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ABSTRACT Bi‐directional power exchange between the lithium‐ion battery and the grid can be achieved using vehicle‐to‐grid (V2G) technology. Bi‐directional battery chargers for V2G applications present opportunities and challenges, as the rapid growth of active components can compromise system reliability. This paper proposes an adaptive control mechanism for a three‐phase bi‐directional battery charging system to address this. The system integrates a bi‐directional active front‐end (AFE) converter (BAC) with an interleaved buck‐boost converter to enable adaptive constant current/constant voltage (CC/CV) mode operation and bi‐directional power flow. A feedforward decoupled current controller and pulse width modulation scheme optimize battery charging, while an optimized voltage‐oriented control (OVOC) model‐based controller ensures stable bi‐directional power exchange between the electric vehicle (EV) and the utility. Experimental results validate the effectiveness of the proposed approach, with findings demonstrating low total harmonic distortion (THD), unity power factor (UPF), and minimal DC voltage ripple. A 12.5 kW prototype further confirms the practical viability of the design, offering a promising solution for enhanced reliability and performance in V2G battery chargers. [ABSTRACT FROM AUTHOR]

Bidirectional DC–DC converters play a crucial role in enabling the transfer of energy between low-voltage and high-voltage sides, a fundamental requirement in applications like vehicle-to-grid and grid-to-vehicle scenarios. The motivation behind the application of common ground converters is the quest for enhanced reliability and safety while also seeking to prevent electromagnetic interferences and address the issue of d v d t between the input and output sides. This paper introduces an enhanced SEPIC converter with a common ground configuration, incorporating a quadratic switched-inductor cell. This innovative design allows for both voltage step-up and step-down operations in both directions of energy flow. The converter exhibits substantial gains and efficiency. To validate the theoretical findings, a 300W laboratory prototype of the converter was constructed and tested. Control and switching operations are managed by a DSP-based microcontroller operating at a sampling speed of 80 Mb/s, utilizing a fuzzy logic control technique. The Arvin converter, as suggested, offers numerous advantages that can be encapsulated as follows: It introduces a common ground-based high-gain topology, governed by a simple fuzzy logic controller, thus eliminating the requirement for intricate current and voltage equations for circuit elements. It makes the bidirectional energy flow possible and adopts a cost-effective, non-isolated configuration with a reduced count of semiconductor devices and passive components. The designed circuit was named ARVIN in honor of my 7-year-old son. [ABSTRACT FROM AUTHOR]

As the adoption of electric vehicles increases, the challenge of managing bidirectional energy flow while ensuring grid stability and respecting user preferences becomes increasingly critical. This paper aims to develop an intelligent framework for vehicle-to-grid (V2G) energy management that balances grid demands with user autonomy. The research presents VESTA (vehicle energy sharing through artificial intelligence), featuring the semantic-aware vehicle access control (SEVAC) model for efficient and intelligent energy sharing. The methodology involves developing a comparative analysis framework, designing the SEVAC model, and implementing a proof-of-concept simulation. VESTA integrates advanced technologies, including artificial intelligence, blockchain, and edge computing, to provide a comprehensive solution for V2G management. SEVAC employs semantic awareness to prioritise critical vehicles, such as those used by emergency services, without compromising user autonomy. The proof-of-concept simulation demonstrates VESTA's capability to handle complex V2G scenarios, showing a 15% improvement in energy distribution efficiency and a 20% reduction in response time compared to traditional systems under high grid demand conditions. The results highlight VESTA's ability to balance grid demands with vehicle availability and user preferences, maintaining transparency and security through blockchain technology. Future work will focus on large-scale pilot studies, improving AI reliability, and developing robust privacy-preserving techniques. [ABSTRACT FROM AUTHOR]

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data. [ABSTRACT FROM AUTHOR]

The increasing number of electric Vehicles (EVs) and their influence on the power grid present difficulties that this article addresses by suggesting optimal planning methods for EV charging and discharging. EV charging and discharging operations are effectively managed by creating both locally and globally optimal planning schemes. Future transportation could be changed by the widespread adoption of dynamic wireless power transfer systems in conjunction with EVs, as they would enable speedier travel and continuous EV battery recharging. Dynamic wireless power transfer is a practical answer to problems with electric vehicles. The electrification of automobiles will have a significant influence on the power infrastructure due to the increase in demand for electricity. In this study, we provide an optimal planning method worldwide and a locally optimal strategy for EV charging and discharging. To minimize the total cost of all EVs that charge and discharge during the day, we propose an optimization problem for global planning in which the charging powers are optimized. The simulation results demonstrate that the proposed planning schemes can effectively reduce the total electricity cost for EV owners while also minimizing the impact on the power grid. The globally optimal planning scheme achieves the lowest electricity cost, while the locally optimal scheme provides a good balance between cost reduction and computational complexity. [ABSTRACT FROM AUTHOR]

The vehicle-to-grid (V2G) service enables electric vehicles (EVs) to discharge battery power to the utility grid for power system stabilization. This study proposes a V2G acceptance model by integrating the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) with two additional constructs: perceived risks of V2G technology and attitude toward V2G service. The model aims to understand individuals' acceptance of V2G services in Taiwan, where the technology is still in the planning phase. Using 365 survey samples (250 EV users and 115 non-EV users) analysed using the structural equation modeling (SEM) method, results show that attitude and price value positively affect V2G usage intention while perceived risks of V2G technology negatively do. Performance expectancy and effort expectancy of V2G services positively influence attitudes. However, the effects of both facilitating conditions of V2G services and social influence on V2G usage intention are found insignificant. Discussions and implications support pilot initiatives for performance evaluation and marketing development, especially for the government, electric bus operators, and aggregators. • The initial research output, utilising an integrated UTAUT2 model, explores the intention to use V2G services in Taiwan. • Attitude and price value positively impact the intention to use V2G services. • Perceived risks negatively affect the intention to use V2G services. • Performance expectancy and effort expectancy of V2G services positively influence attitudes. • Facilitating conditions and social influence have an insignificant impact on the intention to use V2G services. [ABSTRACT FROM AUTHOR]

Microgrid systems face challenges in preserving frequency stability due to the fluctuating nature of renewable energy sources (RESs), underscoring the importance of advanced frequency stabilization strategies. To ensure power system stability in situations where renewable energy significantly contributes to the energy mix, it is essential to implement load frequency controllers (LFCs). Moreover, with the widespread use of electric vehicles (EVs), leveraging battery storage from EVs for microgrid frequency control is becoming increasingly crucial. This integration enhances grid stability and offers a sustainable solution by utilizing renewable energy more efficiently and reducing dependency on traditional power sources. Therefore, this paper proposes an innovative approach to LFCs, using fractional-order control techniques to boost the resilience of the interconnected microgrid systems. The approach centers on a centralized control scheme with a tilt fractional-order integral-derivative featuring an accelerated derivative (TFOID-Accelerated) controller. The accelerated derivative component of this controller is tailored to mitigate high-frequency disturbances, while its tilt feature and fractional integration effectively handle disturbances at lower frequencies. As a result, the proposed controller is expected to efficiently counteract disturbances caused by variability in RESs and/or load changes, achieving a high level of disturbance rejection. Additionally, this paper employs the recent growth optimizer (GO) method for the optimal design of the controller's parameter set, avoiding the need for complex control theories, elaborate disturbance observers, filters, and precise power system modeling. The GO algorithm enhances fractional-order capabilities, offering a robust solution to the challenges of renewable energy variability and demand fluctuations. This is accomplished by optimizing parameters and simplifying the control system design across different microgrid scenarios. The proposed TFOID-Accelerated LFC demonstrates superior performance in enhancing frequency stability and minimizing oscillations compared to existing controllers, including traditional proportional-integral-derivative (PID), PID-Accelerated (PIDA), and tilt-integral-derivative (TID) controllers. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles (FCEVs) are estimated as the future's mobile distributed generators with vehicle-to-grid (V2G) functions. Besides, V2G topologies are required to be designed with an appropriate analog filter by accurately measuring the reference current and voltage values of inverters and converters. Thus, the highest accuracy is required in control structures for charging and grid connection of FCEVs to improve the power quality (PQ). In this study, PQ improvement of V2G operation of FCEVs is proposed by considering the developed real-time digital infinite impulse response (IIR) filters in generating reference signals in the power calculation stage. A band-pass digital IIR filter design is realized with the inverter using LabVIEW infrastructure, and reference signals are smoothed by using IIR filters. The performances of developed different IIR filters' results are examined in the study. Developed filters are designed using the order estimation method. Elliptic, Chebyshev, and Butterworth filters are applied to the voltage and current signals, and the best results are obtained in reference power signals with the Butterworth filter. The Butterworth filter maintains stability in current RMS, induces a 0.09 % decrease in voltage RMS, and shows a slight 0.30 % increase in active power. The proposed approach removes distortions in reference power signals by using a designed filter for a reliable V2G operation of FCEVs. [Display omitted] • IIR filters are proposed to generate reference signals for FCV2G operation. • Butterworth filter demonstrated superior stability in current and voltage RMS. • IIR filters are designed using the improved order estimation method in LabVIEW. • Improving calculation process increases PQ in grid connection of FCEVs with V2G. • A real-time test system in laboratory is presented verifying proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid's reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed "vehicle-for-grid". Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately 1.85 % , compared to the MPC algorithm's THD of about 2.85 % . This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to 0 KVAR in the vehicle-for-grid mode. [ABSTRACT FROM AUTHOR]

Despite the global importance of solar energy, its variability requires energy storage to balance production during peak and off-peak periods. Moreover, the transport sector is undergoing a global transition from internal combustion engines to electric vehicles. Since vehicles are idle 95% of the time, electric vehicle batteries, when connected to a grid, can effectively regulate intermittent photovoltaics using vehicle-to-grid technology. This conceptual study investigates the feasibility of a nationwide energy infrastructure that relies solely on solar energy, replacing other electricity sources, such as solid fuels, petroleum products, and natural gas, and utilizes electric vehicles as the sole battery energy storage system. This study aims to demonstrate the significant potential and benefits of such collaboration. The theoretical study combines historical data, assumptions, and conditions to build a simulation model that is modelled similarly as in previous conceptual studies of nationwide energy systems based solely on photovoltaics and electric vehicles, referenced in this article. In Slovenia, the total surface size suitable for the installation of photovoltaic systems is estimated to be 280 km2. The calculations show that a surface size of 217 km2 for photovoltaic systems can produce enough energy to cover Slovenia's entire energy demand, Slovenia's final energy consumption. However, simulations comparing photovoltaic production, total energy consumption (electricity, solid fuels, etc.), and the capacity of electric vehicle batteries show that a surface size of more than 500 km2 with photovoltaic systems and a 200% share of electric vehicles in the Slovenian vehicle fleet in 2022 will provide satisfactory results. Therefore, for a country like Slovenia, in addition to a solar power plant with a surface size of 280 km2, additional renewable energy sources are needed to cover the total energy demand, as well as additional battery energy storage systems in addition to electric vehicles. [ABSTRACT FROM AUTHOR]

This paper unveils a groundbreaking wide-range DC-DC converter with significant voltage gain and bidirectional capability, engineered explicitly for Hybrid Electric Vehicle (HEV) chargers. This converter facilitates both Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) operations. It aims to revolutionize efficiency, voltage range, and bidirectional power flow capabilities, marking a significant leap forward from existing solutions. A meticulous comparative analysis between established systems and the proposed converter highlights its distinct advantages and evolutionary strides within the HEV charging infrastructure landscape. By enhancing the versatility and performance of HEV chargers, this converter promises to address critical challenges in energy management and integration. Its innovative design not only optimizes energy transfer but also supports future advancements in smart grid technology and sustainable transportation. The results of this study underscore the converter's potential to drive forward the next generation of electric vehicle infrastructure, paving the way for more efficient and resilient energy systems. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are growing in popularity so, power electronic converter research and development is necessary to provide high power, low cost, as well as efficient methods for EV battery charging. Two kinds of EV chargers exist i.e. on-board and off-board. However, off-board chargers have to be utilized for DC rapid and ultra-quick charging to reduce EV volume and weight significantly. This paper reviews the state-of-the-art literature on power electronics converter systems for both AC-DC and DC-DC power stages for off-board chargers, which interface with the utility grid, PV systems, and EVs. In addition to the novel topologies of AC-DC rectifiers and DC-DC converters, the control schemes of AC-DC rectifiers and DC-DC converters are also explained in this manuscript. Comparisons are made regarding their topologies, isolation, power & voltage ranges, efficiency, bi-directional power capabilities, control variables, advantages, and disadvantages.

The increasing number of electric Vehicles (EVs) and their influence on the power grid present difficulties that this article addresses by suggesting optimal planning methods for EV charging and discharging. EV charging and discharging operations are effectively managed by creating both locally and globally optimal planning schemes. Future transportation could be changed by the widespread adoption of dynamic wireless power transfer systems in conjunction with EVs, as they would enable speedier travel and continuous EV battery recharging. Dynamic wireless power transfer is a practical answer to problems with electric vehicles. The electrification of automobiles will have a significant influence on the power infrastructure due to the increase in demand for electricity. In this study, we provide an optimal planning method worldwide and a locally optimal strategy for EV charging and discharging. To minimize the total cost of all EVs that charge and discharge during the day, we propose an optimization problem for global planning in which the charging powers are optimized. The simulation results demonstrate that the proposed planning schemes can effectively reduce the total electricity cost for EV owners while also minimizing the impact on the power grid. The globally optimal planning scheme achieves the lowest electricity cost, while the locally optimal scheme provides a good balance between cost reduction and computational complexity.

Leveraging the Vehicle-to-Grid (V2G) concept, this research explores how a decentralized energy reserve from hybrid electric vehicles can enhance the power system, particularly in large-scale implementations. The study introduces a V2G solution designed for effective microgrid frequency control over a full day. Targeting a scenario with minimal usage, typically in spring or fall, the microgrid is scaled to represent a community of 2000 homes. This is exemplified by integrating 500 Electric Vehicles (EVs) based on a 1:4 vehicle-to-household ratio, reflecting a plausible future scenario. The research conducts a comprehensive examination of the microgrid's voltage, current, and active power. By synchronizing the management of diesel and Renewable Energy Source (RES) generation, power transactions, and EV generation, the microgrid's frequency is effectively regulated through V2G devices adjusting load demand. The implemented V2G-enriched microgrid demonstrates improved energy management and mitigates the inconsistencies and fluctuations inherent in RES power generation, showing notable performance enhancements. In various operational contexts, system parameter fluctuations have been analyzed, revealing that deviations are maintained below a 5% threshold. [ABSTRACT FROM AUTHOR]

The impact of air pollution on the world has been highlighted by the World Health Organization (WHO), which has contributed to the growth of emission-free and ecologically friendly transportation options such as electric vehicles (EVs). Between 2019 and 2020, EV sales climbed by 43%, and by 2025, 14 million EVs are expected to be sold worldwide. On the other hand, EVs have drawbacks such as expensive batteries, short lifespans, lack of EV dependability, limited driving range, and long charging periods. Integrating renewable energy sources (RESs) such as biomass, solar, and wind power into EV charging infrastructures is gaining popularity. PV solar-powered EV charging has benefits like cheaper fuel costs, easier installation, less demand on the grid for power, and cost savings. Hybrid and on-board charging systems offer benefits such as reduced weight, faster charging, and improved communication protocols. On-board chargers have higher energy transfer but are more expensive and difficult to integrate with charging stations. Off-board charging systems include public, rapid, induction, and home charging stations. Advanced charging management systems can optimize charging to preserve battery life. It is recommended that future research concentrate on the development of sophisticated control algorithms, integration of wireless charging, extension of fast-charging infrastructure, innovation in materials, compatibility with many vehicles, and optimization of grid interface to enhance efficiency and user experience. The charging of electric vehicles in standalone and grid-connected photovoltaic systems is covered in this paper, along with an explanation of the various modes of operation for these systems. There is also a quick explanation of the various kinds of EV chargers, their power capacities, and their specifications. The research and comparisons in this paper are expected to be useful resources for scholars who are interested in learning more about EV charging facilities.

Abstract Electric vehicles (EVs) are rapidly replacing conventional fuel vehicles, offering powerful, emission-free performance. This paper introduces an innovative three-phase bidirectional charger for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications, strengthening the connection between EVs and the power grid. The charger employs a two-stage power conversion approach with advanced converters and a simplified dq-based charging control strategy. An efficient AC-DC converter facilitates smooth transitions between modes, responding to grid directives for active and reactive power. A soft-switching dual active bridge (SS-DAB) DC-DC converter optimally interfaces with the EV battery pack, while dual active LCL filters suppress harmonics, enhancing system performance. Simulated results confirm the charger’s effectiveness in a 3.5-kW prototype using MATLAB/Simulink. The proposed SS-DAB converter-based bidirectional on-board charger introduces a groundbreaking unified Voltage Source Converter (VSC) control approach, enabling efficient power transfer in both vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes. This innovation ensures rapid dynamic response, exceptional steady-state performance, and robustness against grid demand changes, optimizing EV integration.

As the adoption of electric vehicles increases, the challenge of managing bidirectional energy flow while ensuring grid stability and respecting user preferences becomes increasingly critical. This paper aims to develop an intelligent framework for vehicle-to-grid (V2G) energy management that balances grid demands with user autonomy. The research presents VESTA (vehicle energy sharing through artificial intelligence), featuring the semantic-aware vehicle access control (SEVAC) model for efficient and intelligent energy sharing. The methodology involves developing a comparative analysis framework, designing the SEVAC model, and implementing a proof-of-concept simulation. VESTA integrates advanced technologies, including artificial intelligence, blockchain, and edge computing, to provide a comprehensive solution for V2G management. SEVAC employs semantic awareness to prioritise critical vehicles, such as those used by emergency services, without compromising user autonomy. The proof-of-concept simulation demonstrates VESTA’s capability to handle complex V2G scenarios, showing a 15% improvement in energy distribution efficiency and a 20% reduction in response time compared to traditional systems under high grid demand conditions. The results highlight VESTA’s ability to balance grid demands with vehicle availability and user preferences, maintaining transparency and security through blockchain technology. Future work will focus on large-scale pilot studies, improving AI reliability, and developing robust privacy-preserving techniques.

Electric vehicles (EVs) are rapidly replacing conventional fuel vehicles, offering powerful, emission-free performance. This paper introduces an innovative three-phase bidirectional charger for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) applications, strengthening the connection between EVs and the power grid. The charger employs a two-stage power conversion approach with advanced converters and a simplified dq-based charging control strategy. An efficient AC-DC converter facilitates smooth transitions between modes, responding to grid directives for active and reactive power. A soft-switching dual active bridge (SS-DAB) DC-DC converter optimally interfaces with the EV battery pack, while dual active LCL filters suppress harmonics, enhancing system performance. Simulated results confirm the charger's effectiveness in a 3.5-kW prototype using MATLAB/Simulink. The proposed SS-DAB converter-based bidirectional on-board charger introduces a groundbreaking unified Voltage Source Converter (VSC) control approach, enabling efficient power transfer in both vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes. This innovation ensures rapid dynamic response, exceptional steady-state performance, and robustness against grid demand changes, optimizing EV integration. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology's business schemes are investigated. Furthermore, the challenges faced by the V2G technology's business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology's research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector. [ABSTRACT FROM AUTHOR]

The proliferation of intelligent transportation systems is driving the rapid growth of Vehicle-to-Grid (V2G). Efficient vehicle scheduling can promotes the development of power grid. However, the existing vehicle scheduling strategies lack the combination with social networks. Therefore, it is particularly important to analyze the relationship between social networks and vehicle scheduling. To achieve the efficient vehicle scheduling, an Attribute and Closeness based Influence Maximization Optimization Algorithm (AC-IMOA) is designed. At first, the relationship among information dissemination, user influence and vehicle scheduling is analyzed. According to these analyses, the user closeness is defined and measured by Influence Measurement Algorithm based on Attribute and Closeness (IMAAC). Then, user influence is calculated by the Random Walk algorithm based on the Closeness between Users (RWACU). In addition, the user optimization set is selected by Simulated Annealing Optimization Algorithm (SAOA). At last, the experimental results show that AC-IMOA is effective in V2G behavior scheduling prediction and has better scheduling performance. [ABSTRACT FROM AUTHOR]

To address the challenge of optimizing the real-time scheduling for electric vehicles on a large scale, a day-ahead–intraday multi-timescale electric vehicle cluster division strategy is proposed based on the different expected charging completion times of the accessed electric vehicles. In the pre-day phase, historical travel statistics are used to model and determine the moments when the electric vehicles are on-grid and off-grid. In the intraday phase, the EV clusters are carefully divided by real-time data collection, taking into full consideration the response willingness and ability of vehicle owners. For each scheduling period, a real-time optimal scheduling model for EV clusters based on the V2G mode is established by taking into account the constraints of the power grid, vehicle owners, batteries, and other parties. The model is divided into two layers to find the charging and discharging plans: the upper model aims to determine the aggregate charging and discharging power of the cluster during the current time period by targeting the distribution grid's minimum variance load curve within the scheduling interval; the lower model takes the lowest cost to the EV owner as the goal to find the charging and discharging plan of a single EV and, at the same time, introduces the scheduling penalty factor to adjust the difference with the cluster charging and discharging plan. The simulation outcomes indicate that the suggested approach successfully mitigates load fluctuations and has a good optimization effect and fast solution speed for dealing with large-scale EV access problems. The simulation results show that the proposed strategy can effectively smooth load fluctuations and can significantly reduce the difficulty of optimal real-time scheduling for electric vehicles on a large scale, and it has a better optimization effect and faster solution speed for dealing with large-scale electric vehicle access problems. [ABSTRACT FROM AUTHOR]

Abstract The past two decades have seen a rapid increase in electric vehicles (EVs) for several reasons, such as policy directives to reduce carbon emissions in the transport sector and technology advancements in the EV industry. However, this has increased the load demand on the power grid, especially in the low‐voltage (LV) network, as most EVs are charged at EV owner premises. This paper investigates the impact of EVs on the LV residential distribution network using a probabilistic modelling framework. Probability distribution functions for EV charging power are derived using the United Kingdom (UK) EV dataset. The study has investigated multiple EV penetration levels, different probability distribution functions for EV charging representation, vehicle‐to‐grid (V2G), solar photovoltaic (PV) generation, and the volt‐var capability of the solar‐PV inverter. The results have shown that as EV penetration increases in the distribution network, there is a significant increase in transformer loading and a decrease in the steady‐state voltage levels. V2G has positively impacted the distribution network. A case study carried out on a real LV feeder with solar‐PV generation has shown how PV generation and volt‐var functionality of the PV inverter help reduce the impact of EV charging and V2G.

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid’s reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed “vehicle-for-grid”. Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately 1.85%, compared to the MPC algorithm’s THD of about 2.85%. This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to 0KVAR in the vehicle-for-grid mode.

Despite the global importance of solar energy, its variability requires energy storage to balance production during peak and off-peak periods. Moreover, the transport sector is undergoing a global transition from internal combustion engines to electric vehicles. Since vehicles are idle 95% of the time, electric vehicle batteries, when connected to a grid, can effectively regulate intermittent photovoltaics using vehicle-to-grid technology. This conceptual study investigates the feasibility of a nationwide energy infrastructure that relies solely on solar energy, replacing other electricity sources, such as solid fuels, petroleum products, and natural gas, and utilizes electric vehicles as the sole battery energy storage system. This study aims to demonstrate the significant potential and benefits of such collaboration. The theoretical study combines historical data, assumptions, and conditions to build a simulation model that is modelled similarly as in previous conceptual studies of nationwide energy systems based solely on photovoltaics and electric vehicles, referenced in this article. In Slovenia, the total surface size suitable for the installation of photovoltaic systems is estimated to be 280 km2. The calculations show that a surface size of 217 km2 for photovoltaic systems can produce enough energy to cover Slovenia’s entire energy demand, Slovenia’s final energy consumption. However, simulations comparing photovoltaic production, total energy consumption (electricity, solid fuels, etc.), and the capacity of electric vehicle batteries show that a surface size of more than 500 km2 with photovoltaic systems and a 200% share of electric vehicles in the Slovenian vehicle fleet in 2022 will provide satisfactory results. Therefore, for a country like Slovenia, in addition to a solar power plant with a surface size of 280 km2, additional renewable energy sources are needed to cover the total energy demand, as well as additional battery energy storage systems in addition to electric vehicles.

In order to solve the problem of difficulty in achieving individual scheduling for large-scale electric vehicles entering the grid and the existence of "dimensionality disaster" in cluster scheduling, a hierarchical and partitioned optimization operation model for active distribution network based on vehicle-to-grid(V2G) mode is established. The upper level optimization model schedules the electric vehicle agent (EVA) of electric vehicles, optimizes the charging and discharging power of EVA in each region, and serves as input for the lower level optimization model. Lower level optimization model adjusts various voltage regulation methods. In terms of optimization algorithm, a self adaptive differential evolution biogeography based optimization (SaDE-BBO) algorithm is proposed and simulated in the improved IEEE 33-node distribution system. The results show that under different charging control strategies, the coordinated interaction between V2G mode and various voltage regulation methods has significant advantages in reducing EVA operating costs in various regions, suppressing load fluctuations, and ensuring the safe and economic operation of active distribution networks. Compared with other optimization algorithms, the SaDE-BBO algorithm has better solutions and convergence.

Aiming at the power fluctuation problem for the randomness of the time-space transfer of electric vehicles (EV), taking into account the gender difference of car owners, different real-time travel purpose and two-way travel of car owners, the charge-discharge dispatch strategy based on the two-way travel chain for the travel randomness of EV is proposed. Firstly, the two-way travel chain of EV is established by considering the actual travel process of car owners. Secondly, the real-time travel probability model is given by considering the gender differences of the male and female owners travel. Then, the dispatch model is set up by reducing the power fluctuation as the objective function according to the real-time travel demand of the owner and the real-time state of charge (SOC) of EV. Finally, the charge and discharge of EV are dispatched according to the established model, and the simulation if given by analyzing the cars in a area of 280 square meters. The simulation results show that the two-way travel model is closer to the actual travel law of owners, and the dispatch strategy based on the dispatched model of the two-way travel chain can better restrain the power fluctuation.

Electric vehicles (EVs) have emerged as a promising solution to address environmental concerns and transform the transportation sector. This paper examines the state-of-the-art EVs, encompassing their history, definition, and different types. The advantages and limitations of each type are compared and contrasted, providing insights into their unique characteristics and potential applications. Critical components of EVs are thoroughly analyzed. The paper explores various types of batteries, emphasizing their features and potential for improving energy density, range, and charging capabilities. Charging infrastructure is discussed, highlighting the importance of establishing a robust network of charging stations. The impact of EVs on power grids is examined, emphasizing grid integration, load management, capacity upgrades, and power quality. Challenges and barriers hindering the wide adoption of EVs are identified, including technological limitations, infrastructure constraints, economic considerations, consumer perception, and policies. The paper delves into the environmental and economic impacts of EVs, including lifecycle analysis, air pollution reduction, and cost-effectiveness. Thus, the paper provides valuable insights into the current state of EVs and their potential to shape the future. The findings highlight the need for collaborative efforts from industry stakeholders, governments, researchers, and consumers to overcome challenges and accelerate the transition to a sustainable transportation system. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) market is growing rapidly due to the necessity of shifting from fossil fuel-based mobility to a more sustainable one. Smart charging paradigms (such as vehicle-to-grid (V2G), vehicle-to-building (V2B), and vehicle-to-home (V2H)) are currently under development, and the existing implementations already enable a bidirectional energy flow between the vehicles and the other systems (grid, buildings, or home appliances, respectively). With regard to grid connection, the increasingly higher penetration of electric vehicles must be carefully analyzed in terms of negative impacts on the power quality; and hence, the effects of electric vehicle charging stations (EVCSs) must be considered. In this work, the interactions of multiple electric vehicle charging stations have been studied through laboratory experiments. Two identical bidirectional DC chargers, with a rated power of 11 kW each, have been supplied by the same voltage source, and the summation phenomenon of the current harmonics of the two chargers (which leads to an amplification of their values) has been analyzed. The experiment consisted of 100 trials, which considered four different combinations of power set-points in order to identify the distribution of values and to find suitable indicators for understanding the trend of the harmonic interaction. By studying the statistical distribution of the Harmonic Summation Index, defined in the paper, the impact of the harmonic distortion caused by the simultaneous charging of multiple electric vehicles has been explored. Based on this study, it can be concluded that the harmonic contributions of the electric vehicle charging stations tend to add up with increasing degrees of similarity of the power set-points, while they tend to cancel out the more the power set-points differ among the chargers. [ABSTRACT FROM AUTHOR]

Transformerless power converters are trending in automotive sector as the demand for onboard chargers rises. The high efficiency and leakage current mitigating property of highly efficient and reliable inverter concept (HERIC) converter make it an excellent choice among transformerless topologies. By using appropriate modulation technique, the HERIC converter can transmit bidirectional power. This article presents design of sliding mode controller (SMC) for bidirectional HERIC converter to enhance the tracking performance. The SMC parameters are selected utilizing Harris hawks optimization (HHO) algorithm. During vehicle-to-grid (V2G) operation, sliding mode controller is developed for reactive power regulation of the grid connected HERIC converter and for grid-to-vehicle (G2V) operation, the converter is operated with very low voltage ripples in the DC side. The stability analysis is done by considering model uncertainties to guarantee sinusoidal grid current, low THD, and unity power factor even in the presence of grid disturbances and parametric variations. The Typhoon Hardware in the Loop (HIL) 402 device is used to execute the real-time testing. The results confirmed the efficacy and robustness of the designed controller under different scenarios for both modes of operation. [ABSTRACT FROM AUTHOR]

Aiming at the distributed resources of electric vehicles with photovoltaics (PVs) on the user side, a trading mode of surplus energy sharing for electric vehicles based on the user-side PVs is proposed by utilizing the bidirectional mobility of information and energy. Power transfer can be implemented between different electric vehicle users through vehicle-to-grid (V2G) technology with a reasonable distribution of benefits taken into account. First, the operational framework of electric energy trading is presented, and the transmission architecture of each body of interest in the system is analyzed. Second, the portraits of EV users' charging behaviors are established considering their different charging habits, and electric vehicle users are divided into electricity buyers and sellers in each trading time period. An electricity transaction model based on "multi-seller–multi-buyer" is established, and all electricity transactions are realized through blockchain-based decentralized technology. Finally, the benefit to each interest group is maximized using the improved Northern Goshawk Optimization (NGO) algorithm. Simulation results of a sample system indicate that the new power trading mode proposed in this study could lead to reasonable reuse of the electric energy of private electric vehicles and can achieve a win–win situation for all stakeholders. [ABSTRACT FROM AUTHOR]

The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.

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Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electric vehicles (EVs) have shown promise in providing ancillary services, e.g., frequency regulation. This is mainly due to their capacities and fast response. On the contrary, the rapid integration of EVs in the grid poses challenges, such as frequency and voltage stability. In order to mitigate the above-mentioned issues, several dispatching strategies have been introduced in the recent literature to optimize the charging/discharging rates of EVs. In this paper, a comparative study of power management strategies for secondary frequency regulation (SFR) employing a fleet of EVs is presented. A hierarchical control scheme is employed to compare two cases, namely control at the charging station (CS) level and novel control at the EVs level. Under both cases, a multi-objective optimization approach is utilized to define the optimal charging and discharging rates of EVs using a pattern search algorithm. Furthermore, the performance of the two models is experimented under contingency cases, a notable contribution of this study. Finally, simulations are carried out using OPAL-RT real time simulator to validate the performance of the two models based on real-time traces obtained from Pennsylvania, New Jersey, and Maryland (PJM) interconnection and California independent system operator (CAISO). To further validate the proposed model, a comparison with a mixed-integer linear programming (MILP) based model is presented.

Due to their fast response and strong short-term power throughput capacity, electric vehicles (EVs) are promising for providing primary frequency support to power grids. However, due to the complicated charging demands of drivers, it is challenging to efficiently utilize the regulation capacity of EV clusters for providing stable primary frequency support to the power grid. Accordingly, this paper proposes an adaptive primary frequency support strategy for EV clusters constrained by the charging-behavior-defined operation area. First, the forced charging boundary of the EV is determined according to the driver's charging behavior, and based on this, the operation area is defined. This ensures full utilization of the available frequency support capacity of the EV. An adaptive primary frequency support strategy of EV clusters is then proposed. The output power of EV is adaptively regulated according to the real-time distance from the EV operating point to the forced charging boundary. With the proposed strategy, when the EV approaches the forced charging boundary, its output power is gradually reduced to zero. Then, the rapid state-of-charge declines of EVs and sudden output power reductions in EV clusters caused by forced charging to meet the driver's charging demands can be effectively avoided. EV clusters can then provide sustainable frequency support to the power grid without violating the driver's charging demands. Simulation results validate the proposed operation-area-constrained adaptive primary frequency support strategy, which outperforms the average strategy in terms of stable output maintenance and the optimal utilization of regulation capacities of EV clusters.

Electric vehicles (EVs) are pivotal in the global shift towards sustainable energy systems. As EV adoption rises, effective integration into the power grid becomes vital. Vehicle-to-grid (V2G) operations, enabling bidirectional power flow between EVs and the grid, enhance grid resilience and leverage EVs as distributed energy resources. This article presents a comprehensive framework addressing gaps in EV integration. It combines EV travel energy forecasts and electricity market engagement, coordinating forecast-based optimization with real-time management. Our approach introduces a novel prioritization mechanism, optimizing power allocation based on forecasted state-of-charge for upcoming trips. The optimization follows a multi-objective function that aims to minimize the operational costs and maximize both the upward and downward flexibility to provide ancillary services. The study assesses EV participation in Replacement Reserve, Secondary Reserve, and energy arbitrage. Simulations compare scenarios, unveiling optimal performances. The framework showcases EVs' potential to bolster grid stability, yielding economic value for EV owners and the power system. This article pioneers a holistic approach to EV integration, bridging current limitations, and emphasizing economic and technical benefits.

This research paper proposes a novel grid-connected modular inverter for an integrated bidirectional charging station for residential applications. The system is designed to support the electrical grid by providing buffering services and enhancing the stability of the grid. The proposed system consists of a modular bidirectional inverter which can work as an EV charger. The system can operate in different modes such as charging and discharging the EV battery, storing excess energy from the grid during low-demand periods, and supplying energy back to the grid during high-demand periods. A low-level control strategy based on the droop control technique and feedforward decoupling is developed to manage the power flow between the EV battery, household load and the grid. The performance of the proposed system is evaluated through simulation studies in MATLAB/Simulink software. The results show the system’s ability to support the grid during peak demand and boost renewable energy integration. It also acts as a backup power supply during outages, increasing reliability. The experimental validation in a laboratory environment confirms an 87% charging efficiency with 35% SoC, drawing 6.3 kW from the grid. Returning power to the grid achieves a V2G efficiency of around 94%, delivering 8.6 kW AC output and 8.77 kW battery input at a 55% SoC.

Green transportation has become our top priority due to the depletion of the earth’s natural resources and rising pollutant emission levels. Plug-in electric vehicles (PEVs) are seen as a solution to the problem because they are more cost and environment friendly. Due to rapid industrialization and government incentives for zero-emission transportation, a significant challenge is also constituted in power grids by the self-interested nature of PEVs, with the asymmetry of information between the charging power demand and supply sides. In this paper, we propose an optimal strategy in industrial energy management system, based on evolutionary computing, to characterize different charging situations. The proposed approach considers stochastic, off-peak, peak, and electric power research institute charging scenarios for attaining the vehicle-to-grid capacity in terms of optimal cost and demand. An extensive scheduling of charging cases is studied in order to avoid power outages or scenarios in which there is a significant supply-demand mismatch. Furthermore, the proposed scheme model also reduces the greenhouse gases emission from generation side to build a sustainable generation infrastructure, which maximizes the utility of fuel-based energy production in the presence of certain nonlinear constraints. The simulation analysis demonstrates that PEVs can be charged and discharged in a systematic manner. The participation of transferable load through the proposed methodology can significantly reduce the economic costs, pollutant impacts, efficiency, and security of power grid operation.

Electric vehicles (EVs) can be used as energy storage devices to provide auxiliary frequency regulation service. However, the lack of inertia and damping caused by vehicle-to-grid (V2G) technology will affect the grid's stability. In addition, the users' charging plans are difficult to be completed on time by the current frequency regulation strategies. Therefore, the technology of virtual synchronous generator is utilized to make EVs have similar inertial and damping characteristics to synchronous generator. Then, based on the T-S fuzzy controller, an auxiliary frequency regulation control algorithm for EVs considering user's charging plans is proposed, and the V2G power is jointly determined by the charging states of EVs and the grid frequency. Simulation results demonstrate that the proposed strategy can intelligently control the power flow between the grid and EVs under the conditions of different charging plans, battery states and grid status. As a result, the proposed strategy can effectively improve the frequency stability of the power grid and avoid the risk of overcharge and overdischarge of the batteries while meeting the charging demand of users.