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A vehicle-to-grid (V2G) technology enables bidirectional power exchange between electric vehicles (EVs) and the power grid, presenting enhanced grid stability and load management opportunities. This study investigates a comprehensive microgrid system integrating EVs with solar (8 MW), wind (4.5 MW), and diesel generation sources, focusing on peak load reduction and frequency regulation capabilities. Through MATLAB/Simulink simulations using a Phasor model, we analyze five distinct scenarios with varying EV fleet sizes (20–100 vehicles, 40 kW each). Results demonstrate that V2G integration effectively maintains grid frequency within 59.5–60.5 Hz across all test cases, achieving optimal performance using 100 EVs. Through strategic EV discharge scheduling, the system successfully reduces evening peak loads from 3000 to 2200 kW. Economic analysis reveals decreasing payback periods from 5.2 to 2.8 years as fleet size increases, with ROI improving from 12.5 to 23.1%. These findings establish quantitative benchmarks for V2G implementation in microgrids and demonstrate its viability for grid stability enhancement and load management applications. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) play a vital role in the transition to renewable energy and decarbonisation, and there is increasing global interest in expanding their use. However, the growing number of EVs poses challenges to the electricity grid due to increasing energy demand. Vehicle-to-grid (V2G) technology can address these issues by allowing for EVs to charge and discharge energy, thus helping to balance the grid when needed. Aggregating vehicles in designated parking areas optimises energy transfer, making it crucial to identify suitable parking locations and forecast the energy available from parked vehicles. A spatial–temporal framework ensures that V2G services operate efficiently considering both the location and the timing of vehicle parking. This paper reviews studies on temporal–spatial V2G parking demand, identifying high-demand areas such as shopping centres and workplaces, where vehicles park for extended periods. Strategic locations of V2G hubs in these areas ensures seamless integration into existing mobility patterns without disrupting users' routines. In addition, this review examines user acceptance, technical feasibility, and V2G's role in reducing grid demand peaks. The findings indicate the potential of effectively implemented V2G services to enhance electricity grid stability and efficiency while minimising disruptions to EV users. [ABSTRACT FROM AUTHOR]

In a global context characterized by climate warming, the transport sector has found the use of electric vehicles to be one of the possible measures of decarbonization. Although the purchase rate of this type of vehicle is still low, there are many research fields related to both the development of the electric charging network and the improvement of batteries to ensure features that meet the expectations of users. Moreover, the increase of the use of electricity can cause issues in electrical network stability, especially during the peak hours. Therefore, this sector is facing new challenges, including the case of vehicle-to-grid (V2G), which is a solution that allows the use of vehicle batteries, not only as a source of energy for the vehicles, but also as stabilizers of the supply network when the vehicles are parked (i.e., no energy is needed for their activity). In the recent years, the researchers mainly focused on the energy infrastructure and technologies, neglecting problems related to the identification of the best locations for V2G services and the potential acceptance of the electric vehicles' owners, as well as on the potential energy that can be transferred to the grid according to the users' needs (e.g., to continue to use their vehicle for completing the daily activities). This paper proposes a methodology aimed at identifying potential areas for deploying V2G services by using floating car data (FCD) and at estimating the potential energy to be transferred to the grid without interfering with the daily activities. This methodology is finally applied to a case study of five provinces of the Veneto region, showing the significant results obtained. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles (EVs), the spatial–temporal coupling between the transportation network (TN) and the power distribution network (PDN) has intensified greatly. Large-scale uncoordinated charging of EVs significantly impacts both the PDN and TN. In this paper, an optimal scheduling strategy for EV charging and discharging in a coupled power–transportation network (CPTN) with Vehicle-to-Grid (V2G) scheduling and dynamic pricing is proposed. The strategy considers the influence of dynamic transportation road network (DTRN) information on EV driving patterns, as well as the unique vehicle characteristics and mobile energy storage capabilities of EVs. Firstly, a DTRN model is established. Subsequently, the dynamic Dijkstra algorithm is utilized to accurately simulate the EV driving paths and predict the spatial–temporal distribution of the EV charging load. Secondly, optimal scheduling for EV charging and discharging within the CPTN is performed, guided by a V2G model coupled with a multi-time dynamic electricity price (MTDEP) strategy to optimize the grid load curve while accommodating the charging requirements of EVs. Finally, the effectiveness and superiority of the proposed optimization scheduling model are validated by the IEEE 33-node PDN test system. [ABSTRACT FROM AUTHOR]

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder. [ABSTRACT FROM AUTHOR]

In order to solve the optimal scheduling problem of large scale electric vehicles into the network, a two layer optimal scheduling strategy of integrated energy system with electric vehicles is proposed in this study. The upper layer is the optimal dispatching layer, in which electric vehicle agents group electric vehicles into clusters according to the dispatchable time and upload the cluster information to the system dispatching center, which cooperates with electric vehicle clusters and energy systems and builds an economic dispatching model with the goal of minimizing the dispatching cost by considering integrated demand response and ladder type carbon trading mechanism. The lower layer is the power allocation layer, where electric vehicle agents build a power allocation model with the goal of satisfying users travel demand, and guide electric vehicles to participate in system scheduling in an orderly manner. The simulation algorithm is constructed and solved by using CPLEX solver. The simulation results show that the proposed strategy can not only effectively reduce the scheduling cost of integrated energy system, smooth the system load curves and reduce carbon emissions, but also significantly reduce the cost of electricity consumption of customers on the basis of securing their travel demand, thus achieving a win-win situation for both supply and demand. [ABSTRACT FROM AUTHOR]

With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems. [ABSTRACT FROM AUTHOR]

Abstract A vehicle-to-grid (V2G) technology enables bidirectional power exchange between electric vehicles (EVs) and the power grid, presenting enhanced grid stability and load management opportunities. This study investigates a comprehensive microgrid system integrating EVs with solar (8 MW), wind (4.5 MW), and diesel generation sources, focusing on peak load reduction and frequency regulation capabilities. Through MATLAB/Simulink simulations using a Phasor model, we analyze five distinct scenarios with varying EV fleet sizes (20–100 vehicles, 40 kW each). Results demonstrate that V2G integration effectively maintains grid frequency within 59.5–60.5 Hz across all test cases, achieving optimal performance using 100 EVs. Through strategic EV discharge scheduling, the system successfully reduces evening peak loads from 3000 to 2200 kW. Economic analysis reveals decreasing payback periods from 5.2 to 2.8 years as fleet size increases, with ROI improving from 12.5 to 23.1%. These findings establish quantitative benchmarks for V2G implementation in microgrids and demonstrate its viability for grid stability enhancement and load management applications.

In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G technology is the bidirectional AC/DC converter. This study concentrates on the non-isolated bidirectional AC/DC converter, providing a detailed analysis of its two-stage operation and creating a mathematical model. A dual closed-loop control structure for voltage and current is designed based on nonlinear control theory, along with a constant current charge–discharge control strategy. Furthermore, midpoint potential balance is achieved through zero-sequence voltage injection control, and power signals for the switching devices are generated using Space Vector Pulse Width Modulation (SVPWM) technology. A simulation model of the V2G system is then constructed in MATLAB/Simulink for analysis and validation. The findings demonstrate that the control strategy proposed in this paper improves the system's robustness, dynamic performance, and resistance to interference, thus reducing the effects of large-scale, uncoordinated electric vehicle charging on the power grid. [ABSTRACT FROM AUTHOR]

The integration of electric vehicles (EVs) into the electricity grid through vehicle-to-grid (V2G) technology represents a promising opportunity to improve energy efficiency and stabilize grid operations in the context of building sustainable cities. This paper provides a systematic review of the literature to assess the status of the research and identify the road ahead. Using bibliometric analysis and systematic assessment, the critical factors that influence the charging behavior of electric vehicles, the adoption of V2G, and the effective use of EVs as dynamic energy resources are identified. The focus is particularly on the ecological transitions toward sustainability, travel characteristics, technical specifications, requirements, and barriers in real use, and the behavioral and psychological aspects of stakeholders. The results lay the foundation for accurate forecasts and the strategic implementation of V2G technology to support the needs of the electric grid. They emphasize the importance of considering the psychological and behavioral aspects of users in the design of V2G strategies and define the key factors to predict the demand for electric vehicle charging. Furthermore, they highlight the main barriers to V2G adoption, which are primarily related to concerns about battery degradation and economic issues. Privacy and security concerns, due to data sharing with electric vehicle aggregators, also limit the adoption of V2G. Addressing these challenges is essential for the successful integration of electric vehicles into the grid. [ABSTRACT FROM AUTHOR]

The profitable commercialization and swift growth in electrified transportation requires load management controllers. Effective load management controllers can reduce the peak load on the power grid, balance the load demand and improve the stability of the power grid. Several techniques are available to optimize the load, including battery swapping, vehicle-to-grid (V2G), vehicle-to-vehicle (V2V) and vehicle-to-home (V2H) approaches. This paper addresses effects of rising adoption of electric vehicles (EVs) and the potential for an increased load on the distribution system, which requires load optimization to ensure sustainable development. This paper offers a comprehensive review of all possible energy management controllers used in EVs for load optimization and power split controllers for splitting power between the battery and super-capacitor in hybrid energy storage systems (HESS) to increase battery life span. These controllers are classified into three categories, fuzzy logic controller, voltage-based controller and soft computing-based controller, with their constraints, advantages and disadvantages and finally sum up with their current challenges and future recommendation. The paper also reveals the wireless charging with their pros and cons. The findings have implications for the design and implementation of controllers for EVs to ensure their sustainable development and reduce fossil fuel usage. This study will be highly helpful to the researchers, industrialist and investors as a ready literature of energy management controllers for EVs, with information of their recommendations and challenges. [ABSTRACT FROM AUTHOR]

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid. [ABSTRACT FROM AUTHOR]

The increasing utilisation of the distribution grid caused by the ramp-up of electromobility and additional electrification can be eased with flexibility through smart and bidirectional charging use cases. Implementing market-oriented, grid-, and system-serving use cases must be tailored to the different national framework conditions, both in technical and regulatory terms. This paper sets out an evaluation methodology for assessing the implementation of smart and bidirectional charging use cases in different countries. Nine use cases are considered, and influencing factors are identified. The evaluation methodology and detailed analysis are applied to Austria, the Czech Republic, Denmark, Finland, France, Germany, Italy, the Netherlands, Spain, and Sweden. In every country, the implementation of vehicle-to-home use cases is possible. Realising market-oriented use cases is feasible in countries with a completed smart meter rollout and availability of tariffs with real-time pricing. Grid-serving and ancillary service use cases depend most on country-specific regulation, which is why no clear trend can be identified. Use cases that require direct remote controllability are the most distant from implementation. The overarching analysis provides orientation for the design of transnational products and research and can serve as a basis for a harmonisation process in regulation. [ABSTRACT FROM AUTHOR]

In a global context characterized by climate warming, the transport sector has found the use of electric vehicles to be one of the possible measures of decarbonization. Although the purchase rate of this type of vehicle is still low, there are many research fields related to both the development of the electric charging network and the improvement of batteries to ensure features that meet the expectations of users. Moreover, the increase of the use of electricity can cause issues in electrical network stability, especially during the peak hours. Therefore, this sector is facing new challenges, including the case of vehicle-to-grid (V2G), which is a solution that allows the use of vehicle batteries, not only as a source of energy for the vehicles, but also as stabilizers of the supply network when the vehicles are parked (i.e., no energy is needed for their activity). In the recent years, the researchers mainly focused on the energy infrastructure and technologies, neglecting problems related to the identification of the best locations for V2G services and the potential acceptance of the electric vehicles’ owners, as well as on the potential energy that can be transferred to the grid according to the users’ needs (e.g., to continue to use their vehicle for completing the daily activities). This paper proposes a methodology aimed at identifying potential areas for deploying V2G services by using floating car data (FCD) and at estimating the potential energy to be transferred to the grid without interfering with the daily activities. This methodology is finally applied to a case study of five provinces of the Veneto region, showing the significant results obtained.

La introducción de vehículos eléctricos (VE) se destaca como una estrategia fundamental para reducir las emisiones de gases de efecto invernadero y avanzar en la descarbonización del transporte. No obstante, este cambio plantea desafíos considerables en las redes eléctricas. La adopción generalizada de VE puede generar fluctuaciones en la demanda, picos de carga y afectar la estabilidad de la red eléctrica y la calidad de la energía. En respuesta a estos desafíos, este artículo presenta una revisión sistemática de la literatura utilizando el método PRISMA para evaluar los impactos de la carga lenta de VE en la calidad de la energía de las redes de distribución. Los resultados destacan la tecnología de vehicle-to-grid (V2G) como una solución eficaz al permitir que los VE funcionen como fuentes de generación distribuida. Se mencionan enfoques como algoritmos de distribución de carga, estrategias de carga inteligente y modelos de optimización. A pesar de estos avances, se subraya la limitación de datos reales y estudios locales en América Latina, evidenciándose la necesidad de investigaciones contextualizadas en la región para abordar adecuadamente los desafíos específicos de la integración de VE en las redes eléctricas en un contexto local.

Smart grids with EVs have been proposed as a great contribution to sustainability. Considering environmental sustainability is of great importance to humanity, it is essential to assess whether electrical vehicles (EVs) actually contribute to improving it. The objectives of the present study are, from a macro (broad-scope) perspective, to identify the sources of emissions and to create a framework for the calculation of CO2 emissions resulting from large-scale EV use. The results show that V2G mode increases emissions and therefore reduces the benefits of using EVs. The results also show that in the best scenario (NC mode), an EV will have 32.7% less emissions, and in the worst case (V2G mode), it will have 25.6% more emissions than an internal combustion vehicle (ICV), meaning that sustainability improvement is not always ensured. The present study shows that considering a macro perspective is essential to estimate a more comprehensive value of emissions. The main contributions of this work are the creation of a framework for identifying the main contributions to CO2 emissions resulting from large-scale EV integration, and the calculation of estimated CO2 emissions from a macro perspective. These are important contributions to future studies in the area of smart grids and large-scale EV integration, for decision-makers as well as common citizens. [ABSTRACT FROM AUTHOR]

This article compares potential revenue from electric storage in retail and wholesale electric markets. The retail value can be extracted when storage responds to time-of-day retail prices. The wholesale value is enabled by the recent US Federal Energy Regulatory Commission Order 2222, which requires regional transmission operators (RTOs) to allow distributed storage behind the meter to participate in wholesale electric markets. To quantify the value of these markets, we use realistic time-of-day rates and market prices in one RTO's ancillary service market. Formulae are developed to estimate the value of behind-the-meter storage in wholesale and retail markets, using an example electric vehicle in a fleet setting. The formulae are also used to compare whether or not net metering is available and different charging rates. The aggregate national storage behind the retail meter is very large, given the projected growth of electric vehicles. Our findings indicate the revenue from wholesale markets can be significantly more than that of retail opportunities. However, the potential in either retail or wholesale markets is currently limited by both state policy and incomplete RTO implementation of FERC orders. [ABSTRACT FROM AUTHOR]

Today, the negative impact of charging on the grid is becoming a major concern as the number of electric vehicles increases. The charging topologies such as V2G, V2H and V2V are being studied to solve this problem. The most critical issue in this context is the design and use of appropriate infrastructure and equipment. In this study, firstly a low-cost and high-efficiency non-isolated bidirectional 120W DC-DC converter suitable for DC charging stations is designed. The designed converter was analysed both by analytical methods and by operating at low powers. Secondly, a real-world implementation of V2V and V2H topologies has been performed using these designed converters. In the application study, a DC charging station model was created by using 4 of these converters as charging units. The 90-minute operation of the designed charging station was realised according to a rule-based algorithm. Accordingly, it has been shown that the efficiency of the non-isolated bidirectional DC-DC converter designed is 95%. It is also proved that 54.16% of the EVs load can be shifted to off-peak time period using V2V topology in real world application. According to the results, it is understood that the isolated and high-power version of the designed converter is suitable for charging stations. [ABSTRACT FROM AUTHOR]

With the rapid growth in the number of EVs, a huge number of EVs are connected to the power grid for charging, which places a great amount of pressure on the stable operation of the power grid. This paper focuses on the development of V2G applications, based on the current research status of V2G technology. Firstly, the standards on V2G applications and some pilot projects involving more representative V2G systems are introduced. Comparing V2G applications with ordered charging and unordered charging, the social and economic benefits of V2G applications are highlighted. Analysis of the social benefits of V2G applications concerns three points: the grid demand response, personalized charging, and the coordination of renewable energy sources. And analysis of the economic benefits of V2G applications is divided into three parties: the grid, the aggregator, and individuals. From the perspective of innovative EVs expanding the application scenarios through V2G technology, V2G applications for commercial EVs, emergency power applications, and vehicle-to-vehicle energy trading are introduced. The current challenges related to V2G applications are presented: users' willingness to participate in V2G applications, battery loss, charging and discharging tariffs, privacy and security, and power loss. Finally, some research recommendations for the development of V2G applications are given and the current state of research in regard to those recommendations is presented. [ABSTRACT FROM AUTHOR]

The electricity consumption and electricity cost of industrial and commercial enterprises (ICEs) are high, and the disorderly charging and discharging of electric vehicles (EVs) brings a great risk to the safe and stable operation of the power grid. Therefore, this paper proposes a V2G demand response pricing strategy for ICE based on cooperative game. Firstly, a V2G demand response mode of ICE is proposed, in which the costs and benefits of both ICE and the corresponding EV employees (EVEs) are analysed, laying the foundation for determining the optimal V2G demand response price. Then, an optimal pricing strategy of V2G demand response for ICE based on cooperative game is put forward, considering the benefits of both ICE and EVE. Finally, an enterprise located in a high-tech zone in Zhejiang province is taken as a case study. The results show that the proposed cooperative game-based pricing strategy of V2G demand response for ICE can increase the benefit of EVE, and reduce the electricity consumption cost of ICE, thus realizing the win–win situation for both ICE and EVE.

The construction of microgrid has fully promoted the large-scale access of distributed power and the rapid development of electric vehicles (EVs). And microgrid system faces operational issues such as strong random disturbances from distributed power sources and loads, as well as unexpected events during operation. These issues can lead to unstable microgrid frequency, excessive discharge of EVs, and increased control costs. To solve the issues, a frequency cooperative control strategy for multimicrogrids with EVs based on improved evolutionary-deep reinforcement learning (EDRL) is proposed in this paper. First, consider the effect of the vehicle-to-grid (V2G) process on the minimum time required to fully charge an EV, and the impact of the output distribution of micro turbines (MTs) on the regulation cost, and based on the coupling relationship between generator terminal voltage regulation and system frequency control, a multimicrogrid comprehensive control model is constructed. Second, in order to deal with engineering tasks with deceptive rewards and sparse rewards such as integrated frequency control, evolutionary algorithms and deep reinforcement learning algorithms are combined to effectively assist the training process to jump out of local optimal solutions and approach optimal control strategies. Meanwhile, the algorithm is improved based on the novelty search and intelligent partition strategy, so that the strategy has better convergence characteristics and can reduce the cost of information transmission and computational complexity under the premise of ensuring the control effect. Furthermore, the state space, action space and reward function of the controller are defined. Finally, the simulation results show that the proposed controller has the ability of coordinated control, can effectively reduce the adjustment cost of MT unit and the unnecessary discharging of EVs while ensuring the frequency regulation requirements of each submicrogrid, which is far superior to PID control, fuzzy control, and traditional deep reinforcement learning control.

The large-scale application of distributed power generation and the prospects of vehicle to grid (V2G) technology lead to unstable operating voltage of the distribution network, but also new possible approaches to the regulation of the power system. Therefore, a multilayer voltage intelligent control strategy is proposed for a distribution network with V2G and power energy production-consumption units (PECUs). First, a model of the PECU includes facilities such as electric vehicle (EV) charging stations (CSs), urban loads, and distributed energy. The model includes a basic energy unit that can accept distribution network scheduling, provide flexible resources for the power system, and enhance system regulation capabilities. Second, based on the impact of V2G process on the demand of EV users, a CS frequency modulation power controllable boundary evaluation model and a sum power response model were obtained, which enable the CSs to participate in power regulation. Furthermore, to address complex engineering tasks with hidden rewards such as comprehensive control, an evolutionary deep reinforcement learning (EDRL) algorithm is applied and improved based on novelty search operations. This algorithm further enhances the scalability of intelligent agent learning to design a multilayer voltage control structure for the distribution network. Finally, the simulation results show that the applied models can fully utilize the power regulation capabilities while satisfying the internal power supply and demand balance in the PECUs. The proposed strategy has the best coordinated control ability, compared to particle swarm optimization (PSO), network losses are reduced by 23.93 %, and voltage deviations are significantly lowered by 71.95 %, it also ensures the charging demands of EV users, reducing the discharge process of EVs by 6 times.

Electric vehicles (EVs) play a vital role in the transition to renewable energy and decarbonisation, and there is increasing global interest in expanding their use. However, the growing number of EVs poses challenges to the electricity grid due to increasing energy demand. Vehicle-to-grid (V2G) technology can address these issues by allowing for EVs to charge and discharge energy, thus helping to balance the grid when needed. Aggregating vehicles in designated parking areas optimises energy transfer, making it crucial to identify suitable parking locations and forecast the energy available from parked vehicles. A spatial–temporal framework ensures that V2G services operate efficiently considering both the location and the timing of vehicle parking. This paper reviews studies on temporal–spatial V2G parking demand, identifying high-demand areas such as shopping centres and workplaces, where vehicles park for extended periods. Strategic locations of V2G hubs in these areas ensures seamless integration into existing mobility patterns without disrupting users’ routines. In addition, this review examines user acceptance, technical feasibility, and V2G’s role in reducing grid demand peaks. The findings indicate the potential of effectively implemented V2G services to enhance electricity grid stability and efficiency while minimising disruptions to EV users.

With the increasing penetration of electric vehicles (EVs), the spatial–temporal coupling between the transportation network (TN) and the power distribution network (PDN) has intensified greatly. Large-scale uncoordinated charging of EVs significantly impacts both the PDN and TN. In this paper, an optimal scheduling strategy for EV charging and discharging in a coupled power–transportation network (CPTN) with Vehicle-to-Grid (V2G) scheduling and dynamic pricing is proposed. The strategy considers the influence of dynamic transportation road network (DTRN) information on EV driving patterns, as well as the unique vehicle characteristics and mobile energy storage capabilities of EVs. Firstly, a DTRN model is established. Subsequently, the dynamic Dijkstra algorithm is utilized to accurately simulate the EV driving paths and predict the spatial–temporal distribution of the EV charging load. Secondly, optimal scheduling for EV charging and discharging within the CPTN is performed, guided by a V2G model coupled with a multi-time dynamic electricity price (MTDEP) strategy to optimize the grid load curve while accommodating the charging requirements of EVs. Finally, the effectiveness and superiority of the proposed optimization scheduling model are validated by the IEEE 33-node PDN test system.

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems.

The paper provides a novel approach for controllably meshing traditional medium-voltage networks by means of a fast-charging parking station with multiple points of delivery connected to different radial feeders. Regulating power flows at each point of delivery while the charging service is being provided, which means actively controlling power exchanges between radial distribution feeders can significantly increase the hosting capacity of the power system. Remarkable benefits are expected when the distribution networks to which the charging infrastructure is connected differ in terms of main characteristics, e.g., rated voltage level, end-user type and operating profiles, and the number and type of renewable plants. The paper focuses on technical targets, such as loss reduction and power quality in terms of admitted voltage deviation from the rated value. The power exchanges between distribution feeders are made possible by a controlled DC link, where bi-directional DC/DC converters are connected so as to charge or discharge vehicles according to the Vehicle-To-Grid approach. A multiplexer topology in which several vehicles can be alternatively connected to the same DC/DC converter is modeled. The proposed concept can contribute to network flexibility by controllably meshing distribution feeders and, jointly, by modulating charging processes according to assigned charging constraints. [ABSTRACT FROM AUTHOR]

The transportation sector is undergoing a profound transformation, shifting from fossil fuel reliance to electric and hybrid semi-electric alternatives. In response, European countries are implementing novel concepts like electrified highways for trucks and buses, bridging the gap between traditional and electric mobility. This paper centers on the management of electric vehicle (EV) charging infrastructure within industrial zones, crucial nodes for charging networks due to their concentrated economic activity and vehicular movement. The study delves into optimal strategies for deploying charging stations in these zones, considering factors such as station placement, capacity planning, and integration with smart grids to ensure efficient and accessible EV charging. Moreover, the research extends its focus to the integration of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies, illustrating their potential within industrial zones. In our research, we have developed algorithms tailored for the infrastructure of industrial zones, focusing on the integration of storage systems and the charging and discharging dynamics of electric vehicles (EVs). Our case study, supported by numerical simulations, illustrates the outcomes of a 24-hour timeframe, where 126 vehicles were charged, and 134 were discharged. The results provide a comprehensive view of how the grid-maintained balance throughout these operations, ensuring that industrial facilities received the required power to fulfill their operational demands. [ABSTRACT FROM AUTHOR]

Global warming, pollution, and increasing energy demand have compelled electrification of the transport sector. Electric vehicles are not only an attractive and cleaner mode of transport, but they also possess the capacity to offer flexible storage alternative based on bidirectional vehicle-to-grid schemes. Vehicle-to-grid or V2G technology permits electric vehicles' batteries to store energy and discharge it back to the power grid during peak-load periods. However, the feasibility and economic viability of V2G is still a matter of concern and needs investigation. In this paper, the authors delved into the feasibility of V2G technology by analysing the real time-charging data of a V2G demonstration project named EV-elocity, located at the University of Nottingham campus in the UK. The authors analysed the charging data and trip-status data of two charging sites and put forward some insights regarding the feasibility of V2G and the behavioural traits of the vehicles. This paper will enlighten the research community regarding the feasibility and benefits of V2G in a real-world environment by analysing the charging/discharging and vehicle behaviour and reporting the opportunities and benefits of vehicle-to-grid technology. [ABSTRACT FROM AUTHOR]

It is noteworthy today that the creation and popularization of new energy has piqued the world's interest. As a result, new energy electric cars are liked and acknowledged by most customers as a representation of the development and use of new energy. The advancement of electric vehicles (EVs) has important implications for the sustainable use of energy resources. As the number of new energy EVs grows, so does the need for charging stations for these vehicles. Maximum simplification of charging station distribution may successfully satisfy the charging demands of EVs. As a result, determining the appropriate arrangement of EV charging stations has become an essential study issue. This paper proposed a novel algorithm for EV charging station optimization based on a neural network. The main idea is to optimize the cost of charging cost and the user's budget. Then, considering the target planning region of the charging station, the historical data is deployed to predict the time distribution of EVs based on the backpropagation neural network algorithm. Finally, the performance of swarm optimization is improved through the dynamic probability mutation method. Simulation results show that the proposed algorithm has better performance than existing algorithms in terms of global economic cost and low-power and high-power charging station's spatial location. [ABSTRACT FROM AUTHOR]

This paper presents an electric vehicle (EV) switched reluctance motor (SRM) drive with incorporated operation capabilities integrated into the utility grid, the microgrid, and another EV. The motor drive DC-link voltage is established from the battery through an interleaved boost/buck converter with fault tolerance. The varied DC-link voltage can improve driving performance and reduce battery energy consumption over a wide speed range. Through a well-designed current control scheme, speed control scheme, and dynamic commutation tuning scheme, the established SRM drive possesses good performance in the motor driving mode. During deceleration, the regenerative braking energy can be effectively recovered to the battery. When the EV is in idle mode, the grid-to-vehicle (G2V) charging operation can be conducted through the bidirectional switch mode rectifier (SMR) and CLLC resonant converter. Satisfactory charging performance with good line drawn power quality and galvanic isolation is preserved. Conversely, the vehicle-to-grid (V2G) discharging operation can be performed. The EV can make movable energy storage device applications. Finally, the interconnected operations of the developed EV SRM drive to vehicle and microgrid are presented. Through vehicle-to-vehicle (V2V) operation, it can supply energy to the nearby EV when the battery is exhausted and needs roadside assistance. In addition, microgrid-to-vehicle (M2V) and vehicle-to-microgrid (V2M) operations can also be conductible. The EV battery can be charged from the microgrid. Conversely, it can also provide energy support to the microgrid. [ABSTRACT FROM AUTHOR]

Electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) have been widely integrated into distribution systems. Electric vehicles (EVs) offer advantages for distribution systems, such as increasing reliability and efficiency, reducing pollutant emissions, and decreasing dependence on non-endogenous resources. In addition, vehicle-to-grid (V2G) technology has made EVs a potential form of portable energy storage, alleviating the random fluctuation of renewable energy power. This paper simulates the optimal design of a photovoltaic/wind/battery hybrid energy system as a power system combined with an electric vehicle charging station (EVCS) using V2G technology in a grid-connected system. The rule-based energy management strategy (RB-EMS) is used to control and observe the proposed system power flow. A multi-objective improved arithmetic optimization algorithm (MOIAOA) concept is proposed to analyze the optimal sizing of the proposed system components by calculating the optimal values of the three conflicting objectives: grid contribution factor (GCF), levelled cost of electricity (LCOE), and energy sold to the grid ( E S O L D ). This research uses a collection of meteorological data such as solar radiation, temperature, and wind speed captured every ten minutes for one year for a government building in Al-Najaf Governorate, Iraq. The results indicated that the optimal configuration of the proposed system using the MOIAOA method consists of eight photovoltaic modules, two wind turbines, and thirty-three storage batteries, while the fitness value is equal to 0.1522, the LCOE is equal to 2.66 × 10 − 2 USD/kWh, the GCF is equal to 7.34 × 10 − 5 kWh, and the E S O L D is equal to 0.8409 kWh. The integration of RESs with an EV-based grid-connected system is considered the best choice for real applications, owing to their remarkable performance and techno-economic development. [ABSTRACT FROM AUTHOR]

To solve the problems of large switching losses and the need for large-capacity electrolytic capacitances in three-phase DC/AC on-board chargers for vehicle-to-grid (V2G) applications, this paper proposes a single-stage bidirectional high-frequency isolated converter that eliminates the need for large-capacity capacitances. Combined with the proposed modulation scheme, it can theoretically reduce the switching loss by about two-thirds with the three-phase converter compared with the conventional modulation scheme, improving the converter's operating efficiency and power density. Firstly, based on the characteristics of the proposed topology, a hybrid modulation scheme is proposed, which combines a phase-shift modulation scheme based on double modulation waves and a sawtooth carrier with a 1/3 modulation scheme, and the theoretical feasibility of the hybrid modulation scheme is verified using a mathematical modeling equation. Secondly, this paper provides a detailed analysis of the four operating modes of the two full-bridge circuits and the commutation process of the three-phase converter within 1/6 of the fundamental frequency cycle (P1 modulation interval). Then, the control strategy is given for the constant-current and constant-voltage charging and constant-current discharging for electric vehicle batteries. Finally, simulation results verify the correctness of the proposed topology and modulation scheme in vehicle–grid interaction. [ABSTRACT FROM AUTHOR]

Energy management systems (EMSs) play a central role in improving the performance of microgrids by ensuring their efficient operation while minimizing operational costs and environmental impacts. This paper presents a comprehensive study of mixed integer linear programming (MILP) based EMSs developed and implemented in MATLAB 2021a using YALMIP software for the energy management of a new positive energy district in the city of Savona, Italy, as part of the Interreg Alpine Space Project ALPGRIDS. The main objective of this research is to optimize the functioning of the microgrid, focusing on cost efficiency and environmental sustainability. In pursuit of this objective, the EMS undergoes comprehensive testing and analysis, replicating actual conditions and addressing the diverse demands of end-users across typical days throughout the year, considering real electricity selling and purchase prices. The EMS also accounts for the reactive power capabilities of the various technologies integrated into the microgrid. The levelized cost of electricity (LCOE) serves as a metric for assessing curtailment costs, while penalties related to reactive power absorption from the distribution network are appraised in alignment with prevailing regulatory guidelines. The case study provides valuable insights into the practical implementation of EMS technology in microgrids and demonstrates its potential for sustainable energy management in complex urban energy districts. In all scenarios, the battery energy storage system (BESS) and combined heat and power (CHP) are pivotal for load satisfaction and microgrid resilience. BESSs balance supply and demand, which are crucial in periods of low renewable energy availability, while the versatile CHP efficiently addresses energy demands, contributing significantly to overall microgrid effectiveness. Their synergy ensures reliable load satisfaction, showcasing the dynamic and adaptive nature of microgrid energy management across diverse scenarios. [ABSTRACT FROM AUTHOR]

Over the past decade, the widespread adoption of global green energy has emerged as a predominant trend. However, renewable energy sources, such as wind and solar power, face significant wastage due to challenges in energy storage. Electric vehicles (EVs) are considered an effective solution to address the energy storage dilemma. "Vehicle-to-grid" (V2G) technology, allowing vehicles to feed electricity into the grid, enhances the efficiency of renewable energy utilization. This paper proposes an electric vehicle game model that comprehensively considers user choices, corporate profits, and green energy utilization. The model, based on difference in prices, electricity rates, and fuel prices, establishes user utility models to determine optimal driving distances and driving decisions. It separately formulates the maximum profit functions for selling conventional electric cars and V2G electric cars, deriving optimal pricing for enterprises and user adoption rates. The research findings indicate that when price difference can offset V2G battery costs, increasing price difference and reducing battery costs effectively enhance electric vehicle adoption rates, increase corporate profits, and improve green energy utilization. Moreover, compared to conventional electric vehicles, V2G electric vehicles demonstrate a comparative advantage, with the implementation of V2G expanding corporate profits and green energy utilization. Validation using Chinese data reveals that when price difference can offset V2G battery costs, drivers are more inclined to choose V2G electric vehicles. Both battery electric vehicles (BEVs) and V2G electric vehicles exhibit adoption rates that can increase by over 35%. This study provides theoretical and model support for the future development of V2G and policy formulation, underscoring the comparative advantages of V2G in enhancing green energy utilization efficiency. Additionally, this study offers valuable insights as a reference for business models in the V2G electric vehicle industry. [ABSTRACT FROM AUTHOR]

In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states—such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users’ requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %. • The response capability of EV is non-zero during state transformations • Users’ travel demand assessment • Seamless integration of vehicle-to-grid technology into the power grid.

The energy produced from various sources in modern power systems needs to be optimally planned for scheduling and managing the power system under specific conditions. In recent times, the world’s growing population, increasing energy needs, technological advancements, environmental concerns, and global climate change have led to a rise in the demand for electric energy. One significant solution to meeting this energy demand is the use of renewable energy sources (RESs) in power systems. However, integrating RESs has made the construction of power systems more complex. Moreover, Plug-in electric vehicles (PEVs) stand out as a highly promising technology for reducing carbon emissions in the transportation sector, aligning with the global Net-zero target. The optimal power flow (OPF) is a challenging problem when dealing with integrating renewable energy sources (RESs) and plug-in electric vehicles (PEVs) into modern power systems. In this paper, we address the OPF problem that arises from power systems with a high penetration of controllable renewable sources and PEVs. We simulate uncertain solar irradiance, wind speed, and PEV behavior using Weibull, lognormal, and normal probability distribution functions (PDFs) respectively. The proposed stochastic OPF problem, which incorporates controllable renewable sources and Vehicle-to-Grid (V2G) technology, is addressed using the hybrid metaheuristic optimization algorithm (AHMRFO). The AHMRFO hybrid algorithm combines the exploratory power of the manta ray foraging optimization (MRFO) with the exploiting strength of artificial hummingbird algorithm (AHA) resulting in a more balanced, robust, and efficient optimization tool. It can effectively handle complex problems, avoid premature convergence, and provide high-quality solutions across a wide range of optimization challenges. Initial evaluations for the proposed algorithm are executed on 7 Congress on Evolutionary Computation (CEC) test functions and its obtained results was compared with recent optimization algorithms such as original MRFO, AHA, supply demand-based optimization (SDO), hunter prey optimization (HPO), weighted mean of vectors (INFO), and northern goshawk optimization (NGO). Moreover, simulations carried out on different test systems demonstrate the effectiveness and efficiency of the proposed AHMRFO algorithm when compared to other algorithms such as the MRFO and AHA. The AHMRFO technique proves to be highly effective, yielding the lowest fitness values of 781.5836 ${\$}$ /h and 808.3611 ${\$}$ /h in the first two scenarios for the modified IEEE 30-bus system with RES and it has the minimum fitness values of 790.2783 ${\$}$ /h and 819.6573 ${\$}$ /h in their respective scenarios for the modified IEEE 30-bus system with stochastic Vehicle-to-grid and RES. These results highlight the precision and strength of AHMRFO technique in effectively addressing various instances of the OPF problem. Additionally, the Wilcoxon signed-rank test has been employed to demonstrate the superiority, effectiveness, and robustness of the proposed AHMRFO algorithm.

Over the past ten years, global electricity networks have undergone rapid advancements, primarily driven by the widespread adoption of renewable energy resources (RES). While these sources have accompanied in a gathering of advantages, such as cost-effective operation of wind and solar photovoltaic (PV) installations and the mitigation of environmental hazards linked with traditional power sources, they have also introduced a host of challenges to planning and operation of power systems. Also, Plug-in electric vehicles (PEVs) stand out as a highly promising technology for reducing carbon emissions in the transportation sector, aligning with the global Net-zero target. The standard optimal power flow (OPF) problem, which is naturally nonlinear, has become even more complex with the addition of renewable energy sources and plug-in electric vehicles along with traditional thermal power generators. This increased complexity comes from the unpredictable and intermittent nature of these new resources. Monte Carlo techniques are used to estimate the production costs of renewable energy sources and plug-in electric vehicles (PEVs) and analyze their viability. The uncertainty of the renewable sources and PEVs is modeled using Weibull, lognormal, and normal probability distribution functions (PDFs). The comprehensive OPF, incorporating renewable energy components and PEVs, is cast as a single objective problem encompassing various objectives, such as decreasing fuel costs, total emissions, voltage deviations, and real transmission losses. This research shares a common objective by introducing a novel hybrid metaheuristic optimization algorithm (MRGTO) to address the OPF challenge. Additionally, the study explores the impact of renewable energy resources and Vehicle-to-Grid (V2G) on the stochastic OPF problem. The MRGTO employs artificial gorilla troops optimizer (GTO) with manta ray foraging optimization (MRFO) algorithm to achieve the optimal results for the OPF problem with stochastic RES and V2G. The developed technique is expected to increase the solution accuracy through increasing the solution diversity through an optimization procedure. Initial assessments are executed on benchmark functions. After that, a combined model of wind and PV-integrated IEEE 30-bus system are executed by the proposed MRGTO algorithm and other well-known optimization algorithms. Additionally, the effectiveness of the proposed method is assessed using the IEEE 30-bus test system under different scenarios. The evaluations have shown the proposed MRGTO technique to be superior at attainment a best solution for the OPF problem considering stochastic wind and PV power and V2G. Moreover, the obtained solutions confirmed the MRGTO technique would be more effective in optimization with quicker convergence rate, and higher convergence precision. After testing, the effectiveness of the MRGTO algorithm has been found to be much robust than the conventional artificial gorilla troops optimizer, manta ray foraging optimization, and other well-known published heuristics, metaheuristics, and hybrid optimization techniques.INDEX TERMS Optimal power flow, plug-in electric vehicle, renewable energy sources, V2G, artificial gorilla troops optimizer, manta ray foraging optimization.

In recent days the high penetration of EVs have an impact on the load demand and it will be increased further. This results in stress on the grid and proper energy management in the charging stations. Recent development also enables smart communication between EV user and charging station. A bidirectional converter-based charging station works on V2G and G2V modes for charging the EV battery and supports the grid or isolated power station when it is needed. In this paper, a brief discussion on the previous development of bidirectional conversion is presented. A bidirectional converter is modeled and simulated in Simulink. In G2V mode, the time of energy-based tariff is used for the cost calculation, and for V2G mode, the system monitoring of the EV charging parameters is presented where the pricing varies from 11.09 Rs. To 16.27 Rs. For an hour of power consumption using a 2.54 kW charger on a probabilistic model. For V2G operation, the inverter voltage and grid voltage in synchronization is presented. An IoT-based system is initiated in Simulink using thing speak. This put the data in the cloud and further the data is monitored and fetched from the cloud using a web application.

The overutilization of electric vehicles (EVs) has the potential to result in significant challenges regarding the reliability, contingency, and standby capabilities of traditional power systems. The utilization of renewable energy distributed generator (REDG) presents a potential solution to address these issues. By incorporating REDG, the reliance of EV charging power on conventional energy sources can be diminished, resulting in significant reductions in transmission losses and enhanced capacity within the traditional power system. The effective management of the REDG necessitates intelligent coordination between the available generation capacity of the REDG and the charging and discharging power of EVs. Furthermore, the utilization of EVs as a means of energy storage is facilitated through the integration of vehicle-to-grid (V2G) technology. Despite the importance of the V2G technology for EV owners and electric utility, it still has a slow progress due to the distrust of the revenue model that can encourage the EV owners and the electric utility as well to participate in V2G programs. This study presents a new wear model that aims to precisely assess the wear cost of EV batteries, resulting from their involvement in V2G activities. The proposed model seeks to provide EV owners with a precise understanding of the potential revenue they might obtain from participating in V2G programs, hence encouraging their active engagement in such initiatives. Various EV battery wear models are employed and compared. Additionally, this study introduces a novel method for optimal charging scheduling, which aims to effectively manage the charging and discharging patterns of EVs by utilizing a day-ahead pricing technique. This study presents a novel approach, namely, the gradual reduction of swarm size with the grey wolf optimization (GRSS-GWO) algorithm, for determining the optimal hourly charging/discharging power with short convergence time and the highest accuracy based on maximizing the profit of EV owners.

Electric vehicles (EVs), as a nascent industry, have seen significant advancements in technology and policy, allowing for a clearer development trend and promising future prospects. The integration of EVs with the energy sector is becoming a key focus, with vehicle-to-grid (V2G) technology and efficient collaboration serving as major areas for technical breakthroughs. This paper begins by analyzing the impact of disordered charging of EVs on the power grid. Given that EVs can function as mobile energy storage units, they have the potential to provide flexible support for the secure operation of the power grid. Building upon this, the paper proposes an optimized V2G power control strategy that is based on time-of-use electricity pricing and comprehensive load cost optimization. The principle behind this proposed control strategy is elaborated upon in the subsequent sections. To validate the effectiveness of the proposed V2G optimized power control strategy, the paper provides simulation results. These results serve to demonstrate the practical feasibility and benefits of the proposed strategy in leveraging the capabilities of EVs and promoting grid stability and efficiency.

In response to the challenges posed by large-scale, uncoordinated electric vehicle charging on the power grid, Vehicle-to-Grid (V2G) technology has been developed. This technology seeks to synchronize electric vehicles with the power grid, improving the stability of their connections and fostering positive energy exchanges between them. The key component for implementing V2G technology is the bidirectional AC/DC converter. This study concentrates on the non-isolated bidirectional AC/DC converter, providing a detailed analysis of its two-stage operation and creating a mathematical model. A dual closed-loop control structure for voltage and current is designed based on nonlinear control theory, along with a constant current charge–discharge control strategy. Furthermore, midpoint potential balance is achieved through zero-sequence voltage injection control, and power signals for the switching devices are generated using Space Vector Pulse Width Modulation (SVPWM) technology. A simulation model of the V2G system is then constructed in MATLAB/Simulink for analysis and validation. The findings demonstrate that the control strategy proposed in this paper improves the system’s robustness, dynamic performance, and resistance to interference, thus reducing the effects of large-scale, uncoordinated electric vehicle charging on the power grid.

The integration of electric vehicles (EVs) into the electricity grid through vehicle-to-grid (V2G) technology represents a promising opportunity to improve energy efficiency and stabilize grid operations in the context of building sustainable cities. This paper provides a systematic review of the literature to assess the status of the research and identify the road ahead. Using bibliometric analysis and systematic assessment, the critical factors that influence the charging behavior of electric vehicles, the adoption of V2G, and the effective use of EVs as dynamic energy resources are identified. The focus is particularly on the ecological transitions toward sustainability, travel characteristics, technical specifications, requirements, and barriers in real use, and the behavioral and psychological aspects of stakeholders. The results lay the foundation for accurate forecasts and the strategic implementation of V2G technology to support the needs of the electric grid. They emphasize the importance of considering the psychological and behavioral aspects of users in the design of V2G strategies and define the key factors to predict the demand for electric vehicle charging. Furthermore, they highlight the main barriers to V2G adoption, which are primarily related to concerns about battery degradation and economic issues. Privacy and security concerns, due to data sharing with electric vehicle aggregators, also limit the adoption of V2G. Addressing these challenges is essential for the successful integration of electric vehicles into the grid.

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

Abstract Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs charging and discharging operations, that can be used to provide ancillary services. To achieve scalability, the monolithic problem is decomposed using the alternating direction method of multipliers (ADMM). Furthermore, we propose a method to handle bilinear constraints of the original problem inside the ADMM iterations, which changes the problem class from Mixed-Integer Quadratic Program (MIQP) to Quadratic Program (QP), allowing for a substantial computational speed up. We test the algorithm using real data from the largest carsharing company in Switzerland and show how our formulation can be used to retrieve flexibility boundaries for the EV fleet. Our work thus enables fleet operators to make informed bids on ancillary services provision, thereby facilitating the integration of electric vehicles.

Este artigo apresenta a análise e operação de um conversor CA-CC trifásico, bidirecional e isolado em alta frequência, de único estágio com correção do fator de potência, destinado para aplicação como carregador embarcado de baterias de veículos elétricos. Este conversor pode operar nos modos Grid-to-Vehicle (G2V) e Vehicle-to-Grid (V2G). No lado primário tem-se um conversor boost trifásico intercalado (interleaving) com modulação PWM senoidal para conexão com a rede elétrica, e na saída um barramento CC com ponto médio. O lado secundário é composto por uma topologia em ponte completa com modulação PWM convencional de razão cíclica de 50%. A transferência de potência do lado primário ao secundário é realizada por meio da técnica de defasamento angular (phase-shift), a mesma utilizada em conversores Dual-Active Bridge (DAB). Devido a defasagem natural do sistema trifásico, é possível obter transferência de potência constante, de reduzida ondulação, por meio do controle do defasamento angular também constante. Os princípios de operação do conversor são apresentados e demonstrados por análises qualitativas e quantitativas. Resultados experimentais de um protótipo de potência de 3 kW, tensão de entrada da rede elétrica de 127 V/60 Hz, tensão de saída de 300 V e frequência de comutação de 50 kHz são apresentados. Em conformidade com as análises, o protótipo doconversor apresentou fator de potência de 0,997, potência transferida entre primário e secundário constante e de baixa ondulação e dinâmica satisfatória e rápida durante ensaios de inversão do fluxo de potência.

In this paper, a multi-functional quasi-single stage, bi-directional electric vehicle charger topology is proposed to realize high efficiency power conversion in all AC/DC, DC/DC, and DC/AC forms. The proposed circuitry includes a noninverting buck boost converter (NBB) and an H-bridge inverter. The NBB converter generates the desired output voltage waveform in the rectified form then the inverter unfolds the waveform to the AC waveform. The advantages of this circuit are the reduced losses due to the high frequency switching only occurring at the NBB converter and passive element sizes are smaller leading to reduced losses and cost. The proposed charger is designed for 2 kVA rating and simulated for all vehicle, grid, and another vehicle interaction modes. Then the circuit is experimentally tested and is validated that the proposed circuit can operate in all three modes at a wide range of loading and power factor conditions with over 92% efficiency.

The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners' disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid. [ABSTRACT FROM AUTHOR]

Battery electric vehicles (BEVs) offer substantial potential to enhance the electric grid through bi-directional charging technologies. In essence, BEVs, functioning as portable battery energy storage systems, play a pivotal role in enabling the seamless integration of renewable energy, grid optimization, and ancillary services. This article sets out to explore the value of BEVs equipped with Vehicle-to-Grid (V2G) for grid operators, particularly in the context of alleviating congestion. This valuable service, though not accompanied by direct monetary compensation for users, holds significant promise in minimizing congestion and renewable energy curtailment. This study utilizes the Day-Ahead Locational Marginal Price (LMP) data obtained from various locations within California Independent System Operator (CAISO) to ascertain the financial benefits to BEVs located on either side of congestion at different grid nodes, across various months. Similar analysis is performed on some of the largest solar energy plants in California. Mixed-integer linear programs are used to optimize the charging/discharging decisions for the BEV for maximizing revenue from LMP arbitrage and for minimizing the congestion component of LMP. Additionally, we take into account the impact of battery degradation, quantified as a cost per kilowatt-hour ($/kWh), and integrate this factor into our assessment to understand the evolving discharging behavior of BEVs. The article compares the benefits from the BEVs towards congestion minimization for the two different optimization scenarios, discusses seasonality, and addresses the importance of adequately compensating BEV users and incentivizing them to prioritize congestion relief during specific time intervals. [ABSTRACT FROM AUTHOR]