0214, and atom search optimization [ASO]-9.5214). For the triple-diode model, the computation time of the chaotic variants (CTSA1-1.2658, CTSA2-1.3520, CTSA3-1.3056, CTSA4-1.2290, and CTSA5-1.3859) is better than the rest of the compared algorithms (PSO-18.5421, TSA-3.2568, HHO-9.8754, ALO-13.5841, and ASO-11.8412). The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation.

Numerical Modeling of High Conversion Efficiency FTO/ZnO/CdS/CZTS/MO Thin Film-Based Solar Cells: Using SCAPS-1D Software

The numerical modeling of a copper zinc tin sulfide (CZTS)-based kesterite solar cell is described in detail in this article. To model FTO/ZnO/CdS/CZTS/MO structured solar cells, the Solar Cell Capacitance Simulator-one-dimension (SCAPS-1D) program was utilized. Numerical modeling was used to estimate and assess the parameters of various photovoltaic thin film solar cells. The impact of different parameters on solar cell performance and conversion efficiency were explored. Because the response of a solar cell is partly determined by its internal physical mechanism, J-V characteristic characteristics are insufficient to define a device’s behavior. Regardless of the conviction in solar cell modeling, variable attributes as well as many probable conditions must be handled for simulation. Promising optimized results were obtained with a conversion efficiency of (η% = 25.72%), a fill factor of (FF% = 83.75%), a short-circuit current of (JSC = 32.96436 mA/cm2), and an open-circuit voltage of (VOC = 0.64 V). The findings will aid in determining the feasibility of manufacturing high-efficiency CZTS-based solar cells. First, in the SCAPS-1D environment, the impacts of experimentally constructed CZTS solar cells were simulated. The experimental data was then compared to the simulated results from SCAPS-1D. After optimizing cell parameters, the conversion efficiency of the improved system was observed to rise. The influence of system factors, such as the thickness, acceptor, and donor carrier concentration densities of the absorber and electron transport layers, and the effect of temperature on the efficiency of CZTS-based photovoltaic cells, was explored using one-dimensional SCAPS-1D software. The suggested findings will be extremely useful to engineers and researchers in determining the best method for maximizing solar cell efficiency, as well as in the development of more efficient CZTS-based solar cells.

Enhanced efficiency in hollow core electrospun nanofiber-based organic solar cells

Over the last decade, nanotechnology and nanomaterials have attracted enormous interest due to the rising number of their applications in solar cells. A fascinating strategy to increase the efficiency of organic solar cells is the use of tailor-designed buffer layers to improve the charge transport process. High-efficiency bulk heterojunction (BHJ) solar cells have been obtained by introducing hollow core polyaniline (PANI) nanofibers as a buffer layer. An improved power conversion efficiency in polymer solar cells (PSCs) was demonstrated through the incorporation of electrospun hollow core PANI nanofibers positioned between the active layer and the electrode. PANI hollow nanofibers improved buffer layer structural properties, enhanced optical absorption, and induced a more balanced charge transfer process. Solar cell photovoltaic parameters also showed higher open-circuit voltage (+ 40.3%) and higher power conversion efficiency (+ 48.5%) than conventional architecture BHJ solar cells. Furthermore, the photovoltaic cell developed achieved the highest reported efficiency value ever reached for an electrospun fiber-based solar cell (PCE = 6.85%). Our results indicated that PANI hollow core nanostructures may be considered an effective material for high-performance PSCs and potentially applicable to other fields, such as fuel cells and sensors.

Bayesian Estimation of Equivalent Circuit Parameters of Photovoltaic Cell with S‐Shaped Current–Voltage Characteristic

An S‐shaped current–voltage characteristic is frequently observed in a photovoltaic cell using emerging materials such as organic semiconductors and organic–inorganic perovskite materials. The opposed two‐diode equivalent circuit model, whose explicit analytical solution is known, can reproduce the S‐shaped characteristic. However, since the number of parameters is as high as eight, it is not easy to extract the equivalent circuit parameters from experimental data by curve fitting. Herein, the Bayesian estimation in extracting the equivalent circuit parameters of the opposed two‐diode model is demonstrated. Unlike the traditional nonlinear least‐squares method, the Bayesian estimation practically does not require any tuning of the initial values and gives the information on the error of the estimates.

A new hybrid method based on differential evolution to determine the temperature-dependent parameters of single-diode photovoltaic cells

A new hybrid method based on differential evolution to determine the temperature-dependent parameters of single-diode photovoltaic cells

Developing Photovoltaic Cells Parameter Estimation Algorithm Based on Equilibrium Optimization Technique

Nowadays all the world does its best to develop the power generation systems that depend on nature in order to reduce the dependence on fuel. Photovoltaic (PV) systems are considered one of the most important renewable energy resources. Scientific research has gained a high interest, especially in PV cell modeling and parameter estimation. The estimation of optimum parameters for the PV model has been considered the main target of the paper optimization problem. Equilibrium optimization (EO) algorithm is considered one of optimization algorithms inspired from nature physical phenomena. EO algorithm has been inspired from the nature physical process of controlling mass balance through specific volume until reaching equilibrium state. In this paper, an EO algorithm has been proposed and applied to prepare a mathematical model for photovoltaic solar cell. The challenge in this optimization problem is the non-linearity in PV solar cell characteristic. The EO algorithm has been evaluated through the following items. EO has been applied to estimate the parameters of different PV models such as single, double and triple PV models, which have different complexity. Applying the previous item for real PV application. The obtained results have been compared though different functions such as root mean square value and absolute mean error. In all cases, EO obtained results have been compared with the more recent optimization algorithms such as Particle swarm optimization (PSO), Teaching learn Based Optimization (TLBO) and Harries Hawk optimization (HHO). From the all obtained results, EO algorithm gives more accurate PV models in comparison with other optimization algorithms.

Chaos Induced Coyote Algorithm (CICA) for Extracting the Parameters in a Single, Double, and Three Diode Model of a Mono-Crystalline, Polycrystalline, and a Thin-Film Solar PV Cell

The design of a solar PV system and its performance evaluation is an important aspect before going for a mass-scale installation and integration with the grid. The parameter evaluation of a solar PV model helps in accurate modeling and consequently efficient designing of the system. The parameters appear in the mathematical equations of the solar PV cell. A Chaos Induced Coyote Algorithm (CICA) to obtain the parameters in a single, double, and three diode model of a mono-crystalline, polycrystalline, and a thin-film solar PV cell has been proposed in this work. The Chaos Induced Coyote Algorithm for extracting the parameters incorporates the advantages of the conventional Coyote Algorithm by employing only two control parameters, making it easier to include the unique strategy that balances the exploration and exploitation in the search space. A comparison of the Chaos Induced Coyote Algorithm with some recently proposed solar photovoltaic cell parameter extraction algorithms has been presented. Analysis shows superior curve fitting and lesser Root Mean Square Error with the Chaos Induced Coyote Algorithm compared to other algorithms in a practical solar photovoltaic cell.

Metaphor-free dynamic spherical evolution for parameter estimation of photovoltaic modules

How to effectively realize the simulation, evaluation, and control of the photovoltaic (PV) system established on the actual measured voltage and current PV cells and module data has attracted widespread attention. The original SE possesses the disadvantages of slow convergence and poor accuracy in the parameter identification of PV cells and modules. This paper proposes an enhanced spherical evolution algorithm (SE) based on a novel dynamic sine-cosine mechanism (DSCSE). The introduction of the dynamic sine-cosine mechanism significantly promotes the information communication of disparate individuals and increases the diversity of diverse populations. To assess the performance of DSCSE, it is compared with ten comparative algorithms to estimate unknown parameters of PV cell and module at fixed and varying temperature and light conditions, including single diode model (SDM), double diode model (DDM), three diode model (TDM) and PV module. The experimental results indicate that the root mean square of the error (RMSE) gained by DSCSE outperforms most competing algorithms. The results of RMSE by DSCSE for SDM, DDM, and TDM of commercial solar cells R.T.C. France and PV module of Photowat-PWP201 is the percentage of improvement of 48.45%, 6.85%, 11.81%, and 4.73% compared to SE, respectively. Furthermore, for three manufacturers, including Mono-crystalline (SM55), Thin-film (ST40), and Multi-crystalline (KC200GT), the results of RMSE by DSCSE harvest the maximum and minimum increase of 96.1% and 31.36%. Therefore, DSCSE is expected to become a novel promising technology to estimate the parameter of PV cells and modules.

Adaptive teaching–learning-based optimization with experience learning to identify photovoltaic cell parameters

Parameter identification of photovoltaic cell and module plays a key role in the simulation, evaluation, control, and optimization of photovoltaic systems. In order to improve the accuracy and reliability of the identified parameters, in this paper, a new adaptive teaching–learning-based optimization with experience learning referred as ELATLBO, is proposed. In ELATLBO, the population is first sorted according to the objective function value, and then it is divided into two parts: fit solutions with good objective function values and inferior solutions with poor objective function values. The fit solution that selects the teacher phase with experience learning is used for local search to improve the exploitation capability of the algorithm. While the inferior solution that selects the learner phase with experience learning is applied for global search to enhance the exploration capability of the algorithm. The performance of ELATLBO is verified by testing the photovoltaic cells and module parameter identification problems, i.e., the single diode model, the double diode model, and the single diode photovoltaic module. The simulated result shows that the proposed ELATLBO exhibits remarkable performance on the accuracy and reliability when compared with other reported parameter identification techniques, especially for the double diode model.

PV Cell Parameter Extraction Using Data Prediction–Based Meta-Heuristic Algorithm via Extreme Learning Machine

To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction–based accurate modeling of PV cells is extremely crucial. However, its inherent high nonlinear and multimodal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current–voltage (I–V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this article proposes a novel parameter extraction strategy by data prediction–based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I–V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). Consequently, MhAs can undertake a more stable search for optimal solution through extended I–V data; thus, PV cell parameters can be obtained with high accuracy and convergence rate. Its effectiveness is validated via three typical PV cell models, that is, single diode model (SDM), double diode model (DDM), and three diode model (TDM). Last, comprehensive case studies illustrate that the DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.

Investigation of the Photon to Charge Conversion and Its Implication on Photovoltaic Cell Efficient Operation

Efficient photon to charge (PTC) transfer is considered to be the cornerstone of technological improvements in the photovoltaic (PV) industry, while it constitutes the most common process in nature. This study aims to investigate the parameters that impact efficient PV-cell photon to charge conversion in two ways: (a) providing a brief research analysis to extract the key features which affect the electrical and optical performance of PV cells’ operation, and (b) investigating the dependance of these characteristics on the photon to charge mechanisms. The former direction focuses on the latest advances regarding the impacts of the microenvironment climate conditions on the PV module and its operational performance, while the latter examines the fundamental determinants of the cell’s efficient operation. The electrical and optical parameters of the bulk PV cells are influenced by both the external microenvironment and the intrinsic photon to charge conversion principles. Light and energy harvesting issues need to be overcome, while nature-inspired interpretation and mimicking of photon to charge and excitation energy transfer are in an infant stage, furthering a better understanding of artificial photosynthesis. A future research orientation is proposed which focuses on scaling up development and making use of the before mentioned challenges.

A Powerful Bio-Inspired Optimization Algorithm Based PV Cells Diode Models Parameter Estimation

Accurate and reliable photovoltaic (PV) cell parameter identification is critical to simulation analysis, maximum output power harvest, and optimal control of PV systems. However, inherent high-nonlinear and multi-modal characteristics usually result in thorny obstacles to hinder conventional optimization methods to obtain a fast and satisfactory performance. In this study, a novel bio-inspired grouped beetle antennae search (GBAS) algorithm is devised to effectively identify unknown parameters of three different PV models, i.e., single diode model (SDM), double diode model (DDM), and triple diode model (TDM). Compared against beetle antennae search (BAS) algorithm, optimization efficiency of GBAS algorithm is markedly enhanced based on a cooperative searching group that consists of multiple individuals rather than a single beetle. Besides, a dynamic balance mechanism between local exploitation and global exploration is designed to increase the probability for a higher quality optimum. Comprehensive case studies demonstrate that GBAS algorithm can outperform other advanced meta-heuristic algorithms in both optimization precision and stability for estimating PV cell parameters, e.g., standard deviation (SD) of root mean square error (RMSE) obtained by GBAS algorithm is 64.34% smaller than the best value obtained by other algorithms in SDM, 61.86% smaller than that in DDM.

Triple diode parameter estimation of solar PV cell using hybrid algorithm

Solar photovoltaic (PV) systems are now one of the most prominent green energy technologies for producing a significant proportion of electricity. With the increased attention towards solar PV-based systems, the effective and precise estimation of PV cell parameters has received considerable attention from researchers. Extracting the parameters of the solar PV model is quite important for precise modelling, assessment and control of the PV system. Mostly theoretical, computational and meta-heuristic in the last few years have been proposed which extract parameters of PV cell based on the experimental results. Extracting PV model parameters, however, remains a major challenge. The application of a new hybrid algorithm that relies on the Grey Wolf Optimizer and Cuckoo Search Algorithm is being proposed in this manuscript to extract the parameters of various PV cell models. The parameter optimization results are obtained using GWOCSA and are further compared with those obtained with five other algorithms, i.e. PSO, MVO, SCA, CSA, and GWO. The complete error analysis is carried out for TDM of PV cells to establish the superiority of GWOCSA. The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation.

Parameter identification of PV cell via adaptive compass search algorithm

Photovoltaic (PV) cell parameter identification is of great significance to accurate PV cell modelling, which can further critically influence overall optimal control and output characteristics simulation design of PV systems. Nevertheless, this high non-linearity obstacle often simultaneously exists multiple local optimums, thus conventional optimization approaches can hardly maintain a consistently satisfactory performance to obtain global optimum. Hence, an adaptive compass search (ACS) algorithm is employed in this paper to identify several critical unknown parameters of the most common utilized PV cell model, i.e., double diode model (DDM). Compared with fixed sequence based original compass search (CS) algorithm, ACS algorithm can dramatically improve global exploration ability via adaptive sequence of exploration directions via historical searching results. Particularly, case studies verify the feasibility and merits of ACS algorithm, which validates that it can achieve more desirable performance compared against whale optimization algorithm (WOA) in terms of optimization precision and convergence rate.

Unveiling the prospect of a novel chemical route for synthesizing solution-processed CdS/CdTe thin-film solar cells

In this paper, an in-depth analysis of the photovoltaic parameters of solar cells is carried out to reveal the prospect of fabricating TX-100 surfactant-induced thiol-amine cosolvent-assisted CdS/CdTe thin-film solar cells (TFSCs) via the spin-coating process. The research is carried out through SCAPS-1D simulations using the parameters obtained from thin-film characterizations of the as-deposited and/or pre-annealed and annealed CdS and CdTe thin-films deposited on the SLG substrates at various annealing temperatures (200–350​ ∘C), time (3–8 min) and circumstances (low-vacuum, glass-protected air, open-air) with and without surfactant, respectively. First, the effects of surfactant and different annealing conditions on the device performance are reproduced through simulations. Thereafter, the temperature-annealed solar cell with added surfactant is optimized by extensive variation in thickness, doping, bulk, and interface defect densities, resistance, and temperature. This article also provides a plausible description of the function and choosing guidelines of the TCO layer and back metal in ameliorating the performance/cost ratio of the device. Results show that the use of surfactant and heat treatment on the deposited thin films of CdS window and CdTe absorber layers can greatly enhance the performance. Under global AM 1.5 irradiance at 300 K, the optimized CdS/CdTe TFSC presents an efficiency of 18.37% with JSC of 25.64 mA/cm2, VOC of 870 mV, FF of 82.31%, and QE of 90.44%, respectively. This study reveals that a novel chemical route employing TX-100 surfactant and thiol-amine cosolvent for synthesizing solution-processed CdS/CdTe solar cells using a low-cost facile spin-coating method could be a cutting edge TFSC technology for industrial scaling in the near future.