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Hydrogen has become an essential element in the pursuit of sustainable and clean energy solutions. Especially with the fast-paced advancement in demand, supply, and policy environment, its impact on hybrid renewable energy (HRE) management is becoming increasingly relevant. Efficient energy consumption, cost reduction, and enhanced user comfort are now critical factors in energy optimization. The production of green hydrogen, which is generated through water electrolysis using renewable energy sources (RES), has shown great potential as a sustainable energy solution. It offers several advantages, such as zero greenhouse gas emissions, high energy density, and versatile applications. This paper presents a detailed study on the power management and control of a hybrid renewable system (HRES) equipped with a diesel generator (DG) as a backup power source. The main objectives of the hybrid system are to satisfy the load power demand, ensure the most efficient use of the HRES, and keep the battery bank charged to prevent blackouts and extend the battery's life. To guarantee the system's reliability, the DG should be sized to meet the peak load demand when the RES generates less electricity than the load demand. This study explores the feasibility of modified versions of the load following and cycle charging control strategies to overcome the limitations of managing generation and storage systems' operations in different operating modes and to enhance the performance of an HRES with a DG that supplies electricity to a small and remote location. The proposed method not only maximizes the use of RES production but also enables multi-energy source management under different power generation and load demand scenarios. The study's outcomes demonstrate the feasibility of this proposed power dispatch strategy in a remote location environment. The paper includes a detailed discussion of overall control, mathematical models, energy storage in the battery model, and energy dispatching based on load following. To design and simulate the hybrid model system, MATLAB-SIMULINK is used, and the results are analyzed to identify the appropriate operation requirements, component selection, and energy management of the hybrid renewable energy system. [ABSTRACT FROM AUTHOR]

The advancement of renewable energy technology has been significantly aided by solar photovoltaics (PV). Since solar PV is a weather‐dependent source, it cannot be dispatched. To ensure that the solar PV system can harvest the maximum amount of electricity for the available irradiance level, maximum power point tracking (MPPT) algorithms are used. For standalone low‐voltage DC (LVDC) microgrids to utilize the energy storage system as efficiently as possible, maximum power extraction is essential. The sensed PV voltage and current are essential for these MPPT algorithms to ensure that the maximum power point of the panel is captured. This work proposes an effective fault‐tolerant control (FTC) scheme for the solar PV subsystem in the LVDC microgrid that can seamlessly extract the maximum power despite the PV voltage sensor being faulty. The proposed FTC scheme uses a sliding mode observer (SMO)‐based method to detect and isolate PV voltage sensor faults in the standalone LVDC microgrid. The efficacy of the proposed FTC is assessed in a range of circumstances involving load disturbance, irradiance change, and various sensor fault scenarios. The performance of the proposed FTC is validated using experimental analysis on the LVDC microgrid testbed and MATLAB simulations. Given a faulty PV voltage sensor, at a given operating condition of the microgrid, the proposed FTC scheme is successful in reducing the additional power burden on the battery storage by at least two times. Consequently, the additional discharge in terms of SoC is also seen to be decreased by at least 9%. The proposed FTC technique outperforms the popular MPPT approaches for solar PV in terms of PV voltage sensor fault tolerance in the microgrid. [ABSTRACT FROM AUTHOR]

The traditional short circuit ratio index does not consider the impact of energy storage devices (ESDs) and cannot be used for the collaborative optimization of ESDs and renewable energy sources (RESs). Therefore, this paper proposes a novel synergistic capacity short circuit ratio (SCSCR) index, which can reflect the interaction between multiple RESs and ESDs on each bus under different capacities. Based on the proposed SCSCR, a coordinated optimization method of RESs and ESDs is proposed. It ensures system strength while determining the optimal location and capacity proportion of RESs and ESDs. Firstly, the location and capacity proportion of RESs are obtained through optimization method because the system strength is mainly determined by RESs connection points. Secondly, the optimal location and capacity proportion of the ESDs are found under the optimal configuration of the RESs. The proposed optimization method ensures the system strength while obtaining the optimal location and optimal capacity proportion of RESs and ESDs. Finally, the simulation results verify the effectiveness of this method on the IEEE 9 bus and 39 bus systems, providing reference for the site selection and efficient operation of RESs and ESDs. [ABSTRACT FROM AUTHOR]

The variable pressure source system is widely used in renewable energy recovery scenarios. However, the instability of the new energy input and the nonlinearity of the power generation system can lead to problems such as more difficult tracking and control of the maximum power point and poor power generation quality. To address the problem of lower efficiency under varying inputs, which is prevalent in renewable energy generation, this paper establishes an energy recovery system model based on power conversion rectifier topology, designs a speed current double closed‐loop control strategy, proposes an online variable‐step maximum efficiency point tracking method based on experience curves, and tests the overall energy recovery effect through system simulation and comparative experiments. The simulation results show that the maximum efficiency point tracking method proposed in this paper reduces the number of optimization searches by 50% and improves optimization speed. The experiment results show that, under the drastic changes of the input, the method proposed in this paper could reduce the total harmonic distortion to 5.65%, improve the energy recovery efficiency by 10.976%, and reduce the fluctuation ratio of the voltage to 2.43%. This study can provide an important reference for the collection and utilization of new energy sources. [ABSTRACT FROM AUTHOR]

Due to the increasing energy demand, traditional fossil fuels are gradually decaying day by day as analyzed by many researchers. Fossil fuels are not sufficient to fulfil the requirement of energy demand and it also produces greenhouse gas emissions. In this regard, worldwide research is going on related to renewable energy sources (RESs) like solar photovoltaic (SPV), wind turbines, fuel cells etc. The source of SPV is plentiful and environment friendly which converts solar radiation to non-linear electrical power. This power is not suitable for a stable system. Therefore, the maximum power point tracking (MPPT) controller is required to find the optimum maximum power point (MPP) to the load. The MPPT technology regulates the duty-cycle in favour of the DC-DC converter to continuously obtain maximum power from the SPV arrays. In the past few decades, the learning of MPPT techniques has made substantial progress in the RESs. This research article analyzes the performance of various MPPT techniques in the proposed SPV framework. The main investigation is to assess different MPPT techniques to optimize power from the SPV framework. The artificial neural network (ANN)-MPPT method has been observed to be more effective in output power production and transient response about the MPP than conventional perturb and observe (P&O)-MPPT and fuzzy logic controller (FLC)-MPPT technology. [ABSTRACT FROM AUTHOR]

The Photovoltaic system (PV) is gaining popularity as a future energy source due to its vast, secure, essentially limitless, and widely accessible nature. However, a prevalent challenge in PV systems is the impact of solar cell radiation intensity and temperature on the output power induced in the photovoltaic module. As a result, monitoring the input source's maximum power point becomes essential for maximizing the effectiveness of the renewable energy system. In order to harvest the most power from the photovoltaic system and channel it to the load through a boost converter, which raises the voltage to the necessary level, most Power Point Tracking, or MPPT, is essential. This study uses the Perturb and Observe (P&O) method to track the photovoltaic module's Maximum Power Point (MPP) and maximize the system's overall power production to overcome this obstacle. By varying the array terminal voltage or current, the P&O approach perturbs the system and then compares the PV output power to the preceding perturbation cycle. Therefore, the main goal of this study is to simulate and assess the effectiveness of the P&O MPPT algorithm in MATLAB/Simulink when it comes to maximizing power extraction from a photovoltaic system. The simulation findings show that the suggested P&O approach is a reliable means of tracking MPP from PV panels, even when there are fluctuations in solar irradiation. [ABSTRACT FROM AUTHOR]

Abstract Solar photovoltaic energy is a vital renewable resource because it is clean, endless, and pollution‐free. Due to the fast growth of the semiconductor and power electronics sectors, photovoltaic (PV) technologies are climbing significant attention in modern electrical power applications. Operating PV energy conversion systems at the maximum power point is essential for getting the maximum power output and raising efficiency. This paper proposes a regression‐based Perturb and Observe method to quickly find a global maximum power point, avoiding being stuck in local maxima, likewise analytical and metaheuristic methods. The improved control focuses on the narrowed search areas by linear and non‐linear regression analyses using the generated PV model on a flexible Python environment. Furthermore, the method's accuracy is validated in real time under variable temperatures, irradiations, and loads. This method was proven with a hardware implementation. The proposed method is more than 98% accurate and can withstand long‐term modelling. The suggested regression‐based perturbation and observation method provided a short learning time and easy implementation. Additionally, the dynamic recorded results can be visualized for researchers to utilize efficiently.

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system's performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches. [ABSTRACT FROM AUTHOR]

The present power production companies are working on renewable energy systems because their features are more reliable for the local energy consumers, high continuity in the energy production, and less cost is required for maitainence. In this article, the proton exchange membrane fuel stack (PEMFS) renewable energy is utilized to supply energy to the automotive systems. Here, the PEMFS is selected because of its merits are high energy density, quick system response concerning the source operational temperature, and more suitable for electric vehicle application. However, the PEMFS supplied voltage is completely nonlinear which is solved by utilizing the modified particle swarm optimization with adaptive neuro-fuzzy inference system (MPSO with ANFIS) controller. This hybridization-based maximum power point tracking controller provides more accuracy, high power point identifying speed, best dynamic response at different fuel stack functioning temperature conditions, and easy maitainence. Here, the fuel stack generated current is very high which is optimized by introducing the new DC–DC converter. The advantages of this DC–DC converter are more voltage transformation ratio, low-level voltage stress appearing across the switches, and wide voltage gain. The overall system is investigated by utilizing the MATLAB/Simulink tool. [ABSTRACT FROM AUTHOR]

One of the most known and usable renewable energy is photovoltaic solar energy. Its properties such as recursivity, cleanness, limitlessness, and reliability make it the most useful source of energy. Some undesirable natural (irradiance and temperature) variation affects the photovoltaic solar modules' characteristics. However, photovoltaic PV solar power needs to be monitored accurately, effectively, and efficiently in order to keep the global maximum power point (GMPP) steady. Therefore, this paper presents a modified multi-stepped constant current based on the gray wolf optimization (MMSCC-GWO) algorithm for photovoltaic systems. The proposed technique utilizes a multi-step constant current to the pack of wolves by monitoring the inside and the outside step of the hierarchical wolves. This proposed (MMSCC-GWO) algorithm accurately tracks the maximum power point from the photovoltaic systems under different partial shading conditions (PSCs). A comparative result with other conventional methods is investigated and the simulation results carried out in the MATLAB/Simulink environment reveal that the proposed approach outperforms the existing techniques in terms of efficiency, tracking time, and accuracy. [ABSTRACT FROM AUTHOR]

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads. [ABSTRACT FROM AUTHOR]

Integrating photovoltaic systems (PV) into the grid has garnered significant attention due to increasing interest in renewable energy sources. Maximizing the PV systems output power is crucial for improving energy efficiency and reducing operating costs. This paper presents a comparative analysis of two different techniques of maximum power point tracking (MPPT): perturb and observe (P&O) and artificial neural network (ANN) MPPT, focusing on their application in grid-connected PV systems. The paper evaluates their performance under various operating conditions, including changes in irradiance and temperature, that are discussed in three cases. The comparative analysis includes metrics such as voltage regulation and power loss. MATLAB Simulink is utilized to implement P&O and ANN MPPT methods, which include a PV cell connected to an MPPT-controlled boost converter. The simulation demonstrates the power loss of the PV model as well as the voltage regulation in the three cases for the two methods. The results obtained in simulation and implementations show that the ANN method outperforms the P&O in the three cases discussed in terms of power loss, voltage regulation, and efficiency. The results also show that the change in output power from PV is noticeable when compared to changes in radiation, while the change is slight when temperatures change. [ABSTRACT FROM AUTHOR]

DC microgrids are getting more attention because majority of the renewable energy sources generate DC output voltage and also modern gadgets require DC voltage for its operation. In this work, high gain SEPIC (HGSC) topology is derived from switched inductor voltage boosting cell (SIVBC). The HGSC converter provides continuous source current due to SIVBC and high conversion ratio and achieves maximum efficiency of 97.88% when compared with the existing SEPIC topology. The operating modes, conversion ratio expression, power loss distribution, voltage drop, current stress of the semiconductor devices, and efficiency are also analysed. In DC microgrids, the HGSC intends to track the peak power from solar PV array. An incremental conductance algorithm is employed to track the peak power of the solar PV modules. The power flow in the microgrid system is analysed by employing synchronous reference frame theory‐based current controller. In order to validate the theoretical concepts of the HGSC converter, the hardware model is developed for the load rating of 1,000 W/380 V output voltage. [ABSTRACT FROM AUTHOR]

This paper compares different maximum power point tracking (MPPT) control strategies in microgrid dynamics, focussing on perturb and observe (P&O), adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimisation (PSO), and PSO sliding mode controller techniques. The study investigates their performance under varying microgrid conditions, considering factors like weather and load variations. The simulation results provide a detailed comparative analysis of the power at the point of common coupling (PCC) for MPPT techniques at different time intervals. Both the P&O and PSO sliding mode recorded a power output of 287 kW, while PSO-ANFIS achieved a slightly higher power output of 294 kW. At 2.5 seconds, the P&O method recorded a power output of 712 kW, while the PSO sliding mode and the PSO-ANFIS techniques achieved 717 kW and 738 kW, respectively. Overall, the PSO-ANFIS technique consistently outperformed the other methods in terms of power output, demonstrating its effectiveness in maximising energy extraction and adaptability to dynamic conditions. These findings provide valuable insights for designing and implementing MPPT controllers in microgrid systems, emphasising the efficiency of the hybrid PSO-ANFIS technique in enhancing the overall performance and stability of renewable energy systems. [ABSTRACT FROM AUTHOR]

Solar systems are becoming widely used renewable energy source since photovoltaic cell generates high power output. However, during partial shading conditions (PSC), the existing MPPT techniques failed to determine the actual maximum point of the photovoltaic (PV) cell, and the photovoltaic system is stuck at a local maximum thus output is gradually reduced. The Optimized Grid-Connected Photovoltaic system aims to extract the most power from the solar array under partial shading conditions. It uses an optimisation algorithm named Sovereign Butterfly Optimization Algorithm and a sequence controller to optimize power value selection. The voltage harmonics and phase delay cause the system to be in an unbalanced state, so the proposed model uses an anomaly obliteration filter fusion technique in which the Butterworth filter is utilised for reducing voltage harmonics and removing phase delay completely by avoiding negative sequence by converting three-phase current to dq0 frames. To increase the overall power quality in the photovoltaic system without synchronization problems, a Subsystem Synchronization Control Strategy is applied which effectively synchronizes the active and non-active currents injected into the grid. Simulated results using the proposed model showed high tracking accuracy of 99.23% and minimum power oscillation of 2.53%, thus enhancing the overall power quality. [ABSTRACT FROM AUTHOR]

Renewable energy is undergoing significant advancements through the utilization of new technologies. Among these technologies, fixed solar panels serve as a fundamental type of solar photovoltaic energy generator. Monitoring and optimizing the performance of these panels is achieved through the implementation of controller algorithms. However, there is a growing emphasis on axis tracking systems for solar panels, as that has the feasible to extract more power compared to fixed panels. In this context, the integration of the Internet of Things (IoT), big data analytics, deep learning, and cloud computing presents a contemporary solution for addressing various challenges associated with monitoring renewable solar energy systems. By employing sensors and actuators grouped within an IoT framework, real-time data can be extracted and stored in a cloud server, allowing remote access from anywhere. Furthermore, the use of meta-heuristic optimization algorithms, such as the Whale Optimization Algorithm, has become commonplace in solving engineering issues. In this proposed system, the performance of horizontal axis trackers is analyzed and compared with fixed panels using the WOA-based axis tracking analysis. Through the application of a control algorithm, optimal values can be obtained under typical weather conditions. The system's simulation and performance are thoroughly assessed and evaluated. [ABSTRACT FROM AUTHOR]

Global electricity demand is increasing with the rising population and rapid urbanization. Building Integrated photovoltaic (BIPV) system is a new method of renewable energy generation where solar photovoltaic (PV) modules are integrated into the building surfaces such as façade, shades, windows, roofs, and tiles. BIPV systems reduce the urban energy demand. Utilization of vertical surfaces makes the BIPV system a preferable choice where land scarcity affects the implementation of large PV systems. The economic viability depends on the maximum power generated by the BIPV array. In urban environments, the BIPV arrays experience severe partial shading conditions (PSCs). The PSCs cause mismatch losses, reducing the global maximum power of the BIPV array and efficiency. Fixed array configurations such as series-parallel (SP), total-cross-tied (TCT), triple-tied (TT), and quarter-tied (QT) are designed to solve this issue. The cross ties across the rows of the BIPV array improve the performance at the expense of more wiring. Researchers proposed various optimal array configurations with different shading patterns. This research attempts to generalize the design of the BIPV array configurations by considering the trade-off between wiring requirements and shading losses. The performance of SP, TT, QT, and TCT configurations under four different shading conditions is simulated with the proposed BIPV array design algorithm. A 9 × 8 BIPV array of 3.6 kW is considered. QT configuration reduces the wiring requirement by 10.45% compared to TCT and improves up to 8.43% maximum power than SP. The fill factor is improved to 48.49%, and the mismatch loss is limited to 34.10%. Therefore, QT and TCT are considered favorable configurations for BIPV array design. [ABSTRACT FROM AUTHOR]

Active Distribution Networks are Multi-Input Multi-Output (MIMO) systems with coupled dynamics, which cause interactions among the control loops of Distributed Energy Resources (DERs). This undesired effect leads to performance degradation of voltage control. To mitigate the effects of this unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In detail, each DER is equipped with a double-loop PI to control the active and reactive power output of the voltage source converter, which connects the DER to the network's node. Furthermore, to guarantee ancillary services, the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a procedure with two sequential steps based on the Internal Model Control (IMC) technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in the presence of model parameter uncertainty in the MIMO plant, modeling various operating conditions of the ADN, including a step connection of large loads, renewable energy source variations, and changes in the substation transformer ratio. [ABSTRACT FROM AUTHOR]

Energy is an essential element in the development of mankind and is the engine that has made possible all the discoveries and innovations of recent decades. It is found behind the comfort we have in everyday life, whether at home, at the office, or when traveling: an optimal temperature in the room, light at anytime, appliances to ease us and make our lives more beautiful. An energy system based on renewable energy has the ability to provide electricity in an economical, sustainable, long-term, non-polluting way and can serve the increasing population. One such example is represented by photovoltaic (PV) panels, which produces electricity from captured solar energy and is one of the solutions considered for reducing energy. The dimensioning of a photovoltaic system, from a technical and economic efficiency point of view, involves the analysis of input data, output data and hence the characteristics of an efficient system. The analysis can be done analytically, but also with the help of dedicated software, the result being, in the end, a safe system in terms of preventive operation and maintenance. In the article was made the dimensioning of a system, analytical and automatic – using software application – to determine if the result obtained – the photovoltaic system – is the same regardless of the method used. [ABSTRACT FROM AUTHOR]

A substantial level of significance has been placed on renewable energy systems, especially photovoltaic (PV) systems, given the urgent global apprehensions regarding climate change and the need to cut carbon emissions. One of the main concerns in the field of PV is the ability to track power effectively over a range of factors. In the context of solar power extraction, this research paper performs a thorough comparative examination of ten controllers, including both conventional maximum power point tracking (MPPT) controllers and artificial intelligence (AI) controllers. Various factors, such as voltage, current, power, weather dependence, cost, complexity, response time, periodic tuning, stability, partial shading, and accuracy, are all intended to be evaluated by the study. It is aimed to provide insight into how well each controller performs in various circumstances by carefully examining these broad parameters. The main goal is to identify and recommend the best controller based on their performance. It is notified that, conventional techniques like INC, P&O, INC-PSO, P&O-PSO, achieved accuracies of 94.3, 97.6, 98.4, 99.6 respectively while AI based techniques Fuzzy-PSO, ANN, ANFIS, ANN-PSO, PSO, and FLC achieved accuracies of 98.6, 98, 98.6, 98.8, 98.2, 98 respectively. The results of this study add significantly to our knowledge of the applicability and effectiveness of both AI and traditional MPPT controllers, which will help the solar industry make well-informed choices when implementing solar energy systems. [ABSTRACT FROM AUTHOR]

Due to their inherent ability and environmentally friendly nature, renewable energy sources are the only real option for producing pollution-free energy in the modern era. Solar energy is one of the best possibilities in this family for supplying civilization with the power and energy it needs. Researchers can efficiently boost a PV panel's efficiency by using the maximum power point tracking (MPPT) approach to extract the most power from the panel and send it to the load. The authors of this study examined and surveyed the sequential advancement of solar PV cell research from one decade to the next, and they elaborated on the upcoming trends and behaviours. Many maximum power point tracking algorithms (MPPTs) that are employed in photovoltaic systems (PVSs) that function under both uniform and partial shade situations are structurally summarized in this work. Well-written descriptions of the features of photovoltaic modules are followed by a variety of effective control strategies, including both AI-based and traditional controllers. In addition, appropriate knowledge of the various controllers is essential when the PV system is exposed to partial shade, keeping in mind the different control systems' classifications in this situation. A thorough analysis of several soft computing-based techniques is also included, as well as many classical controller-based PV systems. First, well-developed traditional MPPT methods are used, followed by artificial intelligence-based MPPT approaches. Later, a thorough comparison of the various MPPT-controlling approaches is established. For PV systems operating under partial shade conditions (PSCs), the advantages and disadvantages of the various MPPT techniques are outlined, contrasted, and assessed. Future research directions for MPPT are also being investigated. A collection of several datasets pertaining to various control processes that were gleaned from various research articles has also been presented. Researchers working on PV-based MPPT and those working in the sectors of renewable energy production and environmentally sustainable development would be very interested in the findings of this review study. [ABSTRACT FROM AUTHOR]

The main objective of this work is to evaluate the energy efficiency improvement obtained in grid-connected photovoltaic systems based on a dynamic reconfiguration strategy. The MIX and team reconfigurable photovoltaic system topologies have been considered since both minimize the operation of the inverters in low-load conditions. A numerical method is used to analyze the energy flows within the photovoltaic system, with a specific focus on the plant-oriented configuration. In this work, MIX systems are only presented briefly, while team reconfigurable photovoltaic systems are analyzed in more detail. This is because team systems can be implemented using conventional commercial inverters, electromechanical switches to redirect power flows, and a simple digital controller (as based on the Arduino platforms). The energy supplied to the grid by two grid-connected photovoltaic systems will be evaluated: one based on a classic non-reconfigurable strategy and another based on the team strategy. The measurement of the energy generated by these two systems, tested under various irradiance levels (emulating different climatic conditions), shows that reconfigurable systems always exhibit greater energy efficiency. However, this energy improvement can only be considered substantial in certain situations. [ABSTRACT FROM AUTHOR]

Hybrid Energy Systems (HESs) are offering several advantages over conventional generation systems because of the rising need for Renewable Energy Resources (RERs) and its high capability and flexibility of achieving the desired performance for both urban and rural areas. The main goals of HESs are to improve the system performance by increasing its flexibility, reliability and reducing generation instability. In the current work, a hybrid Photovoltaic/Wind Energy Conversion System (PV /WECS) is proposed to supply and support the utility grid. The generated power from the system is greatly responding to the change in both the wind speed and PV irradiance level, and as a result impacting the DC-Bus voltage of the system converters. Therefore, precise, fast and optimum controllers are required for the PV/WECS system to ensure the extraction and generation of stable maximum power during weather variations along with maintaining the DC-Link voltage at the desired value. Further, this study proposes a novel Linear Active Disturbance Rejection Control (LADRC) combined with a hybrid Salp Particle Swarm Algorithm (SPSA) to a grid-connected PV/WECS for extracting the Global Maximum Power (GMP), regulating the DC-Link voltage, and enhancing the active and reactive powers control. The capability of the proposed SPSA-LADRC based-MPPT approach is examined according to the tracking time, accuracy and response to internal and external disturbances and then comparing its performance with other techniques such as SPSA-SMC, SPSA-PID and SPSA-PI controllers respectively. The findings revealed that the LADRC can track and keep the DC-Link voltage as the desired reference value,1500 V, respond much faster than the PI, SMC, and PID controllers as well as keep the THD of the injected current into the grid at a low level of 2.25%. Finally, the performance of the proposed system is experimentally verified using Hardware-In-Loop (HIL). The obtained findings verify the capability and accuracy of the system and its great potential in the management of HESs. • A novel hybrid SPSA based LADRC is designed for grid-connected PV/WECS. • The SPSA-LADRC performance was investigated under wind speed and irradiance variation. • It regulates the DC-Link voltage, improves GP tracking speed and dynamic performance. • The Hardware-In-Loop experiment validates the implementation feasibility. [ABSTRACT FROM AUTHOR]

With the large-scale integration of renewable energy systems, the power inertia and voltage support of the grid are low. Using the inverter with temporary storage capabilities cope with shortages and power fluctuations. This paper presents a new sensorless control of a single-phase grid-connected PV system based on a multilevel flying capacitor inverter MFCI) able to smooth out the power produced. The designed controller involves two cascade loops, an outer loop control extracts the maximum power despite climate change, and an inner loop consists of a model predictive control based on the Lyapunov theory for power factor correction and flying capacitor voltage balancing. Furthermore, an interconnected Kalman-like high-gain observer is combined with the predictive control algorithm to estimate the flying capacitor voltages accurately based on the actual grid current. The stability study of the suggested observer in the closed loop has been described. An algorithm that generates reference signals to manage power flows is also developed. The performance of the proposed controller has been verified on the MATLAB/Simulink platform, and the simulation results confirm the effectiveness of the proposed controller over different grid operating modes. • the proposed PV system is based on MFC inverter able to smooth out the grid-injected power. • The designed controller combines a Lyapunov-based model predictive control with a Kalman-like high-gain observer. • The PV conversion system optimizes energy extraction, injecting it smoothly into the grid. • The inverter, with temporary storage, copes with power fluctuations without external energy storage during grid faults. • The closed-loop stability of the controller is rigorously proved during the normal mode and grid fault conditions. [ABSTRACT FROM AUTHOR]

Currently, the majority of the country has moved to renewable energy sources for electricity generation, and power companies are concentrating their efforts on renewable resources. Solar, wind, hydropower, and biomass are examples of renewable resources; of these, due to a lack of non-renewable resources, the solar industry is expanding. All year long, solar electricity is available, and it creates a calm, quiet atmosphere. The majority of large and small companies, as well as individual consumers, have shifted to PV solar cells for electricity generation. A trustworthy and precise simulation design of a photovoltaic system prior to installation is required to predict a photovoltaic system's performance. The current research aims to build models for solar PV systems with one, two, and three diodes and determine which model is most appropriate for each environmental circumstance to forecast performance accurately. By contrasting the experimental data of solar panel with simulated results of single-, double-, and triple-diode models, this study examines the accuracy of each model. These models' comparative performance study has been done using the MATLAB/Simulink, taking into account the influence of changing model parameters and the performance of the models under varying climatic circumstances. These models, despite their simplicity, are quite sensitive and react to even a little change in temperature and irradiance. Under conditions of low solar irradiance or shading conditions, three-diode photovoltaic models are shown to be more accurate. We can forecast the power output of solar photovoltaic systems under changeable input circumstances by understanding the I-V curves with the help of the performance assessment of the models used in this work. [ABSTRACT FROM AUTHOR]

In pursuit of sustainable transportation, green energy sources (GES) are taking center stage, propelling traction vehicles like tramways and trolleybuses towards a zero-emission future. Solar is the most prominent source among the available renewable sources and also for the transport system applications. To utilize photovoltaic (PV) source effectively, model predictive controller-based PV maximum power tracking algorithm is used to identify the PV parameters. Equipped with the smart energy storage system (SESS), light traction vehicles rely on the efficiency and reliability of brushless DC (BLDC) motors for smooth operation. However, BLDC motors operate at high voltages, which requires them to be connected to high voltage microgrids. Bridging the gap between ESS/SESS and the highvoltage microgrid with traditional DC-DC boost converters incurs efficiency losses and component stress. This paper tackles high-voltage needs in microgrids through innovative, efficient DC-DC boost converter designs. An innovative finite-control-set based model-predictive control (FCS MPC) controller tackles clean energy harnessing from PV and grid stability in traction applications, enabling optimized power sharing within microgrid constraints. [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]

The world depends heavily on electrical energy for accessing technologies. For the generation of electricity, technology can utilize renewable energy sources like solar energy and wind energy. Solar photovoltaic (PV) systems occupy space among consumers due to their feasibility, flexibility, cost, and simple implementation procedures. The solar PV system experiences many factors causing power loss like partial shading, hotspots, and diode failure. In this work, a new static PV array configuration, named Renzoku puzzle patternbased array configuration, is proposed. This proposed configuration technique was designed to overcome the drawbacks of the previously proposed array configurations in terms of power generation, fewer mismatch losses, a high shade-dispersion rate, and consistent performance under any level of partial shading. The proposed array configuration has been validated using both simulation and hardware. The simulation is carried out in a 9 x 9 PV array in MATLAB/Simulink®. The performance analysis, results, and corresponding characteristic curves are presented in this manuscript. [ABSTRACT FROM AUTHOR]

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration. [ABSTRACT FROM AUTHOR]

Research into renewable energies is expanding quickly, especially photovoltaic (PV) systems. PV systems are employed extensively in several renewable energy applications. The primary challenge with PV systems is maximizing electricity output. Consequently, a significant amount of research into modeling PV continues to focus on maximizing the generated power. Maximum power point tracking (MPPT) refers to the optimization of PV power generation. Accordingly, an effective MPPT approach deploying a converged barnacles mating optimizer (CBMO)-based adaptive neuro-fuzzy inference system (ANFIS) is introduced in this paper. The mentioned strategy is utilized to detect and track the maximum power point (MPP) in two phases. At the initial stage, ideal voltages are determined using the CBMO algorithm in various temperatures and irradiances in the offline mode. After being trained, the ANFIS calculates the ideal voltage depending on the radiation conditions on solar panels. It then enters the tracking cycle and attempts to identify the MPP. To evaluate the behavior of the suggested technique, a Matlab/Simulink-based MPPT model is created. The proposed approach is evaluated under various weather conditions. The results demonstrate that the suggested methodology for tracking is efficacious under all environmental circumstances. Simulation of the suggested technique is carried out, and the results demonstrate that the introduced MPPT algorithm will effectively give the global maximum under a variety of climatic circumstances. Additionally, this approach is exceedingly effective, rapid, and stable. The findings demonstrate that the suggested technique properly identifies the optimum MPP at 99.3% efficiency. [ABSTRACT FROM AUTHOR]

Due to its abundant natural supply and environmentally friendly features, solar photovoltaic (PV) production based on renewable energy is the ideal substitute for conventional energy sources. The efficiency of solar power generation under partial shading conditions (PSCs) is significantly increased by maximizing power extraction from the PV system. The maximum power point tracking (MPPT) method is to track maximum PowerPoint (MPP). This research proposes a photovoltaic MPPT control in partial shading conditions using Loxo-Canis (LOXOCAN) optimization algorithm. The ultimate goal of the novel method is to track the solar photovoltaic system's maximum power point under conditions of partial shading using the LOXOCAN algorithm. The proposed LOXOCAN algorithm is a combination of Elephant-herd optimization (EHO) and Coyote Optimization Algorithm (COA). The K p , K i , and K d parameters of the PID controller of the MPPT controller will be tuned to their optimum values using the proposed optimization strategy. Higher MPPT performance and a quick convergence at the global maxima are shown in the proposed Loxo-Canis approach. Also, the recommended hybrid Loxo-Canis MPPT approach offers faster MPPT, less computational work, and higher efficiency. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of controlling and optimizing a hybrid renewable energy system, specifically consisting of a photovoltaic (PV) generator and a wind energy conversion system comprising a wind turbine and a permanent magnet synchronous machine. The primary control objectives involve maximizing power extraction from both the PV and wind turbine while ensuring efficient injection of the extracted energy into the electrical network and maintaining a suitable power factor. This study presents two key innovations that distinguish it from existing research. Firstly, it introduces a new hybrid PV/aerogenerator energy source that eliminates the need for a DC/DC converter in the PV structure. Secondly, unlike previous works that rely on various measurements such as wind speed, temperature, and irradiance, our approach only requires measurements of electrical variables. This advancement significantly reduces the number of sensors required and enhances the overall reliability of the system. [ABSTRACT FROM AUTHOR]

Photovoltaics are one of the most important renewable energy sources to meet the increasing demand for energy. This led to the emergence of Microgrid s, which revealed a number of problems, the most important of which is managing and monitoring their operation, this research contributes mainly by using a maximum power tracking algorithm Which depends on artificial neurons and integrating it with a proposed algorithm for energy management in Standalone DC Microgrid, in order to control the distribution of power and maintain the DC bus voltage level. Maximum Power Point Tracking (MPPT) algorithm based on ANN+PID is used. Where ANN tracks the maximum power point by estimating the reference voltage using real-time data such as temperature and solar radiation. The PI controller reduces the error between the measured voltage and the reference voltage and makes the necessary adjustments in order to control the boost converter connected to the photovoltaic panels. While the process of controlling the DC bus voltage level is done by controlling the battery charging and discharging process through the power management algorithm and controlling the Bidirectional converter switches according to the battery's state of charge. The simulation results obtained by used MATLAB Simulink are shown that the used MPPT algorithm achieved the maximum power with the least amount of fluctuation, the method's efficiency was 99.92%, and its accuracy was 99.85%, as well as the success of the power management algorithm controlling the battery charging/discharging process and maintaining the DC voltage level at the specified value in different operating scenarios. [ABSTRACT FROM AUTHOR]

This article is an editorial that discusses the potential of solar photovoltaic (PV) systems in contributing to the achievement of the United Nations' Sustainable Development Goals (SDGs). The authors highlight the importance of renewable energy systems, particularly solar PV systems, in addressing global disparities and improving living standards. They emphasize the need for research and innovation to enhance the efficiency and cost-effectiveness of solar PV systems. The article also provides summaries of various studies that explore different aspects of solar PV systems, such as performance assessment, power quality improvement, PV array layout, and environmental impact. The authors conclude by advocating for the transition to sustainable energy sources like solar PV to achieve carbon neutrality. [Extracted from the article]

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

The need for environmentally-friendly energy generation and massive exploitation of fossil fuels are leading to the increased demand for renewable energy sources like photovoltaic (PV) and wind energy, etc. The fluctuating behaviour of PV causes various disadvantages, of which the generation of Inter-harmonics is the major problem. The production of Inter-harmonics by the load-interfacing inverters is primarily due to the Maximum Power Point Tracking (MPPT) techniques employed in PV system. The requirement of multiple perturbations in tracking peak points is the primary cause behind the inter-harmonic generation. Since solar irradiation varies throughout a specific period, there can be situations, where the operating irradiance changes abruptly from one state to the other. These abrupt changes generate enormous fluctuations in the MPPT power output, resulting in an inter-harmonics generation. This paper proposes an evolved INter-HARmonic-Elimination (INHARE) algorithm for minimizing the PV fluctuating behaviour, leading to lower voltage ripple and settling time. The proposed INHARE algorithm is examined for a 0.52 kW PV system, where the Inter-harmonics content is reduced considerably in load current for the case of varying irradiances at a constant temperature. [ABSTRACT FROM AUTHOR]

This article dealt the application of three-phase all-pass filter integrated with phase-locked loop (3Ø-APF-PLL) control approach in islanded electrical supply system for enhanced power quality (PQ). It also covers the effectiveness of proposed control under various operating conditions such as intermittent wind speed, variable solar irradiance, and unbalanced nonlinear load. An islanded electrical supply system includes wind energy generation, solar photovoltaic (SPV), and battery energy storage (BES). The proposed control technique provides a coordinated and balanced operation under a dynamical load. A 3Ø-APL-PLL-based control approach is proposed to extract the maximum value of fundamental component of load current and phase angle. The proposed control approach is utilized to operate voltage source converter (VSC) that performs multi-functions, such as reducing source current harmonics, power compensation (real & reactive), balancing of loads, and improving the overall power quality of the islanded SPV-wind-BES electrical supply system. The SPV system is developed using an incremental conductance (IC)-based maximum power point tracking (MPPT) technique to extract maximum power during the intermittent conditions of solar irradiance. A bidirectional controller (BDC) is designed to operate the charging/discharging modes of a BES. Generally, an islanded electrical supply system suffers from poor voltage & frequency regulation due to the intermittency of power generation sources, which can be solved by utilizing a BES with BDC control to manage the power leveling between the power generation sources and the unbalanced load. A 3Ø-APF-PLL control approach is compared to existing controllers like a least mean square (LMS) (Singh et al. in IET Power Electron 13:2767–2774, 2020) and second-order generalized integrator-based frequency locked loop (SOGI-FLL) (Golestan et al. in IEEE Trans Power Electron 34:213–224, 2018). Numerous parameters, such as the evaluation of the maximum value of the fundamental component, stability, steady-state error, and harmonic compensation capabilities, are taken into account when comparing 3Ø-APF-PLL to the control approaches described above. The dynamic parameters of the islanded electrical supply system are justified under the IEEE-519 standard. [ABSTRACT FROM AUTHOR]

This study proposes an integrated design of isolated three-port high-gain DC-DC converters to link PV (photovoltaic) and batteries for a standalone system. Three switches are required to process power between inputs and outputs while maintaining minimal circulating current flow between ports and permitting bidirectional power flow at the battery port. Furthermore, owing to the leakage inductance of the transformers, ZVS (zero-voltage switching) of all the switches may be done using a mutual active clamp circuit throughout the whole operating range. As a result, the converter has fewer components and can still operate in all power conversion modes. Because the bulk of renewable energy sources have a low voltage level, the converter maintains a high voltage gain. An effective and reasonably priced converter is the outcome of these qualities. Using MATLAB simulation, the suggested converter's detailed operating principles, characteristics, design concerns and control method are examined. In order to confirm the viability and efficacy of the suggested solution, an experimental prototype is constructed and tested. [ABSTRACT FROM AUTHOR]

Grid-connected inverters are considered vital elements for effectively connecting renewable energy sources and distributed generation system applications. Ripple-induced current harmonics in DC link and high switching frequency are the disadvantages of grid-connected inverters that are reduced by LCL filters. However, the intrinsic resonance in the LCL filter leads to instability of the power transmission system. As a result, suitable damping is essential for removing resonance in the LCL filters. The contribution of this paper is to improve the quality of injectable power of LCL filter-based gridconnected photovoltaic array. For this contribution, the stability of the grid-connected inverter has been investigated using active damping method, and maximum power point tracking (MPPT) for the PV array has been performed. The capacitor voltage feedforward active damping method considering computational delay is presented in this paper. By using the inverter-side current feedback beside this method, the proposed control maintains the system's low-frequency specifications independent of the grid impedance changes. It provides high harmonic rejection capability without additional compensators. Also, the number of sensors is decreased due to the alternative measurement of the capacitor voltage instead of the grid voltage for the phase lock loop (PLL). Meanwhile, maximum power point tracking is implemented using the incremental conductance (IC) technique in the boost converter. In addition, a simple and suitable computational method for designing LCL filter parameters is presented, and the system's sensitivity is analyzed. Finally, the simulation has been implemented in MATLAB software that indicates the accurate performance of the control system in injecting the maximum power of the photovoltaic array into the grid and the highly desirable quality of the injectable current to the grid. [ABSTRACT FROM AUTHOR]

In this study, we modeled and designed a novel, efficient controller for a hybrid renewable energy system with an integrated converter and associated grid interface. The proposed grid-interfaced Hybrid Renewable Energy System (HRES) model is built by properly connecting the photovoltaic (PV) system, Wind Energy Conversion System (WECS), battery, DC/DC converter, Maximum Power Point Tracking (MPPT) controller, microgrid, and load to obtain the desired output. The integrated converter used in this is a modified high-conversion ratio converter for improving conversion efficiency. A maximum power point tracker is used to track the maximum power from renewable energy sources. We have designed an efficient controller with a successful control strategy to provide optimal switching for the grid-side inverter to achieve optimal energy management in the system. A novel control method is developed by combining the Adaptive Grasshopper Optimization Algorithm (AGOA) and the Gradient Boosting Decision Tree Algorithm (GBDT), which is named the AGOA-GBDT method. The proposed approach uses AGOA as an evaluation technique to develop accurate command signals and enrich the command signal database for offline use. The data sets collected from the sensors are also used to build a control system with fast feedback for online training of the GBDT system. The main purpose of using and proposing this novel control technique is to improve efficiency by achieving optimal energy management in the model. The formulation of the problem considers several constraints, such as the intermittent nature of renewable energy sources, the state of charge of the storage components, and the power demand. The MATLAB/Simulink platform simulates the proposed system model with the proposed controller and other controllers. This proposed control technique proved to be the best in efficacy and convergence properties when compared with other existing control techniques in the literature. [ABSTRACT FROM AUTHOR]

In recent years, the installed capacity of renewable energy systems has seen rapid growth, particularly in photovoltaic (PV) power. Photovoltaic modules, being the fundamental elements of the PV system, play a crucial role in determining system performance. However, the challenge arises from the inconsistent decay rates of PV modules, which significantly impact the accuracy of PV system modeling. To address this issue, this paper introduces a novel MIC-LSTM based parameter extraction method for the single-diode PV model. This method focuses on accurately deriving PV module model parameters under various decay rates. By establishing a mapping relationship between the current-voltage (I-V) curve characteristics and the five unknown parameters in the photovoltaic module model, the proposed method demonstrates high precision in parameter extraction. Simulation and experimental verifications are carried out to validate the proposed method, where the extraction accuracy is 99.3%, 98.39%, 98.85%, 97.91%, and 98.36% for the five unknown model parameters. [ABSTRACT FROM AUTHOR]

Systems for driving motors are frequently employed with renewable energy sources. By using cutting-edge methods and algorithms, the power converter controlling technology boosts performance. The switching state of the converter from the solar module's generation unit is where the QPSO (Quantum Particle Swarm Optimization) technique is applied in the currently available work. The duty cycle depends on the swarm velocities, which indirectly depends on the QPSO parameters. Hence, the QPSO parameters inertia (W), and learning coefficients C1 and C2, were determined using the Cuckoo search algorithm (CSO). This approach helps to extract the most powerful solar panel output and continuously operate a BLDC motor performance increasing the power converter controlling technology by utilizing state-of-the-art techniques and algorithms. Every module of the suggested boost converter was examined by the experimental findings. Based on generation and load, a boost converter module is obtained. The Models for single-ended primary inductors (SEPIC) and zeta converters are contrasted with the suggested logic. The overall design model is done by using MATLAB/Simulink 2021a. [ABSTRACT FROM AUTHOR]

In recent times, the research in sustainable energy resources was integrated with the control approaches of various hybrid renewable energy systems for balancing the electricity generation. The accelerated hybrid energy storage systems and steadily growing energy consumption make the variability and the predictable behavior occur in the renewable energy sources, which requires an energy management system. This paper aims to control hybrid renewable energy systems for power management. The systems consist of photovoltaic (PV) arrays, diesel generators, wind systems with boost converter DC-DC, and inverters. The optimum of the real-time process is less due to prediction errors of multiple renewable sources, which track the output voltage and load current. Model predictive control (MPC) is proposed for problem-solving time operation to address this issue and compensate for the prediction error. The major impact factors, such as optimal split power, stability, reliability of energy, and desired dynamic responses, have been identified by applying the MPC technique. A simulation model has been constructed using MATLAB/Simulink environment to confirm the effectiveness of the MPC technique model. [ABSTRACT FROM AUTHOR]

The development of distributed renewable energy sources for power production is popular nowadays. To combine many renewable energy sources of various sorts into a single source for numerous uses. In a technology wave of high-quality power demand, multi-DC–DC converters have been proposed for effective power management and grid integration for diverse sources and advancements. Multi-port DC–DC Converters are a few input ports that interact with numerous photovoltaic (PV) panels and battery sources. The multi-port DC–DC converter handles the crucial maximum power point tracking feature in addition to the low-level DC voltage (MPPT). The unenthusiastic rule soft switching control (URSSC) technique, which is developed in this work, depends on switching as another isolated multi-DC–DC converter for the concurrent power execution of various renewable energy sources. Using maximum power point tracking (MPPT), the suggested converter is used to regulate a solar hybrid generating system composed of two separate photovoltaic (PV) boards. The outcomes of both the simulator and the experiment are shown to validate the ability to get MPPT simultaneously for both PV boards using the suggested converter. To approve the MPPT control of the proposed multi-port DC–DC converter architecture, simulation studies are carried out in MATLAB Simulation. [ABSTRACT FROM AUTHOR]