Model Of Photovoltaic Module Research Articles

Modeling and Analysis of Simplified PV Module With Various Temperature And Irradiance Inputs Using MATLAB Simulink Algorithm

Photovoltaic (PV) systems offer a potential solution to the global energy crisis. Modeling study and analysis involving solar PV module is an important task in a PV system to be more user friendly, improve reliability and performance. However, the output characteristics of PV modules are nonlinear, since they are dependent on environmental conditions such as solar irradiance and temperature, as well as local climate conditions such as humidity and wind. The various methods used have some common gaps as the absence of step-by-step procedure which causes difficulties to understand. Indeed, accurate modeling of PV modules is important to provide a better understanding of their operation and output characteristics, since simulations can be used to understand the behavior of PV modules under various operating conditions. The objective of the project is first, to build and model a solar photovoltaic (PV) module algorithm using Matlab/Simulink. Second, to simulate and analyze the behavior of the model in various temperature and irradiance input conditions. Third, to validate the results obtained from the simulations by comparing the output characteristics with the manufacturer’s datasheet. The methodology used by presenting the principles of detailed modeling of PV modules using Matlab/Simulink software. The constructed model is taken from the equations obtained from the equivalent circuit of a single diode model. The PV Module block is a five-parameter model using a light-generated current source (IL), diode, series resistance (Rs), and shunt resistance (Rsh) to represent the irradiance and temperature-dependent I-V and P-V characteristics of the module. This method provides a simple, reliable, and highly flexible method to adapt PV modules to different environmental conditions such as irradiance, temperature and physical parameters of the solar modules such as resistance, current, voltage, ideality factors and others. The simulation results and data obtained show that the block module developed and simulated using Matlab/Simulink with various input values ​​of temperature and irradiance is almost identical to the real PV module and user-friendly.

Comparative Estimation of Electrical Characteristics of a Photovoltaic Module Using Regression and Artificial Neural Network Models

Accurate modeling of photovoltaic (PV) modules under outdoor conditions is essential to facilitate the optimal design and assessment of PV systems. As an alternative model to the translation equations based on regression methods, various data-driven models have been adopted to estimate the current–voltage (I–V) characteristics of a photovoltaic module under varying operation conditions. In this paper, artificial neural network (ANN) models are compared with the regression models for five parameters of a single diode solar cell. In the configuration of the proposed PV models, the five parameters are predicted by regression and neural network models, and these parameters are put into an explicit expression such as the Lambert W function. The multivariate regression parameters are determined by using the least square method (LSM). The ANN model is constructed by using a four-layer, feed-forward neural network, in which the inputs are temperature and solar irradiance, and the outputs are the five parameters. By training an experimental dataset, the ANN model is built and utilized to predict the five parameters by reading the temperature and solar irradiance. The performance of the regression and ANN models is evaluated by using root mean squared error (RMSE) and mean absolute percentage error (MAPE). A comparative study of the regression and ANN models shows that the performance of the ANN models is better than the regression models.

Estimation of photovoltaic waste spatio-temporal distribution by 2060 in the context of carbon neutrality.

In recent decades, large-scale deployment of photovoltaic (PV) power leads to management challenges for recycling PV module waste in China. With the growth of waste PV volumes, it is necessary to figure out the spatio-temporal distribution of PV waste at the provincial level. Based on China's carbon neutrality goal by 2060, six development pathways of PV installed capacity are proposed to identify in-use stocks of PV capacity. In particular, we develop the retired flow estimation model for PV modules that is constructed by three PV module degradation scenarios. The results show that a relatively large scale of PV waste will be started to emerge in China by 2030 and the cumulative waste is expected to reach 1100[Formula: see text]1450 GW by 2060. Our findings also indicate an unequal distribution of PV waste across regions and the highest PV waste volumes by 2060 is the East China region at 31.4%, with Shandong (8.99%) and Hebei(8.65%) ranking as the top provinces. This prospective research will help the PV industry plan the location and capacity of recovery facilities at an appropriate time to advance toward a more resource efficient and circular economy.

Photovoltaic Models’ Parameter Extraction Using New Artificial Parameterless Optimization Algorithm

Identifying parameters in photovoltaic (PV) cell and module models is one of the primary challenges of the simulation and design of photovoltaic systems. Metaheuristic algorithms can find near-optimal solutions within a reasonable time for such challenging real-world optimization problems. Control parameters must be adjusted with many existing algorithms, making them difficult to use. In real-world problems, many of these algorithms must be combined or hybridized, which results in more complex and time-consuming algorithms. This paper presents a new artificial parameter-less optimization algorithm (APLO) for parameter estimation of PV models. New mutation operators are designed in the proposed algorithm. APLO’s exploitation phase is enhanced by each individual searching for the best solution in this updating operator. Moreover, the current best, the old best, and the individual’s current position are utilized in the differential term of the mutation operator to assist the exploration phase and control the convergence speed. The algorithm uses a random step length based on a normal distribution to ensure population diversity. We present the results of a comparative study using APLO and well-known existing parameter-less meta-heuristic algorithms such as grey wolf optimization, the salp swarm algorithm, JAYA, teaching-learning based optimization, colliding body optimization, as well as three major parameter-based algorithms such as differential evolution, genetic algorithm, and particle swarm optimization to estimate the parameters of PV the modules. The results revealed that the proposed algorithm could provide excellent exploration–exploitation balance and consistency during the iterations. Furthermore, the APLO algorithm shows high reliability and accuracy in identifying the parameters of PV cell models.

A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction

Currently, solar energy is one of the leading renewable energy sources that help support energy transition into decarbonized energy systems for a safer future. This work provides a comprehensive review of mathematical modeling used to simulate the performance of photovoltaic (PV) modules. The meteorological parameters that influence the performance of PV modules are also presented. Various deterministic and probabilistic mathematical modeling methodologies have been investigated. Moreover, the metaheuristic methods used in the parameter extraction of diode models of the PV equivalent circuits are addressed in this article to encourage the adoption of algorithms that can predict the parameters with the highest precision possible. With the significant increase in the computational power of workstations and personal computers, soft computing algorithms are expected to attract more attention and dominate other algorithms. The different error expressions used in formulating objective functions that are employed in extracting the parameters of PV models are comprehensively expressed. Finally, this work aims to develop a comprehensive layout for the previous, current, and possible future areas of PV module modeling.

Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

AbstractThe determination of photovoltaic (PV) parameters is of great importance for the reliability of solar system operation, continuity of the load power consumption, and control management of the energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching and learning‐based optimization (TLBO), named TLBOBSA, to accurately simulate the PV model. During the evaluation of the proposed algorithm, the concept of teaching from TLBO is introduced into the BSA to guide optimal individuals, thus improving the convergence rate of the algorithm. The learning behavior among individuals in the student phase of TLBO facilitates interindividual learning and provides beneficial information for its evolution, which is introduced into the BSA to ensure the diversity of the population. The comprehensive test results of different PV module models in different environmental conditions show that the proposed algorithm is more advantageous for parameter extraction than other existing algorithms. This can be seen in the simulation experiments of two commercial PV models, where the simulated current is consistent with the measured current at each measured voltage. This demonstrates that the proposed TLBOBSA is an accurate and reliable tool for evaluating unknown parameters of PV models.

Thermal effect on curved photovoltaic panels: Model validation and application in the Tabuk region.

This paper aimed to investigate the temperature effect on photovoltaic (PV) cell parameters. The PV cell parameters such as series and parallel resistances, diode ideality factor, and diode saturation current, are not considered in the reported stepwise modeling. The present work aims to improve available models used in the modeling and simulation of PV modules to support the researcher and power project developer. All the required temperature-dependent parameters are determined to model the simulated PV module with high accuracy using Simulink/MATLAB software. To validate the method, a 36-cell-50W solar panel with different radii of curvature is set up to assess solar power outputs under varying irradiance and temperature conditions. For the present application, the Tabuk region (Saudi Arabia) is chosen based on its location and climatic conditions. The method provided conformity to the measured power outputs for varying Global Horizontal Irradiance (GHI) and temperature conditions. The maximum power output of the PV module increases from 14.4 W to 25.8 W when the received solar power density varies from 307 W/m2 to 526 W/m2 depending on the level of curvature starting from a semi-cylindrical shape to a vaulted shape to a flat shape. The curved PV module shows slightly higher power variation with temperature as compared to the flat one. Above 25°C, the power output is about 20% less at a maximum temperature of 65°C. When the temperature drops below 25°C, the power outputs increase about 6% and 11.5% for corresponding temperatures of 15°C and 5°C, respectively.

Physics-based electrical modelling of CIGS thin-film photovoltaic modules for system-level energy yield simulations

Copper indium gallium selenide (CIGS) is a commercialized, high-efficiency thin-film photovoltaic (PV) technology. The state-of-the-art energy yield models for this technology have a significant normalized root mean square error (nRMSE) on power estimation: De Soto model—26.7%; PVsyst model—12%. In this work, we propose a physics-based electrical model for CIGS technology which can be used for system-level energy yield simulations by people across the PV value chain. The model was developed by considering models of significant electrical current pathways from literature and adapting it for the system-level simulation. We improved it further by incorporating temperature and irradiance dependence of parameters through characterisation at various operating conditions. We also devised a module level, non-destructive characterization strategy based on readily available measurement equipment to obtain the model parameters. The model was validated using the measurements from multiple commercial modules and has a significantly lower power estimation nRMSE of 1.2%.

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.

A comprehensive multi-physics model of photovoltaic modules with non-uniform solar concentration and serpentine cooling

This paper focuses on the multi-physics modeling of photovoltaic modules with solar concentration and cooling. The first step of the proposed modeling approach is optical modeling of the solar concentrators to determine the solar flux distribution on the photovoltaic module. Using the solar flux distribution by the concentrator, the thermal performance of the photovoltaic module was calculated for various cooling cases. The temperature and solar irradiance distributions were then used to model the electrical performance using a newly proposed cell-based electrical model for photovoltaic modules. The model was used to study the impact of solar concentrator design and cooling system design on the electrical performance of the photovoltaic system. Multiple cases with different working fluid velocities showed that the compound parabolic concentrator focuses the solar flux close to the edges of the photovoltaic module on focal points of the parabolic reflectors, where the concentration ratio rose to 18. It resulted in extreme non-uniformity of irradiance and cell temperature over the module, which resulted in only a 7 % increase in the electrical power output when the 2x compound parabolic concentrator was added to the photovoltaic module. Water cooling significantly improved over 35 % in power output and a 16 % reduction in average temperature even for the most straightforward heat exchanger design (4 bends) and with the lowest inlet velocity (0.1 m/s). The study results show that a simple V-trough concentrator design improves electrical performance compared to the more complicated compound parabolic concentrator.

Applications of Artificial Intelligence to Photovoltaic Systems: A Review

This article analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training. Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.

Modeling of Photovoltaic modules under shading condition and an error evaluation criterion

Energy is the focus of recent years. As one of the most representative new energy sources, solar energy has the characteristics of large reserves and no pollution. The main way to use solar energy is photovoltaic (PV) power generation, and the PV module in the PV power generation system is the component that converts solar energy into electric energy. In the actual power generation process, PV modules often receive uneven solar illumination due to the shadow caused by clouds, trees, buildings, etc., resulting in changes in the output characteristics of PV modules and reduced output efficiency. Therefore, modeling of PV modules under shading condition is very important. This paper presents two simple methods for solving the parameters of PV module models, which require few parameters and are easy to obtain. A modeling method of PV modules under shading condition is also presented. And then the modeling method is verified with the experiment result. Finally, an error criterion for PV module modeling under shading condition is proposed to measure the goodness of the modeling. Combining with the two parameter solving methods, the errors of this PV module modeling method under shading condition of the error criterion proposed in this paper are 3.24% and 2.51%, respectively, which meet the requirements for engineering use.

Photovoltaic parameters estimation of poly-crystalline and mono-crystalline modules using an improved population dynamic differential evolution algorithm

<span>Photovoltaic (PV) parameters estimation from the experimental current and voltage data of PV modules is vital for monitoring and evaluating the performance of PV power generation systems. Moreover, the PV parameters can be used to predict current-voltage (I-V) behavior to control the power output of the PV modules. This paper aimed to propose an improved differential evolution (DE) integrated with a dynamic population sizing strategy to estimate the PV module model parameters accurately. This study used two popular PV module technologies, i.e., poly-crystalline and mono-crystalline. The optimized PV parameters were validated with the measured data and compared with other recent meta-heuristic algorithms. The proposed population dynamic differential evolution (PDDE) algorithm demonstrated high accuracy in estimating PV parameters and provided perfect approximations of the measured I-V and power-voltage (P-V) data from real PV modules. The PDDE obtained the best and the mean RMSE value of 2.4251E-03 on the poly-crystalline Photowatt-PWP201, while the best and the mean RMSE value on the mono-crystalline STM6-40/36 was 1.7298E-03. The PDDE algorithm showed outstanding accuracy performance and was competitive with the conventional DE and the existing algorithms in the literature.</span>

Information sharing search boosted whale optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

With the recent emphasis on new energy sources, solar photovoltaic cells have received widespread attention from scholars as a highly efficient and clean new energy source. Researchers model solar photovoltaic cells and analyze the values of various unknown parameters to improve their efficiency in converting solar energy into electric energy. To extract unknown parameters more accurately and efficiently, we propose a whale optimization algorithm (WOA) driven by the information sharing search mechanism and the Nelder-Mead simplex (NMs) mechanism, which ISNMWOA. The WOA can perform a global search of the parameters of the photovoltaic model to obtain a number of initial vectors of model parameters that satisfy the parameter range. The information sharing search strategy performs a further coarse local search on a number of feasible solutions generated. In contrast, the NMs can perform a further refined local search in the vicinity of the optimal parameters to obtain the final vector of optimally performing model parameters. First, we tested ISNMWOA against six advanced algorithms in CEC 2014. We then used the ISNMWOA to evaluate the parameters of the solar cells and PV modules. The RMSE of ISNMWOA on single diode model (SDM), double diode model (DDM), three diode model (TDM), and photovoltaic module model (PV) are 9.8602E-04, 9.8248E-04, 9.8248E-04 and 2.4251E-03 respectively. Compared to the original WOA, the RMSE of ISNMWOA on SDM, DDM, TDM, and PV decreased by 30.01%, 24.24%, 50.98%, and 6.94%, respectively. Moreover, compared to the high-performing GOFPANM, ISNMWOA runs 298 times faster than GOFPANM with almost identical optimization results. We evaluated the performance of three commercial PV modules under different irradiance and temperature conditions using ISNMWOA to verify the reliability of the experiment. The experimental results show that the ISNMWOA outperforms similar algorithms currently available.

Optimal parameter assessment of Solar Photovoltaic module equivalent circuit using a novel enhanced hybrid GWO-SCA algorithm

Solar Photovoltaic (PV) modules provide a reliable and clean electricity source that can suit various applications. Based on the quality and type of crystals, the PV modules are classified as monocrystalline, polycrystalline, and thin film. The improvements on different solar PV modules can be made efficiently with accurate mathematical models, which requires extracting its parameters. This paper suggests a novel enhanced hybrid grey wolf optimizer-sine cosine algorithm (EHGWOSCA) to extract solar PV module parameters. The recommended algorithm is validated on CEC-C06 2019 benchmark functions which contain ten standard functions. The PV module parameter extraction is performed on different kinds of PV modules, namely, monocrystalline type Shell CS6K280M, polycrystalline type Shell S75, and thin-film type Shell ST40. Two well-known models of PV modules are considered with a requirement of 5 parameters and 7 parameters extraction. The proposed algorithm is implemented by minimizing the sum of squared errors at open circuit point, short circuit point, and maximum power point (MPP). The results obtained show that in the Monocrystalline model using the proposed hybrid approach with single diode model, an error is 1.0718E−15, and it is further reduced to 1.3229E−16 with the double diode model. The proposed EHGWOSCA Error in the Polycrystalline model with double diode is 6.1594E−19, and the Thin-film model error is 2.91E−22. The effectiveness of the proposed approach is verified by comparative analysis with other methods available in the literature.

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.

Procedure Used to Estimate the Power Production of a Photovoltaic Module Operating under Partial Shading Conditions

This paper presents a methodology used to estimate the energy generated during one year by a photovoltaic module (PVM) operating under partial shading conditions. The methodology starts by calculating the solar paths and contours of nearby objects that produce shadows. Then, a method was proposed to estimate the shading factors of each submodule. Afterwards, the solar resource data and the calculated shading factors were used to feed a detailed PVM model to calculate the power–voltage curves for each hour, which were used to obtain a power profile and estimate the energy generated by the PVM in one year. The procedure was validated through simulation and experimental results. The simulation results consider a case study available in the literature, which was simulated to evaluate the effect on the PVM energy estimation considering and disregarding the partial shading conditions. The experimental results illustrate the capacity of the proposed methodology to predict the shaded and unshaded submodules and the module power–voltage curve. The results show that the proposed method avoids the energy overestimation introduced by classical estimation methods, which affects the sizing of a photovoltaic generator.

Validation of an outdoor efficiency model for photovoltaic modules

The study of the efficiency of photovoltaic modules in outdoor conditions allows determining their correct operation and to detect abnormal behavior. Most efficiency models developed use experimental data that are difficult to obtain as they require specialized equipment and a long measurement time under laboratory conditions to identify or adjust the model parameters. In this article, we propose a linear equation that estimates the efficiency of photovoltaic modules using irradiance and back panel temperatura measurements as input variables. The main contribution is that the parameters of the proposed model can be obtained directly from the reference information (IEC61853-1 power values) without any data regression. The proposed model was validated using experimental data obtained from three different climatic zones during a whole year. The model shows the best fit to the data of four analyzed models, and its normalized root mean square deviation for all photovoltaic modules is less than 3.6%; this dispersion could be explained by the fact that the outdoor data have an uncertainty of around 3.0%

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.

An approach on mathematical modeling of PV module with sensitivity analysis: a case study

An approach on mathematical modeling of PV module with sensitivity analysis: a case study