Commercial Photovoltaic Modules Research Articles

A comparative study of optimization algorithms for parameter estimation of PV solar cells and modules: Analysis and case studies

The parameter assessment of solar cells and photovoltaic (PV) modules is a challenging task due to the non-linearity behavior of the current–voltage (I–V) characteristic curve. This paper presents two hybrid nature-inspired algorithms for estimating the unknown parameters of the Single-Diode Model (SDM), and Double-Diode Model (DDM). These algorithms are based on borrowed exploration and exploitation schemes from three well-known optimization algorithms: Whale optimization algorithm (WOA), Marine Predators Algorithm (MPA), and Generalized Normal Distribution Optimization (GNDO) algorithm. The first proposed algorithm is called Marine-Whale-Generalized (MWG) algorithm. In addition, MWG is effectively integrated with novel exploration and exploitation schemes to further its exploration and exploitation operators in a new variant known as (MWGG). A solar cell from RTC France and five commercial PV module models, including Photowatt-PWP201 (PWP), Ultra 85-P (Ultra), and STM6-40/36 (STM), are being used to test the efficacy and efficiency of the proposed algorithms. The experimental findings proved that MWGG is more accurate and faster at convergent results compared to other comparators.

Mathematical modeling in MATLAB of a photovoltaic panel

With the increase in energy demand, the necessity for photovoltaic panels to be as efficient as possible has also increased. In order to achieve the highest possible performance, solar cells depend on a number of environmental elements such as: temperature, shading, cloudy weather, deposits on the surface, dust, solar irradiation, nighttime darkness, etc. All these factors ultimately result in a decline in photovoltaic panels performances. With the help of modeling and simulation programs, complex systems with high performance can be designed and realized, considering the advantage of avoiding manufacturing costs through their physical testing. Based on the electrical and thermal characteristics extracted from the Sanyo producer's data sheet, the proposed commercial photovoltaic module HIT-240HDE4, is implemented in the MATLAB environment under standard and real test conditions. The article presents the modeling and simulation in the MATLAB program of the proposed photovoltaic module, for the analysis of the electrical performances under the described conditions. Also, the article addresses the analysis issue of the operation considering the chosen module, under the same conditions.

Photovoltaic model parameters identification using Northern Goshawk Optimization algorithm

The massive integration of photovoltaic (PV) systems into electric power grids creates a slew of new issues in today's power systems. Accurate modeling of photovoltaic modules is critical in strengthening the characteristics of its systems in simulation assessments. Modeling such PV systems is represented by a nonlinear current-voltage characteristic curve behavior with numerous unknown parameters due to insufficient data in the cells' datasheet. This manuscript presents an application of a recently introduced optimization algorithm called Northern Goshawk Optimization (NGO) for parameter identification of the triple diode model of the PV module. Three commercial PV modules are utilized in this study to accomplish this task. These models are multi-crystalline structures like Photowatt-PWP201 and Kyocera KC200GT and mono-crystalline like Canadian Solar CS6K-280 M. The simulation results show the ability of the NGO to extract the model parameters accurately. Experimental validation of the estimated parameters using the NGO optimizer is accomplished and compared with the simulation results under various environmental conditions. The simulation results show the superiority of the NGO over competitive optimization algorithms in terms of convergence speed and accuracy. The NGO can reduce the cost function to 1.35E-05, 9.42E-05, and 0.000195 for the PWP-201, KC200GT, and Canadian Solar CS6K-M modules. Moreover, the robustness of the NGO is evaluated by the statistical analysis and the Wilcoxon rank test.

A general algorithm for the optimization of photovoltaic modules layout on irregular rooftop shapes

Although the installation of photovoltaic systems on roofs is a successful investment, rooftop space availability has been identified as a significant limiting factor in achieving zero-energy buildings, especially by building components such as chimneys, elevator machine rooms, fans, plumbing vents, etc. This study presents a general algorithm for the optimal deployment of photovoltaic module rows installed on irregular flat roof shapes. The presented algorithm takes into account the irregular rooftop shape, the self-shading of photovoltaic modules, the inclusion of building components, commercial photovoltaic modules with different sizes, mounting systems with different configurations, distances required for maintenance, and the technical reports to minimize shading effects. The proposed algorithm allowed to increase in the amount of solar energy received by the photovoltaic modules. The optimization process takes into account the weather conditions at the specific location. The optimization algorithm was implemented using a specific Mathematica™ code in which the commands used in the development of the code were introduced to facilitate its replication. The optimization algorithm output provides the essential parameters for the optimal photovoltaic system design such as: the optimum number of mounting systems and their configuration, the optimum tilt angle of the mounting system and its dimensions, the photovoltaic module model, the maximum total area of the photovoltaic field and the maximum annual energy captured by the photovoltaic modules. We compared the mounting system layout obtained with the proposed algorithm with the tilt angle photovoltaic module layout recommended by three technical papers ( IDAE Technical Report, Lorenzo’s equation and Jacobson’s equation) with respect to photovoltaic field area gain, energy gain and levelized cost of energy .The optimal photovoltaic module layout obtains the maximum photovoltaic field area gain of 35.52% with respect to the Jacobson’s equation and the minimum of 32.29% with respect to the IDAE Technical Report. The optimal photovoltaic module layout obtains the maximum energy gain of 27.83% with respect to the Jacobson’s equation and the minimum of 24.84% with respect to the IDAE Technical Report. The levelized cost of energy of the optimal P V module layout is lower than that of the other arrangements studied. The algorithm presented may be useful for decision-makers or policymakers in determining the optimal distribution of photovoltaic modules on irregular rooftop shapes. • Algorithm for the optimal PV modules layout on irregular rooftop is presented. • Building components and irregular roof shapes are considered in the research. • The influence of the mounting system configuration is investigated. • The algorithm obtains 28% more energy than the obtained by a classical method. • The LCOE efficiency of optimal PV module layout is the best.

A novel chaotic-driven Tuna Swarm Optimizer with Newton-Raphson method for parameter identification of three-diode equivalent circuit model of solar photovoltaic cells/modules

Photovoltaic (PV) modeling is becoming an increasingly popular field of study. When it comes to the effective performance and reliability of PV systems, the reliability of PV models is critical. Following that, numerous PV models have been created to simulate the flow of current in photovoltaic cells. The estimate of the parameters of these models is a difficult undertaking. In this article, an enhanced version of the Tuna Swarm Optimizer (TSO) is proposed to handle the parameter estimation optimization problem. The basic TSO algorithm performance is poor when applied to the real-world optimization problem, especially parameter estimation of a Three-Diode Model (TDM) of the PV cell/module. Therefore, the exploration and exploitation phases of TSO have been improved by the chaotic tent random number generator, and therefore, the proposed algorithm is named Chaotic Tuna Swarm Optimizer (CTSO). To quickly find the optimal parameters of the TDM of the PV cell/module, the proposed CTSO is also hybridized with the Newton-Raphson method. The proposed CTSO algorithm is applied to three different case studies, which include a standard PV cell and two commercial PV modules. The performance of the proposed CTSO on the parameter estimation optimization problem of the PV model is contrasted with seven state-of-the-art optimization algorithms. With an average root-mean-squared error of 3.41E-03 for all case studies, the CTSO algorithm stood first among all selected algorithms. The acquired results demonstrated the superiority of the proposed algorithm and suggested that the proposed CTSO is a reliable tool for estimating the unknown parameters of the three-diode PV model.

Design, Analysis, and Modeling of Curved Photovoltaic Surfaces Using Composite Materials

Currently, the use of photovoltaic solar energy has increased considerably due to the development of new materials and the ease to produce them, which has significantly reduced its acquisition costs. Most commercial photovoltaic modules have a flat geometry and are manufactured using metal reinforcement plates and glass sheets, which limits their use in irregular surfaces such as roofs and facades (BIPV) and the transportation sector (VIPV). The purpose of this study is to analyze the design implications of curved photovoltaic surfaces using composite materials. Considering operation and maintenance requirements, the most suitable reinforcement and encapsulation materials are selected based on references and experimental tests. It was found that the maximum radius of curvature that a polycrystalline silicon cell with the dimensions of a SunPower C60 model can achieve is 6.51 m for a failure probability lower than 5 %, which allows us to define the maximum curvature that this photovoltaic surface can reach. Additionally, an analytical model of the reinforcement was implemented using macromechanical models in Matlab™, which was validated by the finite element method employing the composite materials module in Ansys®. Therefore, this paper presents a detailed analysis of the shear stresses between the layers and of the deformations generated in the curved solar panel reinforcement. Finally, under the operating conditions assumed here, carbon fiber presents the best structural behavior in the reinforcement material, while epoxy resin exhibits a better performance in the encapsulation material. These results can facilitate the manufacturing of curved photovoltaic surfaces.

Accurate Three-Diode model estimation of Photovoltaic modules using a novel circle search algorithm

Three-diode model (TDM) of photovoltaic (PV) cells is more accurate model that includes most of the optical and electrical losses of PV module. However, the nonlinearity and parameters of this TDM are higher, which make it complex and challengeable model. This article utilizes an efficient algorithm called the Circle Search Algorithm (CSA) for accurate electrical modeling of PV modules. The CSA is inspired by the circle's well-known geometrical properties and the perpendicular relationship between its radius and tangent line. The CSA and other four algorithms are applied to determine the nine parameters of the TDM of three commercial PV modules. The objective function is the root mean square error (RMSE) between the estimated and measured output currents. The statistical optimization results and convergence curves proved the robustness and speed of CSA in determining the optimum TDM among the other methods. The predicted I - V curves are plotted against observed I - V curves at various temperatures and solar irradiance to demonstrate the CSA's superiority under various scenarios. The absolute current error of the CSA-TDM at maximum voltage is less than that of other algorithms.

PV Module Energy Rating Standard IEC 61853-3 Intercomparison and Best Practice Guidelines for Implementation and Validation

The IEC 61853 standard series aims to provide a standardized measure for photovoltaic (PV) module energy rating, namely the Climate Specific Energy Rating(CSER). For this purpose, it defines procedures for the experimental determination of input data and algorithms for calculating the CSER. However, some steps leave room for interpretation regarding the specific implementation. To analyze the impact of these ambiguities, the comparability of results, and the clarity of the algorithm for calculating the CSER in Part 3 of the standard, an intercomparison is performed among research organizations with ten different implementations of the algorithm. We share the same input data, obtained by measurement of a commercial crystalline silicon PV module, among the participating organizations. Each participant then uses their individual implementations of the algorithm to calculate the resulting CSER values. The initial blind comparison reveals differences of 0.133 (14.7%) in CSER. After several comparison phases, a best practice approach is defined, which reduces the difference by a factor of 210 to below 0.001 (0.1%) in CSER for two independent PV modules. The best practice presented in this article establishes clear guidelines for the numerical treatment of the spectral correction and power matrix extrapolation, where the methods in the standard are not clearly defined. Additionally, we provide input data and results for the PV community to test their implementations of the standard's algorithm. To identify the source of the deviations, we introduce a climate data diagnostic set. Based on our experiences, we give recommendations for the future development of the standard.

A New Photovoltaic Emulator Designed for Testing Low-Power Inverters Connected to the LV Grid

Assessing the performance of photovoltaic systems, particularly dedicated DC/AC inverter devices, requires the use of photovoltaic panels operating under natural environmental conditions, such as variations in solar radiation intensity, temperature and wind speed. Environmental testing is obviously very troublesome, inconvenient and limited. An alternative solution is to use a device that emulates the output photovoltaic panel curves in variable weather conditions, which allows the carrying out of all necessary tests at the laboratory. This paper presents a new photovoltaic emulator (PVE), mimicking the output characteristics of the photovoltaic panels. The proposed PVE is designed and constructed at the renewable energy laboratory for testing low-power PV inverters connected to the LV grid. A novelty of this solution is the method for shaping emulated current–voltage characteristics I–V. The concept of this method assumes autonomous regulation of slopes and shapes of emulated curve fragments. This allows us to obtain the desired shapes of the output characteristics for a wide range of both voltages and currents. The proposed PVE is not a pulse device; it belongs to linear analogue circuits. In order to confirm the assumed concept, a prototype is designed and constructed, and laboratory tests are conducted. Satisfactory results are obtained, confirming the correctness of the adopted concept. High compliance of the emulated characteristics is found in comparison to the characteristics of the selected commercial photovoltaic module. Very good results of dynamic tests and energy efficiency measurements are achieved.

Damp heat-stable perovskite solar cells with tailored-dimensionality 2D/3D heterojunctions.

If perovskite solar cells (PSCs) with high power conversion efficiencies (PCEs) are to be commercialized, they must achieve long-term stability, which is usually assessed with accelerated degradation tests. One of the persistent obstacles for PSCs has been successfully passing the damp-heat test (85°C and 85% relative humidity), which is the standard for verifying the stability of commercial photovoltaic (PV) modules. We fabricated damp heat-stable PSCs by tailoring the dimensional fragments of two-dimensional perovskite layers formed at room temperature with oleylammonium iodide molecules; these layers passivate the perovskite surface at the electron-selective contact. The resulting inverted PSCs deliver a 24.3% PCE and retain >95% of their initial value after >1000 hours at damp-heat test conditions, thereby meeting one of the critical industrial stability standards for PV modules.

Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer

Parameters of the solar cell equivalent circuit models have a significant role in assessing the solar cells’ performance and tracking operational variations. In this regard, estimating solar cell parameters is a difficult task because cells have nonlinear current-voltage characteristics. Thus, a fast and accurate optimization algorithm is usually required to solve this engineering problem effectively. This paper proposes two hybrid variants of honey badger algorithm (HBA) and artificial gorilla troops optimizer (GTO) to estimate solar cell parameters. The proposed algorithms minimize the root mean square error (RMSE) between measurement and simulation results. In the first variant, GTO is used to determine the initial population of HBA, while in the second variant, HBA is used to determine the initial population of GTO. These variants can efficiently improve convergence characteristics. The proposed optimization algorithms are applied for parameter estimation of different equivalent circuit models of solar cells and various photovoltaic (PV) modules. Namely, the proposed algorithms test three solar cell equivalent models: single-diode, double-diode, and triple-diode equivalent circuit models. Different photovoltaic modules are investigated, such as the RadioTechnique Compelec (RTC) France solar cell, Solarex’s Multicrystalline 60 watts solar module (MSX 60), and the Photowatt, France solar panel (Photo-watt PWP 201). In addition, the applicability of the proposed optimization algorithms is verified using obtained results from a commercial solar module called Shell Monocrystalline PV module (SM55) with different irradiation and temperature levels. The good results of the proposed algorithms show that they can efficiently improve convergence speed and the accuracy of the obtained results than other algorithms used for parameter estimation of PV equivalent circuit models in the literature, particularly in terms of the values of the RMSE and statistical tests. In addition, the parameters estimated by the proposed methods fit the simulation data perfectly at different irradiance and temperature levels for the commercial PV module.

Modeling of the operating characteristics of photovoltaic modules based on particle swarm optimization

AbstractThis work evaluates the use of particle swarm optimization to extract the parameters that represent a photovoltaic cell in the model of one diode and five parameters (1D5P) to obtain current versus voltage characteristic curves (I × V) of a photovoltaic module operating under a wide range of temperature and irradiation scenarios. In all the modeling and simulation processes, only information of the module's data sheet provided by the manufacturers was used, without depending on results from other simulations or external data collections. To validate the study, the simulation of the current versus voltage (I × V) curves of a commercial photovoltaic module was made, and this was compared with data provided by the manufacturer and also compared with Chenni's method, which is consolidated by the literature. Errors of less than 0.3% were obtained for the simulations performed under the standard test conditions and in temperature and irradiation situations commonly found in practice, as is the case, of G = 700 W/m2 and Tc = 40°C, the 1D5P method showed an average error of approximately 1%, slightly surpassing the Chenni's method where the error was approximately 1.4%.Statement of Industrial RelevanceIn developing countries, such as Brazil, a significant increase in the portion referring to solar energy in the Brazilian energy grid is noted. As in 2016, this portion was only 0.02%, though in 2020, according to the Brazilian Solar Energy Association (ABSOLAR),1 this portion had an increase of 75 times and reached the value of 1.5%. In addition, ABSOLAR's forecast for 2030 is that solar energy will reach 10% of the Brazilian energy grid. One of the reasons for this is that industrial consumer units are increasingly looking for alternative energy sources due to high energy inflation. Photovoltaic generators are an excellent option for industries because they are clean and sustainable energy.2 It is silent and brings a financial return for the investor. Although, even with all the benefits, the photovoltaic generator has a high acquisition cost, especially for consumer units that demand much energy, as is the case of industries. Thus, correct and precise dimensioning for the photovoltaic generator is necessary. One way to increase the accuracy of dimensioning is to use simulation of the photovoltaic modules, which is the objective of this article.Novelty or SignificanceThe developed method uses the particle swarm optimization with constriction factor to simulate I × V curves of a photovoltaic module for any temperature and irradiation values, using only the module's datasheet information. This study demonstrates that the proposed method presents more accurate results than consolidated methods in the literature, as is the case with the Chenni's method.3

Monocrystalline silicon photovoltaic mitigation of potential-induced degradation using SiO2 thin film and + 1000 V biasing

Potential-induced degradation (PID) of photovoltaic (PV) modules is a severe form of degradation in PV modules, where power losses depend on the strength of the electric field, the relative humidity, and temperature. Therefore, understanding how PID influence the performance of PV modules is fundamental to reducing problems caused by such degradation. Previous studies have only considered single effects of PID; however, this work investigates the power losses and the development of hotspots in two different monocrystalline silicon PV modules. The origin of the hotpots is when the examined modules are being affected by severe PID, which results in cracks and materials deterioration. The first module is SiO2-free, and the second module contains this thin film layer. In addition, a PID mitigation procedure of + 1000 V has been applied and its effect examined on the modules. The modules were examined under electroluminescence (EL) and thermal imaging. Following these PID experiments, it was found that the SiO2 layer is suitable for deploying commercial PV modules to prevent hotspots and PID. However, applying + 1000 V PID mitigation does not necessarily improve the modules' performance after being affected by PID.

An evolutionary Nelder–Mead slime mould algorithm with random learning for efficient design of photovoltaic models

The efficiency of solar cells in converting solar energy into electrical energy can be improved by efficient and accurate solar photovoltaic cell modelling. However, the key to solar photovoltaic cell modelling is the accuracy of the solar cell parameters. Therefore, to obtain the unknown parameters of the solar cell accurately and efficiently, we proposed an improved slime mould algorithm (ISMA) combining the Nelder–Mead simplex (NMs) mechanism with a random learning mechanism. Specifically, the NMs mechanism ensures that the population is intensive and keeps moving closer to food as the population evolves. At the same time, the random learning mechanism incorporating the monitoring mechanism encourages optimal individuals to continuously learn the results of random communication among different agents and effectively improves the local search capability of the traditional SMA. To validate the performance of the proposed ISMA, it has been utilized to identify the optional parameters of a single diode, double diode, and PV modules. Based on experimental results, it is shown that ISMA outperforms most existing techniques in terms of convergence accuracy, convergence speed, and stability. In addition, ISMA also shows excellent stability in identifying the unknown parameters of three commercial photovoltaic modules under different environmental conditions. In summary, the proposed ISMA can be a promising technology in extracting the parameters of the photovoltaic models.

Analysis of the tilt and azimuth angles of photovoltaic systems in non-ideal positions for urban applications

A professional point of view suggests that photovoltaic systems should be installed at the optimum tilt angle and orientation. However, in photovoltaic systems integrated in buildings the flexibility of installation is common. This paper is organized in two different parts. In the first one, the energy losses caused by deviations from the tilt angle (β) and the orientation (γ) of the installation in relation to the ideal position are evaluated. This work considers the cloudy-sky conditions in each locality and theoretically calculates by applying the Cavaleri’s principle, the energy losses. Ten cities around the world, in the northern hemisphere, have been studied with a MATLAB code and the findings demonstrate that non-ideal tilt and azimuth angles can also lead to acceptable levels of electric energy generation. A photovoltaic system installed in South orientation (γ=0°) and β deviations of up to 10 (°) in relation to the optimum tilt angle has a very small influence on the energy losses. The energy losses are: 5%, 10%, 15% and 20% when β deviations are respectively: 21–23 (°), 31–33 (°), 37–40 (°) and 43–47 (°). Then, in the second part, an important application of this previous outcome comes out: the best distribution of the photovoltaic modules on a flat roof of irregular shape of an urban building is achieved.The aim of this work is to maximize the amount of energy get by a photovoltaic system. This engineering problem is highly complex as it involves 10 variables: the available flat roof area, the shape and the orientation of the available flat roof area, the dimensions (length and width) of the commercial photovoltaic modules, the orientation and the position of the photovoltaic modules, the number of the photovoltaic modules, the minimum distances (maintenance operations, to avoid shadowing effects) between rows of photovoltaic modules, and the minimum distance to the terrace boundary. In this context, this work aims to present a study to assist the decision-making.This paper shows a packing algorithm (in Mathematica™) which maximizes the energy generation area of the solar photovoltaic system, considering shadings and distances required for maintenance. Eventually, using the initial study, it comes out the influence of β on the potential capacity of the solar photovoltaic system and it is demonstrated that a decrease in the optimal tilt angle results in an increase up to 24% in the amount of obtained energy keeping invariable the available area. For example, in Almeria, with an optimum tilt angle of 30.3 (°) the amount of obtained energy is 149.8 (MWh) while with a tilt angle of 14 (°) the amount of obtained energy is 186.2 (MWh). This analysis enables to find the optimal answer to the following practical questions: what number of photovoltaic modules is required?, which is the right position for the photovoltaic modules?, and what orientation of photovoltaic modules is the right one?. There are many installers of photovoltaic systems who would benefit from studies about this issue.

Vortex Search Algorithm Applied to the Parametric Estimation in PV Cells Considering Manufacturer Datasheet Information

This paper addresses the problem of parametric estimation in solar cells considering manufacturer datasheet information regarding open-circuit, short-circuit, and maximum power points from the point of view of mathematical optimization. To represent this problem a single-objective function is formulated associated with the minimization of the mean square error of the single-diode model evaluated in the operational points reported by the manufacturer. The solution of this nonlinear non-convex optimization model is addressed with a metaheuristic optimization technique known in specialized literature as a vortex search algorithm (VSA). This metaheuristic optimization method works with Gaussian distribution functions and variable radius to explore and exploit the solution space by generating hyperspheres that move through the solution space as a function of the best current solution. The VSA is implemented in MATLAB environment by using commercial photovoltaic module information, where numerical results demonstrate the efficiency of this optimization method with objective functions lower than 1times10-25 and processing times around 6.13s.

Buck-based DMPPT emulator: a helpful experimental demonstration unit

<span lang="EN-GB">Distributed control strategyrepresents the most promising solution to enhance the lackluster energetic performance of mismatched PhotoVoltaic (PV) systems. Moreover, many factors that contribute to such poor performance are still to be explored. To fully understand the advantages offered by the Distributed Maximum Power Point Tracking (DMPPT) approach, the implementation of a DMPPT emulator is necessary. Based on the above needs, this paper describes the realization and use of a Buck based DMPPT emulator and shows its high flexibility and potential. The realized device is capable to emulate the output current vs. voltage (I-V) characteristics of many commercial PV modules with a dedicated Buck DC/DC converter not only in controlled atmospheric conditions but also with different currents rating of the switching devices. The system implementation is based on a commercial power supply controlled by a low-cost Arduino board. Data acquisition is performed through a low-cost current and voltage sensor by using a multichannel board by National Instruments. Experimental results confirm the validity and potential of the proposed DMPPT emulator.</span>

A novel equivalent circuit-based model for photovoltaic sources

This paper presents a novel circuit-based model of photovoltaic (PV) source (cell, module or array) that can be easily integrated into any circuit-oriented simulators such as PSpice, PSCAD/EMTDC, PSIM, PowerSys of MATLAB/Simulink, etc. This proposed model is able to simulate accurately any commercial PV module behavior either exposed to uniform or non-uniform irradiance distributions. Moreover, it can also simulate perfectly PV arrays behavior in large scale PV systems. It is mainly derived from the conventional one-diode equivalent circuit model, which consists of a current source, a diode and two resistors. By modifying the linear part of this latter and including implicitly the effect of weather parameters (irradiance and cell temperature) on the circuit elements, our proposed model will encompass two types of dependent sources (voltage and current sources) and two resistances.To validate the truthfulness of the proposed novel model, comparisons between simulated and outdoor measured I-V characteristics on commercial PV module are done. Preliminary results have shown promising outputs and accuracy of this model.

Parameters Identification of PV Triple-Diode Model Using Improved Generalized Normal Distribution Algorithm

To simulate the behaviors of photovoltaic (PV) systems properly, the best values of the uncertain parameters of the PV models must be identified. Therefore, this paper proposes a novel optimization framework for estimating the parameters of the triple-diode model (TDM) of PV units with different technologies. The proposed methodology is based on the generalized normal distribution optimization (GNDO) with two novel strategies: (i) a premature convergence method (PCM), and (ii) a ranking-based updating method (RUM) to accelerate the convergence by utilizing each individual in the population as much as possible. This improved version of GNDO is called ranking-based generalized normal distribution optimization (RGNDO). RGNDO is experimentally investigated on three commercial PV modules (Kyocera KC200GT, Ultra 85-P and STP 6-120/36) and a solar unit (RTC Si solar cell France), and its extracted parameters are validated based on the measured dataset points extracted at generalized operating conditions. It can be reported here that the best scores of the objective function are equal to 0.750839 mA, 28.212810 mA, 2.417084 mA, and 13.798273 mA for RTC cell, KC200GT, Ultra 85-P, and STP 6-120/36; respectively. Additionally, the principal performance of this methodology is evaluated under various statistical tests and for convergence speed, and is compared with a number of the well-known recent state-of-the-art algorithms. RGNDO is shown to outperform the other algorithms in terms of all the statistical metrics as well as convergence speed. Finally, the performance of the RGNDO is validated in various operating conditions under varied temperatures and sun irradiance levels.

Nondestructive silicon wafer recovery by a novel method of solvothermal swelling coupled with thermal decomposition

• 86.11% of Si-wafers in commercial PV module can be reclaimed without damage. • The formation of gas release channels contributes to the nondestructive recovery. • PV backsheet removal avoids the fluorine-containing gas emission. • Solvothermal swelling strategy provides significant time and organic solvent savings. • The reclaimed silicon wafer meets the requirements of new PV module fabrication. End-of-life (EoL) photovoltaic (PV) waste is becoming a severe environmental issue worldwide. Developing technologies to reclaim nondestructive and reusable silicon wafers (Si-wafers) is the most appealing way to solve this problem, saving ~40% on PV module production costs, but it remains a great challenge. Herein, we develop a novel method of coupling solvothermal swelling with thermal decomposition (SSTD) for structure-intact Si-wafers recovery. Laboratory-scale studies reveal that undamaged Si-wafers can be easily obtained by conducting a solvothermal swelling process in advance to build channels for the quick, horizontal release of ethylene-vinyl acetate (EVA) thermal decomposition gas. Using scaling-up equipment, 86.11% of Si-wafers in commercial PV module are reclaimed without any damage, nearly 10-fold higher than that of thermal decomposition alone (9.26%). Moreover, PV backsheet can be depolymerized with specially designed solvents (toluene-ethanol), greatly facilitating swelling by exposing EVA directly to toluene vapor and reducing the emission of fluorine-containing gas pollutants. The reclaimed Si-wafers are comparable to the originals, with the following properties: interstitial oxygen, <6 × 10 17 atoms·cm −3 ; substitutional carbon, <5 × 10 17 atoms·cm −3 ; resistivity, 1.84 Ω·cm; and minority carrier lifetime, 3.92 μs.