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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]

Renewable energy‐based electric vehicle (EV) charging systems have become increasingly popular in recent years, particularly in commercial and industrial environments. This study looks at a broad‐spectrum bidirectional buck boost DC to DC converter employing solar photovoltaic (PV) technology. This combination is intended for usage in vehicle to grid (V2G) and grid to vehicle (G2V) modes for electric car applications. In both operating modes, the converter attains a high‐voltage transfer gain ratio (VTGR) without any theoretical output voltage restrictions. It provides evidence on suitable nature for EV applications with versatility, redundancy, and practicality. The theoretical evaluation was verified by the development and testing of a 500 W experimental model. A peak efficiency of 97.8% and 97.4% in V2G and G2V modes is indicated by the data, confirming the usefulness and efficacy of this integrated strategy. These results show that the bidirectional buck boost DC to DC converter, when combined with the suggested integration of PV and EV, improves V2G‐G2V operations in EV charging systems and is both practicable and feasible. [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]

Electric Vehicle (EV) standardization is of paramount importance in the rapidly evolving automotive landscape, playing a pivotal role in ensuring interoperability, safety, and global harmonization. This review delves deep into the multifaceted challenges and pillars of standardizing EVs, the FAME I, FAME II schemes initiated by the Indian government. With the advent of EVs, the convergence of technologies from these traditionally distinct sectors has brought forth unique challenges, from communication protocols to global harmonization and safety concerns. The paper sheds light on the prevailing international standards set by bodies such as IEC, IEEE, UL, BIS, CHAdeMO, Standardization Administration of China (SAC), SAE and ISO. Moreover, the status of Indian standards and worldwide standards are discussed. The voltage levels and maximum charging capacity of the different chargers are discussed. Furthermore, it elucidates the advantages of EV and PV charging stations and impact of charging stations on 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]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press 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.)

As renewable energy resources dwindle and the world's population grows, the balance between energy supply and demand is being adversely affected. The rapid increase in the number of EVs causes power quality problems in existing conventional grids. Topologies such as V2G, G2V, and V2H are being studied in order to overcome these problems. In this study, a microgrid is designed for power flow in single-phase V2G topology. In this microgrid, there is one EV, one home, and one PV panel. The EV is charged by the PV panel during the daytime when it is in the parking lot. When there is a power quality problem in the microgrid, the V2G topology is activated and power flows from the EV battery to the grid. A single-phase grid-connected inverter and a bidirectional DC-DC converter were designed to operate this system. The simulations of the designed microgrid were performed in the MATLAB/Simulink program. According to the simulation results, installing this system in homes or workplaces with EVs, charging the EV battery with PV panel, and using the EV battery to eliminate power quality problems that may occur in the existing microgrid provides a solution for single-phase V2G topology. [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]

Copyright of Dicle University Journal of Engineering / Dicle Üniversitesi Mühendislik Dergisi is the property of Dicle Universitesi 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.)

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy 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.)

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]