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Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems. • A hybrid technique for predict fault in standalone PV to improve the performance of PV • The proposed method is the joint execution of ANN and GEO • The major contribution of this work is to improve the performance of PV systems • The use of GEO provides an efficient optimization technique for ANN training • The proposed method is more accurate and effective than existing techniques [ABSTRACT FROM AUTHOR]

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method. [ABSTRACT FROM AUTHOR]

In recent years, the adoption of photovoltaic technology has become increasingly attractive and popular due to the rising global demand for power. The energy conversion structure considered in this study, as well as its investigation, undoubtedly constitute the main tasks for the design and projection of photovoltaic systems connected to the electric distribution network. This paper presents the mathematical model of a photovoltaic system, along with the design of a maximum power point tracking algorithm and control. Subsequently, the dynamic performance of the photovoltaic generator connected to the distribution network via inverters is examined under various climatic conditions. Simulation results demonstrate that the controllers ensure optimal operation of the photovoltaic panel at its maximum power point, maintaining the DC output voltage at its reference level regardless of atmospheric conditions in terms of dynamic response time, overshoot, and reference tracking. [ABSTRACT FROM AUTHOR]

Photovoltaic modules are impacted by overheating, which degrades their conversion efficiency and shortens their lifespan. This work attempts to study the cooling potential for a hybrid photovoltaic/thermal (PV/T) system by employing CuO and Fe 2 O 3 nanofluids with 0.2% and 0.3% volume fractions (φ), circulated in a heat exchanger attached at the rear side of the PV module to lower the temperature of the PV cells and improve its energy and exergy efficiencies. Consequently, a 2D numerical model was analysed given the energy balance across the PV/T system layers, which is evaluated in line with measured experimental data. The results indicate that the successfully synthesised nanomaterials could improve the performance of the nanofluids and increase the heat exchange between PV/T system layers, reducing the PV cells' temperature by 23.49% and 34.58% when cooling by CuO and Fe 2 O 3 nanofluids at φ=0.3%. Compared to the uncooled PV module, the electrical efficiency has increased by 9.21% and 10.30%, while thermal efficiency increased by 38.9% and 43.3% under cooling with CuO and Fe 2 O 3 nanofluids, respectively. Thereby, the exergy destruction (exergy losses) and entropy generation have decreased by about 26% and 68% when cooling with Fe 2 O 3 nanofluid, which improved the exergy efficiency by 13% more than CuO nanofluid. [Display omitted] [ABSTRACT FROM AUTHOR]

The integration of hybrid energy sources, such as batteries and supercapacitors, in off-grid photovoltaic installations is of crucial importance. This method promotes energy autonomy, offers operational flexibility, compensates for fluctuations in solar production, and can result in long-term economical savings. It also allows for optimized energy management through efficient storage and redistribution. This work details the design and simulation of a self-sufficient solar system that uses supercapacitors and batteries as part of a hybrid energy storage system. Recognizing the increasing significance of efficient energy systems, this study addresses the importance of such installations in delivering sustainable energy solutions. The FOPI-PSO controller optimized using the Particle Swarm Optimization (PSO) technique; demonstrates greater flexibility with a greater number of parameters, surpassing the adaptability of the conventional PI controller. By using multiple simulation scenarios that take into consideration both variations in load and irradiance, the study compares the effectiveness of both controllers in terms of synchronizing batteries and supercapacitors. The results demonstrate the PSO-based FOPI control strategy's outstanding performance, showing its optimal efficiency and robustness. [ABSTRACT FROM AUTHOR]

Uncertainty in solar generation forecasts affects electricity market operations, forcing producers to bid conservatively and, consequently, reducing the amount of solar energy fed into the grid. This study analyses the potential of aggregated forecasts to reduce the errors of energy generation in medium-sized PV plants. To this objective, publicly available forecasts from the Global Forecasting System were processed with a basic neural network to generate hourly energy forecasts for individual PV plants of 1.1 MW and for the aggregated production of three of them (3.3 MW). The validation was carried out considering typical day-ahead and intraday market horizons, two zones with different atmospheric complexity, and discerning three types of days in terms of the received irradiation. Results show that the aggregation of forecasts would be beneficial as a general strategy in all cases, but especially in the zone of complex atmospheric dynamics and for the day-ahead horizons (36-42 h). [ABSTRACT FROM AUTHOR]

Access to high-quality electricity has a significant effect on capital savings and economic benefits for manufacturing industries. Indeed, the occurrence of any event that causes distortions in voltage, current, and frequency will lead to improper operation of electrical equipment and a severe reduction in power quality. By connecting the renewable energy sources to the grid or load through electronic power converters, power quality issues can be appropriately improved. This paper proposes an innovative power electronic transformer to meet the power quality requirements, which has been constructed by a new high-gain boost converter, a new multilevel inverter and high frequency transformer. The proposed boost converter can provide two outstanding advantages: reduction of input current ripple and increase of high output voltage for photovoltaic and fuel cell systems. Since the high input current and low output voltage of the dc/dc converter respectively lead to energy discharge and load loss, this structure not only greatly improves the power quality issues, but also promises the high reliability during the compensation process. The proposed multilevel inverter can provide required staircase sinusoidal voltage to compensate all power quality issues. The simulation results under different disturbance scenarios have validated the compensation capability of the proposed power electronic transformer. To further validate the proposed power electronic transformer structure, its prototype model based on the ATmega2560-Arduino has been provided and tested in laboratory. [ABSTRACT FROM AUTHOR]

This paper aims to develop a photovoltaic (PV) performance monitoring system applied on a micro scale using the Internet of Things (IoT). Previous monitoring systems had limitations in platform flexibility, low-cost devices, hardware complexity, and stability of the data transfer process. For this reason, this research proposes an IoT architecture that uses Arduino devices, mini WIFI and an open-source platform, so that it can be easily developed further. This research also develops innovations in controlling the use of electrical energy sources from PV and utility networks. This monitoring system is applied to PV installations with a capacity of 1KW which is capable of monitoring electrical data in the form of current, voltage, power, energy and frequency obtained from PV panels, batteries, loads and electrical utilities. Monitoring data is displayed in a visual form that can be accessed via web-based and mobile applications. This research has developed an innovation in controlling the use of electrical energy sources from PV and Utility networks by using several sensors as parameters to determine the right time and condition in controlling the use of electrical resources. [ABSTRACT FROM AUTHOR]

Chile is a country that has had large renewable energy resources in recent years, and it has demonstrated a commitment to the use of clean energy. However, most solar energy projects are located in northern Chile, neglecting other areas, such as the southern part of the country, where the photovoltaic energy industry has the potential to thrive. This is the case in the Araucanía region, particularly in Temuco, which has diverse agricultural activity. However, being a remote area without access to the electrical grid, it relies on generation sources like oil. This article, a research product, aims to demonstrate the functionality that can be added to agricultural land by combining it with solar parks. To achieve this, an agrovoltaic system was estimated for a plot of land in the agricultural zone of Temuco. This system will serve as a sample of the integration between photovoltaic infrastructure and the agricultural industry for those interested in installing it. [ABSTRACT FROM AUTHOR]

A novel optimization algorithm, namely Harbor Seal Whiskers Optimization Algorithm (HSWOA) is proposed in this work. Harbor seals use their whiskers to find underwater disturbances which are in the form of oscillating spheres and track prey even though they lack lateral-line systems. HSWOA mimics the high-level sensing that seal whiskers possess. As such, HSWOA has an excellent exploration capability for the search space and a high exploitation capacity for exploiting the all-optimum solutions to reach the most optimum solution. To validate these abilities, the proposed HSWOA utilizes two sets of test functions: 33 benchmark functions and five IEEE Congress on Evolutionary Computation (CEC2019) benchmark functions. The results of HSWOA are compared with ten well-established optimization algorithms. The comparison results show that HSWOA offers superior performance indices to reach an optimum solution while requiring less control variables. The results also show that HSWOA is more efficient regarding computational demand and resolution accuracy. Finally, the HSWOA is employed to track Maximum Power Point (MPP) of Photovoltaic (PV) array with partial shading conditions (PSCs) for two case studies. The results show that HSWOA extracts maximum power in minimum tracking time and high average power capturing capability compared to other optimization techniques. [ABSTRACT FROM AUTHOR]

The enhancement of photovoltaic array conversion efficiency during the partial shade operation is essential as the power-voltage curve includes multi-local peaks and one global peak. In such case, the conventional hill climbing-based maximum power point tracker approaches failed in monitoring the global power as they fall in local optima. Therefore, this paper proposes a new metaheuristic approach named modified tunicate swarm algorithm to track the global power of the photovoltaic array in the event of partial shade operation. In the proposed approach, two distinct random numbers are incorporated in the tunicate's updating process to enhance the search ability and avoid falling in local optima. The proposed approach is validated using two applications, CEC'17 test suite and practical one of designing the tracker installed with the photovoltaic array operated at partial shade. The proposed approach controls the dc-dc boost converter at the array terminals via obtaining the suitable duty cycle at which the global power is extracted. Five shade patterns with distinct locations of global power are analyzed in addition to normal operation, the proposed tracker is compared to teaching–learning-based optimizer, particle swarm optimizer, Coot optimization algorithm, capuchin search algorithm, and perturb and observe. Moreover, statistical tests of ANOVA table, Wilcoxon rank test, and Friedman test are conducted to assess the proposed approach. The fetched results clarified that, the best power error obtained via the proposed tracker is 0.000042 W at the third shade pattern while the worst one is 0.0652 W at the fifth pattern. Moreover, the p-value obtained via ANOVA table is 1.85399e-19 which confirms significant difference in the column means. The results proved the superiority and competence of the proposed tracker in solving the CEC'17 test suite and extracting the best global power from the photovoltaic array in all studied operating conditions. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are electric power systems designed to supply usable solar power by means of photovoltaics, which is the conversion of light into electricity using semiconducting materials. PV systems have gained much attention and are a very attractive energy resource nowadays. The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun. PV systems play an important role in reducing feeder losses, improving voltage profiles and providing ancillary services to local loads. However, large PV grid-connected systems may have a destructive impact on the stability of the electric grid. This is due to the fluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels. Thus, the electrical distribution system with high penetration of PV systems is subject to performance degradation and instabilities. For that, this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants. To accomplish this, an indicative representation of solar irradiation datasets is used for planning and power flow studies of the electric network prior to PV systems installation. Those datasets contain lengthy historical observations of solar energy data, that requires extensive analysis and simulations. To overcome that the lengthy historical datasets are reduced and clustered while preserving the original data characteristics. The resultant clusters can be utilized in the planning stage and simulation studies. Accordingly, studies related to PV systems integration into the electric grid are conducted in an efficient manner, avoiding computing resources and processing times with easier and practical implementation. [ABSTRACT FROM AUTHOR]

In this research, an Artificial Bee Colony (ABC) algorithm based Selective Harmonics Elimination (SHE) technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system. Pulse width modulation based on Selective Harmonics Elimination is mostly used to suppress lower-order harmonics. A high gain DC-DC-SEPIC converter keeps the photovoltaic (PV) panel's output voltage constant. The Grey Wolf Optimization (GWO) filter removes far more Photovoltaic panel energy from the sunlight frame. To eliminate voltage harmonics, this unique inverter architecture employs a multi-carrier duty cycle, a high-frequency modulation approach. The proposed ABC harmonics elimination approach is compared to SHE strategies based on Particle Swarm Optimization (PSO) and Flower Pollination Algorithm (FPA). The suggested system's performance is simulated and measured using the MATLAB simulation tool. The proposed ABC approach has a THD level of 4.86%, which is better than the PSO and FPA methods. [ABSTRACT FROM AUTHOR]

Reconfiguration techniques play an essential role in maximum power enhancement from PV arrays under partial shading conditions. Reconfiguration techniques are of two types based on the electrical connection. If the electrical connection remains the same after the rearrangement of panels, then that reconfiguration is static; otherwise, it is dynamic. Out of all the static reconfiguration techniques, the fixed column reconfiguration technique is the most flexible method. For static reconfiguration, the length of the column wire should be more than the usual connection. This paper proposes an algorithm for finding the column wiring resistance for fixed column static reconfiguration of a PV array. The proposed algorithm is applied to reconfigure Total-Cross-Tied (T-C-T) and Triple-Tied-Cross-Linked (T-T-C-L) PV array configurations. The performance of T-C-T and T-T-C-L PV arrays is analyzed in four reconfigured patterns by considering cross-ties resistance and column wiring resistance under dynamic shading conditions. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are electric power systems designed to supply usable solar power by means of photovoltaics, which is the conversion of light into electricity using semiconducting materials. PV systems have gained much attention and are a very attractive energy resource nowadays. The substantial advantage of PV systems is the usage of the most abundant and free energy from the sun. PV systems play an important role in reducing feeder losses, improving voltage profiles and providing ancillary services to local loads. However, large PV grid-connected systems may have a destructive impact on the stability of the electric grid. This is due to the fluctuations of the output AC power generated from the PV systems according to the variations in the solar energy levels. Thus, the electrical distribution system with high penetration of PV systems is subject to performance degradation and instabilities. For that, this project attempts to enhance the integration process of PV systems into electrical grids by analyzing the impact of installing grid-connected PV plants. To accomplish this, an indicative representation of solar irradiation datasets is used for planning and power flow studies of the electric network prior to PV systems installation. Those datasets contain lengthy historical observations of solar energy data, that requires extensive analysis and simulations. To overcome that the lengthy historical datasets are reduced and clustered while preserving the original data characteristics. The resultant clusters can be utilized in the planning stage and simulation studies. Accordingly, studies related to PV systems integration into the electric grid are conducted in an efficient manner, avoiding computing resources and processing times with easier and practical implementation. [ABSTRACT FROM AUTHOR]

In this research, an Artificial Bee Colony (ABC) algorithm based Selective Harmonics Elimination (SHE) technique is used as a pulse generator in a reduced switch fifteen level inverter that receives input from a PV system. Pulse width modulation based on Selective Harmonics Elimination is mostly used to suppress lower-order harmonics. A high gain DC-DC-SEPIC converter keeps the photovoltaic (PV) panel's output voltage constant. The Grey Wolf Optimization (GWO) filter removes far more Photovoltaic panel energy from the sunlight frame. To eliminate voltage harmonics, this unique inverter architecture employs a multi-carrier duty cycle, a high-frequency modulation approach. The proposed ABC harmonics elimination approach is compared to SHE strategies based on Particle Swarm Optimization (PSO) and Flower Pollination Algorithm (FPA). The suggested system's performance is simulated and measured using the MATLAB simulation tool. The proposed ABC approach has a THD level of 4.86%, which is better than the PSO and FPA methods. [ABSTRACT FROM AUTHOR]

In this paper, an electric vehicle (EV) charging station powered by a photovoltaic (PV) system has been created to charge EVs. Furthermore, the maximum available power from the PV system was extracted using a new algorithm called the simplified universal intelligent PID (SUIPID) controller, which has the advantages of simplicity in design and intelligence. The SUIPID controller was compared to the artificial neural networks (ANNs), the fuzzy logic controller (FLC) based on linear membership functions, and the FLC based on particle swarm optimization (PSO) to optimize its parameters under various conditions. The system simulation was first performed using MATLAB software, then an experimental set up was conducted to confirm the theoretical work. The proposed SUIPID responds 28.5%, 44.4%, and 61.5% faster than the PSO-FLC, ANN, and FLC, respectively, whereas the ITAE error was reduced by 27.3%, 52.9%, and 65.2%, respectively. Also, the charging procedure of the EV battery is precisely steady under different atmospheric conditions. The SUIPID controller has several advantages over other intelligent algorithms, most notably its ease of design and implementation. • A new controller (SUIPID) was proposed to extract the MPPT from a PV system. • The dynamic response of the system was improved by 28.5%, 44.4%, and 61.5% compared to the PSO-FLC, ANN, and FLC. • A better tracking of the system maximum power point was recorded. • The proposed controller is much easier in design and implementation compared to other intelligent algorithms. • Experimental results confirm the superiorities of proposed algorithm. [ABSTRACT FROM AUTHOR]

The current use of photovoltaics is often limited to the utilization of roof surfaces or ground-mounted systems. In particular, building integrated photovoltaics (BIPV) have enormous potential to make energy production more sustainable, because the energy is generated where it is used. However, most of these modules either do not meet the aesthetic requirements of the architects as well as the building owner or are uneconomical, since visually appealing building-integrated PV modules cost several times more than standard modules. In this article, an approach for a (semi) automated assembly line that allows geometry- and material-flexible manufacturing of PV modules is presented. The challenges in automating the flexible manufacturing processes include mainly the handling of limp components and the complexity of geometry variability. Appropriate gripper systems are required to ensure safe and reliable handling of the components. A gripper developed in this article offers the ability to flexibly deposit solar strings. Preliminary tests show that 66% of all conducted trials meet the accuracy requirements. [ABSTRACT FROM AUTHOR]

This paper analyzes the system describing the absorption solar collector as an application of nanoparticles for the storage of solar energy. The system involves two fractional partial differential equations (FPDEs) utilizing the Caputo fractional definition (CFD). Explicit solutions are determined for the temperature and velocity in terms of the Wright function (WrFn) via Laplace transform (LT). In addition, the solutions are expressed in terms of some well–known special functions at selected values of the fractional–order. Moreover, the behaviors of the temperature and velocity are investigated graphically using the thermo–physical data of the Cu/Al 2 O 3 –nanoparticles. Furthermore, some numerical results are conducted about the performance of the Cu and Al 2 O 3. The results reveal the higher efficiency/performance of the Cu–nanoparticles over the Al 2 O 3 and thus copper enjoys higher capability than alumina for the purpose of solar energy storage. In addition, it is found that 1.41% in the enhancement of heat transfer is achieved by adding 1% of the Cu–nanoparticles while the corresponding enhancement rate of the other three nanoparticles was less. Besides, the enhancement rate reaches 7.1% by increasing the volume fraction of the Cu–nanoparticles to 5%. Furthermore, the enhancement rates for the TiO 2 , Ag, and Al 2 O 3 are 5.41%, 6.47%, and 6.53%, respectively when using 5% of these nanoparticles. Finally, the advantages/effectiveness of the current approach over a previous work in the literature are discussed in detail. [ABSTRACT FROM AUTHOR]

Within the last years but especially since the beginning of 2022 there is a large increase in the demand of renewable energy. Currently, so-called green hydrogen is one of the most promising energy carriers for renewable energy. To meet the demand for green hydrogen, installation of production technologies like water electrolyzer is necessary. In order to play a role within the hydrogen economy, companies have to be able to design their hydrogen production capacities for a spot-on time-to-market. This leads to difficult ramp-up decisions and trade-offs between costs, technological maturity, and market success. As the phases of research and development influences a significant part of the costs (especially within production) after reaching time-to-market, they determine the later cost structure of the company, the possibilities to gain a competitive advantage or to survive on the market. Analyzing literature with regard to wind and photovoltaic industry shows several factors influencing success. This paper identifies and discusses these critical success factors, transfers their relevance onto the current hydrogen economy, and points out critical aspects for market entry decisions. With respect to a globalized world, in which dependencies from single countries can lead to unwanted constraints, the results become even more important. [ABSTRACT FROM AUTHOR]

Cooling enhances the energy conversion efficiency and output of photovoltaic (PV) panels. In this work, the effects of natural convection, forced convection, and evaporative cooling on the performance of polycrystalline PV panels were investigated. The output and efficiency of a cooled PV panel were monitored and compared to those of an uncooled PV panel under the same conditions. The cooling was conducted using an insulated channel installed below a PV panel. Natural convection cooling was investigated for various channel air gaps (H = 30, 60, 90, and 120 mm). Natural convection currents in the cooling channels were capable of cooling the panel with wide air gaps. In forced convection cooling, the air was introduced by fans installed at the bottom opening of the cooling channel with various air velocities (u a = 1, 2, and 3 m/s). Evaporative natural convection cooling was performed by a wetted fabric along the lower surface of the cooling channel, whereas evaporative forced convection cooling by pushing air along the wetted lower surface of the channel. The experimental data showed that the panel efficiency and output increased due to cooling. The experimental results of natural convection cooling revealed that the use of an air gap of 120 mm to cool the solar panel contributed to an increase in the panel daily energy production and efficiency by 1.7% and 1.2%, respectively. For forced convection cooling, using air at a speed of 3 m/s increased the daily energy production by 4.4% and the efficiency by 4%. Natural convection evaporative cooling increased the daily energy production and the efficiency by 3.6% and 2.7%, respectively. Forced convection evaporative cooling contributed, at a speed of 2 m/s, to an increase in the daily energy production by 3.8% and an increase in efficiency of 3.8%. [ABSTRACT FROM AUTHOR]

Non-linear loads such as computers, televisions, arc furnaces, inverters, etc. generates harmonic currents infecting the quality of the power supply and causing damage. To remedy this problem, the shunt active power filter presents the best way to fight against harmonic currents. This paper gives a new technique of shunt active power filter which has tested to be the satisfactory answer to improve energy high-quality in phrases of removing harmonics caused by means of non-linear power loads distribution networks. Our work presents an SAPF powered by a photovoltaic system whose energy control is managed via metaheuristic cuckoo search optimization as the fastest and most reliable optimization unlike traditional strategies like the Perturb and Observe (P&O) algorithm used as the MPPT for PV systems, which gives us a stable power supply to further eliminate harmonics. The simulation on the Matlab Simulink environment truly suggests that the proposed model improves the excellent quality of energy and the control method is efficient and in accordance to the international standard recommendations IEEE 519-1992. [ABSTRACT FROM AUTHOR]

Silicon and its usage in photovoltaics and advanced Li-ion traction batteries is a key material for the decarbonisation of the energy and mobility sector. To achieve sustainable silicon-based products, an efficient raw material processing is required. However, the production of silicon and its purification is energy- and resource intensive. Further, during the production of photovoltaic cells, about 40% of the silicon used is lost as sawdust, the so-called silicon kerf, which is often not recycled to a high quality. Compared to primary production, silicon recycling requires less electrical energy and no fossil reductants. However, an environmental assessment of the GHG reduction potential of silicon kerf recycling in comparison to primary production of silicon is limited in literature. In this paper, we will provide a comprehensive environmental assessment of silicon kerf recycling located in Sichuan and Xinjiang in China and Germany, since China is the biggest producer of silicon worldwide. For the assessment of the recycling, a detailed process-specific material and energy flow balance based on industry and literature data is used. The results are evaluated with relation to the primary production of metallurgical silicon and a sensitivity analysis of used electricity mix and transport distance is performed. Results show that recycled metallurgical silicon from silicon kerf creates up to 77% lower GHG emissions compared to conventional primary silicon. Further, environmental hotspots are identified and discussed. Based on the findings, recommendations for target-oriented process engineering are given to further reduce the material-related GHG impacts of silicon based products. Finally, the impact of the results is reflected on the market potential of innovative products like photovoltaics and Li-ion batteries. [ABSTRACT FROM AUTHOR]

Significant interest has been shown in switched capacitor (SC)-based multi-level inverters (MLIs), which decrease the need for a DC supply and enhance power quality. The common issues with SC-MLIs include an uneven distribution of conducting paths, increased voltage drop across capacitors, the sum of all inverter DC link voltages across the highest voltage rated switches, and a higher total standing voltage (TSV). The purpose of this paper is to create a SC-MLI with less components in order to maintain a constant voltage across the capacitors, to obtain higher voltage gain with fewer parts, fewer conducting routes, lower TSV, and to create a more affordable and effective inverter. The structure of the MLI is created by a cascade interconnection between the number of SC cells. A single input multiple output (SIMO) converter boosts the DC-link voltage over the stable DC voltage of the solar panels using a modified perturb and observe (P&O) method. Additionally, fewer switches in the conduction path and 50 % of the switches operating at normal frequency guarantee a decrease in an overall loss of power in the proposed network. The benefits of the recommended MLI are made clear by comparing them with 17-level MLIs in terms of the number of elements, stress, gain, and cost factor. Detailed experimental results are shown under various transient conditions to show that the 17-level prototype is operationally viable. The total harmonic distortion (THD) is found to be identical and is less than 5 %, which meets IEEE standards. [ABSTRACT FROM AUTHOR]

Low-voltage (LV) consumers are considered the main cause of loading the electrical systems during peak hours, hence increasing the expansion costs of the distribution system. To stimulate the efficient use of Brazilian electricity, the National Electric Energy Agency (ANEEL) established a new tariff modality for low-voltage consumers, known as White Tariff, effective in Brazil since January 2018. The White Tariff is an hourly rate with three different time schedules throughout a day. In addition to this new tariff modality, ANEEL established the conditions for access to distributed micro-generation and mini-generation since 2012 through the Normative Resolutions No. 482 and No. 687 as a way of encouragement of the inclusion of distributed generation (DG) for the low-voltage consumers. In this sense, this article aims to present a methodology to analyze the implementation of the white tariff for photovoltaic and biomass DG by rural LV consumers. The methodology consists of carrying out simulations using the hybrid optimization model for electric renewables software including combinations to consider energy tariffs from different local utilities, load profiles of rural consumers within different consumption ranges, natural resources and distributed generation technologies. From that, it is possible to conclude that the resulting simulations were more satisfactory for the white tariff together with the DG biomass for consumption within ranges from 501 to 1000 and 1001 to 5000 kWh. [ABSTRACT FROM AUTHOR]

The current study investigated the simultaneous impacts of dust as well as other environmental parameters on cell temperature and the electrical outputs of photovoltaic systems. Based on the experimental and analytical analysis, several linear and polynomial semi-empirical correlation forms were proposed. It was shown that cell temperature depends directly on irradiation and ambient temperature, as well as inversely on humidity, wind speed, and the amount of accumulated dust on the surface. During the four-year outdoor experiments and further indoor measurements, the surface dust density varied from 0 to 10.1174 g.m−2, which resulted in up to a 28 % current loss. Although, it was shown that dust affects the output voltage less, and the most recorded variation was <0.5 % reduction. The electrical behavior of the photovoltaic system was also simulated with a modified single-diode model. Comparing the simulation results, correlation predictions, and experimental measurements confirmed consistency. Moreover, it was shown that considering the prediction precision, simplicity, and computational cost, the proposed correlation forms should be preferred when just the output current, voltage, and power are of concern. The proposed modified semi-empirical correlation forms can be easily coupled with other models to improve their precision and accuracy. • Experimental investigation of parameters affecting a photovoltaic cell temperature. • Provide a semi-empirical correlation form to predict cell temperature. • Provide semi-empirical correlation forms to predict output current and voltage. • Provide semi-empirical correlation forms to predict output power. • Increase precision and reduce computational costs for predicting output behavior. [ABSTRACT FROM AUTHOR]

With the increasing development of EVs, the energy demand from the conventional utility grid increases in proportion. On the other hand, photovoltaic (PV) energy sources can overcome several problems when charging EVs from the utility grid especially in remote areas. This paper presents an effective photovoltaic stand-alone charging station for EV applications. The proposed charging station incorporates PVarray, a lithium-ion battery representing the EV battery, and a lead-acid battery representing the energy storage system (ESS). A bidirectional DC-DC converter is employed for charging/discharging the ESS and a unidirectional DC-DC converter is utilized for charging the EV battery. The proposed controllers achieve maximum power extraction from the PVand regulate the DC-link voltage. It also controls the voltage and current levels of both the ESS and the EV during the charging/discharging process. The study has been applied to two cases with different power levels. Analysis, simulation, and implementation of the proposed system are presented. A 120 W laboratory prototype is carried out to verify the system performance, experimentally. Design guides for higher power levels are proposed to help in choosing the proper parameters of the converters. Both the simulation and experimental results are matched and verify the high performance of the proposed system. [ABSTRACT FROM AUTHOR]

• Multi-objective sizing models of on/off-grid hybrid renewable systems for EVCSs are developed. • Environmental, economic, reliability, and self-sufficiency aspects are considered. • NSGA-II algorithm solves the sizing problem; TOPSIS selects the best suitable solution. • Integrating renewable energy systems into EVCSs is highly profitable in Türkiye. • Renewable integrated EVCS reduces CO 2 emissions caused by EV charging up to 64%. Integrating electric vehicle charging stations (EVCSs) with renewable energy systems requires the consideration of several factors during the planning stage, including environmental impact, economic viability, grid reliability, and self-sufficiency. Therefore, this study conducts a multi-objective optimal sizing of on- and off-grid hybrid renewable energy systems for EVCSs. The sizing problem is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II). Subsequently, the best suitable solutions from the obtained non-dominated solutions are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, prioritizing the objective functions based on diverse interests of different stakeholders (large and small private investors and governmental entities). Finally, a techno-economic analysis is made considering payback period, profitability index (PI), and internal rate of return (IRR). The results show that on-grid systems show high economic viability with payback periods between 1.98 and 7.72 years, an average PI of 5.07 and an average IRR of 23.97%. Although off-grid systems present lower economic viability with payback periods between 8.77 and 22.42 years, an average PI of 1.68 and an average IRR of 4.91%, in certain cases they reach investable levels with payback periods below 10 years, PI above 2, and IRR above the interest rate. [ABSTRACT FROM AUTHOR]

Solar PV power is still under-utilized despite the abundance of solar radiation in Uganda. There is need for empowering renewable energy landscape through unlocking the technical and economic feasibility of solar photovoltaic power. We analyzed data from 56 locations for the techno-economic and environmental assessment of photovoltaic power facilities in Uganda. This was based on weather data availability and accessibility to the national power grid. Analysis of the energy generation and different input factors was done using PVsyst 7.2. A three stage approach to losses was adopted: absorption of sunlight, conversion to DC and DC to AC conversion. Findings indicate that most of the countryside is suitable for construction of large scale grid-connected photovoltaic power facilities. Due to longer sunshine duration and stronger Global Horizontal Irradiance (GHI) which are associated with high energy yield, northern Uganda performed better than the rest of the country, making it a preferential siting for large scale grid-connected photovoltaic facilities. South western Uganda performed the poorest. After a thorough energy accounting and a list of all performance metrics, the viability of investing in grid-connected photovoltaic power facilities was assessed. • Despite the abundance of solar radiation in the country, large scale exploitation of the same is still limited. • Large-scale solar PV use is feasible by assessing the performance of existing grid-connected plants • Environmental analysis of the solar PV power plants shows a significant reduction in Greenhouse gases emission. • Effective assessment combines slope analysis with techno-economic performance analysis. • Stakeholders should focus on large-scale grid-connected solar PV in Northern Uganda. [ABSTRACT FROM AUTHOR]

• The design features and pretreatment requirements in this application are reported. • Adsorption, ion exchange and membrane process in this application are evaluated. • Membrane process is the most promising solution in this application. • Efforts should be put into developing cost competitiveness membrane. Delivering efficient, affordable and sustainable water treatment methods in the removal of heavy metals in wastewater often remains a challenge. This paper seeks to alleviate this challenge by reviewing and assessing the viability and efficiency of different water treatment methods within the realm of end-of-life (EoL) photovoltaic (PV) module recycling. Specifically, this paper evaluates the possible designs, pretreatment requirements, efficiency, relative cost and environmental footprint of adsorption, ion exchange and membrane process, applications of physical-chemical and tertiary treatments, to protect the receiving environment and realise treated water reuse in EoL PV module recycling process respectively. The current results suggest that the membrane process is a promising solution to the various heavy metals removal in EoL PV recycling due to its comparatively good performance in efficiency and environmental footprint. Meanwhile, specifical efforts should be put into the development of cost competitiveness membrane that excels in EoL PV recycling. More importantly, exploring the potential combination of different water treatment methods could also greatly broaden the range of solutions available for addressing the existing water issues in EoL PV recycling. Schematic of wastewater strategy in end-of-life (EoL) photovoltaic (PV) module recycling [Display omitted] [ABSTRACT FROM AUTHOR]

In Poland, in the last 3 years, there has been a sharp increase in the number of photovoltaic micro-installations, especially in the power range of 2–10 kWp. The study analyzes the economic viability and environmental aspects of the resulting installations. A distinction was made according to the size of the installation, which translates into its productivity and the scale effect of the assembly. For the first time in Poland, the emission levels of CO 2 and other pollutants were determined based on the power of PV installations. Both the Life Cycle Assessment and the cash flow analysis were used in the research. In the analyzed range of installation power, changes in the calculated environmental indicators ranged from 5.4 to 7 %, and the most environmentally effective option in the My Electricity program is a 10 kWp installation, while the highest economic efficiency was achieved for installations with a capacity of 2 kWp. In addition, the impact of the installation of many installations under the My Electricity program on the reduction of the carbon footprint, emission of dust, nitrogen oxides and sulfur dioxide in the conditions of the Polish power industry was analyzed. • Evaluation of polish "My Electricity" programme. • Photovoltaic grants. • Life Cycle Assessment and the Cash Flow Analysis for polish prosumer PV microinstallations. • Influence of capacity photovoltaic installation for economic and environmental results. [ABSTRACT FROM AUTHOR]

The present research addresses a new optimum configuration for a Hybrid Renewable Energy System (HRES). The structure includes fuel cell (FC), wind turbine (WT), and photovoltaic (PV) and its auxiliaries including the electrolyzer and the H 2 tank as the storage system. The primary mover in this study is the grid, while, the proposed HRES is used during energy deficiency from the grid side. The main target is to provide an optimum number of the HRES components to get the minimum loss of power supply probability (LOPSP) and Total Net Present Cost (TNPC). For achieving better optimal results, a Modified version of African vulture optimizer (IAVO) is proposed, validated, and used in the system. Final results are applied to a building in Ahvaz, Iran and they are put in comparison with different works of the studied works. The achievements indicated the primacy of the proposed system over the other algorithms. • A new optimal configuration for a HRES. • Using electrolyzer and the H 2 tank as the storage system. • Providing optimal number of the HRES components to get the minimum LOPSP and TNPC. • Enhanced version of African vulture optimizer (IAVO) is used for optimization. • Final results are applied to a building in Ahvaz, Iran. [ABSTRACT FROM AUTHOR]

[Display omitted] • Photovoltaic system is evaluated in extreme operating temperatures. • System thermal performance is analysed using 1D and 2D simulations. • Free convection is improved as a dominant condition. • Models are verified analytically and validated experimentally. This study aims to evaluate the performance of a photovoltaic module under some extreme climate conditions, and with a case study for Iraq. CFD model is developed for the analysis of the photovoltaic module using the commercial CFD software of COMSOL Multiphysics v5.3a for the transient conditions. The results are verified with the analytical solution to the one-dimensional non-linear energy balance equation using Matlab. The results are also compared with measurements reported in the literature for validation. The results reveal that the free convection currents in inclined and horizontal positions of the module were weaker relative to the vertical position. Also, the increase in the length of inclined photovoltaic module, up to 1.3 m, enhances the heat transfer rate. However, beyond this length, the temperature of the module becomes higher, and the convective heat transfer coefficients are reduced regardless of the inclination. In the horizontal position, the convective heat transfer rate is lower, particularly on the bottom surface of PV system. [ABSTRACT FROM AUTHOR]

The goal of this project is to develop and analyze a three-phase grid-connected photovoltaic (PV) system with a 250KW power capacity with expandable property. The PI, Slide, and MPC methodologies are used to test three distinct inverter current control techniques. The dynamic performances of the three inverter control techniques are virtually comparable under grid connected working conditions, although the dynamic performances of the traditional PI control technique are slightly better and smoother than the other two inverter control techniques. The unity power factor criterion was reached by all techniques. The output power levels obtained using the sliding mode inverter control method are higher than those obtained using the PI and MPC methods. MPC's DC link voltage readings are also consistent and match the intended voltage levels. [ABSTRACT FROM AUTHOR]

The paper suggests a novel DPC approach to ameliorate the management and HRES “hybrid renewable energy system” control composed of photovoltaic and wind systems. In the conventional DPC technique, the “switching table” is founded on a “hysteresis comparator”, which poses the problem of fluctuations on the HRES various output variables. The approach proposed here is based on an FLC and shown to diminish the ripples in the “active” and “reactive” powers waveforms. The comprehensive HRES and the advised “control schemes” are implemented using “MATLAB/Simulink” and the results indicate that the advised method had preferable performance over the conventional DPC. [ABSTRACT FROM AUTHOR]

Although solar photovoltaic (PV) module costs have been declining aggressively there has been only modest capital cost improvements for the balance of systems (BoS) in general, and for racking in particular. For small-scale PV systems racking can make up the majority of the capital expenditures. To overcome this challenge, in this study a novel parametric mobile low-cost open source racking system is designed using open source software and validated with numerical modeling. The system is designed to use readily accessible vertically-mounted conduit and steel cables and be built with common hand tools so that it is easily replicable in remote areas and the developing world. It can be assembled and deployed in half an hour and easily dissembled and flat-packed for transport. The system can be fabricated for less than commercial racks and is completely customizable in terms of tilt angle and size. The racking system is designed to resist light to moderate wind loads consistent with everyday breezes rather than those commonly used for structural design which are based on extreme events; as it is assumed the racking system will be disassembled and stowed prior to an extreme wind event. • Small-scale photovoltaic (PV) systems racking is majority of capital expenditures • Novel parametric mobile low-cost open source racking system • Designed using open source software and validated with numerical modeling. • Vertically-mounted conduit and steel cables, built with common hand tools • Fabrication costs < commercial racks; completely customizable for tilt angle + size. [ABSTRACT FROM AUTHOR]

Photovoltaics cell behave in a unique way when light falls on it. The objective of this work is to evaluate the performance of photovoltaic system under different wavelengths from artificial light in a controlled environment. Measurements were first taken from the PV module in the absence of the filters, followed by each filter been placed individually and measurement taken correspondingly. From the results obtained the PV module was most efficient when the yellow colored filter was employed and least efficient with the blue filter (revealing a difference of 2% in efficiency between them). Nevertheless, the photovoltaic module surpassed the efficiencies reached with the application of the colored filters when left open to the natural spectrum. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) technology, particularly silicon solar cells (SSCs), has emerged as a key player in meeting this demand due to its mature technology, prolonged stability, non-toxicity, and material abundance. Heterojunction (HJT) solar cells have shown significant promise by eliminating dopant-diffusion processes and separating c-Si wafers from metal contacts. In recent years, the notable enhancement in the record PCE of SSCs primarily hinges on advancements in HJT technology, incorporating sophisticated passivating selective contacts. This review explores the evolution and recent progress of passivating selective contacts in HJT solar cells, examining doped silicon-based materials, metal compounds, and organic materials. Despite dopant-free contacts still lagging in efficiency, their potential for high fill factor (FF) values suggests viable pathways for future research. This study aims to provide a comprehensive overview, highlighting key advancements, challenges, and prospects in the ongoing development of HJT technology for higher performance, enhanced stability, and reduced costs. [Display omitted] • The study reviews passivating selective contacts for HJT silicon solar cells over the past decade. • Fundamental mechanisms and theory of passivating selective contacts are explored. • The evolution of passivation layers is highlighted, focusing on material modifications. • Different selective layers are systematically evaluated. • Challenges and future perspectives for passivating selective contacts are presented. [ABSTRACT FROM AUTHOR]

With the accelerating deployment of renewable energy, photovoltaic (PV) and battery energy storage systems (BESS) have gained increasing research attention in extremely cold regions. However, the extreme low temperatures pose significant challenges to the performance and reliability of such systems. This paper reviews the current progress in PV-BESS technologies and applications in extreme cold climates. The major technical challenges are discussed, including the impacts of low temperatures on PV and battery efficiency, snow and icing effects, and difficulties in system modeling and optimization. Recent solutions are analyzed, such as advanced PV module designs, battery thermal controls, anti-icing coatings, and sensors. Case studies of existing PV-BESS demonstration projects in cold regions are presented, and key lessons learned from real-world operation data are summarized. The economic feasibility and policy incentives for promoting PV-BESS in cold areas are also examined. Finally, recommendations for further research and development are provided to overcome the limitations and knowledge gaps. This review covers the current state-of-the-art in PV-BESS systems suited for extreme cold environments, providing insights for researchers and engineers working on advancing renewable energy in these challenging climates. Key findings indicate that despite technical hurdles, PV-BESS systems show promising potential in extreme cold regions through continued system optimization, cost reduction, and supportive policies. • We summarize the state-of-the-art in PV-BESS technologies suited for extreme cold climates. • Field experiences and lessons learned from existing PV-BESS demonstration projects and installations in extreme cold regions are analyzed. • We provide recommendations for further research and development towards advancement. [ABSTRACT FROM AUTHOR]

Globally, renewable energy sources (RESs) are being used more frequently, including in arid desert regions such as Kuwait. This small geographical area is planning to expand its RES share to reach 15 % of the national electricity demand by 2030. This study investigates the effects of two large sandstorms on different RESs employed in Kuwait: (1) photovoltaic (PV) power plants, (2) concentrated solar power (CSP) plants, and (3) wind turbines (WTs). Sandstorms exhibit crucial weather-related characteristics as the solar irradiance decreases significantly during storms whereas the wind speed increases. These two weather parameters are the fuel used in RESs. Therefore, the power produced by the PV and CSP plants decreases substantially, whereas the power produced by the WT increases to its rated value for several hours. This causes a decrease in the total energy generated by the PV and CSP plants, in contrast to the WT plants, which always overperform against their typical daily average values. By comparing the daily energy average of May 2021 with no major sandstorms present, the energy generated by the PV is 83.3 % (Storm 1) and 49.1 % (Storm 2), whereas the energy generated by the CSP is 100.4 % (Storm 1) and 18.2 % (Storm 2). Conversely, the energy generated by the WTs is 198.6 % (Storm 1) and 237.6 % (Storm 2). This study illustrates one of the benefits of employing hybrid RESs. Different hybrid configurations are investigated with the PV-WT configuration showing great potential with near-constant produced power and overperformance in the total energy generated. • Sandstorms display two important characteristics: (1) low solar irradiance (2) high wind speed. • Sandstorms reduce output power and energy generated of photovoltaic (PV) power plants. • Sandstorms reduce output power and energy generated of concentrated solar power (CSP) plants. • Sandstorms increase output power and energy generated of wind turbines (WTs). • Sandstorms have adverse effects for few days after the storms due to dirt accumulations. • Hybrid renewable energy sources can alleviate the negative effects of sandstorms. [ABSTRACT FROM AUTHOR]