Photovoltaic System Research Articles

Warranty-driven reliability analysis: a stochastic model for continuous solar power supply system with two photovoltaic cells and an inverter

Warranty-driven reliability analysis: a stochastic model for continuous solar power supply system with two photovoltaic cells and an inverter

Maximizing hydrogen production through photovoltaic generator by using improved incremental conductance algorithm with proportional integral PI controller

Effectively storing energy for prolonged periods poses a primary challenge for renewable and innovative energy sources. This research focuses on two key objectives: first, converting photovoltaic (PV) voltage to the necessary level for electrolysis through a buck converter, and second, utilizing a maximum power point tracking (MPPT) method to optimize the solar generator's efficiency. The simulation of the solar-driven buck converter for the electrolysis load was carried out using MATLAB/Simulink, integrating an Incremental Conductance (INC) MPPT algorithm with a PI controller for system optimization. The simulation results reveal the stabilization of both the generated power from the PV system and the load voltage. Significantly, the proposed system achieves an efficiency surpassing 90% under high irradiance levels.

Apparent Intensity Dependence of Shunts in PV Modules Revision of the Shunt Parameterization in the De Soto Model and PVsyst

ABSTRACTIt is common practice in PV system simulation to use the De Soto model, which describes how to use the 1‐diode equivalent circuit model for modules. De Soto's model scales the shunt with irradiance, making it disappear toward zero W/m2. Also, the commercial software PVsyst uses a parameterization that reduces the shunt effect when the irradiance goes down. However, the solar cells that make up a module typically do not have an illumination‐dependent shunt. We therefore investigate the origin of the intensity‐dependent apparent shunt in modules. We show that this apparent shunt (derived from the slope of the quasi‐linear region from ISC onwards) is a misinterpretation for module I‐V curves and has little to do with a shunt conductance, although this slope method serves well for determining the shunt conductance of individual cells. Instead, the module I‐V curve slope of the quasi‐linear region from ISC onwards is strongly influenced by even small ISC mismatches between the cells. Such mismatch can occur from small illumination inhomogeneity even for A+ solar simulators in the laboratory, or from cell production variation. Abandoning the practice of using the I‐V curve slope to determine the shunt value for equivalent circuit models of modules (and the corresponding shunt scaling in the De Soto model or PVsyst) contributes to physically more meaningful I‐V curve parameterizations and bears the opportunity for further improved accuracy of PV system energy yield prediction.

Low capacitor stress reconfigurable quadratic boost converter with fault tolerant capability for rooftop solar PV application.

Recently, many high-gain topologies have been derived. However, there is a need for a high-gain converter with fault-tolerant features. In this paper, a fault-tolerant reconfigurable quadratic boost converter is proposed for DC microgrid application. In this novel topology, 2-level redundancy is achieved by addressing the fault in the switch and capacitors. The operation of the converter in normal operating conditions and reconfiguration mode is discussed. The derived topology can achieve the same voltage gain even in the reconfiguration mode. The proposed topology exhibits better performance in the reconfiguration state. The voltage stress across the input and output capacitor is reduced in that state. The reliability analysis of capacitors in both states is carried out and compared with the aid of the reliability handbook. Finally, the topology operation in a normal and reconfiguration state is validated by building a 1-kW hardware setup. The results show that the quadratic boost converter in a reconfiguration state operates without altering the voltage gain of the converter and with reduced voltage stress across the capacitors.

Feasibility of a 40kWp Grid-Connected Solar Power Plant in Tiaret, Algeria:

This study evaluates the technical and economic feasibility of a 40kWp grid-connected solar power plant in Tiaret, Algeria. Utilizing comprehensive solar irradiance data and advanced PV system software, we designed and simulated the plant's performance under local conditions. Our analysis incorporates smart grid integration strategies and economic modeling. Results indicate an annual electricity generation of approximately 68,000 kWh, with a levelized cost of energy (LCOE) of 0.12 USD/kWh and an estimated payback period of 5 years. The plant demonstrates a performance ratio of 0.759, reflecting its efficiency under real-world conditions. These findings suggest that grid-connected solar power plants are not only technically viable but also economically attractive in Algeria. The study provides critical insights for policymakers, investors, and engineers, offering a replicable model for assessing and implementing solar projects in similar emerging markets across North Africa and beyond.

Investigation of optical and thermoelectric characteristics of novel zintl-phase alloys CaZn2X2 (X = P, As, Sb) for green energy applications

Abstract This study examines the photovoltaic and thermoelectric response of calcium-based novel Zintl-phase alloys CaZn2X2 (X = P, As, Sb). The structural, optoelectronics, and transport features of Zintl CaZn2X2 (X = P, As, Sb) compounds have been analyzed using the full potential linearized augmented plane wave (FPLAPW) technique. Investigations on formation energy and phonon dispersion have confirmed the formation and dynamical stabilities. These compounds exhibit a semiconductor behavior, as their predicted bandgap values: 1.76 eV for CaZn2P2, 1.14 eV for CaZn2As2, and 0.32 eV for CaZn2Sb2. By investigating the optical properties, we have discovered their potential applicability in optoelectronic and photovoltaic devices, as evidenced by the optical response of these phases. The traditional Boltzmann transport theory has assessed transport characteristics against temperature and chemical potential. Significantly higher values of the Seebeck coefficient are achieved at room and elevated temperatures. Moreover, the power factor demonstrates a linear relationship with rising temperature. The remarkable optoelectronic properties and exceptional power factor values suggest that these materials are suitable for deployment in photovoltaic and transport devices.

Impact of Temperature and Shading on Performance of Solar Photovoltaic Systems in Telecom Sites

Nigeria's rural mobile network boom strains its power generation. Expensive and polluting diesel generators power off-grid base stations, prompting mobile network operators to explore solar PV systems. However, real-life performance under field conditions is a major concern, as manufacturer datasheets often fall short. This study highlighted the performance of a solar PV system powering a telecommunication station in Uli, Anambra State, Nigeria. We examined temperature, irradiance and shading effect. The findings highlight the impact of shading and temperature on energy yield and performance ratio (PR). While stand-alone and solar tracking systems had similar PRs (66 to 68 percent), tracking systems generated the most energy (4.53 energy yield) due to increased solar exposure. However, this benefit came with a trade-off; higher temperature losses (1559 kWh) caused by temperature rise from direct sunlight. Additionally, the nearby structures significantly impacted tracking systems by causing shading. In conclusion, well-designed solar PV systems for telecommunication stations must consider mitigating factors like shading and temperature. This can achieve substantial advantages: lower operational expenses for operators, improved network availability for rural users, and a reduced environmental impact, aligning with UN Sustainable Development Goals.

A hybrid algorithm for photovoltaic maximum power point tracking using Artificial Neural Network and Kinetic Gas Molecular Optimization

The quest for efficient photovoltaic (PV) energy conversion systems has led to the development of Maximum Power Point Tracking (MPPT) algorithms. In this paper, we propose a novel hybrid approach that combines the power of Artificial Neural Network (ANN) with the optimization capability of Kinetic Gas Molecular Optimization (KGMO) for MPPT. We present a high-level outline of the proposed algorithm along with example MATLAB code snippets for each step, highlighting its potential for improving PV system performance. This paper proposes a metaheuristic optimized multilayer feed‐forward artificial neural network (ANN) controller to extract the maximum power from available solar energy. Firstly, to improve the maximum power point (MPP) delivered by PV arrays and to overcome the drawbacks in the conventional MPPT method under irradiation variation, a hybrid MPPT controller is designed, in which the input parameters include the PV array voltage and current. The output parameter is the duty cycle of the DC/DC boost converter. The proposed approach abbreviated as ANN-KGMO MPPT controller is based on the Kinetic Gas Molecular Optimization (KGMO) Algorithm which is useful to train the developed ANN and to evolve the connection weights and biases to get the optimal values of duty cycle converter corresponding to the MPP of a PV array. Finally, the performance of the proposed control system is confirmed by simulation tests on a 2 kW PV system. In addition, the performance of an ANN-KGMO -based MPPT controller is also compared to the conventional perturb and observe (P&O) method. To analyze the results, simulations are performed by using MATLAB software.

An MPPT method using phasor particle swarm optimization for PV‐based generation system under varying irradiance conditions

AbstractThe dependency of photovoltaic (PV) systems‐based generation systems on constantly varying temperatures and incident sunlight affect the non‐linear behaviour of PV panels. Therefore, tracking the maximum power output of the PV panels for efficient utilization becomes a challenge, resulting in power losses and the creation of intense heating spots in the shaded areas of the PV modules when the PV modules receive varying degrees of insolation, that is, under partial shading conditions (PSCs). The inclusion of bypass diodes in parallel to each PV module mitigates this problem to an extent but leads to the formation of several peaks in the P‐V characteristics. As a result, maximum power point tracking (MPPT) to deliver maximum power at the load becomes a limitation for conventional optimization algorithms as they are normally based on hill climbing algorithm and get stuck at local maxima of the P‐V curve. Therefore, metaheuristic algorithms are used thereby eliminating the possibility of getting trapped at the local optima. However, particle swarm optimization (PSO) suffers from delayed convergence, more iterations to reach the optimal point, and random parameter selection. Hence, this study employs an improved version of PSO called Phasor‐PSO (P‐PSO) in an MPPT controller. The proposed algorithm is parameter‐less which results in reduced computational complexity and thus provides quick decision in achieving the maximum power point (MPP). The hardware‐in‐loop real‐time analysis demonstrates the supremacy of P‐PSO over PSO in faster convergence, higher efficiency, and reduced power losses during tracking under various PSCs. The P‐PSO based MPPT method will find application in grid connected and stand‐alone solar PV system with better efficiency of power transfer.

Minimizing Voltage Deficit in Perovskite Indoor Photovoltaics by Interfacial Engineering.

Metal halide perovskites with bandgap of ≈1.8eV are competitive candidates for indoor photovoltaic (IPV) devices, owing to their superior photovoltaic properties and ideal absorption spectra matched to most indoor light sources. However, these perovskite IPVs suffer from severe trap induced non-radiative recombination, resulting in large open-circuit voltage (VOC) losses, particularly under low light intensity. Herein, an effective approach is developed to minimizing trap density by modifying the buried interface of perovskite layer with bifunctional molecular 2-(4-Fluorophenyl)ethylamine Hydrobromide (F-PEABr). The benzene ring of F-PEABr molecules can firmly anchor at the hole transporting layer by π-π stacking interaction, and the other ends can passivate the defects on the buried interface of perovskite layer. Based on that, the F-PEABr modified perovskite IPVs achieved power conversion efficiency (PCE) of 42.3% with a remarkable VOC of 1.13V under 1000 lux illumination from a 4000K LED lamp. Finally, perovskite IPV mini-modules with area of 10.40cm2 are demonstrated with a PCE of 35.2%. This interface modification strategy paves the way for crafting high-performance perovskite IPVs, holding great potential for self-powered internet of things applications.

Electrical characteristics of photovoltaic (PV) solar panel and hybrid PV/thermal (PV/T) solar panel

Solar collectors and photovoltaic (PV) solar panels can convert solar radiation into heat and electrical energy. A hybrid PV/thermal (PV/T) solar panel was tested in this study. The hybrid PV/T solar panel was tested using a spiral absorber and water flow levels range from 0.01 kg/s to 0.03 kg/s. As a result, with variable water flow rates and irradiation intensity of 700 and 900 W/m2, the efficiency of hybrid PV/T solar panels varied. The highest electrical efficiency attained was 4.06% in hybrid PV/T solar panels at 900 W/m2 with 0.025 kg/s of mass flow while 3.65% by PV solar panels. In addition, plotted current-voltage and power-voltage curves were used to show the electrical properties of the solar PV panels and hybrid PV/T solar panels.

Performance evaluation of agrivoltaic system for the synergy among greengram (Vigna radiata L. Wilczek) production and solar electric power generation

AbstractThe concept of agrivoltaic, combining agriculture and solar photovoltaic system, is ideal for populous countries like India as it provides access to eco‐friendly power and crop production from the same land. The main objective of the study was to evaluate the performance of agrivoltaic systems for different geometry of solar photovoltaic strings and their suitability for agricultural practices for the greengram (Vigna radiata L. Wilczek) crop under North‐Gujarat agro‐climatic conditions of India. Eight equal‐capacity strings with different geometry were designed to evaluate power generation and crop production beneath the strings. The experiment involved eight strings taken as eight treatments and the traditional system of greengram (V. radiata L. Wilczek) cultivation practices is taken as the ninth treatment. The greengram (V. radiata L. Wilczek) was shown and all other cultural practices was kept same upto grain yields. The results revealed that 10.5 feet height string with continuous solar panel pattern) provided the highest gross income (Rs. 24,364.00) from power generation and greengram (V. radiata L. Wilczek) yield. In terms of net realization, a 6.0‐feet string with continuous solar panel pattern provided the highest net return of Rs. 12,417.00, as the capital cost was less for the system. Treatment‐5, which involved transparent panels, was found to be better for the photosynthesis process of the greengram (V. radiata L. Wilczek) crop, as it provided the highest yield (12.99 kg) under the agrivoltaic system.

A single switch high step-up DC-DC converter derived from coupled inductor and switched capacitor for a photovoltaic system.

This study suggests a single switch high step-up DC-DC Converter derived from coupled inductor and switched capacitor used in Grid-Connected Photovoltaic systems. In the suggested converter, a part of the yield voltage is produced by parallel charging of the capacitors on the side of the secondary winding and their series discharge together with the secondary winding. The other part of the output voltage is obtained by simultaneously charging the boost and buck-boost capacitors and discharging them in series with the input voltage source. Finally, these two parts make the output voltage in series, so that high voltage gain with proper duty cycle is obtained. In addition, boost and buck-boost capacitors absorb leakage inductance energy leading to a reduction of the switch voltage stress. As a result, switching losses are reduced and by choosing a switch with lower RDS(ON), conduction losses are also reduced. Finally, a prototype with an output power of 200 Watts, and a voltage conversion of 24-400 Volts was made to confirm the performance in the laboratory, which has the highest efficiency of 95.89%.

A new MPPT mechanism based on multi-verse optimization algorithm tuned FLC for photovoltaic systems.

Tracking the Maximum Power Point (MPP) in solar Photovoltaic (PV) systems is a difficult task under changing environmental weather conditions. Furthermore, the tracking method gets more complex under changing irradiance conditions because of the nonlinearity in power voltage characteristics. The Maximum Power Point Tracking Technique (MPPT) under changeable irradiance and temperature-varying conditions is presented in this study. Using a swarm algorithm known as Multi-Verse Optimization (MVO), this technique combines a Fuzzy Logic Controller (FLC). In this work, the MVO algorithm is used to generate the optimized scaling factors of FLC to track the accurate MPP. A MATLAB/Simulink environment is used to model the PV system and the proposed MPPT method. The simulated system was assessed under uniform, abruptly changing, ramp with abruptly changing irradiance and temperature variation circumstances. The results demonstrate that the proposed approach is efficient in tracking the MPP and has a quick response time when compared to FLC-based MPPT and conventional P&O.

Research on photovoltaic MPPT under complex conditions based on an advanced great wall construction algorithm method.

To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. We introduce the LGWGCA-P&O method, which synergistically combines an modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique. The LGWGCA is refined with a positional update mechanism inspired by the Grey Wolf Optimization (GWO) algorithm, optimizing the distribution of solution agents, while a Levy flight strategy is employed to reduce excessive randomness during agent replacement and recombination, thereby accelerating the tracking process. As the algorithm approaches the maximum power point, it seamlessly transitions to the P&O method, leveraging its rapid convergence to ensure precise and efficient power point identification. Experimental results demonstrate that the LGWGCA-P&O method achieves a PV conversion efficiency exceeding 99.96%, significantly minimizing power losses during MPPT. Furthermore, the method enhances convergence speed by approximately 40% compared to traditional GWO-P&O approaches, and it consistently outperforms Particle Swarm Optimization and Cuckoo Search Optimization algorithms under extreme conditions, demonstrating superior computational efficiency and robustness.

Unveiling the Potential of Ultraviolet Fluorescence Imaging as a Versatile Inspection Tool: Insights from Extensive Photovoltaic Module Inspections in Multi‐MWp Photovoltaic Power Stations

UV fluorescence imaging (UVF) has the potential to grow into a powerful, informative, and economically attractive inspection method for photovoltaic (PV) power stations. UVF demonstrates the ability to indicate differences in polymer types and degradation states in backsheets and encapsulation materials of PV modules. The data acquisition rate of UVF measurements is 10 to 15 times higher than that achieved by state‐of‐the‐art near‐infrared spectroscopy, enabling the mapping of the bill of materials (BoMs) and degradation state for every module in large PV arrays. Combinations of UVF with vibrational spectroscopies allow the polymer BoMs to be recognized and correlated with aging phenomena such as metal corrosion or potential induced degradation. UVF imaging can also be used for conventional visualization of nonmaterial‐related anomalies, such as hot cells or cell cracks. The low purchase cost of the equipment makes UVF an affordable and high‐throughput diagnostic method yielding comprehensive information that would otherwise only be accessible by combining several slower and more laborious methods. Automated UVF image analysis and refinements of correlations between different BoMs and UVF pattern geometry can further unlock the potential of UVF as a straightforward tool accessible to all stakeholders and promote proactive strategies for the optimization of the reliability and performance of PV power stations.

How Sophisticated Are Neural Networks Needed to Predict Long-Term Nonadiabatic Dynamics?

Nonadiabatic dynamics is key for understanding solar energy conversion and photochemical processes in condensed phases. This often involves the non-Markovian dynamics of the reduced density matrix in open quantum systems, where knowledge of the system's prior states is necessary to predict its future behavior. In this study, we explore time-series machine learning methods for predicting long-time nonadiabatic dynamics based on short-time input data, comparing these methods with the physics-based transfer tensor method (TTM). To understand the impact of memory time on these approaches, we demonstrate that non-Markovian dynamics can be represented as a linear map within the Nakajima-Zwanzig generalized quantum master equation framework. We further propose a practical method to estimate the effective memory time, within a given tolerance, for reduced density matrix propagation. Our predictive models are applied to various physical systems, including spin-boson models, multistate harmonic (MSH) models with Ohmic spectral densities and for a realistic organic photovoltaic system composed of a carotenoid-porphyrin-fullerene triad dissolved in tetrahydrofuran. Results indicate that the simple linear-mapping fully connected neural network (FCN) outperforms the more complicated nonlinear-mapping networks including the gated recurrent unit (GRU) and the convolutional neural network/long short-term memory (CNN-LSTM) in systems with short memory times, such as spin-boson and MSH models. Conversely, the nonlinear CNN-LSTM and GRU models yield higher accuracy in the triad MSH systems characterized by long memory times. These findings offer valuable insights into the role of effective memory time in non-Markovian quantum dynamics, providing practical guidance for the application of time-series machine learning models to complex chemical systems.

A Rooftop Solar Photovoltaic Tree Solution for Small-Scale Industries

With the increase in population and the growing demands of industrialization, carbon emissions across the globe are increasing exponentially. Furthermore, the demand for clean energy from renewable sources (solar, wind, etc.) is growing at an unparalleled rate to fight against the climate change caused by these increased carbon emissions. However, at present, it is very difficult for small-scale industries in urban areas to install solar power systems due to constraints around the operation area and on rooftops. Therefore, these small-scale industries are not able to install any solar plants and, thus, are not able to reduce their carbon emissions. In the context of this problem regarding the generation of cleaner energy and reducing carbon emissions by small-scale industries in urban areas, a model of a rooftop solar photovoltaic tree (SPVT) has been proposed that may be considered by small-scale industries in the place of a conventional rooftop solar photovoltaic (SPV) system. It is also noted that various models of SPVT systems are commercially available on the market, each with their own unique features. However, no new SPVT model has been designed or provided in this paper, which simply presents simulation studies comparing a conventional rooftop SPV system and an SPVT system. The results show that a 9.12 kWp SPVT system can be installed in just 6 Sq.mt, while a 3.8 kWp conventional SPV system requires 40 Sq.mt of rooftop area. Consequently, an SPVT generates around 128% more electricity than a conventional SPV, leading to greater reductions in carbon emissions. Thus, the objective of this study is to identify the most suitable option for small-scale industries in densely populated urban areas to generate electricity and maximize carbon emission reduction.

Bioinspired Antireflective and Antifogging Surface for Highly Efficient and Stable Inverted Solar Cells.

Photovoltaic devices are essentially solar energy collectors that convert incident photons into charge carriers. However, light reflection losses and external factors (e.g., fog) can lead to an inefficient utilization of incident photons. Therefore, the development of antifogging surface materials that can efficiently reduce reflection is a critical issue in the upgradation of photovoltaic devices. Herein, inspired by the wing scale structures of butterfly Trogonoptera brookiana, an antireflective and antifogging surface (BRFS) was prepared by a method combining biotemplate and sol-gel. Remarkably, the BRFS possesses a relatively large surface roughness and exhibits superhydrophilic property (static water contact angle of 0°), which can quickly split fog droplet film within 6.6 s to realize the antifogging effect. In addition, the final transmittance of BRFS is as high as 90.25%. Furthermore, as an application demonstration, BRFS was applied to the surface of the reverse organic solar cells. Without compromising the inherent performance of the panels, the BRFS enhances the electrical performance of the inverted solar panels by 18%. This work provides a simple and effective strategy for designing surfaces with superior antifogging and antireflective properties and offers significant potential value for the practical application of photoelectric devices.

A butterfly shape acceptor with rigid skeleton and unique assembly enables both efficient organic photovoltaics and high-speed organic photodetectors

Abstract It remains challenging to design efficient bifunctional semiconductor materials in organic photovoltaic and photodetector devices. Here, we report a butterfly-shaped molecule, named WD-6, which exhibits low energy disorder and small reorganization energy due to its enhanced molecular rigidity and unique assembly with strong intermolecular interaction. The binary photovoltaic device based on PM6:WD-6 achieved an efficiency of 18.41%. Notably, an efficiency of 19.42% was achieved for the ternary device based PM6:BTP-eC9:WD-6. Moreover, the photodetection device based on WD-6 demonstrated an ultrafast response speed (205 ns response time at λ of 820 nm) and a high cutoff frequency of -3 dB (2.45 MHz), surpassing the values of most commercial Si photodiodes. Based on these findings, we show-cased an application of the WD-6-based photodetection device in high-speed optical communication. These results offer valuable insights into the design of organic semiconductor materials capable of simultaneously exhibiting high photovoltaic and photo detective performance.