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Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array's current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%. [ABSTRACT FROM AUTHOR]

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The goal is to develop an adaptive energy management method that can predict driving circumstances, reducing stress on the primary storage battery in a hybrid electric vehicle (HEV). To improve battery lifespan and overall efficiency, power allocation among multiple energy storage sources (ESS) must go beyond traditional rule-based schemes. This paper offers a promising solution to address these challenges. Standardized driving cycles, such as the Urban Dynamometer Driving Schedule (UDDS), are used as load torque to evaluate the power demand in hybrid electric vehicles (HEV). First, feature extraction techniques improve the extraction of important features from historical driving cycle data, thereby facilitating the long short-term memory (LSTM) based on multistep velocity predictors using a warm-up algorithm. This predicted future velocity profile serves as input to the HEV system. Second, within the hybrid electric vehicle (HEV) system, the battery is linked to a modified interleaved DC-DC boost converter. The results show that this converter affects factors such as ripple, converter loss of 29%, state of charge (SOC) level, and efficiency improvement to 98%. Finally, the energy management system (EMS) with the modified rule-based scheme is implemented with neural networks for optimization. This EMS is responsible for continuously adjusting the power allocation. Simulation results show a significant average reduction of about 33.33% at peak battery level, thereby extending battery life. Compared with conventional rule-based EMS methods, the proposed strategy significantly improves efficiency by reducing the total energy loss by about 10.36%. These results emphasize the reliability and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) system performance and reliability can be improved through the detection of defects in PV modules and the evaluation of their effects on system operation. In this paper, a novel system is proposed to detect and classify defects based on electroluminescence (EL) images. This system is called Fault Detection and Classification (FDC) and splits into four modules, which are (1) Image Preprocessing Module (IPM), (2) Feature Extraction Module (FEM), (3) Feature Selection Module (FSM), and (4) Classification Module (CM). In the first module (i.e., IPM), the EL images are preprocessed to enhance the quality of the images. Next, the two types of features in these images are extracted and fused together through FEM. Then, during FSM, the most important and informative features are extracted from these features using a new feature selection methodology, namely, Feature Selection-based Chaotic Map (FS-CM). FS-CM consists of two stages: filter stage using chi-square to initially select the most effective features and a modified selection stage using an enhanced version of Butterfly Optimization Algorithm (BOA). In fact, BOA is a popular swarm-based metaheuristic optimization algorithm that has only recently found success. While BOA has many benefits, it also has some drawbacks, including a smaller population and an increased likelihood of getting stuck in a local optimum. In this paper, a new methodology is proposed to improve the performance of BOA, called chaotic-based butterfly optimization algorithm. Finally, these selected features are used to feed the proposed classification model through CM. During CM, Hybrid Classification Model (HCM) is proposed. HCM consists of two stages, which are binary classification stage using Naïve Bayes (NB) and multi-class classification stage using enhanced multi-layer perceptron. According to the experimental results, the proposed system FDC outperforms the most recent methods. FDC introduced 98.2%, 89.23%, 87.2%, 87.9%, 87.55%, and 88.20% in terms of accuracy, precision, sensitivity, specificity, g-mean, and f-measure in the same order. [ABSTRACT FROM AUTHOR]

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions. [ABSTRACT FROM AUTHOR]

The hybrid photovoltaic (PV) with energy storage system (ESS) has become a highly preferred solution to replace traditional fossil-fuel sources, support weak grids, and mitigate the effects of fluctuated PV power. The control of hybrid PV-power systems as generation-storage and their injected active/reactive power for the grid side present critical challenges in optimizing their performance. Therefore, this paper introduces hybrid PV-battery parallel inverters employing a finite control set model predictive control (FCSMPC) method. The proposed FCSMPC-based controller and inverter system achieves multiple functionalities, including maximum power extraction from PV, proper charging/discharging commands for ESS, support for weak grid conditions, support during low-voltage ride-through (LVRT) by increasing reactive power injection to counteract the drop in grid voltage, and economic management based on feed-in-tariff (FiT). The controller significantly improves the performance of the PV-battery system under faulty LVRT conditions and unbalanced grid voltages, satisfying grid code requirements while continuously supplying the microgrid's delicate local load. A real-time simulation hardware-in-the-loop (HiL) setup, utilizing the OPAL-RT platform, is employed to implement the proposed hybrid PV–ESS with its controller. The results affirm the superior ability of FCSMPC in weak-grid conditions and its capability to achieve multiple objectives simultaneously. [ABSTRACT FROM AUTHOR]

A hybrid approach is proposed for optimizing distribution systems (DSs) by integrating clean energy sources, specifically photovoltaic (PV) and wind power (WT). The proposed technique combines the mud ring algorithm (MRA) and quantum neural network (QNN), referred to as the MRA‐QNN technique. The primary objective is to minimize power loss and enhance voltage stability. The MRA method generates the control signal of the converter, while the QNN method predicts the control signal based on the MRA output. The effectiveness of the approach is revealed through simulations on standard IEEE 33 bus and 69 bus systems. Implementation in MATLAB shows superior performance compared to existing methods, with lower power loss values. There has been a sustained rise in the system voltage profile (In the WT and PV situations, 0.950. and 93 p.u), as well as a considerable reduction in the active power (AP) losses (to 132.39 kW with PV and 81.23 kW with WT from 362.86 kW). With PV, the entire yearly economic loss is lowered from $158932.68 to just $57996.939, and with WT, it is decreased to $56805.479. With three PVs, the yearly economic loss and active power losses are decreased to 30419.871 $ and 69.449, and 4.27 kW and 1875.930 $, respectively. [ABSTRACT FROM AUTHOR]

The detailed examination of utilizing renewable energy sources, particularly integrating Photovoltaic (PV) arrays and Multilevel Inverters (MLI), is thorough and underscores the importance of addressing environmental issues linked to fossil fuels. The selection of Sinusoidal Pulse Width Modulation (SPWM) and its benefits, such as low switching losses and high efficiency, are well-articulated. The simulation results showing sinusoidal waveforms for resistive loads and the focus on reducing Total Harmonic Distortion (THD) using an LC filter further highlight the commitment to achieving high-quality power output. THD reduction is crucial for maintaining the stability and reliability of power systems. The incorporation of a seven-level MLI adds complexity and sophistication to the system, potentially allowing for more precise control over the output waveform and enhancing the overall performance of the renewable energy system. The consideration of factors like efficiency, reliability, and grid compatibility aligns with best practices in the design and implementation of renewable energy systems. Your approach clearly aligns with the broader industry trend towards cleaner and more sustainable energy solutions. Overall, the strategic and effective use of renewable energy, SPWM for control, addressing THD through an LC filter, and incorporating an eleven-level MLI showcases multi-order harmonic elimination for maximum power generation as presented in this paper.

Abstract Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

Abstract There are two major forms of solar energy that are typically utilized: photovoltaic and concentrated applications. The application of fractal glass texture to photovoltaic solar panels is a cutting-edge technique in the field of solar panels that generate electricity from exposure to light. When it comes to studying the environmental implications of a product during its development and commercialization, the life cycle assessment (LCA) approach is an excellent technique that can be utilized. The purpose of this study is to offer a thorough understanding of the product’s effects on the environment by taking into consideration a wide range of criteria, including environmental, economic, and other evaluations. Through the utilization of the life cycle assessment (LCA) methodology and the SimaPro software, this paper presents a comparative analysis of conventional solar panels and fractal glass texture panels. During the course of this research, 18 midpoint indicators and three endpoint indices were investigated. In addition, a sensitivity analysis has been carried out on the fractal property of the panel in order to evaluate the impact that it has on environmental impacts and damages. For the purpose of this study, three distinct levels of fractal coating were applied to the panel surface: one percent, three percent, and five percent. As a result of the data, it was determined that the “Photovoltaic cell single-Si wafer” and the “Transport, freight, sea, transoceanic tanker” had the most significant impact on the midpoint and endpoint indices for both panels, respectively. Furthermore, there is a direct association between the rise in fractal coating on panels and the reduction in environmental repercussions, approximately. This correlation exists because of several factors.

Solar photovoltaic (PV) panels suffer from a reduction in performance due to dirt and environmental pollutants accumulating on their surface. As the number of solar panels installed grows, boosting their efficiency becomes more crucial to optimizing electricity output while decreasing the need for additional panels. Cleaning the PV panels can increase their efficiency, and an automated cleaning system with cutting-edge technologies can improve cleaning effectiveness. This article proposes a system that utilizes the Messaging Queuing Telemetry Transport protocol to enhance the efficiency of solar panels. Also, the system harnesses the power of IoT technology to automate the cleaning process. The system incorporates IoT-enabled sensors, actuators, and communication modules, all controlled by ESP-32. We programed the microcontroller using the software Arduino and Visual Studio Code. The Adafruit IO controls and monitors the system. Users can switch between fully automatic and manual modes using the Adafruit dashboard. The system utilizes DC motors, nylon brushes, a metal frame, a relay module, a buck converter, and a boost converter. A 10W solar panel powers the system with the help of a 20 A charge controller and a 12 V/2.5 Ah battery. The Adafruit dashboard fully controls and monitors the developed system. By automating the cleaning process and leveraging real-time data, the system maximizes solar power generation efficiency, minimizing downtime. The system increases the efficiency of tested 30 W solar panels by 30%. [ABSTRACT FROM AUTHOR]

This paper proposes a new structure for a photovoltaic (PV) simulator. The proposed simulator enables obtaining power–voltage (P–V) and current–voltage (I–V) graphs without the need for a PV panel. The main part of the PV simulator includes series-connected cascaded units, and this feature provides a stepped shape voltage form at the simulator output depending on the unit number. As the number of units is increased, P–V and I–V graphs become more similar to the actual data. Simulation studies were first performed to prove the ability of the presented inverter. Then experimental work was performed to confirm the validity of the simulator. The results show that the presented topology is very convenient for operating as a PV simulator in laboratory environments for researchers. Since the number of units can be increased, the voltage level can easily be moved to higher values and so the simulator can be used in high-voltage PV applications. [ABSTRACT FROM AUTHOR]

Renewable energy sources such as solar are becoming increasingly popular worldwide. Mathematical derivation is used to show the structural framework of PV cells and their models with a single-diode configuration. This paper proposed a two-step optimization technique for extracting the unknown parameters of solar PV cells and modules. The implementation of the proposed techniques is to find the unknown parameters of the PV module from Kyocera (KC200GT). First, we configured the single-diode configuration of the PV equation in terms of five unknown parameters ( I ph , I 0 , V t , R s , and R sh ) and in terms of two unknown parameters ( R s and V t ). After that, we implemented the proposed two-step optimization techniques for extracting the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module Kyocera (KC200GT). Also, we used the genetic algorithm (GA) and particle swarm optimization (PSO) techniques to find the unknown parameters ( I ph , I 0 , V t , R s , and R sh ) of the PV module from Kyocera (KC200GT). The performance of the proposed two-step optimization techniques was compared with the traditional single-stage optimization techniques: particle swarm optimization (PSO), genetic algorithms (GAs), grey wolf optimization (GWO), Villalva's method, Accarino's method, Iterative method, and Silva's method. The test results and the output P–V waveform indicate that the proposed method is more efficient and has a greater impact than the standard techniques. [ABSTRACT FROM AUTHOR]

The study emphasizes on the development and evaluation of a PV-powered solar-infrared hybrid dryer (SIHD) for the uninterrupted drying of anchovy fish irrespective of weather conditions and grid connectivity. The SIHD dryer was designed to utilize solar energy as the main source of heat for drying during sunshine hours and an infrared lamp as backup during overcast conditions. It consists of a cylindrical drying chamber, infrared (IR) lamp, drying trays, solar PV panel, battery, and a remote monitoring system. The performance of SIHD was evaluated using anchovy by assessing drying kinetics, drying efficiency, and sensory attributes of the dried product and comparing it with solar and open sun drying. A remote data acquisition unit with a controller and sensors was integrated with the SIHD to obtain drying data and maintain desired drying conditions. The moisture of anchovy is reduced from 83.7 (w.b.) to 15.2% (w.b.) in 6.25 h by SHID, while it takes 10.30 h in a solar dryer and 16.20 h in the open sun drying to achieve the moisture content of 15.3% (w.b.) and 15.5% (w.b.), respectively. The maximum drying efficiency of 30.43% and 41.11% was obtained for solar and solar-infrared hybrid drying, respectively. The dried samples obtained from the solar infrared hybrid dryer received the highest overall acceptability score with superior color and texture. Hence, the outcome of the present study helps to understand the new and innovative approach of using solar-IR hybrid technology in the drying process for controlled and efficient drying with scope for large-scale adoption possibilities. [ABSTRACT FROM AUTHOR]

There are two major forms of solar energy that are typically utilized: photovoltaic and concentrated applications. The application of fractal glass texture to photovoltaic solar panels is a cutting-edge technique in the field of solar panels that generate electricity from exposure to light. When it comes to studying the environmental implications of a product during its development and commercialization, the life cycle assessment (LCA) approach is an excellent technique that can be utilized. The purpose of this study is to offer a thorough understanding of the product's effects on the environment by taking into consideration a wide range of criteria, including environmental, economic, and other evaluations. Through the utilization of the life cycle assessment (LCA) methodology and the SimaPro software, this paper presents a comparative analysis of conventional solar panels and fractal glass texture panels. During the course of this research, 18 midpoint indicators and three endpoint indices were investigated. In addition, a sensitivity analysis has been carried out on the fractal property of the panel in order to evaluate the impact that it has on environmental impacts and damages. For the purpose of this study, three distinct levels of fractal coating were applied to the panel surface: one percent, three percent, and five percent. As a result of the data, it was determined that the "Photovoltaic cell single-Si wafer" and the "Transport, freight, sea, transoceanic tanker" had the most significant impact on the midpoint and endpoint indices for both panels, respectively. Furthermore, there is a direct association between the rise in fractal coating on panels and the reduction in environmental repercussions, approximately. This correlation exists because of several factors. [ABSTRACT FROM AUTHOR]

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

Photovoltaic (PV) aquaculture offers a promising solution for sustainable electricity generation for farm and grid utilization (SEG/FGU). This fusion of solar technology and aquaculture methods is crucial for sustainable food production and eco-friendly power and grid integration. However, there is a significant gap in research, with a lack of comprehensive studies that highlight current advancements, publication patterns, research focal points, key contributors, and future avenues for advancing FGU research. A bibliometric analysis of the SEG/FGU research based on Scopus data revealed a steady rise in publications due to environmental issues, research collaborations, and funding availability. China emerged as the top nation with the most articles published (18 articles and 240 citations) on SEG/FGU growth and development, with Alexander Golberg, Tel Aviv University (Israel), and the National Natural Science Foundation of China (NSFC) as prolific authors, institutions, and funders, respectively. China's supremacy has been attributed to the availability of funding from significant programs like NSFC. Three prominent research focal points emerged: the integration of solar energy, agriculture, and desalination; the progression of PV technology within the renewable energy sector; and the intersection of aquaculture and ecological stewardship. The current research focus is on enhancing efficiency, developing effective energy storage solutions, and expanding the scope of applications for agricultural practices. However, significant research voids persist in sustainable aquaculture applications, designing and integrating hybrid PV systems, and achieving scalability in PV technology. [ABSTRACT FROM AUTHOR]

This article compares traditional and intelligent Maximum Power Point Tracking (MPPT) algorithms for photovoltaic (PV) powered DC Shunt Motors. The study evaluates five MPPT methods under varying DC loads and finds that intelligent algorithms perform well under rapidly changing solar radiation conditions. However, they struggle to track the maximum power point when the temperature changes due to inadequate data for training. The analysis focuses on the time required by each method to reach peak power under different conditions, and the advantages and disadvantages of each algorithm are discussed. The article provides valuable insights into the behavior of MPPT algorithms in real-world scenarios and aims to contribute to the advancement of PV system optimization and control. [Extracted from the article]

In this study, the economic analysis of the architectural part of the building, the use of both the solar thermal and solar electric systems in a poultry farm were discussed. In this economic analysis, the implementation of architectural solutions as well as the utilization of the solar thermal system considering the global gas price and the current gas rates in the country and finally the use of solar electric system with different subsidies were analyzed. Economic analysis was conducted using RETSCREEN software. The results showed that the internal rate of return (IRR) for the architectural solutions of using double-glazed windows and wall insulation, considering the global gas price, was estimated as 53.1% and, the investment return, taking into account the inflation and discount rates, was equal to 2.6 years, which is the breakeven point of investing in this solution. Utilizing the solar thermal system and without considering the cost of the underfloor heating system, the IRR value is equal to 34.1%, and considering the cost of the underfloor heating system, the IRR value is equal to 27.9%, which is economic in terms of these solutions. This option proves to be cost-effective. When employing the solar electric system with an IRR of 4.4%, this solution proves uneconomical under all circumstances due to the IRR falling below the bank rate and yielding a negative Net Present Value (NPV). However, when considering a 50% government subsidy and a 1.5-fold increase in the electricity purchase tariff for the solar electric system, this solution approaches the threshold of economic viability. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) power plants’ performance is severely impacted by multi-level irradiances or partial shading, leading to power losses and voltage instability. Also, partial shading adds further complexity to the maximum power point tracking algorithms by introducing numerous peaks in the power curves, resulting in additional losses. Numerous solutions are presented to deal with shading losses, and dynamic reconfiguration is the most effective; however, higher switch count and complex architecture make it impractical in real-world implementation. Hence, this study proposes a low-complexity architecture based on the grouped string voltage balancing approach. This approach utilizes a voltage balancing converter connected to groups of strings to enhance the power output of the array of PV plants, maintain overall system voltage stability, and eliminate the possibility of multiple peaks formation in the power curves. The effectiveness of the proposed approach is tested under numerous static and dynamic partial shadings and analyzed using power curves, power output, losses, efficiencies, and voltage stability. The validation is done by comparing the proposed approach with conventional and advanced architectures for a 32.5 kW system. The results show that the proposed method requires a 50 % reduced switch count than existing techniques, achieves 99.54 % efficiency, and maintains an average voltage stability of 0.01.

DC-DC converters play a critical role in various applications, including DC microgrids, electrical vehicles, data centres and aerospace power systems. This article provides a description of a non-isolated Dual Input Single Output Quadratic Boost DC-DC Converter (DISOQBC) designed for DC microgrids. PV module and battery are low dc voltage power generators. DC microgrid requires high voltage for its operation effectively. By utilizing the proposed converter, the PV module and battery are integrated with separate sources. Achieving a high efficiency and voltage conversion ratio, the proposed converter is distinguished by low components used. The operation of the converter, component design, steady state analysis, and small-signal model has been reported. The proposed converter's benefits include the converter's ability to handle more than one source, deliver more gain than the conventional converter, simple control and reduced losses. The outcome of simulating the converter validates the proposed converter's theoretical operation. The laboratory prototype is ultimately employed to validate and verify the performance of the proposed DISOQBC.

The integration of distributed generation brings in new challenges for the operation of distribution networks, including out-of-limit voltage and power flow control. Soft open points (SOP) are new power electronic devices that can flexibly control active and reactive power flows. With the exception of active power output, photovoltaic (PV) devices can provide reactive power compensation through an inverter. Thus, a synergetic optimization operation method for SOP and PV in a distribution network is proposed. A synergetic optimization model was developed. The voltage deviation, network loss, and ratio of photovoltaic abandonment were selected as the objective functions. The PV model was improved by considering the three reactive power output modes of the PV inverter. Both the load fluctuation and loss of the SOP were considered. Three multi-objective optimization algorithms were used, and a compromise optimal solution was calculated. Case studies were conducted using an IEEE 33-node system. The simulation results indicated that the SOP and PVs complemented each other in terms of active power transmission and reactive power compensation. Synergetic optimization improves power control capability and flexibility, providing better power quality and PV consumption rate.

A new online scheduling algorithm is proposed for photovoltaic (PV) systems with battery-assisted energy storage systems (BESS). The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions. The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities. The scheduling algorithm is developed by using deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) algorithm that can deal with continuous state and action spaces. One of the main contributions of this work is a new DDPG reward function, which is designed based on the unique behaviors of energy systems. The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation. The new scheduling algorithm is tested through case studies using real world data, and the results indicate that it outperforms existing algorithms such as Deep Q-learning. The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

Utility authorities utilize various methods to promote end-user energy conservation, including higher tariff rates and demand response (DR) strategies. This paper investigates an Optimized Smart Home Energy Management System (OSHEMS) designed to minimize grid dependence and energy bills while ensuring reliable load delivery. A hybrid architecture prototype was implemented, integrating a photovoltaic (PV) array, battery storage, and the electrical grid. The system combines Maximum Power Point Tracking (MPPT) solar chargers and Pure Sine Wave (PSW) inverters for efficient energy management. The Home Energy Management Whale Optimization Algorithm (HEMWOA) was employed to optimize energy usage and achieve cost reduction while enhancing user comfort. Real-time pricing (RTP) tariffs incentivizing flexible energy consumption during peak hours were incorporated. OSHEMS manages, schedules, and monitors energy sources and appliances, determining the optimal consumption mix. Experimental results demonstrate a significant decrease in grid reliance (46.6 %) and energy costs (57.7 %) compared to non-scheduling scenarios. These findings highlight the potential of OSHEMS in promoting sustainable and cost-effective energy consumption in smart homes.

A hybrid Photovoltaic/Thermal(PV/T) approach is proposed in this study based on extensive research and a comparative analysis of several hyperparameter tuning methods. The models analyzed are Linear Regression (LR), Random Forest (RF), XGBoost Regression, AdaBoost Regression, Edge Regression, Support Vector Regression (SVR), elastic net, and lasso (L) models. Grid search optimisation approach was used to maximise all of the model's hyperparameters. A detailed analysis is presented as well as the strategies for tweaking the positive and negative hyperparameters. The suggested hybrid PV/T approach is evaluated in two ways. First, the cumulative yield of solar still was obtained. Second, support vector regression, followed by the hyperparameter tuning function was used to provide the maximum accuracy of the PV output. The findings show that RF and SVR achieved the uttermost precision both before and after the use of the hyperparameter tuning approach, with r2 scores of 0.9952, 0.9935, Root Mean Squared Error values of 0.2583 and 0.5087 while utilising grid search optimisation.

Partial shading in series-connected photovoltaic (PV) systems poses a significant challenge as it considerably restricts the system's output power and has a notable impact on the overall efficiency. Differential power processing (DPP) has emerged as a widely employed solution to mitigate this effect. However, implementing DPP architectures introduces challenges such as system cost, reliability, control implementation, and modularity. This research proposes a simplified voltage equalization (VE) algorithm, executed on a low-cost and modular DPP architecture using bi-directional flyback converters. Notably, under light and moderate partial shading conditions, the PV system's output power exhibits a significant increase compared to the traditional string configuration with bypass diodes. To further optimize the system performance, a methodology adapted to the VE algorithm is suggested, minimizing the processed power while increasing overall efficiency. The proposed solutions have undergone rigorous testing through simulations and experiments. The results validate that, under moderate shading conditions, the system efficiency reaches approximately 90%. [ABSTRACT FROM AUTHOR]

According to recent power quality studies, voltage sags and voltage unbalance in medium-voltage and low-voltage distribution grids are the most common type of power quality problems. Power quality is an essential concern in the modern power system that can affect consumers and utility. In modern age, with an increase in sensitive and expensive electronic equipment, power quality is essential for the power system's reliable and safe operation. This paper proposes a hybrid technique-based unified power quality conditioner (UPQC) of microgrid in an EV charging station (EVCS), and it is the combination of recalling-enhanced recurrent neural network (RERNN) and sand cat swarm optimization (SCSO), together called as RERNN-SCSO. The SCSO creates the converter control signal, and the RERNN method forecasts the control signal from SCSO method. The proposed technique consists of series-active power filter (Se-APF), shunt-active power filter (Sh-APF), and DC-side capacitor as the parts of proposed technique. The major purpose of RERNN-SCSO technique is used to increase the power quality. Many loads, like highly inductive NLL, nonlinear loads (NLL) as well as unbalanced NLL (UNLL), are all linked at the point of common coupling (PCC). When the major structure of UPQC is built into the EVCS, an additional alternating current (AC)/DC converter will help with initialization. At this, the charging position provides charging service to the EVs as well as discharges the EVs into the power grid. The novelty of the proposed work consists in connecting EVs in parallel to the UPQC DC-side using double-layer microgrid (MC) and DC–DC converter. The proposed method ensures EV charging while efficiently handling quality of power issues like harmonic current, voltage disturbance, voltage sag and swell and voltage unbalance. RERNN-SCSO method performance is performed on the MATLAB/Simulink software to analyze the working performances and related with present techniques. RERNN-SCSO method-based THD under voltage sag condition as 2.03% and swell as 2.01%, which is lower than the existing methods. [ABSTRACT FROM AUTHOR]

Renewable energy sources are being increasingly deployed to achieve carbon neutrality, thereby boosting photovoltaic (PV) system adoptions. Accordingly, vertical PV systems designed for specific installations have been developed. We propose a strategy to enhance the PV hosting capacity of a connected distribution line (DL) by combining vertical installations with modules facing east-west and conventional PV systems with modules facing south at an installation angle of approximately 30°. The data were obtained from a real testbed located in South Korea, which is situated in a mid-latitude region. We analyzed the generation patterns of vertical and combined PV systems (vertical and conventional) to enhance the hosting capacity. The results showed that vertical PV combination ratios of 40–60% effectively flattened the peak generation curve. Additionally, the DL hosting capacity improved by 40% under real-world conditions. In an actual industrial scenario, the system feasibility was validated to be within the voltage maintenance range and thermal capacity of lines in South Korea, indicating that this approach can mitigate the need for additional line installations and renewable energy curtailments. Furthermore, the issue of Duck Curves in the power grid can be addressed by smoothing the power production of the PV systems, particularly during low-demand periods. [ABSTRACT FROM AUTHOR]

Summary: The demand for green energy sources has led to the exploration of photovoltaic (PV) systems as a sustainable and cost‐effective solution. However, PV systems have an uncertain nature that makes the tracking of global maximum power point a challenge. To get the optimal benefits from the PV source, the extraction of power is required in a precise manner, especially in the case of irregular complex partial shading conditions (CPSCs). In the case of CPSCs, the global maximum point tracking (GMPP) is a bit difficult to manage by the conventional algorithms presented so far. As the power peaks increase with CPSCs, the conventional algorithms are usually stuck in the local maxima. To avoid sticking at all the local maxima points and to find GMPP with better efficiency, stretching is used here along with the repulsion technique. Stretching and repulsion techniques are merged with the chimp optimization algorithm (SRCA). The tuning parameters of SRCA are so effective that remove all local points and lead to finding the GMPP accurately. The proposed algorithm is applied to the PV array to extract optimal power off‐grid and on‐grid as well. The average tracking time and output power efficiency in the case of off‐grid is observed as 0.4 s and 99.87% while in the case of on‐grid as 0.09 s and 98.88%, respectively. [ABSTRACT FROM AUTHOR]

The development of electricity generation from renewable sources, particularly photovoltaic energy (PV), as well as the significant growth of electric mobility with electric vehicles (EVs), leads us to contemplate the potential impacts of these components may have on an existing distribution electrical network. An electrical distribution network is designed to supply electrical loads in one specific direction while maintaining the quality of its technical parameters, such as current, voltage, and frequency. In this article, through a literature review, we will explore how the integration of each component, photovoltaic energy, and electric vehicles, individually and in combination, can impact the electrical distribution network. In addition, we will examine based on a literature review, the methods proposed by various authors to enhance the impacts occurring on the network due to this integration. [ABSTRACT FROM AUTHOR]