PV Model Research Articles

Design Analysis of 7.5KW Stand Alone Solar Photovoltaic Power System for an Intermediate Household

Aim: Design Analysis of 7.5KW Stand-alone Solar Photovoltaic Power System for an Intermediate Household.
 Methodology: A design analysis of standalone 7.5KW PV system was carried out using PV modeling equations based on load estimated. The analyzed data of the solar photovoltaic components was used to determine the estimated output power of 7.5KW. Therefore, a number of modeling equations and methodologies for designing a PV system based on application have been developed and simplified in order to ensure the optimum performance of the system. The analyzed solar powered 7.5KW system was achieved by designing 24 solar panels of 335W each, 16 deep cycle battery of 200A each, and a pulse width modulation (PWM) charge controller of 60A to monitor the output of the battery for safety operation. The battery will be connected to the inverter circuit (DC-AC) to generate 220V alternating current in its output via a step-up transformer. In this paper, design analysis of a standalone PV system enables of producing power to a household with approximate consumption of 7.5KW was conducted. 
 Results: The standalone PV system along with the cost implications was analysed and designed. In this analysis a sequential design plan of an independent standalone solar powered photovoltaic structure was analysed and overviewed to supply continuous and uninterrupted power to a typical utility with maximum power consumption of 7500W (7.5KVA). The calculated/computed values of all the components yield a result that can serve the purpose. Based on the design analysis, the result implies that the estimate consumption of 7.5KW in a day requires 24 PV panels of 335 Watts each, 16 (12V, 200Ah) batteries, 10KW inverter, (12V, 60A) charge controller and copper wire of cross-sectional area (1.688mm2) for installation. 
 Conclusion: In Nigeria, to generate a solar power of 7.5KVA requires almost $15,585.70 which is equivalent to N 5,965,426.66.

A reinvigorated strategy for the analysis of optimal parameters for modeling of Solar Photovoltaic modules under variable conditions

In today’s world, Solar Photovoltaic energy sources have accredited robustness and reliability in the context of generation, transmission, and distribution of power for the end-users and utility customers. The accurate parameters analysis of Solar PV modules is necessary for the proper assessment, simulation, and optimization of power from Solar PV systems. The main goal of this paper is to apply a suitable approach to identify unspecified parameters of the PV module and at the same time to minimize (%) relative error and computation time to get better-optimized parameters for modeling of PV module. This paper presents a hybrid of a mathematical and iterative approach to analyze the appropriate parameters of the Solar PV model. Here, Root finding algorithms have been applied to extract the optimal unknown parameters of the PV modules by MATLAB programming. These algorithms are demonstrated on three distinct types of PV modules (Thin film, Monocrystalline, and Multicrystalline type) at standard test conditions and nominal operating conditions. These algorithms provide high precision and consume minimal time with ease of implementation. Extraction of parameters and simulation of the model is performed for validation based on datasheets of distinct types of PV modules using MATLAB/ SIMULINK software. The extracted precise parameters give rise to output characteristics of PV modules under variable environmental and electrical conditions. Relative error analysis has been evaluated to distinguish between different techniques applied for parameter extraction of PV modules. The relative error (%) of the proposed techniques for parameters extraction of PV modules varies from −0.5% to ＋0.5%, which shows the supercilious characteristic of the proposed techniques. Future exploration headings concerning the optimization of power from the Solar PV system are suggested.

Parameter Estimation of Modified Double-Diode and Triple-Diode Photovoltaic Models Based on Wild Horse Optimizer

The increase in industrial and commercial applications of photovoltaic systems (PV) has a significant impact on the increase in interest in studying the improvement of the efficiency of these systems. Estimating the efficiency of PV is considered one of the most important problems facing those in charge of manufacturing these systems, which makes it interesting to many researchers. The difficulty in estimating the efficiency of PV is due to the high non-linear current–voltage characteristics and power–voltage characteristics. In addition, the absence of ample efficiency information in the manufacturers’ datasheets has led to the development of an effective electrical mathematical equivalent model necessary to simulate the PV module. In this paper, an application for an optimization algorithm named Wild Horse Optimizer (WHO) is proposed to extract the parameters of a double-diode PV model (DDM), modified double-diode PV model (MDDM), triple-diode PV model (TDM), and modified triple-diode PV model (MTDM). This study focuses on two main objectives. The first concerns comparing the original models (DDM and TDM) and their modification (MDDM and MTDM). The second concerns the algorithm behavior with the optimization problem and comparing this behavior with other recent algorithms. The evaluation process uses different methods, such as Root Mean Square Error (RMSE) for accuracy and statistical analysis for robustness. Based on the results obtained by the WHO, the estimated parameters using the WHO are more accurate than those obtained by the other studied optimization algorithms; furthermore, the MDDM and MTDM modifications enhanced the original DDM and TDM efficiencies.

Supply demand optimization algorithm for parameter extraction of various solar cell models

This article illustrates a new application of the Supply-Demand-Based Optimization (SDO) algorithm to accurately extract the electrical parameters of different PV models. The key goal is to minimize the total error between the experimental data and the proposed approach by optimizing the electrical parameter of three different models. The SDO simulates the stability and instability modes of the supply–demand mechanism in market economy, where the quantity and price converge and diverge from the equilibrium point, respectively. Three different marketable PV modules were provided, and the findings correlate with the experimental data and other well-known optimization techniques denoting the superiority of SDO. These modules are STM6_40_36, STP6_120_36, and Photowatt-PWP 201 are adopted. The validation is introduced, and the findings are correlated with experimental data and other recently well-known optimization techniques which are grey wolf optimization (GWO), Crow search Optimizer (CSO), Bernstein–Levy​ Search Differential Evolution Algorithm (BSDE), and Manta Ray Foraging Optimizer (MRFO), Backtracking Search Algorithm (BSA). In the article a deep analysis has been carried out for the best parameter extraction in a PV module. An online parameter extraction is extended based on the SDO algorithm via SDM under different sunshine irradiation of 200, 400, 600, 800 and 1000 W/m2 and temperature of 25, 50 and 75 °C. The standard deviations of the fitness values, over 30 runs, for three models for the above-mentioned modules are less than 1 × 10−18, 10−17 and 10−6, respectively which denotes the superiority of the SDO. Also, a unique set of the extracted parameters is provided with significant validations of the proposed SDO using measured I–V curves for different irradiance and temperature values. Also, the obtained results were compared to different algorithms with a detailed statistic characterizations for convergence, achieving good fitting correlations. Therefore, the SDO are extremely consistent, competitive one among other 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.

High-Power Closed-Loop SMPC-Based Photovoltaic System Characterization under Varying Ambient Conditions

Photovoltaic energy generation potential can be tapped with maximum efficacy by characterizing the source behaviour. Characterization refers to the systematic terminal measurement-based PV modeling which can further facilitate output prediction and fault detection. Most of the existing PV characterization methods fail for high-power PV array due to increased thermal losses in electronic components. Here, we propose a switched-mode power converter-based PV characterization setup which is designed with input filter to limit switching ripple entering into PV array under test, thereby enhancing system life and efficiency. The high resonant frequency input filter ensures its compactness with high-speed characterization capability. To further enhance the system performance, a closed-loop current control of the system is designed for high bandwidth and stable phase margins. Variation of the controller parameters under varying ambient conditions of 200–1000 W/m2 irradiation and 25–70 °C temperature is documented and an adaptive PI controller is proposed. Experimental and simulation results validate the high performance of the closed loop operation of the PV characterization at 1.2 kW range power level in real-time field conditions. Compared to the open loop operation, the closed-loop operation eliminates the waveform ringing by 100% during characterization.

Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems

The extraction of parameters of solar photovoltaic generating systems is a difficult problem because of the complex nonlinear variables of current-voltage and power-voltage. In this article, a new implementation of the Gorilla Troops Optimization (GTO) technique for parameter extraction of several PV models is created. GTO is inspired by gorilla group activities in which numerous strategies are imitated, including migration to an unknown area, moving to other gorillas, migration in the direction of a defined site, following the silverback, and competition for adult females. With numerical analyses of the Kyocera KC200GT PV and STM6-40/36 PV modules for the Single Diode (SD) and Double-Diode (DD), the validity of GTO is illustrated. Furthermore, the developed GTO is compared with the outcomes of recent algorithms in 2020, which are Forensic-Based Investigation Optimizer, Equilibrium Optimizer, Jellyfish Search Optimizer, HEAP Optimizer, Marine Predator Algorithm, and an upgraded MPA. GTO’s efficacy and superiority are expressed by calculating the standard deviations of the fitness values, which indicates that the SD and DD models are smaller than 1E−16, and 1E−6, respectively. In addition, validation of GTO for the KC200GT module is demonstrated with diverse irradiations and temperatures where great closeness between the emulated and experimental P-V and I-V curves is achieved under various operating conditions (temperatures and irradiations).

Modelling and Simulation of Bifacial PV Production Using Monofacial Electrical Models

In this paper, we investigate the use of monofacial PV models to simulate the production of bifacial PV systems over different albedos. Analytical and empirical models were evaluated using measured data obtained from three identical bifacial PV arrays: (1) with the backside covered by white plastic, (2) with normal albedo, and (3) with high albedo. The front-and rear-side irradiances were measured in order to integrate bifaciality of the modules into the models. The models showed good performance for non-real-time monitoring, especially under clear skies, and the analytical model was more accurate than the empirical model. The heatmap visualization technique was applied to six months of data in order to investigate the site conditions on the rear side of the modules as well as the accuracy of the models. The heatmap results of the rear- and front-sides irradiances showed that the installation conditions, such as the azimuth angles of the sun and the surrounding obstacles, had a strong impact on the energy received from the back of the modules. The heatmap results of the models validated the performance of the analytical model. The average daily errors for the analytical model were less than 1% and 3% for normal and high albedos, respectively.

A software-based approach in designing a rooftop bifacial PV system for the North Hall of Residence, IUT

Abstract Bifacial rooftop photovoltaic panels appear to be an excellent means of power generation in this era of urbanization, especially for land-limited countries like Bangladesh. This paper presents a software-based approach to design and simulate a bifacial solar-panel-based energy model on the rooftop of the North Hall of Residence of the Islamic University of Technology, Gazipur. This vertically mounted model investigates the feasibility and applicability of such an energy model in a university residence, situated in a load-shedding-prone area. Hence, three prominent software platforms, namely PVSOL, PVsyst and System Advisor Model (SAM), are brought into action and rigorous simulations are performed for three different orientations; promising outcomes are observed in terms of annual energy yield, bifacial gain (BG) and consumption coverage of the grid and PV model. The annual energy demand of the North Hall is ~444 733.5 kWh. The three orientations can generate annually 92 508.62, 94 643.48 and 86 758.94 kWh, respectively. Hence, it is evident that the proposed orientations can supply almost 19–21% of the site’s annual demand. Monthly BG analysis shows an overall increase in energy gain of 13%, 15.6% and 6% for Orientation-1, Orientation-2 and Orientation-3, respectively. A rigorous comparative analysis and deviation analysis among the software results has been accomplished to gain more insight into the feasibility of the proposed system. Thus, we have focused on a detailed software-based estimation of energy production for different orientations of the PV panels, considering several factors, which will provide prior knowledge and assessment before going for hardware implementation in the future.

Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules

Improved Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been proposed to identify the parameters of solar PV cells and modules. For this purpose, the traditional PSO algorithm has been adapted and an improved one using the fractional order calculus has been especially designed as well. The proposed FODPSO together with the PSO has been applied in both the single and double diode models to describe the specified PV units for purpose of comparison. To examine the advantages of the proposed FODPSO over conventional PSO, two approaches have been performed; the first approach is implementing the FODPSO and PSO algorithms using input data obtained from previous international publications for different types of PV cells/modules, then comparing the FODPSO algorithm's results to the results of PSO and the other previous optimization techniques that were using the same input data and identical PV cells/modules. The second validation approach is achieved by performing real measurements in outdoor conditions for another two types of PV modules (mono and poly crystalline Silicon) and inserting these measurements' readings as input data to the PSO and FODPSO algorithms. A comparative study has also been done for both algorithms' results. Enhanced results have been achieved using the FODPSO technique.It is worth mentioning that during the development of this work, other efforts have been made with different techniques that may result in similar or even better results, yet this paper is still introducing for the first time the FODPSO technique (as a modified technique of the PSO) as applied on the PV modeling problem, in addition the paper presents the benefits of the proposed FODPSO over the conventional PSO and presents a comparison that shows the superiority of the FODPSO over the conventional PSO.

Validation of the Myelofibrosis Secondary to PV and ET-Prognostic Model in Patients with Post-Polycythemia Vera and Post-Essential Thrombocythemia Myelofibrosis: MD Anderson Cancer Center

Validation of the Myelofibrosis Secondary to PV and ET-Prognostic Model in Patients with Post-Polycythemia Vera and Post-Essential Thrombocythemia Myelofibrosis: MD Anderson Cancer Center

Assessment of Rooftop Solar Power Generation to Meet Residential Loads in the City of Neom, Saudi Arabia

The economic and social development of the Kingdom of Saudi Arabia (KSA) has led to a rapid increase in the consumption of electricity, with the residential sector consuming approximately 50% of total electricity production. The KSA depends largely on non-renewable energy resources, and the government has produced Saudi Vision 2030. This plan aims to lessen the country’s reliance on fossil fuels and reduce associated problems such as air pollution. Saudi Vision 2030 combines renewable energy and new building designs so that, for example, the planned city of Neom will be net zero energy. This study addresses how best to reduce Neom’s reliance on the national grid through rooftop photovoltaic generation in residential buildings. The study develops a techno-economic model of rooftop PV with battery storage suitable for existing residential building types likely to be built in Neom city (villas, traditional houses, and apartments), and assesses the optimal PV size, battery storage capacity, and optimal orientation of the PV panels. The study used HOMER Pro to compute the Net Present Cost, Levelized Cost of Energy, orientation of PV panels, and optimum PV system size. The optimal size of PV system is 14.0 kW for the villa, 11.1 kW for the traditional dwelling, and 10.3 kW for the apartment, each with a single battery of capacity 12 kWh.

Application of an adaptive Bayesian‐based model for probabilistic and deterministic PV forecasting

Accurate prediction of solar photovoltaic plant energy generation is essential for optimal planning and operation of modern power systems, and incorporating such plants into the energy sector. In this study, an adaptive Gaussian mixture method (AGM) and a developed variational Bayesian model (VBM) inference through multikernel regression (MkR) are utilized to assist desirable precise prediction. In this model, the MkR processes the multiresolution solar energy signal, and then the AGM models the complex signals forecasting error. Finally, the proposed model can be optimized, and the concurrent output of the solar energy signal in both probabilistic and deterministic status can be attained through the introduction of the VBM. The solar energy output of an actual plant, including four measurement sites provided the data for the study. The results confirmed that the proposed model delivers higher prediction accuracy for both probabilistic and deterministic forecasts when compared with other well-known models.

Iterative Methods for Finding Roots of Nonlinear Equations

In the present work, two iterative techniques have been displayed in order to solve non-linear equation. These two techniques are free from second derivative and per evaluation; they require five and six iterations with a first derivative of the function. Analysis of the PV model has been studied in term of function evaluations appears that the proposed technique is superior. It is practical interest is investigated by some numerical examples.

An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters

The efficiency of PV systems can be improved by accurate estimation of PV parameters. Parameter estimation of PV cells and modules is a challenging task as it requires accurate operation of PV cells and modules followed by an optimization tool that estimates their associated parameters. Mostly, population-based optimization tools are utilized for PV parameter estimation problems due to their computational intelligent behavior. However, most of them suffer from premature convergence problems, high computational burden, and often fall into local optimum solution. To mitigate these limitations, this paper presents an improved variant of particle swarm optimization (PSO) aiming to reduce shortcomings offered by conventional PSO for estimation of PV parameters. PSO is improved by introducing two strategies to control inertia weight and acceleration coefficients. At first, a sine chaotic inertia weight strategy is employed to attain an appropriate balance between local and global search. Afterward, a tangent chaotic strategy is utilized to guide acceleration coefficients in search of an optimal solution. The proposed algorithm is utilized to estimate the parameters of the PWP201 PV module, RTC France solar cell, and a JKM330P-72 PV module-based practical system. The obtained results indicate that the proposed technique avoids premature convergence and local optima stagnation of conventional PSO. Moreover, a comparison of obtained results with techniques available in the literature proves that the proposed methodology is an efficient, effective, and optimal tool to estimate PV modules and cells’ parameters.

The role of batteries in meeting the PV terawatt challenge

<h2>Summary</h2> What role have batteries to play in the transition toward terawatt levels of photovoltaics? In this perspective, we attempt to answer this question by looking at technical, economic, and ecological features of PV-battery systems. We argue that the window of opportunity for batteries lies in the capacities of a few kWh/kW_P, the exact amount depending on various factors including battery cost, degradation rate, location, load profile, diversification of renewable energy sources, and interconnections. Using a simple PV plus battery model, we illustrate that such storage capacities efficiently reduce fluctuations in electricity generation, enabling higher PV adoption rates at competitive costs, and with a carbon footprint that is at least five times lower than that of the current energy mixes. Using sensible capacities, batteries are a powerful companion for solar energy, yet technical, economic, and policy innovations are needed to expand adoption. We see longer battery lifetimes and low capacity degradation rates are the most impactful technological parameters. Economic efforts should aim to reduce balance of plant costs and create better market opportunities for stationary storage, whereas policies should provide a strong regulatory framework to facilitate multipurpose usage and sector coupling.

Solder Bond Fatigue is Insensitive to Module Size

In this article, we explore the influence of module size on the rate of interconnecting solder bond thermomechanical fatigue (TMF) damage. Structural mechanics models of crystalline silicon PV models are created to solve with the finite-element method. Results conclusively demonstrate that the rate of solder bond TMF damage is independent of module size, interconnect location across the cell and cell location across the module.

Characteristic Testing Of Simulation-Based Photovoltaic Models

This article explains the evaluation of the characteristics of PV implemented in Simulink / MATLAB. The implanted PV model is a model of one diode with a series resistor, a model of one diode with a series and parallel resistor, and a model of Two Diodes with a series and parallel barrier. The results obtained by the PV model with two diodes with series and parallel series are the best because it has an I_mp and V_mp error that opposes the data sheet compared to other models. Increasing the temperature will cause an increase in I_sc, but cause a decrease in V_oc. While decreasing from standard temperature will cause a decrease in I_sc but causes an increase in V_oc. This condition is the opposite of solar radiation changes, whereas I_sc will decrease dramatically with decreasing solar radiation. The diode ideal factor for the PV performance characteristics will cause a maximum increase in power P_m. However, it does not affect the values of I_sc and V_oc. While the greater the value of R_s will cause a decrease in P_m, but it does not affect the value of I_sc and V_oc. This is inversely proportional to the effect of the value of R_sh, while an increase in the value of R_sh will cause an increase in P_m but does not affect the value of I_sc and V_oc.

TVACPSO-assisted analysis of the effects of temperature and irradiance on the PV module performances

The time-varying acceleration coefficients particle swarm optimization method is employed to extract the physical parameters of the single diode PV model. Under the condition of low radiation and high temperature, there is a slight deviation between the calculated and the published data, but this model still has good accuracy in most ranges. The time-varying mechanism is introduced to mitigate the premature convergence problem and establish an appropriate tradeoff between the explorative and exploitative capabilities. The method provides the lowest cost function in the extraction of parameters. Although the experimental conditions of the curves cannot fully represent the working environment of the photovoltaic module, the validity of the model can be verified by comparing the calculated curves with the released curves. Analytical solutions of short-circuit current, open-circuit voltage, maximum power point current and voltage are also derived by the Lambert W function. The variation of maximum power, fill factor, energy efficiency, exergy efficiency and power conversion efficiency of the multi-crystalline and thin-film modules in the range of 10–70 °C were studied. The electrical performance of PV modules is more sensitive to temperature than irradiance. The output performance of thin-film modules is better than crystalline modules at high temperature conditions.

The Thrombopoietin Receptor, MPL, Is a Therapeutic Target of Opportunity in the MPN.

The myeloproliferative neoplasms, polycythemia vera, essential thrombocytosis and primary myelofibrosis share driver mutations that either activate the thrombopoietin receptor, MPL, or indirectly activate it through mutations in the gene for JAK2, its cognate tyrosine kinase. Paradoxically, although the myeloproliferative neoplasms are classified as neoplasms because they are clonal hematopoietic stem cell disorders, the mutations affecting MPL or JAK2 are gain-of-function, resulting in increased production of normal erythrocytes, myeloid cells and platelets. Constitutive JAK2 activation provides the basis for the shared clinical features of the myeloproliferative neoplasms. A second molecular abnormality, impaired posttranslational processing of MPL is also shared by these disorders but has not received the recognition it deserves. This abnormality is important because MPL is the only hematopoietic growth factor receptor expressed in hematopoietic stem cells; because MPL is a proto-oncogene; because impaired MPL processing results in chronic elevation of plasma thrombopoietin, and since these diseases involve normal hematopoietic stem cells, they have proven resistant to therapies used in other myeloid neoplasms. We hypothesize that MPL offers a selective therapeutic target in the myeloproliferative neoplasms since impaired MPL processing is unique to the involved stem cells, while MPL is required for hematopoietic stem cell survival and quiescent in their bone marrow niches. In this review, we will discuss myeloproliferative neoplasm hematopoietic stem cell pathophysiology in the context of the behavior of MPL and its ligand thrombopoietin and the ability of thrombopoietin gene deletion to abrogate the disease phenotype in vivo in a JAK2 V617 transgenic mouse model of PV.