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.

Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades.

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.

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.

Enabling an internet of things (IoT) application in residential distribution systems by integrating houses with IoT windows and occupant behavior can provide numerous advantages to the power grid, including, but not limited to, demand diminution, congestion reduction, and capacity deferral. This paper presents a new framework that mathematically enables an IoT application in residential distribution systems by integrating IoT windows and occupant behavior with houses for load management and energy conservation. With the proposed framework, we model residential loads considering the IoT concept, and then develop a mathematical optimization model that facilitates the integration of IoT-based houses into the residential distribution system. Different case studies considering a 33-bus distribution network are presented and discussed to demonstrate the effectiveness of penetrating IoT-based houses on distribution system operations and household profitability. It is observed that the profit of the local distribution company decreases when houses are transformed to IoT-based houses due to the fact that less energy is sold to the households. On the other hand, the operation cost of the IoT-based house is lower than that of the conventional house because of the better-managed house energy use, thereby resulting in saving money. It is found that 10% and 20% penetrations of IoT-based houses help reduce the maximum power imported through the distribution substation by 30 kW and 60 kW, respectively. It is also found that the load of IoT-based houses and power availability of a rooftop photovoltaic generation are not compatible, and hence, without an action from the customer and/or utility to coordinate them through a demand response program, IoT-based houses would not contribute to increasing the connectivity of PV-distributed generation in the smart grid.

The utilization of renewable energy sources has increased due to concerns about climate change. However, injecting the power from renewable energy sources into grid-tied systems is challenging. The techno-economic analysis a photovoltaic (PV) energy systems is investigated. As a result, this paper presents AC-PV module for Grid-Tied and Off-Grid Scenarios via optimization, modeling, and test results. Even though the output of a PV panel is DC voltage, a three-port inverter and a lithium-ion battery pack are integrated with the back of the PV panel. They are packaged as a PV system module that makes the module output have AC voltage. Therefore, an optimized AC-PV module can be a solution for residential and commercial use, which are grid-tied systems; it can be very efficient for those without access to electricity, which is an off-grid system. An integrated battery and thermal management strategy is crucial for this AC-PV module. In the article, the battery capacity optimization, the electrical and the thermal model of the battery pack, battery heat generation model are discussed by using stochastic analysis techniques; the battery test results are also obtained to identify the models’ parameters and a control algorithm is proposed to extract the battery information such as temperature, current, voltage, SoC and SoH of the battery pack.