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A solution to increase the electrical output of the photovoltaic systems relies on solar tracking mechanisms that increase the amount of received solar energy. The experimental results obtained during a monitoring period of one year are comparatively presented in the paper for five types of photovoltaic modules installed on a fixed platform (as reference) and on a dual-axis solar tracking platform in the Renewable Energy Systems and Recycling R&D Centre of the Transilvania University of Brasov, Romania. The influence of the solar-tracking mechanism and the meteorological conditions specific to the four seasons during the monitoring period on the output of the analysed photovoltaic technologies are discussed in the paper. The solar tracking increases by 28% the amount of the yearly received solar energy and by 29.6% the electrical energy output of the entire PV platform. The solar conversion efficiency of the tracked PV platform is slightly increased (14.34%) when compared with the fixed one (14.17%). When assessing the influence of solar tracking on each type of PV, the results show that the CIGS PV module has the highest relative energy gain (34%) followed by CIS (30.8%), m-Si (30.6%), p-Si (27.3%) and CdTe (23.4%) PV modules. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study on the implementation of photovoltaic (PV) energy systems at Al-Abrar Mosque in Saudi Arabia. The primary objective was to explore optimal regional solar power strategies. By synergistically integrating technical evaluations of the PV system with economic analyses, including the payback period and levelized cost of energy (LCOE), alongside an investigation of net metering and net billing scenarios, we delineated a pathway toward achieving net zero billing for the mosque's energy requirements. This study examined two scenarios: Scenario I involved net metering, while Scenario II explored net billing. Our theoretical and simulation results, derived from detailed analyses conducted using PVsyst software, unequivocally demonstrated the superiority of net metering for this specific application. With net metering, the mosque's energy needs can be efficiently met using minimal infrastructure—comprising only 34 photovoltaic modules and a single inverter. In contrast, net billing requires significantly higher resource demands, underscoring the economic and spatial advantages of net metering. Additionally, the payback period for Scenario I is 7.9 years, while for Scenario II, it extends to 87 years. Through rigorous simulations, this study reaffirmed the practicality and feasibility of the net metering approach within the context of Saudi Arabia. Furthermore, our research provides actionable insights for implementing sustainable solutions at specific sites, such as the Al-Abrar Mosque, and contributes to advancing renewable energy knowledge in the region. [ABSTRACT FROM AUTHOR]

In the context of an insulated area with a subtropical climate, such as La Réunion island, it is crucial to reduce the energy consumption of buildings and develop local renewable energy sources to achieve energy autonomy. Direct current (DC) nanogrids could facilitate this by reducing the energy conversion steps, especially for solar energy. This article presents the deployment and efficiency evaluation of a 48 VDC low-voltage direct current (LVDC) nanogrid, from conception to real-world installation within a company. The nanogrid consists of a photovoltaic power plant, a lithium–iron–phosphate (LFP) battery, and DC end-use equipment, such as LED lighting and DC fans, for two individual offices. For identical test conditions, which are at an equivalent cabling distance and with the same final power demand, the total power consumed by the installation is measured for several stages from 50 to 400 W, according to a 100% DC configuration or a conventional DC/AC/DC PV configuration incorporating an inverter and AC/DC converter. The methodology used enables a critical view to be taken of the installation, assessing both its efficiency and its limitations. Energy savings of between 23% and 40% are measured in DC for a power limit identified at 150 W for a distance of 25 m. These results show that it is possible to supply 48 VDC in an innovative way to terminal equipment consuming no more than 100 W, such as lighting and air fans, using the IEEE 802.3 bt power over ethernet (PoE) protocol, while at the same time saving energy. The nanogrid hardware and software infrastructure, the methodology employed for efficiency quantification, and the measurement results are described in the paper. [ABSTRACT FROM AUTHOR]

Energy crises and the growth of the energy demand have increased the interest in utilizing unconventional power sources. Thus, renewable energy sources have become a topic of interest to mitigate rising energy concerns and cope with increased electricity demand. With remarkable merits including cleanness and abundance, photovoltaic (PV) solar energy systems are a key to solving these issues. The employed inverters should effectively utilize the maximum available power from the PV solar system and transfer this power to the utility grid without posing any further limitations. However, the unequal power generation of different PV systems caused by partial shading (PS) and other PV panel degradation factors leads to a reduction in generation capacity. One of the relatively new solutions to mitigate the mismatch concerns between the PV modules and sub-modules is to extract the maximum power of each sub-module individually. The main objective of this paper is to present a comprehensive review of such PV grid-connected inverters topologies associated with sub-module connection and control. It will classify the PV grid-tied inverters in accordance with the level where the maximum power point tracking (MPPT) system is implemented. A special focus has been placed on sub-module microinverters (MI) in terms of circuit topologies, conversion efficiency, and controller design. This paper provides a comprehensive analysis of employing the distributed MPPT (DMPPT) approach to maximize the power generation of PV systems by mitigating the mismatch issues inside the PV module. The circuit topology, PV system configuration, and MPPT algorithms used for applying DMPPT solutions in PV SMs are discussed in detail in this study. [ABSTRACT FROM AUTHOR]

This paper provides a review of the implementation of different materials and how they have impacted the efficiency of solar cells. This work elaborates on all solar generation methods that have been developed in the past and covers disparate technologies that are being implemented in different generations. A review of the characterization and factors involved in these processes are also discussed briefly. Furthermore, the economic, environmental, and technical perspectives related to solar cells have also been expounded. This paper also provides some insights into potential research directions that can be pursued in the field of solar energy. Energy demands are increasing all over the world, and substantial amounts of fossil fuels are currently exhausted all over the world in order to meet those needs, which in turn contaminates our environment; moreover, non-renewable sources of energy are diminishing at higher rates as well. Solar energy is of prime importance in all renewable energy sources as the Sun shines at the Earth for 8 to 10 h on average. Thus, heat can be harnessed to generate electricity, but solar cells are not substantially efficient because the materials used in them are quite costly and waste a considerable amount of energy, mostly as heat, which subsequently reduces the efficiency of the cell and increases the overall price as well. These challenges can be dealt with by designing more efficient, economical systems of storage and manufacturing PV cells with high efficacy. Scientists and engineers are more inclined toward advanced technologies and material manipulation to enhance the efficiency of solar energy and reduce its cost. In this regard, substantial research is being carried out, especially on the structure of materials and advanced materials like nanomaterials and quantum dots. Due to their distinct electromechanical and material properties, carbon-based nanomaterials like carbon nanotubes, graphene, fullerene, and nanohybrids are being employed as the electrodes, transport layers, active layers, or intermediate (interfacial) layers of solar cells in this regard. [ABSTRACT FROM AUTHOR]

Solar energy has gained prominence because of the increasing global attention received by renewable energies. This shift can be attributed to advancements and innovations in solar cell technology, which include developments of various photovoltaic materials, such as thin film and tandem solar cells, in addition to silicon-based solar cells. The latter is the most widely commercialized type of solar cell because of its exceptional durability, long-term stability, and high photoconversion efficiency; consequently, the demand for Si solar cells has been consistently increasing. PV modules are designed for an operation lifespan of 25–30 years, which has led to a gradual increase in the number of end-of-life PV modules. The appropriate management of both end-of-life and prematurely failed PV modules is critical for the recovery and separation of valuable and hazardous materials. Effective methods for end-of-life PV waste management are necessary to minimize their environmental impact and facilitate transition to a more sustainable and circular economy. This paper offers a comprehensive overview of the separation processes for silicon PV modules and summarizes the attempts to design easily recyclable modules for sustainable solar module development. Based on the studies summarized in this paper, suggestions are provided for future research. [ABSTRACT FROM AUTHOR]

In the face of the increasing depletion of non-renewable energy sources and increasingly serious environmental problems, the development of green and environmentally friendly renewable energy sources cannot be delayed. Because of the far-reaching development potential of solar energy, solar power has become an important research object for power development. The available solar energy in space is several times greater than that on Earth. Solar energy from space can be collected by a space solar power station (SSPS) and transmitted to the ground by wireless power transfer. In the full-chain ground-based validation system of SSPS-OMEGA, the spherical concentrator is used, and the light intensity distribution on the solar receiver is non-uniform. The non-uniform light intensity makes the output current of each photovoltaic (PV) cell on the solar receiver greatly different, and causes power losses, known as the mismatch problem. This paper proposes a simple, efficient and easy-to-implement method to optimize the structure of PV arrays to reduce the effect of non-uniform light on the output performance of each PV cell, which is beneficial to the topology of PV arrays and also effectively improves the layout rate. Then, a differential power processing (DPP) converter with a simple structure and easy control is designed to further deal with the power mismatch problem between series-connected PV modules. Finally, a simulation circuit model and a physical hardware model of the differential power processing PV system are built and used in the full-chain ground-based validation system of SSPS-OMEGA. The results demonstrate that the influence of non-uniform lighting on PV cells is effectively reduced, the output power of PV modules connected in series under non-uniform light distribution is substantially increased, and the photoelectric conversion efficiency is significantly improved. [ABSTRACT FROM AUTHOR]

Solar hydrogen production technology is a key technology for building a clean, low-carbon, safe, and efficient energy system. At present, the intermittency and volatility of renewable energy have caused a lot of "wind and light". By combining renewable energy with electrolytic water technology to produce high-purity hydrogen and oxygen, which can be converted into electricity, the utilization rate of renewable energy can be effectively improved, while helping to improve the solar hydrogen production system. This paper summarizes and analyzes the research status and development direction of solar hydrogen production technology from three aspects. Energy supply mode: the role of solar PV systems and PT systems in this technology is analyzed. System control: the key technology and system structure of different types of electrolytic cells are introduced in detail. System economy: the economy and improvement measures of electrolytic cells are analyzed from the perspectives of cost, consumption, efficiency, and durability. Finally, the development prospects of solar hydrogen production systems in China are summarized and anticipated. This article reviews the current research status of photovoltaic-photothermal coupled electrolysis cell systems, fills the current research gap, and provides theoretical reference for the further development of solar hydrogen production systems. [ABSTRACT FROM AUTHOR]

The increasing demand for energy and electricity and the depletion of fossil fuels are global problems. In recent years, dye-sensitized solar cell (DSSC) technologies have gained notoriety for their application in solar energy. DSSCs are considered a promising alternative renewable energy source to both inorganic and organic photovoltaic (PV) cells. Many types of dyes are being investigated to enhance the light-harvesting properties of DSSCs, but the actual realization of these absorbers in cell structure requires optimum parameters. The main aim of this study was to simulate proposed zinc phthalocyanine (ZnPC)-based structures to validate their design, assess their performance for commercial implementation, and optimize the cell parameters for optimum efficiency. To that end, Scaps-1D was employed to evaluate the performance of DSSCs to determine their optimum parameters. We found that ZnPC and isopropoxy ZnPC molecules outperform others molecules because of better optoelectronic properties. Several other parametric effects, such as photoactive layer thicknesses, doping densities, trap densities, and charge carrier mobilities, were also evaluated to observe their impact on device performance. The results show that moderate thickness, low defect density, moderate doping, and charge carrier mobility are favorable for better device performance due to low recombination losses, electrical losses, and better transport of charge carriers. The utmost power conversion efficiency values found for ZnPC- and ZnPC: PC70BM-based DSSCs after optimization were 9.50% and 9.81%. This paper also suggests a practical method for efficiently using DSSC cells by modifying factors that are significantly reliant on DSSC performance and output. [ABSTRACT FROM AUTHOR]

As photovoltaics (PV), also known as solar electricity, has been growing over the years, the energy markets have been gradually moving toward decentralization. However, recent media accusations suggest that decentralized renewable energy is slowly becoming unpopular because of the hidden fees being charged to owners of installed PV systems. In response, this paper investigates the potential for alternative approaches to incentivize owners using peer-to-peer (P2P) sharing. This study provides an analytical comparison between the use of the P2P mechanism, the net-metering mechanism, and a combination of these in the commercial sector. Through the use of a simulation, this case study presents the possible outcomes of the implementation of these models in a microgrid. Using technical and economic indexes the comparison was made by looking at the following indexes: peak power, energy balance, economic benefit, and transaction index. Based on a microgrid of 28 commercial buildings, readings of consumption were taken at intervals of one hour, and a Python model was made to find PV size and compare trading mechanisms. It was found that the combination of P2P and net-metering had the best overall performance, followed by net-metering itself, with the best season being all for both, and summer for net-metering by itself. This shows that a P2P model implemented in a microgrid helps create more energy balance, although the combination would achieve the highest performance. This study can be used by policymakers for proposing renewable energy policies and regulations that are more beneficial to all prosumers and consumers. [ABSTRACT FROM AUTHOR]

With attention increasing towards worldwide ecological conservation, the energy policies of various countries are moving towards renewable energy systems, primarily with solar and wind as the primary power sources. The Western Electricity Coordinating Council (WECC) has developed a generic model as a simulation framework for a typical solar wind generation system to analyze the response of the actual equipment. In this paper, we propose a parameter tuning process for a generic model controller that combines Latin hypercube sampling and Pareto optimization to enable a generic model to effectively match the output of an actual inverter device. Different operating scenarios were considered to match the dynamic characteristics of the inverter device using the multi-objective particle swarm algorithm (MOPSO) along with Pareto optimization. The effectiveness of the proposed parameter tuning process was verified on a user-defined test system and, later, the IEEE 39 bus system with a large solar power facility. [ABSTRACT FROM AUTHOR]

The high-efficiency operation of photovoltaic and wind systems is affected by many factors and parameters that should be continuously monitored. Since most of the variable factors are related to weather conditions, they are difficult to predict. Therefore, in order to optimize the operating point of a photovoltaic or wind power plant, it is necessary to observe changes in the subject area. The operation of photovoltaic and wind power plants can complement each other. The results recorded at the hybrid power plant of the Faculty of Electrical Engineering of Bialystok University of Technology are useful for a comprehensive analysis of the power plant operation and the ways to optimize it. This paper presents the influence of environmental factors on the operation of a hybrid photo-voltaic–wind power plant located in the city of Bialystok, Poland. The aim of the study was to present the variable factors on the optimal adjustment of the location of the power plant elements at the stage of its design and selection of the energy management system. The presented measurement data from 2015–2021 allow conclusions to be drawn on the significant impact on the power plant's operation, taking into account both the average conditions corresponding to the analysed location and the full range of changes in the listed factors. [ABSTRACT FROM AUTHOR]

Parameters of the solar cell equivalent circuit models have a significant role in assessing the solar cells' performance and tracking operational variations. In this regard, estimating solar cell parameters is a difficult task because cells have nonlinear current-voltage characteristics. Thus, a fast and accurate optimization algorithm is usually required to solve this engineering problem effectively. This paper proposes two hybrid variants of honey badger algorithm (HBA) and artificial gorilla troops optimizer (GTO) to estimate solar cell parameters. The proposed algorithms minimize the root mean square error (RMSE) between measurement and simulation results. In the first variant, GTO is used to determine the initial population of HBA, while in the second variant, HBA is used to determine the initial population of GTO. These variants can efficiently improve convergence characteristics. The proposed optimization algorithms are applied for parameter estimation of different equivalent circuit models of solar cells and various photovoltaic (PV) modules. Namely, the proposed algorithms test three solar cell equivalent models: single-diode, double-diode, and triple-diode equivalent circuit models. Different photovoltaic modules are investigated, such as the RadioTechnique Compelec (RTC) France solar cell, Solarex's Multicrystalline 60 watts solar module (MSX 60), and the Photowatt, France solar panel (Photo-watt PWP 201). In addition, the applicability of the proposed optimization algorithms is verified using obtained results from a commercial solar module called Shell Monocrystalline PV module (SM55) with different irradiation and temperature levels. The good results of the proposed algorithms show that they can efficiently improve convergence speed and the accuracy of the obtained results than other algorithms used for parameter estimation of PV equivalent circuit models in the literature, particularly in terms of the values of the RMSE and statistical tests. In addition, the parameters estimated by the proposed methods fit the simulation data perfectly at different irradiance and temperature levels for the commercial PV module. [ABSTRACT FROM AUTHOR]

The introduction of solar photovoltaic (PV) systems would provide electricity accessibility to rural areas that are far from or have no access to the grid system. Various countries are planning to reduce their emissions from fossil fuel, due to its negative effects, by substituting with renewable energy resources. The use of solar PV systems is expanding globally because of growing energy demands and depleting fossil fuel reserves. Grid integration of the solar system is expected to increase further in the near future. However, the power output of solar PV systems is inherently intermittent, and depends on the irradiance and the temperature operation of the solar cell, resulting in a wide range of defects. Hence, it is vital to extract peak power from the solar panel in all conditions to provide constant power to the load. This paper presents a tracking control method of the peak output power of a solar PV system connected to a DC-DC boost converter using an improved incremental conductance and integral regulator (IC + IR). The research was carried out because the solar PV output is dependent on environmental parameters, such as solar insolation and temperature. Therefore, it is pertinent to forecast the peak power point in outdoor conditions and to operate at that point, so that solar PV can produce the highest output each time it is used. A peak power point strategy that maximizes the output of a solar PV array is proposed. This method establishes the maximum output operation point under the effects of the solar insolation and the module temperature. An automatic converter restoration scheme with block/de-block signal control is proposed to protect the converters from the higher phase current, total capacitor voltage deviation, grid disturbance, and fault current. The proposed scheme also tracks the peak power point (PPP) of the solar array with stable output voltage under varying operating conditions. It reduces the error signal and ripples at the PPP during instantaneous and incremental conductance to zero. In addition, it controls the solar PV system under constantly changing climatic conditions, and thus improves the system efficiency. [ABSTRACT FROM AUTHOR]

This article describes an example of using the measurement data from photovoltaic systems and wind turbines to perform practical probabilistic calculations around green hydrogen generation. First, the power generated in one month by a ground-mounted photovoltaic system with a peak power of 3 MWp is described. Using the Metalog family of probability distributions, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is calculated. Identical calculations are performed for the simulation data, allowing us to determine the power produced by a wind turbine with a maximum power of 3.45 MW. After interpolating both time series of the power generated by the renewable energy sources to a common sampling time, they are summed. For the sum of the power produced by the photovoltaic system and the wind turbine, the probability of generating selected power levels corresponding to the amount of green hydrogen produced is again calculated. The presented calculations allow us to determine, with probability distribution accuracy, the amount of hydrogen generated from the energy sources constituting a mix of photovoltaics and wind. The green hydrogen production model includes the hardware and the geographic context. It can be used to determine the preliminary assumptions related to the production of large amounts of green hydrogen in selected locations. The calculations presented in this article are a practical example of Business Intelligence. [ABSTRACT FROM AUTHOR]

Sources such as solar and wind energy are intermittent, and this is seen as a barrier to their wide utilization. The increasing grid integration of intermittent renewable energy sources generation significantly changes the scenario of distribution grid operations. Such operational challenges are minimized by the incorporation of the energy storage system, which plays an important role in improving the stability and the reliability of the grid. This study provides the review of the state-of-the-art in the literature on the economic analysis of battery energy storage systems. The paper makes evident the growing interest of batteries as energy storage systems to improve techno-economic viability of renewable energy systems; provides a comprehensive overview of key methodological possibilities for researchers interested in economic analysis of battery energy storage systems; indicates the need to use adequate economic indicators for investment decisions; and identifies key research topics of the analyzed literature: (i) photovoltaic systems with battery energy storage systems for residential areas, (ii) comparison between energy storage technologies, (iii) power quality improvement. The last key contribution is the proposed research agenda. [ABSTRACT FROM AUTHOR]

Increased world energy demand necessitates looking for appropriate alternatives to oil and fossil fuel. Countries encourage institutions and households to create their own photovoltaic (PV) systems to reduce spending money in electricity sectors and address environmental issues. Due to high solar radiation in the Kingdom of Saudi Arabia (KSA), the government urges people and institutions to establish PV systems as the best promising renewable energy resource in the country. This paper presents an optimal and complete design of a 300 kW PV system installed in a limited rooftop area to feed the needs of the Ministry of Electricity building, which has a high energy consumption. The design has been suggested for two scenarios in terms of adjusting the orientation angles. The available rooftop area allowed to be used is insufficient if a tilt angle of 22o is used, suggested by the designer, so the tilt angle has been adjusted from 22o to 15o to accommodate the available area and meet the required demand with a minimum shading effect. The authors of this paper propose a modified scenario "third scenario" which accommodates the available area and provides more energy than the installed "second scenario". The proposed panel distribution and the estimated energy for all scenarios are presented in the paper. The possibility of changing tilt angles and the extent of energy production variations are also discussed. Finally, a comparative study between measured and simulated energy is included. The results show that August has the lowest percentage error, with a value of 2.7%, while the highest percentage error was noticed in November. [ABSTRACT FROM AUTHOR]

The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically. [ABSTRACT FROM AUTHOR]

This paper deals with the monitoring of the performance of a photovoltaic plant, without using the environmental parameters such as the solar radiation and the temperature. The main idea is to statistically compare the energy performances of the arrays constituting the PV plant. In fact, the environmental conditions affect equally all the arrays of a small-medium-size PV plant, because the extension of the plant is limited, so any comparison between the energy distributions of identical arrays is independent of the solar radiation and the cell temperature, making the proposed methodology very effective for PV plants not equipped with a weather station, as it often happens for the PV plants located in urban contexts and having a nominal peak power in the 3÷50 kWp range, typically installed on the roof of a residential or industrial building. In this case, the costs of an advanced monitoring system based on the environmental data are not justified, consequently, the weather station is often also omitted. The proposed procedure guides the user through several inferential statistical tools that allow verifying whether the arrays have produced the same amount of energy or, alternatively, which is the worst array. The procedure is effective in detecting and locating abnormal operating conditions, before they become failures. [ABSTRACT FROM AUTHOR]

In recent times, there have been notable advancements in solar energy and other renewable sources, underscoring their vital contribution to environmental conservation. Solar cells play a crucial role in converting sunlight into electricity, providing a sustainable energy alternative. Despite their significance, effectively optimizing photovoltaic system parameters remains a challenge. To tackle this issue, this study introduces a new optimization approach based on the coati optimization algorithm (COA), which integrates opposition-based learning and chaos theory. Unlike existing methods, the COA aims to maximize power output by integrating solar system parameters efficiently. This strategy represents a significant improvement over traditional algorithms, as evidenced by experimental findings demonstrating improved parameter setting accuracy and a substantial increase in the Friedman rating. As global energy demand continues to rise due to industrial expansion and population growth, the importance of sustainable energy sources becomes increasingly evident. Solar energy, characterized by its renewable nature, presents a promising solution to combat environmental pollution and lessen dependence on fossil fuels. This research emphasizes the critical role of COA-based optimization in advancing solar energy utilization and underscores the necessity for ongoing development in this field. [ABSTRACT FROM AUTHOR]

Industry 4.0 is in continuous technological growth that benefits all sectors of industry and society in general. This article reviews the Digital Twin (DT) concept and the interest of its application in photovoltaic installations. It compares how other authors use the DT approach in photovoltaic installations to improve the efficiency of the renewable energy generated and consumed, energy prediction and the reduction of the operation and maintenance costs of the photovoltaic installation. It reviews how, by providing real-time data and analysis, DTs enable more informed decision-making in the solar energy sector. The objectives of the review are to study digital twin technology and to analyse its application and implementation in PV systems. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into its constituent components, allowing us to find out the impact of the system degradation, maintenance, and weather variations from the inherent seasonal patterns. Our results reveal significant trends in PV system performance, indicating the need for proactive maintenance strategies to mitigate degradation effects. Moreover, we quantify the impact of changing weather patterns and provide recommendations for optimising the system's efficiency based on seasonally varying conditions. Hence, this study not only advances our understanding of the intricate variations within PV system performance but also provides practical guidance for enhancing the sustainability and effectiveness of solar energy utilisation in both residential and commercial settings. [ABSTRACT FROM AUTHOR]

This study investigates the use of a foldable solar panel system equipped with a dynamic tracking algorithm for agrivoltaics system (AVS) applications. It aims to simultaneously meet the requirements for renewable energy and sustainable agriculture. The design focuses on improving solar energy capture while facilitating crop growth through adjustable shading. The results show that foldable panels, controlled by the tracking algorithm, significantly outperform fixed panels in energy efficiency, achieving up to a 15% gain in power generation and uniform power generation throughout the day. Despite the presence of shadows of adjacent panels in the early morning and late evening, the system's effectiveness in creating microclimates for diverse crops demonstrates its substantial value. The foldable design not only protects crops from adverse climate conditions across different seasons but also generates energy efficiently. This demonstrates a step forward in sustainable land use and food security. [ABSTRACT FROM AUTHOR]

In recent years, solar energy has been regarded as one of the most important sustainable energy sources. Under the rapid and large-scale construction of solar farms, the maintenance and inspection of the health conditions of solar modules in a large solar farm become an important issue. This article proposes a method for detecting solar cell faults with unmanned aerial vehicle (UAV) equipped with a thermal imager and a visible light camera, and providing a fast and reliable detection method. The detection process includes a new concept of real-time monitoring of the detected area and analysis of the health of solar panels. An image process is proposed that may quickly and accurately detect the abnormality of a solar module. The whole process includes grayscale conversion, filtering, 3-D temperature representation, probability density function, and cumulative density function analysis. Ten cases in real fields have been studied with this process, including large scale solar farms and small size solar modules installed on buildings. Results show that the cumulative density function is a convenient way to determine the health status of the solar panel and may provide maintenance personnel a basis for determining whether replacement of solar cells is necessary for improving the overall power generation efficiency and simplify the maintenance process. It is worth noting that image recognition can increase the clarity of IR images and the cumulative chart can judge the defect rate of the cell. These two methods were combined to provide an instant, fast and accurate defect judgment. [ABSTRACT FROM AUTHOR]

With the spread of the use of renewable sources of energy, weather-dependent solar energy is also coming more and more to the fore. The quantity of generated electric power changes proportionally to the intensity of solar radiation. Thus, a cloudy day, for example, greatly reduces the amount of electricity produced from this energy source. In the countries of the European Union solar power plants are obligated to prepare power generation forecasts broken down to 15- or 60-min intervals. The interest of the regionally responsible transmission system operators is to be provided with forecasts with the least possible deviation from the actual figures. This paper examines the Visegrad countries' intraday photovoltaic forecasts and their deviations from real power generation based on the photovoltaic power capacity monitored by the transmission system operators in each country. The novelty of this study lies in the fact that, in the context of monitored PV capacities in the Visegrad countries, it examines the regulation capacities needed for keeping the forecasts. After comparing the needs for positive and negative regulation, the author made deductions regarding storage possibilities complementing electrochemical regulation, based on the balance. The paper sought answers concerning the technologies required for the balancing of PV power plants in the examined countries. It was established that, as a result of photovoltaic power capacity regulation, among the four Visegrad countries, only the Hungarian transmission system operator has negative required power regulation, which could be utilized in power-to-gas plants. This power could be used to produce approximately 2.1 million Nm3 biomethane with a 98% methane content, which could be used to improve approximately 4 million Nm3 biogas of poor quality by enriching it (minimum 60% methane content), so that it can be utilized. The above process could enhance the viability of 4–6 low-methane agricultural biogas plants in Hungary. [ABSTRACT FROM AUTHOR]

A robust and efficient segmentation framework is essential for accurately detecting and classifying various defects in electroluminescence images of solar PV modules. With the increasing global focus on renewable energy resources, solar PV energy systems are gaining significant attention. The inspection of PV modules throughout their manufacturing phase and lifespan requires an automatic and reliable framework to identify multiple micro-defects that are imperceptible to the human eye. This manuscript presents an encoder–decoder-based network architecture with the capability of autonomously segmenting 24 defects and features in electroluminescence images of solar photovoltaic modules. Certain micro-defects occupy a trivial number of image pixels, consequently leading to imbalanced classes. To address this matter, two types of class-weight assignment strategies are adopted, i.e., custom and equal class-weight assignments. The employment of custom class weights results in an increase in performance gains in comparison to equal class weights. Additionally, the proposed framework is evaluated by utilizing three different loss functions, i.e., the weighted cross-entropy, weighted squared Dice loss, and weighted Tanimoto loss. Moreover, a comparative analysis based on the model parameters is carried out with existing models to demonstrate the lightweight nature of the proposed framework. An ablation study is adopted in order to demonstrate the effectiveness of each individual block of the framework by carrying out seven different experiments in the study. Furthermore, SEiPV-Net is compared to three state-of-the-art techniques, namely DeepLabv3+, PSP-Net, and U-Net, in terms of several evaluation metrics, i.e., the mean intersection over union (IoU), F1 score, precision, recall, IoU, and Dice coefficient. The comparative and visual assessment using SOTA techniques demonstrates the superior performance of the proposed framework. [ABSTRACT FROM AUTHOR]

Fossil fuel consumption has progressively increased alongside global population growth, representing the predominant energy consumption pattern for humanity. Unfortunately, this persistent reliance on fossil fuels has resulted in a substantial surge in pollution emissions, exerting a detrimental influence on the delicate ecological balance. Therefore, it is imperative to find new renewable energy sources to replace fossil fuels. Solar energy is a clean energy source and has become the most preferred option for human day-to-day needs. Since the construction of the world's first floating photovoltaic power station, humanity has been continuously advancing the technology of power generation by floating photovoltaics. This review comprehensively elucidates the progression of offshore photovoltaic technology and illustrates the composition of the floating photovoltaic system. Each section meticulously contrasts the advantages and drawbacks of various photovoltaic systems. In addition, an in-depth analysis of the offshore photovoltaic application potentials is conducted based on fundamental theories, thereby offering valuable insights for future research. Finally, an encompassing summary of the potential challenges associated with deep-sea floating photovoltaic systems is presented. [ABSTRACT FROM AUTHOR]

The majority of photovoltaic (PV) systems in the Netherlands are small scale, and installed on residential and commercial rooftops, where different objects in many cases may lead to the presence of shading and inevitable energy loss. Nevertheless, the energy loss due to expected shadow must be distinguished from the energy loss due to other malfunctions. In this study an algorithmic tool is presented that automates the process of analyzing monitoring data of partially shaded PV systems. The algorithm compares long-term and high-resolution yield data of a partially shaded PV system with the yield data of an unshaded PV system, as reference PV system, and automatically detects the energy loss due to the expected shadow, caused by any surrounding obstacles, and distinguishes it from any additional energy loss due to other malfunctions. This study focuses on PV systems with module-level power electronics (MLPE) since these are mostly used on PV systems on rooftops. Three different cases of shaded MLPE PV systems are presented to illustrate the versatility of the methodology. Furthermore, suggestions for further research are discussed at the end of the paper. [ABSTRACT FROM AUTHOR]

With the rapidly evolving mobile technologies, the number of cellular base stations (BSs) has significantly increased to meet the explosive demand for mobile services and applications. In turn, this has significantly increased the capital and operational expenses, due to the increased electricity prices and energy consumption. To generate electricity, power plants mainly rely on fossil fuels, which are non-renewable energy resources. As a result, CO 2 emissions also increase, which adversely affect health and environment. For wireless access technologies and cellular networks, BSs are the largest power consumer, and the network energy consumption is mainly dominated by the network infrastructure, which makes the telecommunications sector liable for energy consumption as well as CO 2 emissions around the globe. Alternatively, solar energy is considered as an eco-friendly and economically attractive solution, due to its cost-effectiveness and sustainability. In this paper, the potentials of photovoltaic (PV) solar power to energize cellular BSs in Kuwait are studied, with the focus on the design, implementation, and analysis of off-grid solar PV systems. Specifically, system components, such as the number of PV panels, batteries, and converters needed for the design are determined and evaluated via HOMER software, with the focus on minimizing the net present cost (NPC). A comparison between various PV, diesel generator (DG), and battery bank (BB) system configurations is also performed. Moreover, a comparison of system deployment area will be presented for different PV panels that have different output power and panel sizes, in addition to utilizing a solar tracking system. It is revealed that utilizing a hybrid system configuration (i.e., PV-DG-BB) decreases fuel consumption per year by almost 95% in comparison to the conventional DG-only based electric systems. Not only that, but utilizing a pure off-grid solar PV system (i.e., PV-BB) can significantly reduce the total NPC while completely eliminating CO 2 emissions; however, at the expense of more land. [ABSTRACT FROM AUTHOR]

In this paper, economic feasibility of installing small-scale solar photovoltaic (PV) system is studied at the residential and commercial buildings from an end-user perspective. Based on given scenarios, the best sizing methodology of solar PV system installation has been proposed focusing primarily on the minimum payback period under given (rooftop) area for solar PV installation by the customer. The strategy is demonstrated with the help of a case study using real-time monthly load profile data of residential as well as commercial load/customers and current market price for solar PVs and inverters. In addition, sensitivity analysis has also been carried out to examine the effectiveness of net metering scheme for fairly high participation from end users. Since Saudi Arabia's Electricity and Co-generation Regulatory Authority (ECRA) has recently approved and published the net metering scheme for small-scale solar PV systems allowing end users to generate and export energy surplus to the utility grid, the proposed scheme has become vital and its practical significance is justified with figures and graphs obtained through computer simulations. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources has become strategic in the production of electricity worldwide due to global efforts to increase energy efficiency and achieve a net zero carbon footprint. Hybrid systems can maximize stability and reduce costs by combining multiple energy sources. A conventional metric, such as the levelized cost of energy (LCOE), that is appropriate for assessing the cost-effectiveness of an option may not be appropriate when evaluating the economic feasibility of hybrid systems. This study proposes a stochastic discounted cash flow model (DCF) to assess the economic viability of a hybrid renewable energy system (HRES) in Brazil. The objective is to determine the combinations that will provide the highest 50th percentile internal rate of return (IRR) and the lowest coefficient of variation (CV). Model variables include capital expenditures (CAPEX), operation and maintenance (O&M) costs, sectoral charges, taxes, and long-term energy production metrics. The results demonstrate that the synergies modeled contributed to the higher economic outcomes for the HRES obtained by combining both energy sources rather than opting for a stand-alone configuration. A wind-dominant combination of 60% wind was able to increase the 50th percentile of the IRR, while a solar-dominant combination of 65% solar minimized the CV. [ABSTRACT FROM AUTHOR]

Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations. [ABSTRACT FROM AUTHOR]

Of the different renewable sources of energy, photovoltaic energy has one of the highest potentials. In recent decades, several technological and research advances have contributed to the consolidation of its potential. One current photovoltaic energy research topic is the analysis of the impact of sediments on the panels' performance. The development of models to predict the performance of panels in the presence of sediments may allow for better decision-making when considering maintenance operations. This work contributed to the investigation of the influence of sand on the production of photovoltaic energy in cadmium telluride (CdTe) panels. Six panels of this type with different colors and transparencies were experimentally tested with and without the presence of sand. The impact of the sand on the cells' performance was evaluated by analyzing the change in the 1M5P model's parameters and in the power, efficiency, and fill factors. The experimental results show different negative impacts on the output power of the CdTe panels, from −14% in the orange panel to −36% in the green panel. Based on this study, the development of a model capable of predicting the effect of the sand on these panels was introduced. The developed model was validated experimentally, with a maximum deviation of 4.6%. These results can provide support for the decision-making around maintenance activities and for the development of new techniques to avoid sediment deposition on CdTe panels. [ABSTRACT FROM AUTHOR]

The shift towards sustainable energy sources is gaining momentum due to their environmental cleanliness, abundant availability, and eco-friendly characteristics. Solar energy, specifically harnessed through photovoltaic (PV) systems, emerges as a clean, abundant, and environmentally friendly alternative. However, the efficacy of PV systems is subjective depending on two critical factors: irradiance and temperature. To optimize power output, maximum power point tracking (MPPT) strategies are essential, allowing operation at the system's optimal point. In the presence of partial shading, the power–voltage curve exhibits multiple peaks, yet only one global maximum power point (GMPP) can be identified. Existing algorithms for GMPP tracking often encounter challenges, including overshooting during transient periods and chattering during steady states. This study proposes the utilization of fuzzy sliding mode controllers (FSMC) and fuzzy proportional-integral (FPI) control to enhance global MPPT reference tracking under partial shading conditions. Additionally, the system's performance is evaluated considering potential sensor malfunctions. The proposed techniques ensure precise tracking of the reference voltage and maximum power in partial shading scenarios, facilitating rapid convergence, improved system stability during transitions, and reduced chattering during steady states. The usefulness of the proposed scheme is confirmed through the use of performance indices. FSMC has the lowest integral absolute error (IAE) of 946.94, followed closely by FPI (947.21), in comparison to the sliding mode controller (SMC) (1241.6) and perturb and observe (P&O) (2433.1). Similarly, in integral time absolute error (ITAE), FSMC (56.84) and FPI (57.06) excel over SMC (91.03) and P&O (635.50). [ABSTRACT FROM AUTHOR]

Photovoltaic power is a crucial renewable energy source that has the potential to enhance a city's sustainability. However, in order to identify the various issues that may occur during the lifespan of a photovoltaic module, solar module inspection techniques are crucial. One valuable technique that is commonly used is luminescence, which captures silicon emissions. This article focuses on a specific luminescence technique called partial photoluminescence. This technique involves illuminating a specific portion of the solar cell surface and recording the luminescence emission generated in the remaining area. This method has been trialed in a laboratory environment, utilizing infrared LEDs as the excitation source. An analysis of the main parameters that affect the technique is provided, where pictures have been taken under varying exposure times ranging from 50 ms to 400 ms, irradiance levels ranging from 200 W/m2 to 1000 W/m2, and a percentage of illuminated cells ranging from 10% to 40%. Furthermore, the experimental device has been modified to generate images utilizing sunlight as the excitation source. Several pictures of damaged cells were taken under an irradiance range of 340 W/m2 to 470 W/m2. The quality of the partial photoluminescence images is comparable to conventional electroluminescence images, but longer exposure times are required. [ABSTRACT FROM AUTHOR]

Among the energy targets of the Vietnamese government, solar energy is expected to become the main source of renewable energy in the future. Solar energy is moving forward, with Vietnam outstripping Thailand and becoming the country that installed the largest capacity of solar power generation in Southeast Asia, reaching 16,362 MW in new installations in December 2021. In this study, we have experimentally analyzed and designed a capacity of 47.5 MW grid-connected photovoltaic plant mounted on the floatation system at Da Mi hydropower reservoir in Binh Thuan province. This was selected to be utilized as the first effort to develop the first large-capacity floating solar power plant on a hydroelectric reservoir in Vietnam. A detailed examination of the electrical analysis, including DC to DC converters, AC inverters to the transmission network, and PV module connectivity configurations, are in scope. The present research has the potential to make a contribution to the design of the DC electrical part, the AC electrical part, and the layout PV modules—Inverter—Floatation system—Floating bridge of FPV plants which are less described in the former articles. The performance of the plant after the first 2 years of operation has confirmed that it has met the expectations and exceeded the investor's target, with the power output of the first 2 years being higher than the design by 102.58% to 105.59% and no serious damage has occurred to the equipment from 1 June 2019 to 31 August 2021. [ABSTRACT FROM AUTHOR]

Renewable energy resources like solar energy, wind energy, hydro energy, photovoltaic etc. are gaining much importance due to the day by day depletion of conventional resources. Owing to the lower efficiencies of renewable energy resources, much attention has been paid to improving them. The concept of utilizing phase change materials (PCMs) has attracted wide attention in recent years. This is due to their ability to extract thermal energy when used in collaboration with photovoltaic (PV), thus improving the photoelectric conversion efficiency. In this paper, the objective is to design and fabricate a novel thermal energy storage system using phase change material. An investigation on the characteristics of Potash Alum as a phase change material due to its low cost, easy availability and its usage as an energy storage for the indoor purposes are taken into account. The use of a latent heat storage system using phase change materials (PCMs) is an effective way of storing thermal energy and has the advantage of high-energy storage density and the isothermal nature of the storage process. In the current study, potash alum was identified as a phase change material combined with renewable energy sources, that can be efficiently and effectively used in storing thermal energy at compartively lower temperatures that can later be used in daily life heating requirements.A parabolic dish which acts of a heat collector is used to track and reflects solar radiation at a single point on a receiver tank. Heat transfer from the solar collector to the storage tank is done by using a circulating heat transfer fluid with the help of a pump. The experimental results show that this system is capable of successfully storing and utilizing thermal energy on indoor scale such as cooking, heating and those applications where temperature is below 92 °C. [ABSTRACT FROM AUTHOR]

Solar photovoltaic systems will play a key role in the country's energy mix thanks to their ability to meet increasing energy needs while reducing greenhouse gas emissions. Despite the potential of solar photovoltaic energy, several criticalities remain, such as the intermittent nature and the need for significant land use for its implementation. In this regard, this work aimed at evaluating the photovoltaic potentiality in a national context by 2030 and 2050, considering only installations on the roof surfaces of existing buildings, i.e., without consuming additional land. This study has allowed the answering of three key points: (i) the roof surface could represent a valuable and alternative solution for new installations, since it could amount to around 450 km2, (ii) the national target cannot be reached by only using installations on existing buildings, although some regions could get close to the target by 2050, and (iii) long-term energy incentives should be implemented branching out to each national region, considering their photovoltaic potential. Finally, a regional potential index was also defined, capable of evaluating the photovoltaic potential in each region, helping policymakers to adopt the most suitable energy strategies. [ABSTRACT FROM AUTHOR]

Research on renewable energy sources and power electronic converters has been increasing due to environmental concerns. Many countries have established targets to decrease CO2 emissions and boost the proportion of renewable energy, with solar power being a prominent area of investigation in the recent literature. Techniques are being developed to optimize the energy recovered from PV cells and increase system efficiency, including modeling PV cells, the use of converter topologies to connect PV systems to high-power inverters, and the use of MPPT methods. Certain MPPT algorithms are intricate and demand high processing power. The literature describes several MPPT methods; however, the number of hardware resources required by MPPT algorithms is typically not disclosed. This work proposes a novel MPPT technique based on integral feedback conductance and incremental conductance error, considering the current dynamics of the boost converter. This MPPT algorithm is compared to the most widely used techniques in the literature and evaluates each method's efficiency, performance, and computational needs using an HIL system. Comparisons are made with well-known MPPT algorithms, such as perturb and observe, incremental conductance, and newer techniques based on fuzzy logic and neural networks (NNs). As the NN that is most widely used in the literature depends on irradiation and temperature, an additional NN that is trained using the proposed method is also investigated. [ABSTRACT FROM AUTHOR]

Renewable resources for producing energy for self-consumption are growing, namely solar energy. This work focuses on the comparison of photovoltaic systems for energy production for self-consumption on a property in three different regions of Portugal using traditional and emerging technologies, without batteries' implementation. According to Portuguese law, there is no stipulated value for selling surplus energy produced by a self consumption unit, to the public grid. In order to analyse the economic viability of the project, two scenarios are studied: the delivery, at zero cost of the surplus energy produced to the grid, and its sale. Furthermore, the same analysis is carried out considering partial shading on the photovoltaic generator. The results show that if there is no surplus production sale to the grid, the project becomes economically unviable for the four PV technologies. Otherwise, for the traditional technologies, the project is economically viable, presenting a payback time lower than 10 years. Using emerging technologies, the project becomes economically unviable. When applying partial shading on the generator, the project becomes, in all the scenarios under study, economically unviable. It can be concluded that introducing nanostructures in solar cells to power an infrastructure is not, for now, the best solution from an economic point of view, considering the current legislation. In addition, the shading makes the projects under study unviable, as it is a factor that cannot be controlled in its entirety. [ABSTRACT FROM AUTHOR]

In the step-by-step roadmap for limiting and eliminating power sources that use fossil fuel, especially coal-fired power, as well as for setting the Vietnamese government targets for developing renewable energy for replacement, solar farms and rooftop solar power, followed by floating photovoltaic (PV) power, are considered the best candidates for meeting the goals of land space, PV system operation efficiency, and meeting environmental goals. In addition to the rapid development of PV cell technology are the government's incentives and financial support for PV plants on the water surface area, which is still very large. Floating PV plants will strongly attract investors in the near future. This study could help stakeholders in the market understand the economic–technical aspects from analyzing economic–financial indicators of floating PV plants with a capacity of 47.5 MW connected to the national utility grid 110 kV at Da Mi hydropower reservoir in Binh Thuan province, Vietnam, in May 2019. This is the first floating PV plant on the reservoir in Vietnam, and is the property of a third party. [ABSTRACT FROM AUTHOR]

The management of energy in distribution networks has been gathering attention in recent years. The simultaneous control of generation and demand is crucial for achieving energy savings and can further lower energy pricing. The work aims to develop a control scheme for a hybrid microgrid that can provide stability to the bus voltage and effectively manage the power flow. Solar energy is the current trend in renewable energy sources (RES). There is a surge in the installation of solar PV systems both on a large scale and on a small scale, such as rooftop PV systems. Installation of RES at residential premises has to be conducted with a proper power management scheme. The hybrid microgrid for this work consists of a PV system with a boost converter to extract maximum power, a DC-DC bi-directional converter to charge or discharge the hybrid energy-storing devices, and a three-phase AC-DC interlinking converter for exchange of energy with the utility grid. The control and power management scheme checks the voltage of each unit and maintains the power flow according to operating conditions. Disturbances are introduced in the form of load switching and irradiance variation to check the system performance. The system is tested on the MATLAB (R2021a) Simulink platform for varying its different modes of operations. An experimental set-up has been developed with hardware-in-the-loop to validate the simulation results. [ABSTRACT FROM AUTHOR]

Currently, the increasing energy consumption around the world and the environmental impact resulting from the use of fossil fuel-based energy have promoted the use of renewable energy sources such as photovoltaic solar energy. The main characteristic of this type of energy is its unpredictability, as it depends on meteorological conditions. In this sense, monitoring the power generation of photovoltaic systems (PVS) in order to analyze their performance is becoming crucial. The purpose of this paper is to design a monitoring system for a residential photovoltaic self-consumption system which employs an Internet of Things (IoT) platform to estimate the photovoltaic power generation according to solar radiation and temperature. The architecture of the developed prototype will be described and the benefits of providing the use of IoT for monitoring will be highlighted, since all data collected by the data acquisition system (DAS) may be stored in the Cloud. The comparison of the results with those of other monitoring systems was very positive, with an uncertainty that complies with the IEC61724 standard. [ABSTRACT FROM AUTHOR]

Utilization of solar and wind energy is increasing worldwide. Photovoltaic and wind energy systems are among the major contributing tec4hnologies to the generation capacity from renewable energy sources; however, the generation often does not temporally match the demand. Micro-compressed air energy storage (micro-CAES) is among the low-cost storage options, and its coupling with the power generated by photovoltaics and wind turbines can provide demand shifting, modeled by efficient algorithms. A model based on criteria that are preset according to the demand is presented. The model decides on the distribution of the generated energy, depending on the state of the energy storage and the preset criteria of each storage technology. The satisfaction of the demand by the energy production and micro-CAES is compared to that of storage batteries. The demand originates in a case study of a household and optimal configurations of photovoltaics and wind turbines, and the storage capacities and costs are compared. An optimal configuration of 30 photovoltaic panels and two wind turbines was found for micro-CAES. The annual stored energy of micro-CAES was 114 kWh higher than that of the system with batteries. [ABSTRACT FROM AUTHOR]

An effective MPPT approach plays a significant role in increasing the efficiency of a PV system. Solar energy is a rich renewable energy source that is supplied to the earth in surplus by the sun. Solar PV systems are designed to utilize sunlight in order to meet the energy needs of the user. Due to unreliable climatic conditions, these PV frames have a non-linear characteristic that has a significant impact on their yield. Moreover, PSCs also affect the performance of PV systems in yielding maximum power. A significant progression in solar PV installations has resulted in rapid growth of MPPT techniques. As a result, a variety of MPPT approaches have been used to enhance the power yield of PV systems along with their advantages and disadvantages. Thus, it is essential for researchers to appraise developed MPPT strategies appropriately on regular basis. This study is novel because it provides an in-depth assessment of the current state of MPPT strategies for PV systems. On account of novelty, the authors analyzed the successive growth in MPPT strategies along with working principles, mathematical modeling, and simplified flow charts for better understanding by new learners. Moreover, the taxonomy and pro and cons of conventional and AI-based MPPT techniques are explored comprehensively. In addition, a comparative study based on key characteristics of PV system of all MPPT algorithms is depicted in a table, which can be used as a reference by various researchers while designing PV systems. [ABSTRACT FROM AUTHOR]

The production of electricity from photovoltaic (PV) systems is one of the significant opportunities for novel renewable energy sources. The PV systems can provide energy with minimum environmental harm; however, the usage of this energy source becomes strongly dependent on a wide range of social, economic, and technical factors. Based on the growing demand for renewable energy sources, the aim of the work focuses on the socio-economic analysis of possibilities of producing electricity from photovoltaic installations up to 10 kW in Poland. Based on expert research, the factors influencing energy production from PV systems were assessed based on factor three analysis (level II with 5 factors, level III with 15 factors). Using the expert-mathematical method, a hierarchy of economic, technical, and social factors of PV-based energy production was evaluated. The analysis of socio-economic factors indicated that the greatest impact on the PV systems development in Poland depends on energy purchasing costs and EU law regulations on renewable energy sources (RESs). The most influential factors were then used to forecast the possible cost-effectiveness of renewable energy production of home energy production. The study demonstrates the importance of renewable energy sources utilization and the cost-effectiveness of solar energy production in small PV systems in Poland. [ABSTRACT FROM AUTHOR]

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

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables. [ABSTRACT FROM AUTHOR]

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis. [ABSTRACT FROM AUTHOR]

Solar energy harvesting using Photovoltaic (PV) systems is one of the most popular sources of renewable energy, however the main drawback of PV systems is their low conversion efficiency. An optimal system operation requires an efficient tracking of the Maximum Power Point (MPP), which represents the maximum energy that can be extracted from the PV panel. This paper presents a novel control approach for the Maximum Power Point Tracking (MPPT) based on the differential flatness property of the Boost converter, which is one of the most used converters in PV systems. The underlying idea of the proposed control approach is to use the classical flatness-based trajectory tracking control where a reference voltage will be defined in terms of the maximum power provided by the PV panel. The effectiveness of the proposed controller is assessed through numerical simulations and experimental tests. The results show that the controller based on differential flatness is capable of converging in less than 0.15 s and, compared with other MPPT techniques, such as Incremental Conductance and Perturb and Observe, it improves the response against sudden changes in load or weather conditions, reducing the ringing in the output of the system. Based on the results, it can be inferred that the new flatness-based controller represents an alternative to improve the MPPT in PV systems, especially when they are subject to sudden load or weather changes. [ABSTRACT FROM AUTHOR]