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Energy hub stations represent a transformative approach to modern energy systems, functioning as flexible nodes within distribution networks. By seamlessly integrating electric vehicles (EVs) and battery energy storage systems (BESSs), these hubs address critical challenges such as grid stress, renewable energy utilization, and peak load management. This study introduces a novel mathematical optimization model designed to maximize the operational flexibility of EV charging stations by transforming them into fully functional energy hubs. The model incorporates key parameters such as energy demands, load profiles, and grid conditions to optimize the sizing and operation of distributed energy resources, including photovoltaic (PV) systems and BESSs. The proposed approach minimizes grid dependency, enabling energy hubs to efficiently operate varying levels of operational flexibility, from full reliance on grid power to complete independence. The results reveal that, by effectively shifting energy usage away from peak periods and leveraging PV generation, energy hubs emerge as a critical component of sustainable energy systems and can independently support approximately 45% of their load, with minimal PV and BESS capacities. It also reveals a direct correlation between higher flexibility levels and increased infrastructure requirements for PV systems and BESSs. The proposed model underscores the critical role of energy hubs in facilitating the global shift toward decarbonization, aligning with contemporary goals for resilient and environmentally sustainable energy ecosystems. This work provides a scalable framework for future energy systems, with significant implications for policymakers, researchers, and practitioners in the fields of renewable energy and sustainable mobility. [ABSTRACT FROM AUTHOR]

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a complete analysis of an LLC resonant converter with a customized rectifier structure is presented. The converter is intended for wide, low-input, high-output voltage DC bus applications. The performance of the converter is assessed using comprehensive time-domain and fundamental harmonic approximation (FHA), which demonstrates its capacity to operate across an ample range of voltages by precisely adjusting the rectifier structure. The converter's capability is illustrated by deriving and discussing detailed mode operation, steady-state analysis, and DC gain equations. In order to verify the theoretical analysis, a prototype with a power output of 250 watts is constructed and subjected to testing. The results of the testing demonstrate that the converter is both feasible and effective. The experimental findings illustrate its capacity to manage vast voltage ranges while upholding high efficiency. In addition, the converter utilizes a frequency switching modulation (FSM) to connect with a photovoltaic (PV) panel and control the high output voltage. This demonstrates its adaptability in renewable energy applications. The validation is in accordance with theoretical predictions, demonstrating the converter's high-efficiency performance and versatility. [ABSTRACT FROM AUTHOR]

This paper addresses the critical challenge of optimizing the maximum power point (MPP) tracking of photovoltaic (PV) modules under varying load and environmental conditions. A novel fuzzy logic controller design has been proposed to enhance the precision and adaptability of MPP monitoring and adjustment. The research objective is to improve the efficiency and responsiveness of PV systems by leveraging voltage and power as input parameters to generate an optimized duty cycle for a buck-boost converter. This system is tested through both simulation and experimental validation, comparing its performance against the conventional perturb and observe (P&O) method. Our methodology includes rigorous testing under diverse conditions, such as temperature fluctuations, irradiance variations, and sudden load changes. The fuzzy logic technique is implemented to adjust the reference voltage every 100 µs, ensuring continuous optimization of the PV module's operation. The results revealed that the proposed fuzzy logic controller achieves a tracking efficiency of approximately 99.43%, compared to 97.83% for the conventional P&O method, demonstrating its superior performance. For experimental validation, a 150 W prototype converter controlled by a dSPACE DS1104 integrated solution was used. Real-world testing involved both a resistive static load and a dynamic load represented by a DC shunt motor. The experimental results confirmed the robustness and reliability of the fuzzy logic controller in maintaining optimal MPP operation, significantly outperforming traditional methods. In brief, this research introduces and validates an innovative fuzzy logic control strategy for MPP tracking, contributing to the advancement of PV system efficiency. The findings highlight the effectiveness of the proposed approach in consistently optimizing PV module performance across various testing scenarios. [ABSTRACT FROM AUTHOR]

This study aimed to optimize the sizing and allocation of renewable distributed generation (RDG) systems, with a focus on renewable sources, under N-1 faulty line conditions. The IEEE 30-bus power system benchmark served as a case study for us to analyze and enhance the reliability and quality of the power system in the presence of faults. The firefly algorithm (FFA) combined with the crow search (CS) optimizer was used to achieve optimal RDG sizing and allocation through solving the optimal power flow (OPF) under the most severe N-1 faulty line. The reason for hybridization lies in leveraging the global search capabilities of the CS optimizer for the sizing and allocation of RDGs and the local search proficiency of the FFA for OPF. Two severe N-1 faulty conditions—F27-29 and F27-30—were separately applied to the IEEE 30-bus distribution system. The most severe N-1 faulty line of these two faulty lines was F27-30, based on a severity ranking index including both the voltage deviation index and the overloading index. Three candidate buses, namely 27, 29, and 30, were considered in the optimization process. Our methodology incorporated techno-economic multi-objectives, encompassing overall costs, power losses, and voltage deviation. The optimizer can eliminate the impractical buses/solutions automatically while remaining the practical one. The results revealed that optimal RDG allocation at bus 30 effectively alleviated line overloading, ensuring compliance with the line flow limit, reducing costs, and enhancing voltage profiles, thereby improving system performance under N-1 faulty conditions compared to the equivalent case without RDGs. [ABSTRACT FROM AUTHOR]

Battery State of Health (SoH) estimation is a critical task in the field of battery management, as it provides information about remaining capacity and health of a battery. Various machine learning algorithms, including neural networks, decision trees, support vector machines, and random forests, have been utilized for battery SoH estimation. These models can be trained using different features, such as voltage, current, temperature, impedance, and their combinations. However, the diversity of data is a decisive factor that affects precision of battery SoH estimation using machine learning. In this research, the application of feedforward neural networks (FNNs) and recurrent neural networks (RNNs) is explored for the purpose of accurately estimating the SoH of batteries. These approaches are chosen due to the inherent benefits of FNNs and RNNs in capturing the long‐term dependencies present in sequential data. The battery SoH estimations are evaluated using the single and multichannel input: voltage, current, voltage‐current, voltage‐temperature, and voltage‐current‐temperature. The experimental findings reveal that the proposed RNN model, specifically the RNN with 20 neurons (RNN20) variant, exhibits an enhanced accuracy in predicting the SoH of batteries. For instance, when utilizing voltage and current as inputs, the RNN20 model demonstrated superior performance, achieving an mean absolute error (MAE) of 0.010157 for voltage and 0.010367 for current, outperforming the FNN with 10 neurons (FNN10) model, which yielded an MAE of 0.031635 and 0.065 for voltage and current, respectively. Furthermore, when employing diverse input combinations such as voltage‐current, voltage‐temperature, and voltage‐current‐temperature, the RNN20 model consistently outperformed its counterparts, exhibiting the lowest mean squared error and mean absolute percentage error across all metrics. These results underscore the RNN20 model's robustness and aptitude in accurately predicting battery SoH, affirming the merits of employing RNNs in battery management systems. [ABSTRACT FROM AUTHOR]

For grid‐connected solar photovoltaic (PV) applications, a hybrid cascaded MLI (H‐CMLI) topology based on cascaded H‐bridge MLI is proposed in this study. The proposed configuration is a hybrid of two common MLIs: Cascaded H‐bridge and three‐phase cascaded voltage source inverter (VSI). For the same voltage level, the proposed topology enhances the number of the generated voltage levels while employing fewer components, relative to other traditional MLI topologies. Furthermore, the voltage stresses on switches in the proposed topology are decreased, compared to when each part operates independently. The proposed topology could be utilized for many applications, such as PV‐grid connection, active power filters, and STATCOM. To verify the effectiveness of the proposed topology, the model is developed in MATLAB/Simulink environment, in which a closed‐loop control scheme based on the traditional one is used to achieve the PV‐grid connection. Alongside the simulation results, the proposed topology and its control scheme are experimentally implemented in the laboratory. Two cases were experimentally applied to validate the successful performance of the proposed H‐CMLI: Open‐loop control and closed‐loop control for PV‐grid integration. The proposed control scheme effectively maintains the DC link voltage at its reference voltages. Also, the closed‐loop control scheme is capable of providing the power factor at unity and maintaining the total harmonic distortion (THD) of grid current within the acceptable limit. To demonstrate the proposed topology's functionality and viability, experimental results are provided. [ABSTRACT FROM AUTHOR]