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Simultaneous effects of using cellulose nanocellulose and chemical pulp in paper money were studied with bottom combers cotton pulp. The bottom combers pulp and chemical pulp were prepared in a factory that produced durable paper, and their products were transferred to the laboratory. The nanocellulose was prepared by Nano Novin Polymer Co. and was consumed at 4 levels, i.e., 0%, 0.3%, 0.6%, and 0.9%. After commixing the pulp with the nanocellulose at the identified percentages, handsheets of 90 g∙m-2 papers were produced. The results showed that by increasing the amount of nanocellulose, up to 0.9%, the tensile strength index, burst strength index, tear strength index, and folding endurance increased 22.7%, 38.9%, 7.7%, and 348%, respectively, when compared with the control sample. Enhancement via nanocellulose up to 0.9% increased the surface smoothness up to 7.9% compared with the control sample, while the air resistance and Cobb decreased 69% and 7.9% in comparison with control sample, respectively. By increasing the nanocellulose content up to 0.9% the opacity increased 0.7%; moreover, the brightness and whitening decreased 3.5% and 10.6, respectively. The scanning electron microscopy results indicated that enhancement via nanocellulose decreased the air resistance. [ABSTRACT FROM AUTHOR]

The building sector contributes to 40% of the total final energy consumption and 36% of CO2 emissions in Europe, and these are set to increase in the coming years. International directives are pushing towards a decarbonisation roadmap to improve the quality of cities and the health of citizens. Buildings have a potentially central role in terms of energy transition as a means to produce and save energy. Photovoltaic shading devices (PVSDs) protect buildings from direct solar radiation and overheating while producing renewable electricity onsite and increasing the users' thermal comfort. Even though the potential of the PVSD is considerable, the sector is still unexplored, and few studies on the topic are available in the literature. This systematic review aims to present an exhaustive overview of the current literature on state-of-the-art PVSDs by analysing the scientific framework in terms of the status of the research. It presents a performance-based approach focusing on innovative products, PVSD design strategies, and energetic performance in distinct climate conditions and configurations. In particular, 75 articles and about 250 keywords were identified, selected, and analysed. The literature review serves as a basis for further R&D activities led by both the industrial and the academic sectors. [ABSTRACT FROM AUTHOR]

The development of low‐cost, green, and pollution‐free counter electrode materials with high catalytic activity plays a critical role in improving the photovoltaic performance of dye‐sensitized solar cells (DSSCs). In recent years, transition metal phosphides have been widely used in DSSCs due to their outstanding catalytic activity and stability. Herein, a novel binary phosphide is immobilized on carbon paper (CP) by a two‐step strategy. This strategy involves the preparation of WO3 precursor by the hydrothermal method, and synthesis of tungsten phosphide (WP) with the vapor deposition method, which finally leads to the uniform dispersion of WP on carbon paper. The acquired WP/CP counter electrode demonstrates high electrical conductivity and prefect catalytic ability for reducing triiodide, and the DSSCs assembled with WP/CP counter electrode achieve a high‐power conversion efficiency of 10.29%, which is superior to that of the Pt‐based (7.34%). These findings illustrate that the WP microsheets in‐situ grown on carbon paper are a potential candidate to replace Pt as an economical and efficient counter electrode for DSSCs. [ABSTRACT FROM AUTHOR]

With the increased awareness of environmental protection and the establishment of the spot market of electric power, renewable energy plays a more and more important role in energy structure adjustment. But the intermittence and instability of renewable energy often result in energy waste. Micro-grid energy storage can effectively improve the utilization rate, economy and reliability of renewable energy. To ensure the economical operation of the micro-grid system and improve the load curve, a two-stage optimal dispatch model was established based on non-dominated sorting genetic algorithm (NSGA-II) and deep Q-value reinforcement learning algorithm (DQN). In the first stage, to get the appropriate battery capacity, the minimum comprehensive operation cost and the minimum capacity fading of battery in the cycle life were selected as the optimization objectives. The optimal solution was calculated by using the improved non-dominated sorting genetic algorithm (NSGA-II). In the second stage, the DQN Algorithm was used to propose a scheduling strategy. Compared with the GA method, the DQN method was able to reduce the operating cost from 0.5829% to 1.2294%. The results showed that the method was feasible and effective. [ABSTRACT FROM AUTHOR]

In this paper, we physically modeled passivated ultrathin Cu (In1−xGax) Se2 solar cells with different bandgap grading configurations. Firstly, we have designed the cell architecture according to the fabricated model. The novelty in this work is the modeling of passivated u-CIGS solar cells with different bandgap grading profile configurations in order to achieve high efficiency with a thickness of 500 nm. A significant influence on device performance has been observed while changing absorber doping density, electron affinity, and operating temperature (range of 10–70 °C) for the investigated samples. ZnS has been used as a buffer layer to replace the conventional CdS material in order to improve cell efficiency. The impact of the buffer doping density and electron affinity on u-CIGS cell performance is explored. The simulation results show that a high bandgap at the front and rear sides with an acceptor density of 2 × 1016 provide the best electrical cell parameters: Jsc of 31.53 mA/cm2, Voc of 742.78 mV, FF of 77.50%, η of 18.15%. Our findings can be considered guidelines for new single and/or tandem cell optimization to achieve high efficiency. [ABSTRACT FROM AUTHOR]

The fabrication of heterostructures via solution process is one of the essential technologies for realizing efficient advanced-generation optoelectronics. Hybrid structures comprising colloidal quantum dots (CQD) and organic semiconducting molecules are garnering considerable research interest because of their complementing optical and electrical properties. However, blending both the materials and forming a stable electronic ink are a challenge owing to the solubility mismatch. Herein, a CQD:organic ternary-blended hybrid solar ink is devised, and efficient hybrid solar cells are demonstrated via single-step spin coating under ambient conditions. Specifically, the passivation of the benzoic acid ligand on the CQD surface enables the dissolution in low-polar solvent such as chlorobenzene, which yields a stable CQD:organic hybrid ink. The hybrid ink facilitates the formation of favorable thin-film morphologies and, consequently, improves the charge extraction efficiency of the solar cells. The resulting hybrid solar cells exhibit a power conversion efficiency of 15.24% that is the highest performance among all existing air-processed CQD:organic hybrid solar cells. [ABSTRACT FROM AUTHOR]

Electrochemical conversion of CO2 into formate is a promising strategy for mitigating the energy and environmental crisis, but simultaneously achieving high selectivity and activity of electrocatalysts remains challenging. Here, we report low-dimensional SnO2 quantum dots chemically coupled with ultrathin Ti3C2Tx MXene nanosheets (SnO2/ MXene) that boost the CO2 conversion. The coupling structure is well visualized and verified by high-resolution electron tomography together with nanoscale scanning transmission X-ray microscopy and ptychography imaging. The catalyst achieves a large partial current density of 257.8 mA cm-2 and high Faradaic efficiency of 94% for formate formation. Additionally, the SnO2/MXene cathode shows excellent Zn–CO2 battery performance, with a maximum power density of 4.28 mW cm-2, an open-circuit voltage of 0.83 V, and superior rechargeability of 60 h. In situ X-ray absorption spectroscopy analysis and first-principles calculations reveal that this remarkable performance is attributed to the unique and stable structure of the SnO2/MXene, which can significantly reduce the reaction energy of CO2 hydrogenation to formate by increasing the surface coverage of adsorbed hydrogen. [ABSTRACT FROM AUTHOR]

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions. [ABSTRACT FROM AUTHOR]

The transition process from fossil fuels to environmentally friendly renewable energy sources carries the risk of creating new environmental damages. Photovoltaic technology represents one of the alternatives with the least risk of harmful environmental impact. However, this technology has two important drawbacks: the significant land occupation for the installation of PV systems and the uncontrollability of production. By constructing floating photovoltaic plants on hydroelectric reservoirs, both of these problems can be reduced to an acceptable level. Some artificial reservoirs, originally built for hydroelectric power plants, have acquired a significant secondary function as recreational areas and fish breeding sites. Therefore, there is justified resistance from the local community to change the existing appearance and purpose of such reservoirs. This paper proposes a completely new concept of integrating the interests of the local community into such objects. In addition to preserving existing uses, the concept also offers new features. This can make the entire system environmentally friendly and sustainable. This paper details the technology behind the construction of floating photovoltaic power plants on artificial reservoirs and emphasizes their various advantages. These benefits include the non-utilization of cultivable land, the ease of assembly and construction, integration into existing power grids, and the potential to address electricity storage issues. For instance, Buško Lake, covering an area of 55.8 km2, may host 2.93 km2 of installed floating photovoltaic (FPV) facilities, enabling a total installed capacity of 240 MW. With an average of 5.5 h of daily sunshine, this totals 2007 annual hours, equivalent to a 55 MW thermal power plant. An analysis showed that, with losses of 18.2%, the average annual production stands at 302 G W h , translating to an annual production value of 18 million € at 60 €/MWh. The integration of this production into an existing hydroelectric power plant featuring an artificial reservoir might boost its output by 91%. The available transmission line capacity of 237 MW is shared between the hydroelectric power plant (HPP) and FPV; hence during the FPV maximum power generation time, the HPP halts its production. HPP Orlovac operates a small number of hours annually at full capacity (1489 h); therefore in combination with the FPV, this number can be increased to 2852 h. This integration maintains the lake's functions in tourism and fishing while expanding its capabilities without environmental harm. [ABSTRACT FROM AUTHOR]

To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. Firstly, the edge image features of hot spots were extracted based on residual neural networks. Secondly, by combining the feature pyramid structure, an edge-guided feature pyramid structure was designed, and the hot spot edge features were injected into a Mask R-CNN network. Thirdly, an infrared spatial attention module was introduced into the Mask R-CNN network when feature extraction and the infrared features of the detected hot spots were enhanced. Fourthly, the size ratio of the candidate frames was adjusted self-adaptively according to the structural characteristics of the aspect ratio of the hot spots. Finally, the validation experiments were conducted, and the results demonstrated that the hot spot contours of thermal infrared images were enhanced through the algorithm proposed in this paper, and the segmentation accuracy was significantly improved. [ABSTRACT FROM AUTHOR]

Photovoltaic systems (PV) have been extensively used worldwide as a reliable and effective renewable energy resource due to their environmental and economic merits. However, PV systems are prone to several environmental and weather conditions that impact their performance. Amongst these conditions is dust accumulation, which has a significant adversative impact on the solar cells' performance, especially in hot and arid regions. This study provides a comprehensive review of 278 articles focused on the impact of dust on PV panels' performance along with other associated environmental factors, such as temperature, humidity, and wind speed. The review highlights the importance of modelling dust accumulation along with other ecological factors due to their interactive nature, and the differences between cleaning techniques and schedules effectiveness. Moreover, the study provides a review of statistical and artificial intelligence models used to predict PV performance and its prediction accuracies in terms of data size and complexity. Finally, the study draws attention to several research gaps that warrant further investigation. Among these gaps is the need for proper dynamic optimisation models for cleaning schedules and a more advanced machine and deep learning models to predict dust accumulation while considering environmental and ageing factors. [ABSTRACT FROM AUTHOR]

Due to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution. [ABSTRACT FROM AUTHOR]

To overcome the issues of the existing properties and the non-configurable supervisory control and data acquisition (SCADA) architecture, this paper proposes an IoT-centered open-source SCADA system for monitoring photovoltaic (PV) systems. The system consists of three voltage sensors and three current sensors for data accumulation from the PV panel, the battery, and the load. As a part of the system design, a relay is used that controls the load remotely. An ESP32-E microcontroller transmits the collected data to a Banana Pi M4 Berry (BPI-M4 Berry) through the Message Queuing Telemetry Transport (MQTT) protocol over a privately established communication channel using Wi-Fi. The ESP32-E is configured as the MQTT publisher and the BPI-M4 Berry serves as the MQTT broker. Locally installed on the BPI-M4 Berry, the Node-RED platform creates highly customizable dashboards as human–machine interfaces (HMIs) to achieve real-time monitoring of the PV system. The proposed system was successfully tested to collect the PV system voltage/current/power data and to control the load in a supervisory way under a laboratory setup. The complete SCADA architecture details and test results for the PV system data during the total eclipse on 8 April 2024 and another day are presented in this paper. [ABSTRACT FROM AUTHOR]

The optics is the component that most affects the concentrating photovoltaic (CPV) system performance, depending above all on the concentration factor and optical efficiency. Hence, a basic aspect is the concentrated solar flux measure on the receiving area, the evaluation of which is principally realized by indirect measurement methods. First, a literature review on indirect and direct methods used for the evaluation of concentrated solar flux and optical parameters is presented in this paper. The experimental measurement procedure, which is able to evaluate the optical parameters and concentrated solar flux in CPV systems, is also presented. The main steps of this procedure are represented by experimental system setup, sensor selection for concentrated solar flux estimation, identification of all the factors affecting optical performances, and development of an experimental campaign and output analysis. In particular, the optical characterization results of a CPV system are obtained by means of in-depth experimental analysis using Triple-Junction (TJ) solar cells with areas of 5.5 × 5.5 mm2 and 10 × 10 mm2. Three different setups have been analyzed related to primary and secondary optics composition. The main aim of this paper is the determination of a direct measuring technique, rarely adopted in literature in comparison to the established techniques, that is able to evaluate experimentally the optical parameter values and that can be standardized for other CPV systems. In particular, equations that link the optical concentration factor (C) and efficiency (ηopt) with focal distance (h) represent the fundamental results. They can be used for similar point-focus configurations presenting the same TJ cell size and ranges of C, ηopt and h. Finally, the experimental results of the direct method are compared with those of an indirect method adopting the same CPV system and operational conditions. [ABSTRACT FROM AUTHOR]

Considering the negative consequences of the excessive use of non-renewable energy and the development of technologies related to photovoltaic energy, the present paper aims to analyze if the photovoltaic systems are economically viable for university hospitals. A photovoltaic system was designed in the parking lot of the University Hospital of the Federal University of São Carlos (UFSCar) and analyzed the financial viability of its installation. As a result, the photovoltaic system is financially viable, with an expected generation of 194.2 MWh in the first year and a payback of 7 years. Thus, this paper contributes to the feasibility of photovoltaic projects in university hospitals, reducing electric energy consumption, reducing its operational costs, reducing the emission of pollution, and diversification of the Brazilian energy matrix. Furthermore, the results can be used as a scientific basis for other fields, such as public and private hospitals and clinics. [ABSTRACT FROM AUTHOR]

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the "hot spot" phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values. [ABSTRACT FROM AUTHOR]

The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the used 3LNPC-SAPF topology, aims to ensure the seamless integration of a photovoltaic system into the three-phase four-wire grid while effectively mitigating grid harmonics, grid current unbalance, ensuring grid unit power factor by compensating the load reactive power, and allowing power sharing with the grid in case of an excess of generated power from the PV system, leading to overall high power quality at the grid side. This developed approach is based initially on the application of the four-wire instantaneous p-q theory for the identification of the reference currents that have to be injected by the 3LNPC-SAPF in the grid point of common coupling (PCC). Whereas, the 3LNPC is controlled based on using the finite control set model predictive control (FCS-MPC), which can be accomplished by determining the convenient set of switch states leading to the voltage vector, which is the most suitable to ensure the minimization of the selected cost function. Furthermore, the used topology requires a constant DC-link voltage and balanced split-capacitor voltages at the input side of the 3LNPN. Hence, the cost function is adjusted by the addition of another term with a selected weighting factor related to these voltages to ensure their precise control following the required reference values. However, due to the random changes in solar irradiance and, furthermore, to ensure efficient operation of the proposed topology, the PV system is connected to the 3LNPN-SAPF via a DC/DC boost converter to ensure the stability of the 3LNPN input voltage within the reference value, which is achieved in this paper based on the use of the maximum power point tracking (MPPT) technique. For the validation of the proposed control technique and the functionality of the used topology, a set of simulations has been presented and investigated in this paper following different irradiance profile scenarios such as a constant irradiance profile and a variables irradiance profile where the main aim is to prove the effectiveness and flexibility of the proposed approach under variable irradiance conditions. The obtained results based on the simulations carried out in this study demonstrate that the proposed control approach with the used topology under different loads such as linear, non-linear, and unbalanced can effectively reduce the harmonics, eliminating the unbalance in the currents and compensating for the reactive component contained in the grid side. The obtained results prove also that the proposed control ensures a consistent flow of power based on the sharing principle between the grid and the PV system as well as enabling the efficient satisfaction of the load demand. It can be said that the proposal presented in this paper has been proven to have many dominant features such as the ability to accurately estimate the power sharing between the grid and the PV system for ensuring the harmonics elimination, the reactive power compensation, and the elimination of the neutral current based on the zero-sequence component compensation, even under variable irradiance conditions. This feature makes the used topology and the developed control a valuable tool for power quality improvement and grid stability enhancement with low cost and under clean energy. [ABSTRACT FROM AUTHOR]

Microgrids have increased in popularity thanks to both the integration of renewable energy resources and their energy distribution capability for remote locations. Moreover, the microgrids, mainly using multiple generators connected in parallel, acquire additional advantages by using both Hot-Swap and Current-Sharing techniques. This paper presents the development of a Hardware in the Loop platform to test Current-Sharing algorithms. It is reinforced that the use of a real-time simulation based on Hardware in the Loop is a viable and cost-effective alternative in the validation of controllers. The platform was developed in a graphical programming environment (LabVIEW 2015) and implemented with NI MyRIO 1900 (National Instruments Corp., Austin, TX, USA) development boards for easier reproducibility. The entire code project is openly available and provided in this paper. A system of photovoltaic energy generators was used to evaluate the performance of the HIL platform. As a result, the platform was able to reproduce a similar behavior to the photovoltaic generator, presenting average mean errors of 0.4 V and 0.2 A in its voltage and current, respectively. Additionally, the platform showed its capability to test Current-Sharing algorithms in the occurrence of Hot-Swap events. This work contributes with a validation tool for energy management systems applied to microgrids. [ABSTRACT FROM AUTHOR]

This paper presents a comparative study between traditional and intelligent Maximum Power Point Tracking (MPPT) algorithms for Photovoltaic (PV) powered DC Shunt Motors. Given the nonlinearity of PV systems, they require nonstandard approaches to harness their full potential. Each PV module has a unique maximum power point on its IV curve due to its nonlinear characteristic nature. Power electronic converters are utilized to enable operation at that point. There are many different algorithms described in the introduction, each with its have their own advantages and drawbacks. Recognizing the potential enhancement of PV system efficiency through effective Maximum Power Point (MPP) tracking, this paper evaluates five MPPT methods under varying DC loads. The five algorithms will be as follows: Incremental Conductance and Perturb and Observe as traditional algorithms. Fuzzy Logic Control, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference Systems as Intelligent Algorithms. While traditional algorithms generally produced acceptable results except for Perturb & Observe, intelligent algorithms performed well under rapidly changing solar radiation conditions. Due to inadequate data, intelligent algorithms relying on data training struggled to track the maximum power point when the temperature changed due to inadequate data used for the training. The analysis focuses on the time required by each method to reach peak power under different load conditions, solar irradiance, and temperature variations. The advantages and disadvantages of each MPPT with a shunt DC motor are detailed in the comparative study. [ABSTRACT FROM AUTHOR]

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region's abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season. [ABSTRACT FROM AUTHOR]

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a Symmetric High Voltage-Gain (SHVG) boost converter control for photovoltaic system applications. The concept is based on a multilevel boost converter configuration, which presents an advantage compared to a classic boost converter such as the ability to transfer a high amount of power with less stress on the power electronics components in the high voltage-gain conditions. This advantage allows the power losses in the converter to be reduced. A mathematical-based voltage model of the PV system using variable series resistance depending on solar irradiance and the temperature is proposed. This model is connected to an SHVG boost converter to supply the load's power. A control strategy of the DC-bus voltage with maximum power point tracking (MPPT) from the PV system using PI controllers is developed. The contributions of the paper are focused on the SHVG operating analysis with the passive components' sizing, and the DC-bus voltage control with maximum power point tracking of the PV systems in dynamic operating conditions. The performances of the proposed control are evaluated through simulations, where the results are interesting for high-power photovoltaic applications. [ABSTRACT FROM AUTHOR]

This paper aims to determine the environmental benefits associated with the implementation of a solar PV system to complement the conventional Electricité Du Liban/genset electrical system for the delivery of energy to an industrial facility. The selected case study is a juice manufacturing industrial facility located in Lebanon. Its energy demand is 6000 MWh/year. The components of the PV/hybrid energy system are the following: one 420 modules PV generator, seven three-phase grid-dependent inverters, three 1000 kW generators and one 500 kW generator. The ecoinvent database is used to model the life cycle inventory, and the IMPACT 2002+ methodology is used for the life cycle impact assessment. Results show at the assembly level that the manufacturing of the photovoltaic modules, mounting system components, inverters and cables results in harmful emissions that increase the system's environmental impacts. However, a comparative analysis between the entire life cycle of the PV/hybrid system and the conventional system indicates that the incorporation of the photovoltaic system results in a reduction in environmental impacts across all categories. The end-of-life scenario comprises landfilling/incineration and recycling of the components. Results show that the end-of-life stage poses a minor contribution to the overall life cycle impacts. [ABSTRACT FROM AUTHOR]

This paper demonstrates an enhancement of power quality for a photovoltaic (PV) system connected to the grid with a hybrid energy storage system (HESS). The proposed system utilizes a ramp-rate control (RRC) strategy to limit severe fluctuations in the PV power output. Battery storage is integrated to store surplus energy generated by the PV system and is used for continuous power application. A high-power density device, known as a supercapacitor (SC), is employed to mitigate transient fluctuations in the battery. The proposed system facilitates smooth PV power generation, stabilizes the DC bus voltage (VDC), and eliminates source current harmonics induced by non-linear loads. The Shunt Active Power Filter (SAPF) discussed in this paper serves two primary purposes. Firstly, it acts as a reactive power buffer, smoothing out fluctuations and reducing current harmonic distortions. Secondly, it enables active power injection into the grid, utilizing a specific renewable solar PV source. The efficiency of the modeled compensation system is demonstrated by the sinusoidal shape of the current and the compensation of reactive power (RPC). The targeted system showcases the effectiveness of the current setup by exhibiting low total harmonic distortion (THD). The multifunctional features of the proposed system were implemented using the MATLAB/Simulink software, and the results were validated using an OP5700 Hardware-in-the-Loop (HIL) test bench. This integration of distributed power generation capabilities not only enhances the overall power quality but also improves the efficient utilization of renewable energy resources (RESs). [ABSTRACT FROM AUTHOR]

The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes a method integrating K-means clustering: an improved snake optimization algorithm with a convolutional neural network–bidirectional long short-term memory network to predict short-term photovoltaic power. Firstly, K-means clustering is utilized to categorize weather scenarios into three categories: sunny, cloudy, and rainy. The Pearson correlation coefficient method is then utilized to determine the inputs of the model. Secondly, the snake optimization algorithm is improved by introducing Tent chaotic mapping, lens imaging backward learning, and an optimal individual adaptive perturbation strategy to enhance its optimization ability. Then, the multi-strategy improved snake optimization algorithm is employed to optimize the parameters of the convolutional neural network–bidirectional long short-term memory network model, thereby augmenting the predictive precision of the model. Finally, the model established in this paper is utilized to forecast photovoltaic power in diverse weather scenarios. The simulation findings indicate that the regression coefficients of this method can reach 0.99216, 0.95772, and 0.93163 on sunny, cloudy, and rainy days, which has better prediction precision and adaptability under various weather conditions. [ABSTRACT FROM AUTHOR]

To fully explore the potential low-carbon and economic advantages of a virtual power plant (VPP) that aggregates multiple distributed resources, the paper proposes a VPP scheduling model that considers the carbon emission flow (CEF) and demand response (DR), which is characterized by electro-carbon coupling and source-load interaction. First, the electric-carbon characteristics of each distributed resource under VPP are modeled, and the source-load electric-carbon coupling characteristic model is modeled through the CEF theory. On this basis, a load-side multi-type DR model is established to achieve the purpose of source-load synergy to reduce carbon emissions from VPP. To this end, a two-stage scheduling model of VPP considering the source- load electro-carbon coupling relationship is established, and the implementation of the model can reduce power generation costs, carbon emissions and promote clean energy, and the simulation results of the improved IEEE-14 node system verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Current electricity sectors will be unable to keep up with commercial and residential customers' increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1. [ABSTRACT FROM AUTHOR]

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the "teaching" process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity. [ABSTRACT FROM AUTHOR]

DC collection and transmission is one of the major development directions of large‐scale photovoltaic (PV) power system. In order to achieve low‐cost, high‐efficiency and long‐distance transmission of PV power, this paper adopted a DC grid‐connected topology by using multi‐modular cascaded DC‐DC converters, forming an input‐independent and output‐series (IIOS) system. In this system, an LLC type DC‐DC converter based on IGCT and magnetically integrated transformer was proposed in this paper, which can significantly increase the capacity, efficiency and power density of DC‐DC converter. And the equalization unit based on half‐bridge topology was adopted for the case of uneven irradiation of the PV inputs. In addition, this paper proposed an equalization control method based on the Nearest Level Modulation (NLM), which greatly simplifies the design of the acquisition and control system. Furthermore, the parametric design of a 50MW PV system was presented, and the simulation was carried out to verify the feasibility and advantages of the system in this paper. The proposed IIOS system is more suitable for high‐capacity, long‐distance PV power transmission, which can serve as a reference for the development of large‐scale photovoltaic systems. [ABSTRACT FROM AUTHOR]

The presence of decentralized production in modern electrical systems precludes traditional automatic generation control (AGC) strategies. Renewable energy production is highly dependent on the environment; for example, photovoltaic (PV) system, but its power generation is very unstable, which makes power grids containing PV systems face greater challenges. In this paper, we propose a PV-grid control method based on self-gain system compensation to reduce the negative effects of large fluctuations and uncertainties in solar energy production. First, the performance characteristics of solar modules are discussed and a multi-region model with a system with high penetration of solar systems is built. The AGC power is then displayed with different power variations according to different maximum power point tracking methods to analyze the uncertainty and variation of the PV output power and its effects on the AGC. We have also developed and implemented a compensation unit to eliminate the negative effects of PV power on AGC. Finally, the proposed method was demonstrated using the load from the example step as a disturbance variable to obtain a dynamic model of a dual-zone switched grid AGC with two PV systems based on a DMPC-distributed model. As a result of the simulation, the proposed method in this paper can guarantee the excellent properties of the dynamic behavior of the AGC system with large variations and high uncertainty of the PV system, and the validity of the proposed method have also been confirmed. [ABSTRACT FROM AUTHOR]

Perovskite solar cells (PSCs) have gained much attention in recent years because of their improved energy conversion efficiency, simple fabrication process, low processing temperature, flexibility, light weight, and low cost of constituent materials when compared with their counterpart silicon based solar cells. Besides, stability and toxicity of PSCs and low power conversion efficiency have been an obstacle towards commercialization of PSCs which has attracted intense research attention. In this research paper, a Glass/Cu2O/CH3NH3SnI3/ZnO/Al inverted device structure which is made of cheap inorganic materials, n-type transparent conducting oxide (TCO)-free, stable, photoexcited toxic-free perovskite have been carefully designed, simulated and optimized using a one-dimensional solar cell capacitance simulator (SCAPS-1D) software. The effects of layers' thickness, perovskite's doping concentration and back contact electrodes have been investigated, and the optimized structure produced an open circuit voltage (Voc) of 1.0867 V, short circuit current density (JSC) of 33.4942 mA/cm2, fill factor (FF) of 82.88% and power conversion efficiency (PCE) of 30.17%. This paper presents a model that is first of its kind where the highest PCE performance and eco-friendly n-type TCO-free inverted CH3NH3SnI3 based perovskite solar cell is achieved using all-inorganic transport materials. [ABSTRACT FROM AUTHOR]

The building envelope is the interface between the external atmospheric conditions and the indoor environment. It contributes to approximately 60%-70% of the heating and cooling load. The building envelope constitutes walls, fenestrations, and roofs from which thermal transfer from the roof is significantly very high compared to walls in low-rise buildings. This research paper aims to provide recommendations for energy-efficient roof design considering roof form, material, and external coating suitable for various climate conditions of India. The paper's methodology consists of a literature review of related research papers, learnings from the roofing system of energy-efficient building case studies, and all possible energy-efficient roofing systems that would be simulated for energy efficiency and thermal comfort in five climate types of India. The expected outcome of this paper would be in the form of recommendations for roofing systems in different climate type and their potential for energy saving from the highest to the lowest. This research would be done in three stages viz, Step-1: Literature Review and case study of best practices: A review of the related literature and best practices examples would be done to formulate the maximum possible cases for the simulation. Step-2: The roofing systems, including building form, material (Thermal and insulation), and finishes (reflectance), would be simulated one by one for various climate types of India (composite, hot & dry, warm and humid, moderate and cold) to optimize energy efficiency and payback period. Step-3: Special roof types like Green roofs, cool roofs, Photovoltaic roofs, roof ponds, and the like are simulated and compared for their effectiveness in selected climate types. Based on the literature review, simulation results, and analysis, the recommendations are framed for energy-efficient roofing systems in the selected climate type with respect to heat transfer through the roof and thermal comfort. [ABSTRACT FROM AUTHOR]

Microgrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive exploration of an integrated Buck-Boost converter and Sliding Mode Control (SMC) Maximum Power Point Tracking (MPPT) system for optimizing photovoltaic energy conversion. The study focuses on enhancing solar energy extraction efficiency, regulating output currents, and ensuring effective battery utilization. Through a systematic analysis of converter component sizing and operational modes, the paper delves into the intricacies of the Buck-Boost converter. The unique contribution lies in the innovative integration of SMC with the traditional Perturb and Observe (P&O) algorithm, providing robust and adaptive MPPT under varying environmental conditions. Additionally, the paper introduces a battery management system with three distinct modes, namely, Charging, Direct, and Discharging, offering intelligent control over critical scenarios. Simulation results underscore the robustness of the proposed system under diverse conditions, demonstrating its effectiveness in managing power distribution based on battery charge levels, even in scenarios of insufficient solar power. Overall, this research significantly contributes to advancing the understanding of PV/battery systems and offers a practical, sustainable solution for optimizing energy production, distribution, and storage, marking a substantial stride towards a more efficient and sustainable energy future. [ABSTRACT FROM AUTHOR]

The increasing integration of microgrids into distribution networks has highlighted the significance of evaluating and managing intelligent microgrids from both technical and economic perspectives. In this paper, a decentralized approach using agents is employed to optimize the operation of an intelligent microgrid within the telecommunications platform. The decentralized control method comprises two layers. The first layer represents the main microgrid, which includes loads and their controllers, as well as renewable and conventional resources. In the secondary layer, there is a telecommunication platform in which agents can operate as a control processor along with the means of communication. It should be noted that agents interact with the primary layer and neighbouring agents and exchange information with each other. This exchange takes place until the best state of optimization for the power supply occurs. In this study, the operation cost is calculated for decentralized control rules and considering telecommunication links. Also, the effect of performance on cost reduction is examined and compared with normal conditions and centralized methods. It can be seen that the operation cost of the network has decreased to 9.034% after the implementation of the mentioned method in comparison with the normal condition and it has decreased to 6.957% in comparison with the centralized method. Then, using a demand side management program, the cost will be reduced by 2.5%. In the next step, the uncertainty of available resources is taken into account where the uncertainties increase the cost by 7.8%. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach for implementing the maximum power point that could be generated from a photovoltaic (PV) panel while eliminating the need for current sensors through the application of the Hill Climbing algorithm is proposed. The active power generated by the PV panel is injected into the grid via a three-phase inverter using voltage-oriented voltage control with Spatial Vector Modulation (SVM). The developed strategy ensures minimal ripples for both active and reactive power and produces a sinusoidal alternating current waveform, even under varying lighting conditions. A comprehensive description of the adopted control strategy is provided and validated through numerical simulations conducted in MATLAB/Simulink environment. Furthermore, the performance of the proposed method is assessed by analyzing the simulation results. In an attempt to validate the effectiveness of the proposed approach, an implementation of the inverter control was conducted with the DSpace 1104 board, and the results underscored the feasibility and effectiveness of the employed approach for grid-connected PV systems. [ABSTRACT FROM AUTHOR]

Forecasting the generation of solar power plants (SPPs) requires taking into account meteorological parameters that influence the difference between the solar irradiance at the top of the atmosphere calculated with high accuracy and the solar irradiance at the tilted plane of the solar panel on the Earth's surface. One of the key factors is cloudiness, which can be presented not only as a percentage of the sky area covered by clouds but also many additional parameters, such as the type of clouds, the distribution of clouds across atmospheric layers, and their height. The use of machine learning algorithms to forecast the generation of solar power plants requires retrospective data over a long period and formalising the features; however, retrospective data with detailed information about cloudiness are normally recorded in the natural language format. This paper proposes an algorithm for processing such records to convert them into a binary feature vector. Experiments conducted on data from a real solar power plant showed that this algorithm increases the accuracy of short-term solar irradiance forecasts by 5–15%, depending on the quality metric used. At the same time, adding features makes the model less transparent to the user, which is a significant drawback from the point of view of explainable artificial intelligence. Therefore, the paper uses an additive explanation algorithm based on the Shapley vector to interpret the model's output. It is shown that this approach allows the machine learning model to explain why it generates a particular forecast, which will provide a greater level of trust in intelligent information systems in the power industry. [ABSTRACT FROM AUTHOR]

In the current source photovoltaic grid-connected system, to prevent the DC-link inductor from incurring an opening circuit fault, it is necessary to include the overlap time in the switching signals. However, current error and serious harmonic distortion in the inverter-side and grid-side currents are generated, which will cause additional losses and reduce the power quality of the grid, so it is important to compensate for the current error caused by the overlap time. In this paper, the relationship between the nonlinear current errors caused by the overlap time and the AC-side voltage is analyzed. Then, the mathematical expression of the low-order harmonics with losses caused by the overlap time is derived. On this basis, a current error compensation method with a discrete filter of AC-side voltage is proposed. Finally, a simulation and experiment are carried out to verify the correctness and effectiveness of the theoretical analysis and compensation scheme presented in this paper. With an overlap time of 3 μs, the THD of the grid-side current decreases from 5.93% to 1.59% after compensation. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant increase in the integration of distributed energy resources (DERs) such as small-scale photovoltaic systems and wind turbines in power distribution systems. When the aggregated outputs of DERs are combined, excessive reverse current may occur in distribution lines, leading to overvoltage issues and exceeding thermal limits of the distribution lines. To address these issues, it is necessary to limit the output of DERs to a certain level, which results in constraining the hosting capacity of DERs in the distribution system. In this paper, coordination control methodologies of DERs are developed and executed to mitigate the overvoltage and overcurrent induced by DERs, thereby increasing the hosting capacity for DERs of the distribution system. This paper proposes three coordinated approaches of active and reactive power control of DERs, namely Var Precedence, Watt Precedence, and Integrated Watt and Var Control. The Var and Watt Precedence prioritizes reactive power for voltage (Q–V) and active power for current (P–I) to address network congestion, thereby enhancing hosting capacity. Conversely, the Integrated Var and Watt Precedence propose a novel algorithm that combines four control indices (Q–V, P–V, Q–I, and P–I) to solve network problems while maximizing hosting capacity. The three proposed methods are based on the sensitivity analysis of voltage and current to the active and reactive power outputs at the DER installation locations on the distribution lines, aiming to minimize DER active power curtailment. Each sensitivity is derived from linearized power equations at the operating points of the distribution system. To minimize the computation burden of iterative computation, each proposed method decouples active and reactive power and proceeds with sequential control in its own unique way, iteratively determining the precise output control of distributed power sources to reduce linearization errors. The three proposed algorithms are verified via case studies, evaluating their performance compared to conventional approaches. The case studies exhibit superior control effectiveness of the proposed DER power control methods compared to conventional methods when issues such as overvoltage and overcurrent occur simultaneously in the distribution line so that the DER hosting capacity of the system can be improved. [ABSTRACT FROM AUTHOR]

This study thoroughly compares multiple single bidirectional and multiport converters (MPCs), highlighting the significant role of MPCs in multi-input and multi-output (MIMO) systems. MPCs offer a more efficient and cost-effective solution than multiple single converters, especially in applications involving photovoltaic (PV), electric vehicles (EVs) with storage systems, and power grids. This research emphasizes the importance of multi-input converters (MICs) in integrating diverse voltage sources. It notes the rising popularity of multi-output DC-DC converters in portable electronics, owing to their reduced component count, lower costs, and compact design. This paper emphasizes comparisons based on diverse aspects and applications, shedding light on recent developments in basic bidirectional converters. Additionally, it delves into the advancements in MPC topologies, focusing on efficiency, reliability, and modularity improvements. These advancements are crucial for harnessing cost reduction, simplicity, and compactness. Furthermore, this paper introduces an innovative multiport DC-DC converter tailored for integrating and managing renewable sources. This new converter design enhances PV system and battery storage performance by reducing power conversion steps, using fewer components, and improving voltage-boosting capabilities. Its unique bidirectional buck-boost structure allows for versatile connections between sources and loads with varying voltage and power requirements. The performance of this novel converter is evaluated through MATLAB/Simulink simulations under different scenarios. Experimental studies further validate its effectiveness, marking a significant contribution to power conversion and management in integrating renewable sources such as DC microgrids. [ABSTRACT FROM AUTHOR]

In searching for the optimal control strategy of sub-module capacitance voltage equalization of Modular Multilevel Converter (MMC), the most important issue is how to address the significant increase in computing time of the controller, switching frequency of the sub-module, device switching loss and MMC fault rate led by the increase in the number of sub-modules of the upper/lower bridge arm of the MMC. Therefore, this paper proposes a control strategy based on the retaining factor method + improved quick sort, namely, an improved quick sort algorithm is used to sort the sub-module capacitance voltages to reduce the amount of computation of the MMC controller; at the same time, a retaining factor is introduced to participate in the improved quick sort to significantly reduce the switching frequency of the sub-module. MMC of large ship grid-connected photovoltaic power system is taken as a research object in this paper, a 31-level MMC model is built in Matlab/Simulink. Simulation results verify that the improved quick sort has a good rapidity compared to the algorithms of bubble sort, quick sort, bidirectional bubble sort, and improved merge sort, that the retention factor method has a good frequency reduction effect, and that the proposed retention factor + improved quick sort can provide a good performance compared to improved quick sort and retention factor + improved merge sort under conditions of the normal operation and introducing disturbance. [ABSTRACT FROM AUTHOR]

Solar panels are an environmentally friendly alternative to fossil fuels; however, their useful life is limited to approximately 25 years, after which they become a waste management issue. Proper management and recycling of end-of-life (EOL) solar panels are paramount. It protects the environment because of the high energy consumption of silicon production. We can effectively decrease energy and cost requirements by recovering silicon from recycled solar panels. This is one-third of those needed for manufacturing silicon directly. Moreover, solar panels include heavy metals, such as lead, tin, and cadmium, which pose risks to human health and the environment. Empirical evidence suggests that the costs of mining materials can exceed those of recycled materials, thereby making recycling a more cost-effective means of resource harvesting. This review paper focuses on the techniques developed to delaminate solar panels, which are considered a crucial step in the recycling of EOL solar panels. Initially, various classifications of solar panels are given. Subsequently, an analysis of the diverse methods of solar panel delamination and their efficacy in the retrieval of valued materials is presented. This investigation has identified three primary modes of delamination, namely mechanical, thermal, and chemical. Among these, mechanical delamination is deemed to be a sustainable and cost-effective option when compared to thermal and chemical delamination. The current most popular method of thermal delamination is characterized by its high energy consumption and potential emission, and the chemical delamination generates hazardous liquids that pose their own threat to the environment. This study emphasizes the mechanical delamination techniques, characterized by their environmentally friendly nature, minimal ecological footprint, and capacity to retrieve entire glass panels intact. This paper also discusses the current gaps and potential enhancements for mechanical delamination techniques. For example, some delamination techniques result in crushed materials. Thus, the handling and recovery of materials such as glass and silicon cells require the implementation of an appropriate sorting technique. Also, the value obtained from recovering crushed materials is lower than that of intact glass and silicon cells. [ABSTRACT FROM AUTHOR]

The utilization of accurate models is crucial in the various stages of development for photovoltaic (PV) systems. Modelling these systems effectively allows developers to assess new modifications prior to the manufacturing phase, resulting in cost and time savings. This research paper presents a viable approach to accurately estimate both static and dynamic PV models. The proposed estimation method relies on a novel and enhanced optimization algorithm called leader artificial ecosystem‐based optimization (LAEO), which improves upon the original artificial ecosystem‐based optimization (AEO). The proposed LAEO algorithm integrates the adaptive probability (AP) and leader‐based mutation‐selection strategies to enhance the search capability, improve the balance between exploration and exploitation, and overcome local optima. To evaluate the effectiveness of LAEO, it was tested on 23 different benchmark functions. Additionally, LAEO was applied to estimate the parameters of static three‐diode PV models, as well as integral‐order and fractional‐order dynamic models. This paper showcases practical implementations of photovoltaic (PV) parameter estimation in various scenarios, including the static three‐diode model, dynamic integral order model (IOM), and fractional order model (FOM). The results were assessed from various angles to examine the precision, performance, and stability of the LAEO algorithm. [ABSTRACT FROM AUTHOR]

With the escalating urgency for sustainable energy alternatives, solar power in urban landscapes has gained prominence. Building‐integrated photovoltaic (BIPV) systems are pivotal in this shift, blending efficient energy generation with architectural aesthetics. This review casts a spotlight on BIPV technologies, with a special emphasis on the less‐explored semitransparent photovoltaics (PVs). These systems are not only energy generators but also natural light facilitators, setting them apart from their opaque PV counterparts. Advancements in both transparent and opaque PV, such as crystalline silicon, are discussed. However, the paper prioritizes semitransparent PV's unique benefits, including harmonious integration with building design and simultaneous energy and daylight management. An exhaustive examination of current literature and developments sketches BIPV's trajectory, highlighting applications, challenges, and technological strides. The narrative extols the aesthetic, financial, and efficiency merits of BIPV, particularly the semitransparent variants' blend of functionality and design. Performance optimization of BIPV systems is scrutinized across various environments and architectural styles. This meticulous discussion seeks to clarify the status quo, emerging research avenues, and future prospects of BIPV in fostering a greener energy transition. The review compares BIPV configurations with traditional solar PV systems, charting a path for enhanced energy production, cost efficiency, and aesthetic integration, with semitransparent PV as a key player. By exploring the interplay between architectural design, energy efficiency, and urban planning, this paper aims to solidify the groundwork for future research, reinforcing the sustainability narrative in urban energy infrastructure. [ABSTRACT FROM AUTHOR]

In photovoltaic grid-connected systems, the interaction between grid-connected inverters and the grid may cause harmonic oscillation, which severely affects the normal operation of the system. To improve the quality of the output electrical energy, photovoltaic grid-connected systems often use LCL filters as output filters to filter out high-frequency harmonics. Taking the three-phase LCL-type photovoltaic grid-connected inverter system as an example, this paper addresses the issue of harmonic resonance. Firstly, based on the harmonic linearization method and considering the impact of the coupling compensation term on the grid-side voltage, a modular positive and negative sequence impedance modeling method is proposed, which simplifies the secondary modeling process of the converter under feedback control. Then, the stability analysis is conducted using the Nyquist criterion, revealing the mechanism of high-frequency resonance in photovoltaic grid-connected systems. Furthermore, this paper delves into the impact of changes in system parameters on impedance characteristics and system stability. The results indicate that the proportional coefficient of the internal loop current controller has a significant influence on system impedance characteristics. Additionally, this paper proposes an active damping design method that combines lead correction and capacitor current feedback to impedance-reconstruct the easily oscillating frequency band. Finally, the effectiveness of this method is verified in the simulation platform. Simulation results confirm the effectiveness of this method in suppressing harmonic resonance while maintaining rapid dynamic response. [ABSTRACT FROM AUTHOR]

The evolution of photovoltaic cells is intrinsically linked to advancements in the materials from which they are fabricated. This review paper provides an in-depth analysis of the latest developments in silicon-based, organic, and perovskite solar cells, which are at the forefront of photovoltaic research. We scrutinize the unique characteristics, advantages, and limitations of each material class, emphasizing their contributions to efficiency, stability, and commercial viability. Silicon-based cells are explored for their enduring relevance and recent innovations in crystalline structures. Organic photovoltaic cells are examined for their flexibility and potential for low-cost production, while perovskites are highlighted for their remarkable efficiency gains and ease of fabrication. The paper also addresses the challenges of material stability, scalability, and environmental impact, offering a balanced perspective on the current state and future potential of these material technologies. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification. [ABSTRACT FROM AUTHOR]

At present, electrified railroads with complex road conditions are facing the problems of the existence of a single power supply method, the deterioration in power quality, and the difficulty in recycling regenerative braking energy. In order to improve the above problems, this paper establishes a traction power supply system containing photovoltaic units, proposes an optimized compensation model for the RPC (railroad power conditioner), and improves the particle swarm algorithm for solving the reference value of RPC compensation power. First, the structure of the RPC-based traction photovoltaic power generation system and the establishment of the integrated energy management strategy of the system are constructed. Then, the back-to-back converter compensation model with power quality index parameter constraints is established, which establishes the optimization function with the objectives of minimizing the negative sequence current, maximizing the power factor, and minimizing the RPC compensation power, as well as establishes constraints on the active converter capacity and three-phase voltage imbalance. Then, the particle swarm algorithm for solving the RPC compensation power reference value is improved, specifically in the original PSO by introducing dynamically changing inertia weights and learning factors. This not only solves the problem of the single power supply and realizes the nearby consumption of photovoltaic units in the traction system, but also realizes the recycling of regenerative braking energy and the coordinated control of the traction photovoltaic power generation system, improves the power quality of the system, and meets the demand of the RPC for real-time control. In order to verify the effectiveness of the optimized compensation model established in this paper and improve the convergence of the particle swarm algorithm for solving the RPC compensation power reference value, a simulation model of the traction PV power generation system is established in MATLAB/Simulink, and a real-time simulation is carried out to verify it based on the preset working condition data. The results show that the RPC optimization compensation model developed in this paper can coordinate the control system energy flow and improve the system power quality (the power factor increases and the negative sequence current decreases). The improved particle swarm algorithm is more convergent. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon "3060" plan for the future of China. [ABSTRACT FROM AUTHOR]