Photovoltaic System Research Articles

Risk assessment of photovoltaic system based on cumulant method and mixed copula

Risk assessment of photovoltaic system based on cumulant method and mixed copula

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios.

Solar Powered Electric Vehicle Charging Station With Integrated Battery Storage System

ABSTRACTThe shift towards electrical vehicles (EVs) can be an important alternative to internal combustion engines for sustainable energy solutions. However, increased EV adoption will increase the charging demand, and there will be a load on the grid electricity. Integrating solar photovoltaic systems with EV charging infrastructure will not only support environmental goals, but also ensure a more resilient and self‐sufficient energy system. A standalone PV system is a good option to reduce the stress on the grid for charging EVs. This present work pivots on the design and performance assessment of a solar photovoltaic system customized for an electric vehicle charging station in Bangalore, India. For this purpose, we have used the PVsyst software to design and optimize a standalone PV system with battery energy storage for EV charging stations. The result shows that 51.1 kWp PV system will be sufficient to meet the energy demand of the charging station by producing 98 313 kWh array energy. The proposed system showed a good average performance ratio of 68.90%. This study shows that the integration of standalone solar photovoltaic systems with EV charging stations is crucial in India and other countries to alleviate grid stress and promote sustainable energy use. This approach not only supports the transition to cleaner transportation but also enhances energy security and reduces dependency on fossil fuels.

A novel digital-twin approach based on transformer for photovoltaic power prediction

The prediction of photovoltaic (PV) system performance has been intensively studied as it plays an important role in the context of sustainability and renewable energy generation. In this paper, a digital twin (DT) model based on a domain-matched transformer is proposed using convolutional neural network (CNN) for domain-invariant feature extraction, transformer for PV performance prediction, and domain adaptation neural network (DANN) for domain adaptation. The effectiveness of the proposed framework is validated using a PV power prediction dataset. The results indicate an accuracy improvement of up to 39.99% in model performance. Additionally, experiments with varying numbers of timestamps demonstrate enhanced PV power prediction performance as parameters are continuously updated within the DT framework, offering a reliable solution for real-time and adaptive PV power forecasting.

The impact of quantum-sized nickel nanoparticles on TiO2 in photovoltaic and photocatalytic systems

The study examines the impact of the incorporation of quantum-sized nickel (Ni) nanoparticles in TiO2 (titanium dioxide) matrix at 1%, 3%, and 5% weight percentages by straightforward, easy, and potentially effective synthesis strategy of direct doping. The structural, morphological, optical, and electrical characterization studies of synthesized films are systematically done and the photovoltaic, photocatalytic applications are evaluated. The integration of nickel into TiO2 influences its photovoltaic properties by enhancing the open-circuit voltage (Voc). However, higher concentrations lead to increased recombination and defects, decreasing efficiency. On conducting photocatalytic studies, TiO2 doped with 1 wt. % nickel exhibits superior photocatalytic efficiency, surpassing that of undoped TiO2. This improvement in photovoltaic and photocatalytic performance is attributed to better charge separation and reduced recombination. However, optimizing nickel levels is crucial for maximizing benefits for the applications using the performed synthesis strategy.

Exploring new lead-free halide perovskites RbSnM3 (M = I, Br, Cl) and achieving power conversion efficiency > 32 %

Lead-free ABX3 inorganic perovskites, where A = Cs, Rb; BSn, Ge; and X = I, Br, Cl, have recently gained significant attention due to their remarkable optical, structural, and electronic properties, as well as their potential for solar cell applications. In this study, we thoroughly examined the optical, structural, and electronic properties of RbSnM3 (M = I, Br, Cl) perovskites through first-principles calculations and explored their application in a HTL-free solar cell structure using SCAPS-1D. Our analysis revealed that RbSnI3, RbSnBr3, and RbSnCl3 have direct band gaps of 0.828, 0.988, and 1.242 eV, respectively, using the HSE functional. The electron charge distribution indicates a strong ionic bond between Rb and the halides, as well as a significant covalent bond between Sn and the halides. Additionally, we calculated optical properties such as electron loss function, absorption coefficients, and the real and imaginary parts of the dielectric functions. We also explored the photovoltaic performance of RbSnM3 absorbers paired with a SnS2 ETL layer, investigating different thicknesses, defect densities, doping concentrations, and interface defect densities. The highest power conversion efficiencies (PCE) achieved were 26.38 %, 29.79 %, and 32.53 % for RbSnI3, RbSnBr3, and RbSnCl3 absorber layers, respectively, when paired with a SnS2 ETL. Overall, RbSnCl3 stands out as a highly promising absorber material for future photovoltaic devices, especially when combined with the SnS2 ETL layer.

Investigation of the Combination of Indoline and Naphthalimide in the Preparation of Photosensitizers for Photovoltaic Devices

Investigation of the Combination of Indoline and Naphthalimide in the Preparation of Photosensitizers for Photovoltaic Devices

Second‐Harmonic Ripple in Two‐Stage Single‐Phase Photovoltaic Inverters: Analysis and Mitigation

ABSTRACTTwo‐stage single‐phase photovoltaic inverters exhibit a second‐harmonic ripple at the dc‐link voltage, which can cause variations in the terminal voltage of the photovoltaic array, reducing the efficiency of the maximum power point tracking (MPPT). Initially, this work investigates the efficiency reduction caused by the second‐harmonic component using analytical models. Besides, this work explores a control technique to reduce the ripple in the terminals of the photovoltaic module based on real‐time normalization of the dc/dc converter control signal. This technique is compared with conventional control and with the addition of second‐harmonic resonant control. The case study is based on a 4.4‐kVA/220‐V photovoltaic inverter with input for two photovoltaic strings. The results indicate that both techniques are capable of performing the attenuation of the voltage ripple at the terminals of the photovoltaic array, resulting in efficiency gains in the order of 0.74% for the strategy based on resonant control and 0.95% for the proposed normalization technique. In addition, C‐HIL and experimental results prove the reduction in voltage ripple with the normalization and resonant control. As an advantage, the normalization technique is simpler than the second one, because it does not require modification of the control structure of the dc/dc converter.

Developing an energy storage systems selection methodology for peak shaving purposes in photovoltaic micro-installations

This study investigates the selection methodology for peak shaving purposes in photovoltaic micro-installations. Due to the increasing share of photovoltaic micro-installations in the national electricity production and the consequent changes in the country's energy policy, the need for energy storage is becoming increasingly apparent. Therefore, based on data collected throughout the study of a photovoltaic installation conducted in years 2020-2024 and data on market electricity prices, the correlation between production, market electricity prices, and energy value was analyzed. The analysis established interconnections between these values and identified factors influencing their changes. It was observed that the market price of electricity is relatively low during hours of increased photovoltaic energy production. By comparing the correlations between energy production and electricity prices over different years, an increasing impact of the increasing share of photovoltaic energy production in national electricity production on the market price of electricity was noted, highlighting the importance of energy storage. For the described household, an energy storage system was selected with a view to meeting the energy demand during hours of higher market electricity prices. The selected model was Lithium-Iron-Phosphate battery with a capacity of 5 kWh. Furthermore, the cost effectiveness of household-scale energy storage systems was examined. Implementing the proposed solutions could result in increased auto-consumption and presumably improve cost-effectiveness of photovoltaic installations in the future.

Cell Temperature Determination based on IEC61215: Solar Photovoltaic Experimental Study of Tropical Malaysia

Most commercial photovoltaic (PV) module data sheets include Nominal Operating Cell Temperature (NOCT) values, which assist PV system designers in estimating module temperatures under real outdoor conditions. However, the typical NOCT values of 45°C to 47°C do not account for tropical climates. This study seeks to develop an adjusted NOCT mathematical model and determine revised NOCT values suited for tropical conditions. The new proposed NOCT model follows the international standard IEC61215 but incorporates updated tropical Standard Reference Environment (SRE) parameters: solar irradiance (SI) of 800 W/m², ambient temperature (AT) of 31°C, and wind speed (WS) of 1 m/s. This modified NOCT model demonstrates improved accuracy over the existing model, with an average reduction of 2% in percentage error, root mean square error, and mean average percentage error. Outdoor NOCT testing conducted in Shah Alam, Malaysia, uncovered much higher NOCT values for various PV modules technology: 55°C for monocrystalline, 57°C for polycrystalline, and 59°C for thin film. These results emphasize the inadequacy of current NOCT values in commercial PV module data sheets, which do not accurately reflect conditions in tropical climates. The revised NOCT values from this study offer crucial thermal reference data for PV system designers, integrators, and researchers working in tropical regions, particularly Malaysia.

Environmental impacts of floating solar panels on freshwater systems and their techno-ecological synergies

Abstract Renewable energy sources, such as floating photovoltaic systems (FPVs), are crucial to mitigating the climate crisis. FPV deployments on freshwater bodies are rapidly growing, as they avert land-use change, operate with increased efficiency, and potentially improve water availability by reducing evaporation and the frequency of algal blooms. However, understanding of the ecological consequences for water bodies is very limited despite the variable and far-reaching range of potential impacts. Here, we bring novel insight by combining an established theoretical approach – techno-ecological synergies (TES) - with robust understanding of water body functioning, to direct sustainable FPV deployments. Specifically, we show the potential to integrate consideration of both energy and ecological systems for FPV deployment decisions based on current evidence and fundamental understanding of freshwater body states and processes. Based on 422 pieces of evidence from a systematic review of known physical (n=283), chemical (n=96) and biological (n=43) effects of FPV on freshwater systems, we outline eight potential TES that could be realised through FPV location, design, and operation decisions. There was most evidence (n=114) for the Water Use Efficiency TES, which all reported decreased evaporation rates, or increased water savings, due to panel shading. We highlight a lack of understanding of chemical and biological effects of FPV on hosting water bodies, as well as a need for comprehensive studies in which physical, chemical, and biological aspects of water body dynamics are integrated. Finally, we detail research priorities to ensure future FPV deployments bring benefit for both energy and water bodies. Ultimately, integrated energy and water body system knowledge, FPV deployments could mitigate both the climate and ecological emergencies, with notable benefits for society.

A novel acid-free combined technology to achieve the full recovery of crystalline silicon photovoltaic waste

To achieve the global carbon neutrality commitment, photovoltaics, a clean and renewable source of energy, is increasingly deployed. Photovoltaic panels are core components of photovoltaic systems. As these panels reach the end of their life, recovering the photovoltaic waste becomes crucial. Currently, strong acid reagents are commonly used in the recovery of silver from crystalline silicon photovoltaic waste, posing environmental risks and restricting the industrialization of their recycling. In this study, a novel acid-free technology to achieve the full recovery of crystalline silicon photovoltaic waste was proposed. A pyrolysis process was first conducted for decapsulation, with carbon dioxide being the main gas component at 60.64 %. Next, bioleaching technology was employed to leach silver from waste crystalline silicon photovoltaic cells. The silver leaching rate in a single leaching cycle reached 44.7 %. Meanwhile, the mechanism of silver leaching was further analyzed. Finally, high-velocity fluid frictional separation, a technique that uses high-speed fluid flow to separate material, was chosen to obtain silicon wafers. The reclaimed silicon samples had a total thickness variation of 6.64 µm to11.62 µm, with average carrier lifetimes exceeding 4.9 µs, higher than that obtained by wet etching. This study is expected to advance the industrialization of the recovery of photovoltaic waste, which is also beneficial for the sustainable development of the photovoltaic supply chain.

Comparative Analysis of Structural, Optical, and Electronic Properties of Nickel Oxide and Potassium‐Doped Nickel Oxide Nanocrystals

ABSTRACTMetal oxide semiconductors, known for their exceptional optical transparency, high carrier mobility, and stability, have found extensive use in emerging technologies such as optoelectronics and energy storage devices. Among all metal oxide semiconductors, nickel oxide (NiO) stands out as a highly favorable candidate due to its p‐type conductivity along with its substantial band gap (3.5–4 eV) for the broad range of applications, including gas sensors, high‐rate Lithium‐ion batteries, high‐performance supercapacitors, and photovoltaic devices. In light of these versatile applications, our current study presents a comprehensive comparative analysis of the structural and optoelectronic properties of NiO and potassium (K)‐doped NiO nanocrystals. The nanocrystals were synthesized using the co‐precipitation route and subsequently annealed at 500°C under ambient conditions. The effect of K doping on the structural and optoelectronic characteristics was systematically examined using various techniques, including x‐ray diffraction, UV–visible spectroscopy, Raman spectroscopy, and Hall effect measurements. To explore the structural characteristics, XRD measurements were performed, which confirm the FCC structure of nanocrystals. The optical property analysis suggested that the formation of the energy level can contribute to reduction of the band gap. A sharp peak at 397 cm−1 is associated with NiO bond in FTIR spectra which verifies the formation of nanocrystals. Moreover, the incorporation of K increases the intensity of the Raman peaks, which provides evidence for the higher degree of crystallinity in doped samples. These results of Raman scattering are in good agreement with XRD outcomes. In addition, the resistivity of NiO nanocrystals decreases monotonically with the increasing K concentration. The results of temperature‐dependent resistivity further demonstrate that electrons required more energy to jump from one polaron state to another in the case of x = 0.01 M and 0.03 M doped Ni0.5‐xKxO samples. The combination of a diminished band gap and enhanced conductivity makes these materials exceptionally promising for applications in optoelectronics and energy storage.

Quantum Marine Predator Algorithm: A Quantum Leap in Photovoltaic Efficiency Under Dynamic Conditions

The Quantum Marine Predator Algorithm (QMPA) presents a groundbreaking solution to the inherent limitations of conventional Maximum Power Point Tracking (MPPT) techniques in photovoltaic systems. These limitations, such as sluggish response times and inadequate adaptability to environmental fluctuations, are particularly pronounced in regions with challenging weather patterns like Sunderland. QMPA emerges as a formidable contender by seamlessly integrating the sophisticated hunting tactics of marine predators with the principles of quantum mechanics. This amalgamation not only enhances operational efficiency but also addresses the need for real-time adaptability. One of the most striking advantages of QMPA is its remarkable improvement in response time and adaptability. Compared to traditional MPPT methods, which often struggle to keep pace with rapidly changing environmental factors, QMPA demonstrates a significant reduction in response time, resulting in up to a 30% increase in efficiency under fluctuating irradiance conditions for a resistive load of 100 Ω. These findings are derived from extensive experimentation using NASA’s worldwide power prediction data. Through a detailed comparative analysis with existing MPPT methodologies, QMPA consistently outperforms its counterparts, exhibiting superior operational efficiency and stability across varying environmental scenarios. By substantiating its claims with concrete data and measurable improvements, this research transcends generic assertions and establishes QMPA as a tangible advancement in MPPT technology.

Nonlinear Control of Photovoltaic (PV) Solar-Powered Centrifugal Pump for Irrigation System: A Case Study of Fadak Farm in Karbala

Abstract In both the agricultural and industrial sectors, pumping water is essential. Due to its arid climate, our Saharan area has a substantial solar energy resource, making constructing a PV solar irrigation system possible. Given solar resources' unpredictable and intermittent nature, it is imperative to set up a system that permits optimal usage. This study aims to design and simulate a solar pumping system's effective nonlinear direct torque command. A solar generator provides an asynchronous three-phase machine coupled to a centrifugal pump as part of the system. MPPT monitors the boost converter via duty cycle. The solar generator's functioning at full power will be ensured. The first uses PSO in the MPPT system to supply the motor pump with three phases of power. The second one employs direct torque command (DTC) to regulate the operation of a centrifugal pump coupled with an induction machine. More advancements will be made to the DTC scenario. The proposed mathematical model of the solar irrigation system was simulated using a MATLAB simulation environment, adopting accurate data provided by Fadak Farm. The maximum power extracted from PV tends to peak at 280 Wp with the assistance of particle swarm optimization that energizes the use of the centrifugal pump for the proposed irrigation system. It is evident from numerical simulation results based on accurate input data that the power extracted from PV solar is positively affected by solar radiation and surface temperature variations. It is clear from simulation results that the speed of three–phase I.M motor converges to 22.5 rad/sec, and water flow pumping by centrifugal is decreased and increased, converging to 7.5 *10-8 m/s with the variation of solar irradiance.

Current Compensation for Faulted Grid-Connected PV Arrays Using a Modified Voltage-Fed Quasi-Z-Source Inverter

Large-scale photovoltaic (PV) systems are being widely deployed to meet global environmental goals and renewable energy targets. Advances in PV technology have driven investment in the electric sector. However, as the size of PV arrays grows, more obstacles and challenges emerge. The primary obstacles are the occurrence of direct current (DC) faults and shading in a large array of PV panels, where any malfunction in a single panel can have a detrimental impact on the overall output power of the entire series-connected PV string and therefore the PV array. Due to the abrupt and frequent fluctuations in power, beside the low-PV systems’ moment of inertia, various technical problems may arise at the point of common coupling (PCC) of grid-connected PV generations, such as frequency and voltage stability, power efficiency, voltage sag, harmonic distortion, and other power quality factors. The majority of the suggested solutions were deficient in several crucial transient operating features and cost feasibility; therefore, this paper introduces a novel power electronic DC–DC converter that seeks to mitigate these effects by compensating for the decrease in current on the DC side of the system. The suggested solution was derived from the dual-source voltage-fed quasi-Z-source inverter (VF-qZSI), where the PV generation power can be supported by an energy storage element. This paper also presents the system architecture and the corresponding power switching control. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate the feasibility and capability of the proposed scheme, which contributes in suppressing the peak of the transient power-to-time variation (dP/dt) by 72% and reducing its normalized root-mean-square error by about 38%, with an AC current total harmonic distortion (THD) of only 1.04%.

Efficient multi-objective optimization approach for solving optimal DG placement and sizing problem in distribution systems

Incorporating distributed generations (DGs) like wind turbines (WTs) and solar photovoltaic (SPV) arrays into radial distribution systems (RDSs) has become increasingly important due to their benefits in enhancing system performance. This requires determining appropriate DG locations and their optimal power output when injected into the RDS. To this end, this paper introduces a novel application of a recent multi-objective optimization technique called multi-objective grey wolf optimization (MOGWO) to allocate multiple DG units into the RDS optimally. The primary objectives are to minimize total real power losses (RPL), reduce voltage deviation (VD), and enhance the voltage stability index (VSI) across the entire system while adhering to operational constraints. Various DG operational power factor (PF) scenarios are considered, including unity-PF, fixed-PF, and optimal-PF. The proposed approach generates a Pareto-optimal set of solutions to address the multi-objective problem, after which a fuzzy decision-making method is adopted to select the best trade-off solution from the set. The technique has been implemented on 69-bus and the actual Portuguese 94-bus RDS. Numerical results are compared with other recent well-known algorithms from the literature, highlighting the efficacy of the suggested MOGWO method in handling multi-objective problems. Comparatively speaking, it provides superior solutions than other methods in selecting appropriate control parameters.

Off-grid photovoltaic-powered capacitive deionization for groundwater desalination in rural Africa

ABSTRACT In regions lacking centralized water supply and electricity grid connections, capacitive deionization (CDI) offers a promising approach with its modular and energy-efficient characteristics for low-salinity solutions. This research evaluates the feasibility of using a photovoltaic (PV)-powered CDI system as an off-grid, household-based water purification solution for groundwater desalination in rural Uganda. In this work, the CDI system was integrated with a PV system, comprising an ultrafiltration unit for pretreatment, a CDI stack for desalination, and a PV array for power supply. As evidenced by the performance analysis of the CDI stack in a single-pass mode, the system exhibited commendable desalination efficiency with a salt removal efficiency (>60%) and stable energy performance (0.110 ± 0.014 kWh/mol) observed under different influent conductivities (500–3,000 μS/cm). When applied to groundwater desalination, the CDI-based system successfully reduced the conductivity to meet Uganda's potable water standard (1,500 μS/cm), achieving a water recovery of 63%, and an energy consumption of less than 1.5 kWh/m3. Consequently, the CDI-based system effectively couples with the PV system to meet daily water needs solely through solar energy, demonstrating the practical implementation of innovative off-grid water desalination solutions in resource-constrained settings.

Assessment of Insulation Coordination and Overvoltage for Utility Girds Integrated with Solar Farms

Due to the economic and environmental concerns associated with fossil fuels, many government and private organizations are progressively shifting towards the integration of solar farms with Utility Grids. However, these systems are facing insulation failure issues due to internal and external transient overvoltage’s, in which their shape, magnitude, and duration are unpredictable, and consequently, the insulation stress also becomes unpredictable. To ensure the safety and integrity of the system against any transient overvoltage event, it is important to carry out an insulation coordination analysis. The primary goal of this research work is to achieve this optimization in an economically viable manner, ensuring both operational stability and cost-effectiveness in the design of electrical equipment like surge Arresters. The research work presented in the literature does not fully evaluate all International Electrotechnical Commission (IEC) overvoltage classes as specified in the insulation coordination standards for Utility Grids integrated with solar farms. Therefore, this research paper investigates the impact of various transient and switching overvoltage conditions, as defined in the IEC 60071.4 Insulation Coordination Standard at the Solar and Utility Grid Electrical power system using PSCAD 4.6/EMTP Software. Five distinct simulation scenarios were developed to assess the systems’ resilience against insulation stress events. The proposed system was also examined with and without the application of a lightning surge arrester.

Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study

The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability.