车辆到电网技术: Comparison
View Latest Version
Please note this is a comparison between Version 2 by Rita Xu and Version 1 by shuaikang peng.

车辆到电网（Vehicle-to-grid (V2G）技术作为电动汽车与电网之间的智能互联解决方案受到广泛关注。) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid.

  • vehicle-to-grid
  • electric vehicles
  • grid

1. 引言

电动汽车（

Introduction

Electric vehicles (EV）被认为是最清洁的交通工具[1,2]s) are considered to be the cleanest form of transportation [1][2];然而，电动汽车的普及不仅给电网带来了挑战，也给我们的生活方式带来了挑战。如图 however, the popularization of EVs brings challenges, not only to the power grid but also to our 2lifestyles. 所示，车辆到电网Vehicle-to-grid （V1G）(V2G) technology, as shown in Figure 1, is a pivotal component of the smart grid paradigm and has garnered significant attention in recent years [3]. This technology enables EVs to obtain electricity from 技术是智能电网范式的关键组成部分，近年来备受关注the [3]。该技术使电动汽车能够从电网获取电力grid;它将可再生能源（包括风能 it stores renewable energy, including wind [4], solar [5], and water [6], as mobile energy storage devices and feeds back power to the grid when needed [7]. With the intensified global push for sustainable energy solutions and the improvement in battery capacity and storage efficiency of EVs [8], the integration of EVs with the grid has become increasingly crucial as it can offer potential solutions to challenges such as grid stability [9], peak demand management [4]、太阳能[10][11], [5]and 和水renewable [6]energy ）存储为移动储能设备，并在需要时将电力反馈给电网integration [7]。随着全球对可持续能源解决方案的推动力度不断加大，以及电动汽车电池容量和存储效率的提高[8]，电动汽车与电网的整合变得越来越重要，因为它可以为电网稳定性[9]、峰值需求管理[10,11]和可再生能源整合[12]等挑战提供潜在的解决方案。
[12].
图Figure 1. V2G业务图。 service diagram.
The rapid evolution of V2G技术的快速发展导致了大量的研究工作，旨在应对其多方面的挑战并利用其潜在优势[13]。从与电网整合和电池健康相关的技术复杂性到经济考虑[14,15]和政策影响， technology has led to a plethora of research endeavors aiming to address its multifaceted challenges and harness its potential benefits [13]. From technical intricacies related to grid integration and battery health to economic considerations [14][15] and policy implications, the V2G研究领域既多样化又动态。鉴于这一领域的兴趣日益浓厚，范围广阔，全面调查已成为当务之急 research landscape is both diverse and dynamic. Given the burgeoning interest and the vast scope of this field, a comprehensive survey has become a pressing need;这样的调查可以概括V2G研究的现状，并突出关键发现、趋势和未来方向。 such a survey can encapsulate the current state of V2G research, as well as highlight key findings, trends, and future directions.

2. Keyword Analysis and Application Analysis of V2G关键词分析与应用分析

2.1. 储能、可再生能源和调度Energy Storage, Renewable Energy Sources, and Scheduling

2.1.1. 储能Energy Storage

众多学者对Numerous scholars have conducted in-depth research on the planning and management of V2G和储能的规划与管理进行了深入研究。电动汽车电池由于其容量大，可以作为储能单元，在电网负荷较低时储存多余的电力，然后在需求高峰时释放，从而保证电网的稳定运行[18]。 and energy storage. Electric vehicle batteries, because of their high capacity, can be used as storage units to store excess power when the load on the grid is low and then to release it when the demand peaks, thus ensuring the stable operation of the grid [16]. The combination of V2G技术与储能系统的结合为实现更高效、更灵活、更可靠的电力系统提供了新的机会[19]。电动汽车电池作为一种储能介质，可以减少投资和可再生能源和固定储能的建设，最大限度地减少资源浪费[20] technology and energy storage systems provides new opportunities to realize a more efficient, flexible, and reliable power system [17]. As an energy storage medium, EV batteries can reduce investment and the construction of RE and stationary storage and minimize resource waste [18];智能电池技术的引入进一步促进了 the introduction of smart battery technology further facilitates the realization of V2G的实现[21]。在经济方面，电池衰退成本、售电价格[22]和基础设施成本[23]影响了电动汽车作为储能单元向电网提供短期运营储备的能力[24] [19]. In terms of economics, battery recession costs, electricity sales prices [20], and infrastructure costs [21] affect the ability of EVs to provide short-term operational reserves to the grid as energy storage units [22];然而，电动汽车仍然有潜力为其他设施提供服务。例如， however, EVs still have the potential to service other facilities. For example, V2G可以作为储能嵌入到家庭和IES中，为家庭和商业中心提供能源，从而降低能源成本并提高环境效益[25\u26]。 can be embedded as energy storage in homes and IES to provide energy for domestic and commercial centers, which can reduce energy costs and improve the environmental benefits [23][24]. Sun et al. investigated 等人研究了电动汽车电池的 SOC 如何影响使用电动汽车作为住宅能源系统一部分的住宅能源系统的总成本。结果表明，电池老化成本仍然是影响电动汽车用户电池放电意愿的重要因素[27]。Whow the SOC of EV batteries affects the total cost of residential energy systems that use EVs as part of the residential energy system. The results showed that the battery aging cost is still an important factor affecting the willingness of EV users to discharge their batteries [25]. By embeddi等人将V2G作为储能嵌入IES中，使两者耦合，以最大限度地提高IES的经济和环境效益[25]。ng V2G as energy storage in IES, Wei et al. allowed the two to be coupled to maximize the economic and environmental benefits of IES [23]. Hipolito、, Vandet和Rich估计了电动汽车的储能潜力，并得出结论，只有当电池利用率低于40%时，电动汽车用户的电池放电意愿才会显著增加[28]。, and Rich estimated the energy storage potential of EVs and concluded that the willingness of EV users to discharge their batteries increases significantly only when the battery utilization is below 40% [26]. In summary, it is possible to regulate electricity prices to allow EVs to actively participate in energy storage, but if EVs are allowed to supply power to the grid, a reasonable peak-to-valley electricity differential needs to be established to compensate for the cost of battery depletion to increase the willingness of users to discharge their batteries.

2.1.2. 可再生能源Renewable Energy Sources

风能和光能的高波动性给并网发电带来了相当大的挑战[29]。由于The high volatility of wind and light energy brings considerable challenges to grid-connected power generation [27]. As V2G技术具有响应时间快[30]、灵活的进出机制、相对固定的储能装置等特点，因此不需要额外投资，在清洁能源消耗过剩电力方面发挥着独特且不可替代的作用。 technology has a fast response time [28], a flexible in-and-out mechanism, and an energy storage device that is relatively fixed, it does not require additional investment and plays a unique and irreplaceable role in consuming excess power from clean energy sources. 许多研究人员试图用电动汽车消耗这些可再生能源。Many researchers have tried to consume these renewable energy sources with EVs. Gao等人将具有V2G运行能力的风力发电机和电动汽车集成到配电网中，建立了V2G功率控制的数学模型，并部署了电动汽车的动态功率调节[31]。 et al. integrated the wind power generators and EVs with V2G operation capability in the distribution grid, formulated a mathematical model of V2G power control, and deployed a dynamic power regulation for EVs [29]. Jin等人提出了一种针对AGC涉及的大规模电动汽车、 et al. presented a coordinated control strategy for large-scale EVs, BESS和传统阻燃资源的协调控制策略，可以提高频率稳定性，促进可再生能源的整合[32]。s, and traditional FR resources involved in AGC which could improve the frequency stability and facilitate the integration of renewable energy [30]. Fattori、, Anglani 和, and Muliere 分析了不同渗透情景下光伏和电动汽车对电网的影响，其中 V2G 收益并不乐观，因为电动汽车充电行为与光伏发电的不同步导致对非光伏必须提供的容量的需求增加，并且当时电池成本相对于光伏成本来说很高analyzed the impact of PVs and EVs on the grid under different penetration scenarios, where V2G gains were not promising because the desynchronization of EV charging behavior with PV generation led to increased demand for capacity that non-PVs had to provide, and at the time, battery costs were high in relation to PV costs [11]. Fathabadi等人分析了电源对配电网的影响;这些来源包括传统发电机 （CPG）、DG 可再生能源和电动汽车。实验表明，同时使用CPG、DG可再生能源和电动汽车充电/放电可以降低发电成本和最佳电压曲线;因此，通过在电网中同时使用CPG和DG可以获得最低的功率损耗[33]。 et al. analyzed the impact of power sources on the distribution network; these sources include conventional generators (CPG), DG renewable energy sources, and EVs. Experiments have demonstrated that the simultaneous use of CPG, DG renewable energy sources, and charging/discharging EVs results in the lowest cost of power production and the optimal voltage profile; thus, the lowest power loss is obtained by utilizing both CPG and DG in the grid [31]. Haddadian等人将电动汽车车队部署为分布式储能，并实现了与风能的最佳协调。研究表明，该方法可以降低昼夜运行成本，降低排放，并实现零弃风率的预测风能的彻底消耗[34]。 et al. deployed EV fleets as distributed storage and achieved their optimal coordination with wind energy. Studies indicate that the method could cut the diurnal operation cost, lower the emissions, and enable thorough consumption of forecasted wind with zero wind curtailment [32]. Uddin等人开发了一种电池退化模型，并将其无缝集成到智能电网算法中。这种创新方法表明，将电动汽车连接到这种智能电网系统不仅可以有效地满足电网的需求，注入更多的清洁可再生能源，而且值得注意的是，延长了电动汽车电池的使用寿命[35]。 et al. developed a battery degradation model and seamlessly integrated it into a smart grid algorithm. This innovative approach demonstrated that linking an EV to this intelligent grid system not only effectively addressed the power network’s demand with a greater infusion of clean renewable energy but also, notably, extended the lifespan of the EV battery [33]. Robledo等人介绍了一个示范项目的结果，包括建筑一体化光伏（ et al. introduced the results of a demonstration project, including building-integrated photovoltaic (BIPV）太阳能电池板，这表明FCEV可以将交通和电力部门整合到可持续能源系统中[36]。) solar panels, which indicated that FCEVs can integrate transport and electricity sectors in a sustainable energy system [34]. Bhatti 和and Salam 考虑通过光伏电网系统在办公室停车场进行日间电动汽车充电，并提出了基于规则的能源管理方案 （REMS）。这项工作可以整合其他稀土来源，如风能、潮汐能和生物质能[37]。considered daytime EV charging in an office parking lot by means of the PV grid system and proposed a rule-based energy management scheme (REMS). This work can integrate other RE sources such as wind, tidal, and biomass [35]. Sufyan等人分析了系统成本和能量损失对不同程度的可再生能源集成、电动汽车容量和行驶距离的影响。他们的仿真结果清楚地表明，当可再生能源 et al. conducted an analysis of the influence of system costs and energy losses across varying levels of RE integration, EV capacities, and travel distances. Their simulation results clearly indicate a substantial reduction in operational expenses when renewable energy sources （RES） 无缝集成到配电网络中时，运营费用将大幅降低。此外，V2G技术的使用已被证明对电动汽车用户有利，特别是在可再生能源渗透率高的场景中[38]。(RESs) are seamlessly integrated into the distribution network. Furthermore, the utilization of V2G technology has proven to be advantageous for EV users, particularly in scenarios with a high level of RES penetration [36]. Noorollahi等人为电动汽车聚合器引入了一个新框架，并为四种不同的电动汽车充电场景设计了模型。他们的方法考虑了广泛的可再生能源，包括风力涡轮机、太阳能光伏电池板和地热装置。他们实施了一个能源调度模型来有效地优化这些场景[39]。 et al. introduced a novel framework for an electric vehicle aggregator and devised models for four distinct EV charging scenarios. Their approach takes into account a wide array of renewable energy sources, including wind turbines, solar PV panels, and geothermal units. They have implemented an energy scheduling model to optimize these scenarios effectively [37]. Bartolini等人进行了一项分析，以探讨电动汽车车队如何有助于提高地区的自用能力，特别是在不可控可再生能源（ et al. conducted an analysis to explore how a fleet of EVs can contribute to enhancing the self-consumption capacity of a district, particularly in the context of a high and growing presence of non-controllable renewable energy sources (RES), with a specific focus on PV systems. Furthermore, their study delved into the consequences of both EVs and the extent of RES）大量存在且不断增长的情况下，特别关注光伏系统。此外，他们的研究深入研究了电动汽车的影响以及可再生能源整合对该地区一氧化碳排放的影响 integration on the district’s emissions of CO2 [[40][38]. Rahbari等人提出了一种考虑可再生能源消耗和电动汽车作为储能装置的电动汽车智能停车场规模和选址规划方法，并提出了一种适用于V2G和G2V的自适应智能控制策略，以降低系统电压偏差和功率损耗[12]。 et al. proposed a method for planning the size and siting of EV smart parking lots, considering RE consumption and using EVs as energy storage devices, and they introduced an adaptive intelligent control strategy applicable to V2G and G2V to reduce the system voltage deviation and power loss [12]. Shi等人使用 et al. used V2G技术来稳定可再生能源的间歇性，其中风能的不确定性和电动汽车充电状态使用稳健的最坏情况策略进行建模[41]。 techniques to stabilize the intermittency of renewable energy sources, where the uncertainty of wind energy and the EV charging state are modeled using a robust worst-case strategy [39]. Sangswang 和and Konghirun 开发了一种家庭能源管理系统 （HEMS） 的调度策略，该系统集成了太阳能、储能和 V2G 功能，并使用实时定价和紧急减载来优化电动汽车和家用电池的充放电调度developed a scheduling strategy for a home energy management system (HEMS) that integrates solar, energy storage, and V2G functionality and optimizes the charging and discharging scheduling of electric vehicles and home batteries using real-time pricing and emergency load shedding [40]. Researchers have proposed a variety of strategies and approaches, including dynamic power tuning for EVs, coordinated control strategies, integration of energy systems, and battery life extension, to achieve more efficient integration of renewable energy [42]。sources.

2.1.3. 调度Scheduling

V2G调度受几个关键因素的影响，包括用户对里程的焦虑、电动汽车电池的磨损和状况[43]、用户收入预期以及用户的地理位置。确保从电动汽车到电网的电力传输得到有效控制和管理至关重要。 scheduling is influenced by several key factors, including user anxiety about mileage, the wear and condition of EV batteries [41], user revenue expectations, and the geographic location of users. It is crucial to ensure that the power transfer from electric vehicles to the grid is controlled and managed effectively. Wang和 and Wang研究了联网电动汽车的数量和电动汽车电池组特性对电网调峰和低谷调节的影响[10]。 investigated the impact of the number of networked EVs and the characteristics of EV battery packs on grid peak and trough regulation [10]. Farzin、, Fotuhi-Firuzabad 和, and Moeini-Aghtaie 考虑了电动汽车电池磨损对 V2G 调度的影响，并开发了电池退化成本模型来准确评估considered the impact of EV battery wear on V2G scheduling and developed a battery degradation cost model to accurately assess the economic cost of V2G 的经济成本[42]. [44]。Huang、, Yang和Li介绍了电动汽车用户与电网之间交易价格波动的期权定价模型，并推导了V2G储备合作系数、交易保证金和电力合约价格之间的均衡关系[45]。埃尔多安、额尔登和基萨奇科格鲁提出了两级, and Li introduced an option pricing model for price fluctuations in transactions between EV users and the grid, and derived analytical relationships between V2G reserve cooperation coefficients, trade deposits, and power contract prices in equilibrium [43]. Erdogan, Erden, and Kisacikoglu proposed a two-stage V2G放电调峰控制方案 discharge control scheme for peaking the grid;它基于预测需求和电动汽车移动性模型确定V2G服务的调峰和持续时间，然后通过考虑实际电网负荷和并网电动汽车特性来动态调整电动汽车放电率[46]。 it determines the peak shaving and duration of V2G services based on forecasted demand and EV mobility modeling and then dynamically adjusts the EV discharge rate by considering the actual grid load and the grid-connected EV characteristics [44]. Maeng等分析了电动汽车能源（混合动力或纯电动汽车）对电动汽车用户放电偏好的影响，研究了电动汽车剩余电量对用户放电意愿的影响，这与电动汽车用户里程焦虑对放电意愿的影响相似[47]。 et al. analyzed the effect of EV energy (hybrid or pure EV) sources on EV users’ discharge preferences and investigated the effect of the EV remaining power on the users’ willingness to discharge, which is similar to the effect of EV users’ mileage anxiety on the willingness to discharge [45]. Jiao等人设计了一个模型来研究电动汽车用户的里程焦虑对 et al. designed a model to study the impact of EV users’ mileage anxiety on V2G调度的影响，结果表明，通过提高平均运营成本和放电功率，可以缓解电动汽车用户的里程焦虑，进而影响充放电调度计划[48]。 scheduling, and the result shows that EV users’ mileage anxiety can be mitigated by improving the average operating cost and discharge power, which in turn affects the charge/discharge scheduling plan [46]. Wei、, Yi 和 Yun 使用, and Yun used Q-learning 来预测风力涡轮机的功率，并提出了一种基于强化学习的智能电网优化能量管理方法，以消散 RE，同时提供最佳电力调度并降低电网成本to predict the power of wind turbines and proposed a reinforcement learning-based approach for optimal energy management in smart grids to dissipate RE while providing optimal power scheduling and reducing grid costs [47]. [49]。Wang、, Gao 和, and Tang 创建了一个闪电般的规模转换模型来描述电动汽车用户的需求响应行为，以支持负载聚合器做出调度决策。该模型通过演化为混合双向方法，增强了初始单向尺度变换方法，包括深度尺度变换模式和广义尺度变换模式。这种创新方法促进了空间和时间观测尺度的同步和协同转换[50]。能源调度不仅仅发生在电动汽车和电网之间;许多研究深入研究了各种能源的相互调度[36created a lightning scale transformation model to describe the demand response behavior of EV users in order to support load aggregators in making scheduling decisions. The model enhances the initial unidirectional scale transformation approach,51 encompassing the depth scale transformation mode and the generalized scale transformation mode,52 by evolving it into a hybrid bidirectional method. This innovative approach facilitates concurrent and synergistic transformations of both spatial and temporal observation scales [48]. The energy scheduling does not occur solely between EVs and the grid; many studies have delved into the inter-scheduling of various energy sources [34][49][50][51][52]. Overall,53,54]。 总体而言，未来 V2G调度将更加智能，更加以用户为导向，专注于电池管理、定价策略和可再生能源集成，以满足不断变化的电网需求，提高电力系统的效率和可靠性。 scheduling will be smarter and more user-oriented in the future, focusing on battery management, pricing strategies, and renewable energy integration to meet evolving grid needs and to improve power system efficiency and reliability.

2.2. 频率调节和电压控制Frequency Regulation and Voltage Control

为了便于电网功能，有功功率和无功功率的产生和消耗应不断平衡，以确保频率和电压的幅值接近其额定值。通过调节系统频率可以实现有功功率的有效控制，而无功功率的管理则取决于系统电压的控制。因此，频率和电压是衡量电网电能质量的重要因素。通过To facilitate grid functionality, the production and consumption of active and reactive power should be constantly balanced to ensure that the amplitudes of frequency and voltage are close to their rated values. Effective control of active power is attainable through the regulation of system frequency, whereas the management of reactive power hinges on the control of the system voltage. Therefore, frequency and voltage are important factors in measuring the power quality of the grid. Achieving overall voltage and frequency management through the V2G模式实现整体电压和频率管理，将进一步提高有功和无功电能质量。 mode will go a step further towards active and reactive power quality improvement.

2.2.1. Frequency 频率调节Regulation

Frequency control is essential for power systems to maintain stable operation; it involves the synergistic action of all the generators and loads in the power system and can keep the grid frequency within the normal range. In this context, V2G technology has emerged as a promising resource that can be used to assist in the frequency regulation of power systems. EVs, when utilized as distributed storage devices, hold the potential to offer frequency regulation services owing to their ability to swiftly adjust their charging and discharging power. Liu et al. proposed a V2G control strategy for EV aggregators based on frequency regulation capacity (FRC) and expected V2G (EV2G) power; this control strategy can shift the regulation task from the EV aggregator to the EV charging station to satisfy both the frequency regulation and the charging demand of the EV charging station [55][53]. Lam, Leung, and Li designed a queuing network-based EV aggregation model that can be used to estimate regulation-up and regulation-down capacity to help establish a regulation contract between the aggregator and the grid operator [56][54]. Chen et al. proposed a hierarchical V2G system communication architecture containing a smart V2G aggregator (SVA), and they designed a multilevel online V2G (MLOV) algorithm for hierarchical V2G scheduling to achieve a balance between service quality and computation time [57][55]. Wang, Wang, and Liu proposed a dynamic scheduling strategy for V2G frequency regulation capacity based on deep Q-learning to evaluate the hourly regulation capacity in real time for maximizing the revenue of frequency regulation services provided by battery swapping stations [58][56]. Alfaverh, Denaï, and Sun scheduled EV battery charging and discharging using the deep deterministic policy gradient (DDPG) to meet driving needs and to participate in frequency regulation, as well as to meet the driving needs of car owners and the interests of aggregators [59][57]. Frequency regulation of the grid by V2G is related to the state of EVs, the charging demand, and the changing grid frequency, which requires understanding the state of EVs on the grid; however, accessing these states involves issues such as communication security, user privacy security and control security between the information network and the energy network [60][58].

2.2.2. Voltage Control

Voltage control is one of the key elements in the stable operation of power systems, and large generators and transformers are conventional means of controlling voltage. However, the operation and control of power systems have become more complex with the popularization of distributed energy resources [61,62][59][60]. In this context, V2G technology offers new possibilities for voltage control. With V2G, EVs can act as distributed energy storage devices to provide the necessary reactive power support to the grid [63][61], thus helping to maintain voltage stability [64][62]. García-Villalobos et al. solved voltage problems and load balancing issues in low-voltage distribution networks by optimizing the charging behavior of PEVs. This was conducted by executing intelligent charging algorithms through the processor unit built into the PEV and by developing an optimal charging strategy based on factors such as the price of electricity and battery life [65][63]. AThis research proposes an intelligent sag control method that is independent of line parameters, and it devises a new strategy to enable PHEVs to participate in voltage and frequency control of islanded microgrids (MGs) without a communication link, thus maintaining MG stability more efficiently [66][64]. Huang proposed a day-ahead optimal control model based on three-phase power flow and sensitivity methods that could solve the overrun voltage problem and mitigate the neutral potential rise, thus improving the voltage regulation of residential grids [67][65]. Nimalsiri et al. introduced a network-aware EV charging and discharging scheduling approach known as N-EVC(D). This method is designed to efficiently coordinate the charging and discharging of EVs within a distributed network, with the primary objectives of reducing operational costs and ensuring the stability of the supply voltage [66]. In conclusion, V2G technology is driving advancements in voltage control for power systems and harnessing EVs as dynamic energy resources. These developments are expected to enhance voltage stability and regulation, particularly as distributed energy resources become more prevalent. Researchers are focusing on intelligent algorithms and strategies to optimize voltage control through V2G for a more resilient and reliable power grid in the future.

2.3. Smart Grid and Communication Networks

2.3.1. Smart Grid

As with the application of V2G and smart grid technology [69][67], EV grid technology is an important part of “smart grid technology” in the solving of the problems related to the large-scale development of EVs brought about by the grid load pressure [70][68]. Moreover, EVs, as mobile distributed energy storage units, are connected to the power grid for peak shaving, valley filling, and rotating standby in order to improve the flexibility of the power supply and the reliability and efficiency of energy utilization and to slow down investment in power grid construction. Waraich et al. proposed an agent-based transport simulation framework tailored for PHEVs. Furthermore, they expanded the framework to encompass V2G capabilities and decentralized smart grid functionalities [71][69]. In a separate study, Jian et al. explored the concept of a household smart microgrid. They investigated how to potentially reduce load variance within the household microgrid by adjusting the charging patterns of family PHEVs. Their findings revealed a significant reduction in the variance of load power when such regulations were implemented [72][70]. Kennel, Goerges, and Liu proposed an energy management system designed for smart grids. This system incorporates load frequency control (LFC), economic efficiency optimization, and the integration of electric vehicles through a HiMPC approach. The simulation findings underscore the fact that electric vehicles can play a significant role in mitigating fluctuations in renewable energy generation, thereby contributing to grid stability [60][58]. Vachirasricirikul and Ngamroo presented the new coordinated V2G control and frequency controller for robust LFC in the smart grid system with wind power penetration. The simulation results clearly showcase the resilience and effectiveness of the suggested V2G control strategy and proportional–integral (PI) controllers in LFC, even when subjected to altered system parameters and diverse operating conditions [53][51]. Morais et al. considered the evaluation of the EV impact on the power demand curve under a smart grid environment and further addressed the impact of EVs on system operation costs and on the power demand curve for a distribution network with the deep penetration of distributed generation (DG) units [73][71]. In another study by Jian et al., they discussed a V2G implementation scenario within regional smart grids. They introduced a double-layer optimal charging (DLOC) strategy to address the computational challenges posed by large-scale PEVs and charging stations [74][72]. Liang and Zhuang believe that the forthcoming smart grid is anticipated to take the form of an interconnected network comprising small-scale, self-sustained microgrids. They also provided an overview of the latest advancements in stochastic modeling and optimization tools which was aimed at facilitating the planning, operation, and control of these microgrids [75][73]. The integration of EVs and smart grid technology continues to evolve, and numerous studies have been conducted to explore the potential benefits and challenges of this integration. These studies encompass various aspects, from enhancing the self-consumption of PV power to the development of comprehensive battery degradation models. Van der Kam and van Sark presented a model designed to investigate the augmentation of the self-consumption of PV power through the utilization of smart charging EVs with smart grid technology. The outcomes of their study conspicuously illustrate the advantages of employing smart charging and V2G technologies within a microgrid context [5]. López et al. proposed an optimization-driven model for executing load shifting within the framework of smart grids. They presented findings based on a test system derived from the IEEE 37-bus distribution grid. These results not only demonstrate the efficacy of their approach but also highlight the impact of hourly energy prices on the smoothing out of the load curve [76][74]. Xing et al. concentrated on acquiring load shifting services through the optimal scheduling of PEVs for charging and discharging within smart grids, adopting a decentralized approach. Their research culminated in the development of a decentralized algorithm rooted in iterative water-filling techniques [77][75]. Soares et al. presented a decision-making framework designed to aid virtual power plants (VPPs) in the management of smart grids characterized by a substantial presence of sensitive electricity loads. They further devised a two-stage optimization algorithm, leveraging the weighted sum methodology, to facilitate this management process [78][76]. Uddin et al. developed a comprehensive battery degradation model based on long-term ageing data and integrated a comprehensive battery ageing model into a smart grid algorithm. The result demonstrated that linking an EV to this smart grid system can effectively meet the power network’s demand, particularly as the proportion of clean renewable energy in the system increases [79][77]. Dileep gave an overview of the evolution of the smart grid and explained various smart grid technologies, like smart meters, smart sensors, V2G, and PHEV, and their application in the smart grid [35][33]. The integration of EVs and smart grid technology holds significant promise for the future of sustainable energy. Through various models, algorithms, and frameworks, researchers are paving the way for a more efficient, reliable, and environmentally friendly power grid system. Ambiguous definitions of smart grids, or technologically oriented definitions, often lead to the ignoring of the differences between the smart grid concepts and to the overlooking of the important aspects for the customers that implement them. For the time being, most of the research on smart grids has focused on distributed smart grids and less on centralized grids or centralized–decentralized connections.

2.3.2. Communication Networks

The integration of communication networks with battery-operated vehicles and the electrical grid is a transformative step in the evolution of energy systems. This integration streamlines the coordination of the electric load, but it introduces challenges, especially with regard to security and privacy. Communication networks bridge the gap between user battery-operated vehicles and the electrical grid, enhance the coordination of electric loads, and improve energy efficiency and reliability. Zhang et al. discussed the V2G network architecture and described the different security challenges during V2G power and communication interactions. A new context-aware authentication solution was also proposed for this problem [80][78]. Saxena et al. described the challenges of the security and privacy in smart V2G networks and proposed a cybersecurity architecture that attempted to address the problem; this architecture encompassed anonymous authentication, blind signatures, fine-grained access control, and payment system security [81][79]. Tao et al. proposed a hybrid computing model based on fog and cloud computing for V2G networks in 5G networks. This hybrid computing model can respond more flexibly and in a more timely manner to EV mobility; it can provide auxiliary power services for renewable energy sources in V2G systems, as well as manage and monitor power usage [82][80]. A privacy-friendly and efficient secure communication (PESC) framework was proposed by He, Chan, and Guizani [83][81]. It used group signatures and public key cryptography for access control from each EV to the aggregator and established shared keys (with the Diffie–Hellman key exchange algorithm), which could reduce the communication and computation overhead while ensuring privacy and security. Ahmed et al. designed a signature-encrypted, privacy-preserving authentication key agreement scheme that enables all participants to verify each other; it uses a one-way hash function to improve verification efficiency [84][82]. Umoren, Shakir, and Tabassum proposed a strategy to balance the efficiency of both spectral considerations and cost by expressing resource efficiency as a weighting factor, which improved the efficiency of V2G wireless communication networks [85][83]. Pokhrel and Hossain developed a novel, adaptive demand-side energy management framework for the wireless charging of V2G systems that employed privacy-preserving techniques based on federated learning [86][84]. Hossain, Pokhrel, and Vu proposed a demand-side energy management approach based on reinforcement learning using rechargeable batteries for V2G cost-friendly privacy, efficient scheduling, and accurate billing [87][85].

References

  1. Taiebat, M.; Xu, M. Synergies of four emerging technologies for accelerated adoption of electric vehicles: Shared mobility, wireless charging, vehicle-to-grid, and vehicle automation. J. Clean. Prod. 2019, 230, 794–797.
  2. Yao, X.; Fan, Y.; Zhao, F.; Ma, S. Economic and climate benefits of vehicle-to-grid for low-carbon transitions of power systems: A case study of China’s 2030 renewable energy target. J. Clean. Prod. 2022, 330, 129833.
  3. Ravi, S.S.; Aziz, M. Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives. Energies 2022, 15, 589.
  4. Ahmadian, A.; Asadpour, M.; Mazouz, A.; Alhameli, F.; Mohammadi-Ivatloo, B.; Elkamel, A. Techno-economic evaluation of PEVs energy storage capability in wind distributed generations planning. Sust. Cities Soc. 2020, 56, 102117.
  5. van der Kam, M.; van Sark, W. Smart charging of electric vehicles with photovoltaic power and vehicle-to-grid technology in a microgrid; a case study. Appl. Energy 2015, 152, 20–30.
  6. Román, A.; García De Jalón, D.; Alonso, C. Could future electric vehicle energy storage be used for hydropeaking mitigation? An eight-country viability analysis. Resour. Conserv. Recycl. 2019, 149, 760–777.
  7. Liu, C.; Chau, K.T.; Wu, D.; Gao, S. Opportunities and Challenges of Vehicle-to-Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies. Proc. IEEE 2013, 101, 2409–2427.
  8. Martyushev, N.V.; Malozyomov, B.V.; Khalikov, I.H.; Kukartsev, V.A.; Kukartsev, V.V.; Tynchenko, V.S.; Tynchenko, Y.A.; Qi, M. Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption. Energies 2023, 16, 729.
  9. Gajduk, A.; Todorovski, M.; Kurths, J.; Kocarev, L. Improving power grid transient stability by plug-in electric vehicles. New J. Phys. 2014, 16, 115011–115014.
  10. Wang, Z.; Wang, S. Grid Power Peak Shaving and Valley Filling Using Vehicle-to-Grid Systems. IEEE Trans. Power Deliv. 2013, 28, 1822–1829.
  11. Fattori, F.; Anglani, N.; Muliere, G. Combining photovoltaic energy with electric vehicles, smart charging and vehicle-to-grid. Sol. Energy 2014, 110, 438–451.
  12. Rahbari, O.; Vafaeipour, M.; Omar, N.; Rosen, M.A.; Hegazy, O.; Timmermans, J.; Heibati, S.; Bossche, P.V.D. An optimal versatile control approach for plug-in electric vehicles to integrate renewable energy sources and smart grids. Energy 2017, 134, 1053–1067.
  13. Liu, X.; Zhao, F.; Hao, H.; Liu, Z. Opportunities, Challenges and Strategies for Developing Electric Vehicle Energy Storage Systems under the Carbon Neutrality Goal. World Electr. Veh. J. 2023, 14, 170.
  14. Hidrue, M.K.; Parsons, G.R. Is there a near-term market for vehicle-to-grid electric vehicles? Appl. Energy 2015, 151, 67–76.
  15. Liu, J.; Zhong, C. An economic evaluation of the coordination between electric vehicle storage and distributed renewable energy. Energy 2019, 186, 115821.
  16. Ioakimidis, C.S.; Thomas, D.; Rycerski, P.; Genikomsakis, K.N. Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot. Energy 2018, 148, 148–158.
  17. Hosseini, S.S.; Badri, A.; Parvania, M. A survey on mobile energy storage systems (MESS): Applications, challenges and solutions. Renew. Sustain. Energy Rev. 2014, 40, 161–170.
  18. Forrest, K.E.; Tarroja, B.; Zhang, L.; Shaffer, B.; Samuelsen, S. Charging a renewable future: The impact of electric vehicle charging intelligence on energy storage requirements to meet renewable portfolio standards. J. Power Sources 2016, 336, 63–74.
  19. Teodorescu, R.; Sui, X.; Vilsen, S.B.; Bharadwaj, P.; Kulkarni, A.; Stroe, D. Smart Battery Technology for Lifetime Improvement. Batteries 2022, 8, 169.
  20. Rahman, M.M.; Gemechu, E.; Oni, A.O.; Kumar, A. The development of a techno-economic model for assessment of cost of energy storage for vehicle-to-grid applications in a cold climate. Energy 2023, 262, 125398.
  21. Gough, R.; Dickerson, C.; Rowley, P.; Walsh, C. Vehicle-to-grid feasibility: A techno-economic analysis of EV-based energy storage. Appl. Energy 2017, 192, 12–23.
  22. Atia, R.; Yamada, N. More accurate sizing of renewable energy sources under high levels of electric vehicle integration. Renew. Energy 2015, 81, 918–925.
  23. Wei, H.; Zhang, Y.; Wang, Y.; Hua, W.; Jing, R.; Zhou, Y. Planning integrated energy systems coupling V2G as a flexible storage. Energy 2022, 239, 122215.
  24. Dik, A.; Kutlu, C.; Omer, S.; Boukhanouf, R.; Su, Y.; Riffat, S. An approach for energy management of renewable energy sources using electric vehicles and heat pumps in an integrated electricity grid system. Energy Build. 2023, 294, 113261.
  25. Sun, Y.; Yue, H.; Zhang, J.; Booth, C. Minimization of Residential Energy Cost Considering Energy Storage System and EV With Driving Usage Probabilities. IEEE Trans. Sustain. Energy 2019, 10, 1752–1763.
  26. Hipolito, F.; Vandet, C.A.; Rich, J. Charging, steady-state SoC and energy storage distributions for EV fleets. Appl. Energy 2022, 317, 119065.
  27. Shang, B.; Dai, N.; Cai, L.; Yang, C.; Li, J.; Xu, Q. V2G Scheduling of Electric Vehicles Considering Wind Power Consumption. World Electr. Veh. J. 2023, 14, 236.
  28. Singh, M.; Kumar, P.; Kar, I. A Multi Charging Station for Electric Vehicles and Its Utilization for Load Management and the Grid Support. IEEE Trans. Smart Grid 2013, 4, 1026–1037.
  29. Gao, S.; Chau, K.T.; Liu, C.; Wu, D.; Chan, C.C. Integrated Energy Management of Plug-in Electric Vehicles in Power Grid With Renewables. IEEE Trans. Veh. Technol. 2014, 63, 3019–3027.
  30. Zhong, J.; He, L.; Li, C.; Cao, Y.; Wang, J.; Fang, B.; Zeng, L.; Xiao, G. Coordinated control for large-scale EV charging facilities and energy storage devices participating in frequency regulation. Appl. Energy 2014, 123, 253–262.
  31. Fathabadi, H. Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems. Energy 2015, 90, 1100–1110.
  32. Haddadian, G.; Khalili, N.; Khodayar, M.; Shahiedehpour, M. Security-constrained power generation scheduling with thermal generating units, variable energy resources, and electric vehicle storage for V2G deployment. Int. J. Electr. Power Energy Syst. 2015, 73, 498–507.
  33. Uddin, K.; Jackson, T.; Widanage, W.D.; Chouchelamane, G.; Jennings, P.A.; Marco, J. On the possibility of extending the lifetime of lithium-ion batteries through optimal V2G facilitated by an integrated vehicle and smart-grid system. Energy 2017, 133, 710–722.
  34. Robledo, C.B.; Oldenbroek, V.; Abbruzzese, F.; van Wijk, A.J.M. Integrating a hydrogen fuel cell electric vehicle with vehicle-to-grid technology, photovoltaic power and a residential building. Appl. Energy 2018, 215, 615–629.
  35. Bhatti, A.R.; Salam, Z. A rule-based energy management scheme for uninterrupted electric vehicles charging at constant price using photovoltaic-grid system. Renew. Energy 2018, 125, 384–400.
  36. Sufyan, M.; Rahim, N.A.; Muhammad, M.A.; Tan, C.K.; Raihan, S.R.S.; Bakar, A.H.A. Charge coordination and battery lifecycle analysis of electric vehicles with V2G implementation. Electr. Power Syst. Res. 2020, 184, 106307.
  37. Noorollahi, Y.; Golshanfard, A.; Aligholian, A.; Mohammadi-Ivatloo, B.; Nielsen, S.; Hajinezhad, A. Sustainable Energy System Planning for an Industrial Zone by Integrating Electric Vehicles as Energy Storage. J. Energy Storage 2020, 30, 101553.
  38. Bartolini, A.; Comodi, G.; Salvi, D.; Ostergaard, P.A. Renewables self-consumption potential in districts with high penetration of electric vehicles. Energy 2020, 213, 118653.
  39. Shi, R.; Li, S.; Zhang, P.; Lee, K.Y. Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization. Renew. Energy 2020, 153, 1067–1080.
  40. Sangswang, A.; Konghirun, M. Optimal Strategies in Home Energy Management System Integrating Solar Power, Energy Storage, and Vehicle-to-Grid for Grid Support and Energy Efficiency. IEEE Trans. Ind. Appl. 2020, 56, 5716–5728.
  41. Tabatabaee, S.; Mortazavi, S.S.; Niknam, T. Stochastic scheduling of local distribution systems considering high penetration of plug-in electric vehicles and renewable energy sources. Energy 2017, 121, 480–490.
  42. Farzin, H.; Fotuhi-Firuzabad, M.; Moeini-Aghtaie, M. A Practical Scheme to Involve Degradation Cost of Lithium-Ion Batteries in Vehicle-to-Grid Applications. IEEE Trans. Sustain. Energy 2016, 7, 1730–1738.
  43. Huang, S.; Yang, J.; Li, S. Black-Scholes option pricing strategy and risk-averse coordination for designing vehicle-to-grid reserve contracts. Energy 2017, 137, 325–335.
  44. Erdogan, N.; Erden, F.; Kisacikoglu, M. A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system. J. Mod. Power Syst. Clean. Energy 2018, 6, 555–566.
  45. Maeng, K.; Ko, S.; Shin, J.; Cho, Y. How Much Electricity Sharing Will Electric Vehicle Owners Allow from Their Battery? Incorporating Vehicle-to-Grid Technology and Electricity Generation Mix. Energies 2020, 13, 4248.
  46. Jiao, Z.; Ran, L.; Zhang, Y.; Ren, Y. Robust vehicle-to-grid power dispatching operations amid sociotechnical complexities. Appl. Energy 2021, 281, 115912.
  47. Wei, L.; Yi, C.; Yun, J. Energy drive and management of smart grids with high penetration of renewable sources of wind unit and solar panel. Int. J. Electr. Power Energy Syst. 2021, 129, 106846.
  48. Wang, A.; Gao, X.; Tang, M. A space variable-scale scheduling method for digital vehicle-to-grid platform under distributed electric energy storage. Appl. Soft Comput. 2023, 133, 109911.
  49. Wu, T.; Yang, Q.; Bao, Z.; Yan, W. Coordinated Energy Dispatching in Microgrid With Wind Power Generation and Plug-in Electric Vehicles. IEEE Trans. Smart Grid 2013, 4, 1453–1463.
  50. Honarmand, M.; Zakariazadeh, A.; Jadid, S. Integrated scheduling of renewable generation and electric vehicles parking lot in a smart microgrid. Energy Conv. Manag. 2014, 86, 745–755.
  51. Vachirasricirikul, S.; Ngamroo, I. Robust LFC in a Smart Grid With Wind Power Penetration by Coordinated V2G Control and Frequency Controller. IEEE Trans. Smart Grid 2014, 5, 371–380.
  52. Paterakis, N.G.; Erdinc, O.; Pappi, I.N.; Bakirtzis, A.G.; Catalao, J.P.S. Coordinated Operation of a Neighborhood of Smart Households Comprising Electric Vehicles, Energy Storage and Distributed Generation. IEEE Trans. Smart Grid 2016, 7, 2736–2747.
  53. Liu, H.; Hu, Z.; Song, Y.; Wang, J.; Xie, X. Vehicle-to-Grid Control for Supplementary Frequency Regulation Considering Charging Demands. IEEE Trans. Power Syst. 2015, 30, 3110–3119.
  54. Lam, A.Y.S.; Leung, K.; Li, V.O.K. Capacity Estimation for Vehicle-to-Grid Frequency Regulation Services With Smart Charging Mechanism. IEEE Trans. Smart Grid 2016, 7, 156–166.
  55. Chen, X.; Leung, K.; Lam, A.Y.S.; Hill, D.J. Online Scheduling for Hierarchical Vehicle-to-Grid System: Design, Formulation, and Algorithm. IEEE Trans. Veh. Technol. 2019, 68, 1302–1317.
  56. Wang, X.; Wang, J.; Liu, J. Vehicle to Grid Frequency Regulation Capacity Optimal Scheduling for Battery Swapping Station Using Deep Q-Network. IEEE Trans. Ind. Inform. 2021, 17, 1342–1351.
  57. Alfaverh, F.; Denaï, M.; Sun, Y. Optimal vehicle-to-grid control for supplementary frequency regulation using deep reinforcement learning. Electr. Power Syst. Res. 2023, 214, 108949.
  58. Kennel, F.; Gorges, D.; Liu, S. Energy Management for Smart Grids With Electric Vehicles Based on Hierarchical MPC. IEEE Trans. Ind. Inform. 2013, 9, 1528–1537.
  59. Azzouz, M.A.; Shaaban, M.F.; El-Saadany, E.F. Real-Time Optimal Voltage Regulation for Distribution Networks Incorporating High Penetration of PEVs. IEEE Trans. Power Syst. 2015, 30, 3234–3245.
  60. Cheng, L.; Chang, Y.; Huang, R. Mitigating Voltage Problem in Distribution System With Distributed Solar Generation Using Electric Vehicles. IEEE Trans. Sustain. Energy 2015, 6, 1475–1484.
  61. Sun, X.; Qiu, J. A Customized Voltage Control Strategy for Electric Vehicles in Distribution Networks With Reinforcement Learning Method. IEEE Trans. Ind. Inform. 2021, 17, 6852–6863.
  62. Hu, J.; Ye, C.; Ding, Y.; Tang, J.; Liu, S. A Distributed MPC to Exploit Reactive Power V2G for Real-Time Voltage Regulation in Distribution Networks. IEEE Trans. Smart Grid 2022, 13, 576–588.
  63. García-Villalobos, J.; Zamora, I.; Knezović, K.; Marinelli, M. Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks. Appl. Energy 2016, 180, 155–168.
  64. Pournazarian, B.; Karimyan, P.; Gharehpetian, G.B.; Abedi, M.; Pouresmaeil, E. Smart participation of PHEVs in controlling voltage and frequency of island microgrids. Int. J. Electr. Power Energy Syst. 2019, 110, 510–522.
  65. Huang, Y. Day-Ahead Optimal Control of PEV Battery Storage Devices Taking Into Account the Voltage Regulation of the Residential Power Grid. IEEE Trans. Power Syst. 2019, 34, 4154–4167.
  66. Nimalsiri, N.I.; Ratnam, E.L.; Mediwaththe, C.P.; Smith, D.B.; Halgamuge, S.K. Coordinated charging and discharging control of electric vehicles to manage supply voltages in distribution networks: Assessing the customer benefit. Appl. Energy 2021, 291, 116857.
  67. Shaukat, N.; Khan, B.; Ali, S.M.; Mehmood, C.A.; Khan, J.; Farid, U.; Majid, M.; Anwar, S.M.; Jawad, M.; Ullah, Z. A survey on electric vehicle transportation within smart grid system. Renew. Sustain. Energy Rev. 2018, 81, 1329–1349.
  68. Shen, J.; Jiang, C.; Li, B. Controllable Load Management Approaches in Smart Grids. Energies 2015, 8, 11187–11202.
  69. Waraich, R.A.; Galus, M.D.; Dobler, C.; Balmer, M.; Andersson, G.; Axhausen, K.W. Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation. Transp. Res. Part C Emerg. Technol. 2013, 28, 74–86.
  70. Jian, L.; Xue, H.; Xu, G.; Zhu, X.; Zhao, D.; Shao, Z.Y. Regulated Charging of Plug-in Hybrid Electric Vehicles for Minimizing Load Variance in Household Smart Microgrid. IEEE Trans. Ind. Electron. 2013, 60, 3218–3226.
  71. Morais, H.; Sousa, T.; Vale, Z.; Faria, P. Evaluation of the electric vehicle impact in the power demand curve in a smart grid environment. Energy Conv. Manag. 2014, 82, 268–282.
  72. Jian, L.; Zhu, X.; Shao, Z.; Niu, S.; Chan, C.C. A scenario of vehicle-to-grid implementation and its double-layer optimal charging strategy for minimizing load variance within regional smart grids. Energy Conv. Manag. 2014, 78, 508–517.
  73. Liang, H.; Zhuang, W. Stochastic Modeling and Optimization in a Microgrid: A Survey. Energies 2014, 7, 2027–2050.
  74. Lopez, M.A.; de la Torre, S.; Martin, S.; Aguado, J.A. Demand-side management in smart grid operation considering electric vehicles load shifting and vehicle-to-grid support. Int. J. Electr. Power Energy Syst. 2015, 64, 689–698.
  75. Xing, H.; Fu, M.; Lin, Z.; Mou, Y. Decentralized Optimal Scheduling for Charging and Discharging of Plug-In Electric Vehicles in Smart Grids. IEEE Trans. Power Syst. 2016, 31, 4118–4127.
  76. Soares, J.; Ghazvini, M.A.F.; Vale, Z.; de Moura Oliveira, P.B. A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads. Appl. Energy 2016, 162, 1074–1088.
  77. Dileep, G. A survey on smart grid technologies and applications. Renew. Energy 2020, 146, 2589–2625.
  78. Zhang, Y.; Gjessing, S.; Liu, H.; Ning, H.; Yang, L.; Guizani, M. Securing vehicle-to-grid communications in the smart grid. IEEE Wirel. Commun. 2013, 20, 66–73.
  79. Saxena, N.; Grijalva, S.; Chukwuka, V.; Vasilakos, A.V. Network Security and Privacy Challenges in Smart Vehicle-to-Grid. IEEE Wirel. Commun. 2017, 24, 88–98.
  80. Tao, M.; Ota, K.; Dong, M. Foud: Integrating Fog and Cloud for 5G-Enabled V2G Networks. IEEE Netw. 2017, 31, 8–13.
  81. He, D.; Chan, S.; Guizani, M. Privacy-friendly and efficient secure communication framework for V2G networks. IET Commun. 2018, 12, 304–309.
  82. Ahmed, S.; Shamshad, S.; Ghaffar, Z.; Mahmood, K.; Kumar, N.; Parizi, R.M.; Choo, K.R. Signcryption Based Authenticated and Key Exchange Protocol for EI-Based V2G Environment. IEEE Trans. Smart Grid 2021, 12, 5290–5298.
  83. Umoren, I.A.; Shakir, M.Z.; Tabassum, H. Resource Efficient Vehicle-to-Grid (V2G) Communication Systems for Electric Vehicle Enabled Microgrids. IEEE Trans. Intell. Transp. Syst. 2021, 22, 4171–4180.
  84. Pokhrel, S.R.; Hossain, M.B. Data Privacy of Wireless Charging Vehicle to Grid (V2G) Networks With Federated Learning. IEEE Trans. Veh. Technol. 2022, 71, 9032–9037.
  85. Hossain, M.B.; Pokhrel, S.R.; Vu, H.L. Efficient and Private Scheduling of Wireless Electric Vehicles Charging Using Reinforcement Learning. IEEE Trans. Intell. Transp. Syst. 2023, 24, 4089–4102.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Feedback