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Review

Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives

1
Physical Science and Engineering Division, Mechanical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
2
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
*
Author to whom correspondence should be addressed.
Energies 2022, 15(2), 589; https://doi.org/10.3390/en15020589
Received: 12 December 2021 / Revised: 5 January 2022 / Accepted: 11 January 2022 / Published: 14 January 2022
(This article belongs to the Section E: Electric Vehicles)

Abstract

:
With every passing second, we witness the effect of the global environmental impact of fossil fuels and carbon emissions, to which nations across the globe respond by coming up with ambitious goals to become carbon-free and energy-efficient. At the same time, electric vehicles (EVs) are developed as a possible solution to reach this ambitious goal of making a cleaner environment and facilitating smarter transportation modes. This excellent idea of shifting towards an entirely EV-based mobility industry and economy results in a range of issues that need to be addressed. The issues range from ramping up the electricity generation for the projected increase in consumption to developing an infrastructure that is large enough to support the higher demand for electricity that arises due to the market penetration of EVs. Vehicle to grid (V2G) is a concept that is largely in a testing phase in the current scenario. However, it appears to offer a solution to the issues created by a mobility sector that the constantly growing EV fleet will dominate. Furthermore, the integration of EVs with the grid seems to offer various cost-wise and environment-wise benefits while assisting the grid by tapping into the idle energy of parked EVs during peak hours. This review aims to present some of the possible ancillary service potentials of such a system while also discussing the potential challenges, impacts, and future market penetration capabilities of V2G technology.

1. Introduction

Electric vehicles (EVs) are believed to be feasible solutions for reducing greenhouse gases (GHGs) and, more broadly, global anthropogenic emissions that predominantly emanate from the transportation and energy sectors. In addition, they also contribute to the diversification of the energy market and present new economic opportunities. Since EVs primarily receive their electricity from the electric grid, synchronizing these grids with low-carbon electricity production by adopting renewable energy with high energy-conversion efficiency will undoubtedly create a cleaner landscape in both the energy and mobility sectors. In addition, electric vehicles also tend to have higher overall efficiency when compared with their conventional gasoline counterparts, the internal combustion engine (ICE)-based vehicles. This is due to the higher efficiency in grid electricity generation and regenerative braking [1].
According to the International Energy Agency (IEA), the global number of battery electric vehicles (BEVs) in 2019 reached 4.79 million, with more than half (2.58 million) of the BEVs population coming from China [2]. Moreover, the number keeps increasing at a significant rate, at about 36% annually, hinting at a possible projection of the global EVs stock growing as large as around 245 million EVs in 2030 (under IEA sustainable development scenario) [2]. Assuming that the average battery capacity installed in each EV is 50 kWh, the total battery capacity of all EVs in 2030 can reach up to 12.5 TWh [3].
The global share of EVs in the mobility market is rapidly increasing due to favorable policies, incentives, and subsidies from the government, reduction in the costs of manufacturing and battery, increasing social acceptance, and broader infrastructure to support EVs, such as charging stations [4].
Seventeen countries have committed to phasing out the ICE-based vehicles while making changes on multiple levels to consciously adopt more environmentally friendly vehicles and zero-emission vehicles by 2050 [2]. In 2019, the total GHG emitted due to the electricity generated for consumption by EVs was 51 Mt-CO2-eq, which was about half of the total GHG emitted by the same number of ICE-based vehicles in that year (53 Mt-CO2-eq) [2].
The need for EVs to be equipped with a battery pack that is good enough to meet the energy demands of their propulsion, air-conditioning, and other auxiliaries, while ensuring and maintaining high reliability, capacity, and energy density strongly influenced the expansion of the battery manufacturing industry. This expansion is expected to bring down the costs of battery units and ramp up the pace at which the technological developments took place in the realm of battery research. These factors then contributed to the gradual reduction in the total costs of EV manufacturing.
The average duration over which EVs are used as a transportation instrument is only about 5%, which is comprised mostly of commuting during the weekdays and traveling during the weekends [5]. Therefore, for the remaining 95% of their time (idling time), EVs can be utilized for other purposes by tapping into their batteries and communication capabilities, which forms the basis for the vehicle-to-grid (V2G) concept.
The IEA has predicted that the demand for EVs charging in 2030 under a sustainable development scenario can reach about 1000 TWh. This demand is primarily expected to spring from regions like China (263 TWh), the USA (153 TWh), Europe (187 TWh), India (83 TWh), and Japan (21 TWh). This charging demand makes up about 2% and 6% of the total electricity demand in Japan and Europe, respectively. In a conventional charging system, EV charging is performed in a uni-directional mode, in which the electricity only flows from the charger (grid) to the EV battery, but not in the reverse direction. This uni-directional charging could potentially lead to uncoordinated charging, resulting in an unpredicted, fluctuating, and concentrated electricity demand at some points of time.
Managing the charging patterns of EVs is considered a crucial step for the penetration of EVs in the global markets since it strongly affects the quality of transmission through the electrical grids. The IEA has also predicted that in 2030, there will be a significant rise in electricity demand, especially during the evening hours, while attributing this spike in demand to the unmanaged and concentrated EVs charging. This demand is estimated to be around 5.5% for the US, 6.5% for the EU, and 9.5% for China [2]. Furthermore, through appropriate management and control, it is possible to tap into the massive battery reserves of EVs for utilization in other secondary applications, especially when EVs are connected to the electrical grid.
The term vehicle-grid integration (VGI) is a broader term or a concept that hints at a possible synergistic utilization of both the grid-to-vehicle (G2V) and V2G systems. While the former refers to the flow of electricity from the grid to EVs (which would be the case during charging), while the latter facilitates the flow of electricity from EVs to the grid (discharging, electricity return). It is also helpful to note at this point that V2G as a term has been used synonymously with that of VGI to mean the flow of electricity in both directions (both from and to the grid). That being said, not only can a system like that of VGI bring down the load to the grid (that might arise due to a higher charging demand created by the EVs at a particular point of time), but it also positively supports the grid by effectively controlling both the charging and discharging behaviors [6].
Integration of EV charging systems and renewable energy has a huge impact on the quality of electricity that can be made available from the grid. In the event of a spike in power demand due to a large number of EVs charging simultaneously, the grid overload will negatively affect the power quality. This is proposed to be overcome by integrating renewable energy systems (RES) with charging stations, which will also improve grid stability. Such vehicles that are compatible with the grid and allow for bi-directional power flow are commonly referred to as gridable electric vehicles (GEVs). It is also important for the grid, RES, and EVs to be in constant communication with each other for efficient grid functioning, which could be reliably achieved using V2G telematics.
An illustration of the V2G, or in general as VGI, concept during different times of the day is presented in Figure 1. This paper presents an overview of the current scenario with the V2G technology while outlining some of the potential ancillary services, such as frequency regulation, voltage regulation, peak shaving, load leveling, spinning reserve, congestion mitigation, renewable energy storage, reduction of intermittence and curtailment that can be provided with an infrastructure that supports vehicle grid integration. The infrastructure and system architecture in terms of charging stations, communication protocols, security, networked grid, and control algorithms that are required to essentially support the sustainability of this technological development and the subsequent planning and optimization needed to promote sustainable development across all of the involved sectors are also discussed This review aims to provide a comprehensive picture of the present developments with EV and grid integration while also throwing light on the possible ancillary services that could be made available due to V2G while also showing the possible room for economic developments and business opportunities that arise with an increasing number of global electric vehicle fleets and the impact and challenges they pose in various aspects, such as the economic, environmental and technological fronts.
The benefits of V2G cover various aspects and subjects and can be summarized as follows:
  • For the EVs owner: V2G can reduce the total ownership cost of EVs, and V2G also can be extended for local utilization as a home energy storage and emergency backup storage.
  • For the grid operator: V2G serves as a new resource for both up- and down-regulation and power storage. It provides and facilitates a solution to the fluctuation due to the high share of renewable energy, as well as the solution to the grid congestion and circumvents the need to upgrade the grid infrastructure.
  • For the government: V2G creates a new circular economy in society, provides higher energy security (supply and quality), facilitates a greener environment, and reduces the noise due to vehicle engines. EVs and V2G will restructure the lifestyle and infrastructure in the city, leading to huge movement in economic activities.
  • For the aggregator/EV operator: V2G presents a new business opportunity in the electricity sector, including grid balancing services (in correlation with utilities, grid operators, and consumers) and renewable energy storage services (e.g., storage and minimization of curtailment and fluctuation).
  • For the office and real estate owners and business entities (e.g., office, factory): V2G can facilitate local peak shaving, load leveling, and balance out the electricity demand. Therefore, the total cost of electricity might be reduced.

2. V2G Potential

2.1. Possible Ancillary Services

The ancillary services provided by GEVs are popularly referred to as V2G services, which can be uni-directional or bidirectional in nature [8]. Uni-directional V2G (uni-V2G) services involve the flow of controllable uni-directional power to the EVs and are offered by actively charging EVs. Bi-directional V2G (bi-V2G) services involve active power flow in both directions while utilizing the power stored by the standby EVs. There are certain advantages that are posed by the uni-V2G over the bi-V2G, such as lower battery degradation, reduced cost and initial investments, relatively simpler control, minimal social barriers, and also not having to need a bi-directional charger and all the associated communication that comes with it [9]. The potential of parked EVs in a charging station studied by Raveendran et al. [10] found them to offer an improvement in power quality. Unveiling the V2G potential of EVs requires smarter charging techniques to attain different objectives at different levels of the grid (shown in Figure 2). The potential services include virtual power plant (VPP), frequency and voltage regulations, spinning reserve, peak shaving, load leveling, reduction of intermittence and renewable energy curtailment, renewable energy storage, congestion mitigation [11], and economy-based services, such as reduction of the charging cost [12]. Some of these objectives/ancillary services are explained in the following sections.

2.1.1. Virtual Power Plant (VPP)

Like the other objectives/services mentioned before, VPP is not a direct objective per se, but a means to achieve some objectives, such as better grid stability and frequency and voltage regulations. Depending on the given market environment and needs, VPPs can achieve different objectives. In a more general sense, the objective here is to network the distributed energy resources (which is the standby EVs in our case) by monitoring, forecasting, optimizing, and trading their power to balance the fluctuations in the generated electricity by renewables, which is summarized in Figure 3. That aside, VPPs can also serve by integrating renewable energies into the markets. Since individual smaller plants cannot provide balancing services, by aggregating the power of these individual units, a VPP can work just like a large central power plant by delivering essentially the same service and redundancy.

2.1.2. Frequency Regulation

Some of the critical aspects that need some considerations regarding frequency regulation with V2G are the issues that revolve around the grid stability and economy. Maintaining the stability of the grid frequency by managing the EVs integrated into the grid is conducted together with efforts to increase and encourage the number of EVs integrated into the system and break the social barriers to participate in the frequency regulation service. The criterion for stability is that the grid frequency must match the frequency of the load consumption. The feasibility of EV integration to the grid and its role in frequency regulation in the Great Britain power grid was analyzed in [14]. The simulation shows that the EVs integration to the grid can stabilize the grid by significantly reducing frequency deviations. A quasi-Monte Carlo (QMC) based probabilistic small signal stability analysis (PSSSA) method is suggested to assess the stability of the grid that arises from the frequency regulation ancillary service [15]. Figure 4 shows the two simulation scenarios where the input frequency signal with negative and positive frequency deviation is considered. In scenarios where users want to maintain their battery state of charge (SOC) level, the simulation time was 80 s in order to study the response of charging power of the EVs, before and after the frequency deviation. Moreover, in the case where the users demand a higher SOC level, the simulation time became 3 h. It was proven that EVs can participate in both a positive and negative frequency control market.

2.1.3. Voltage Regulation

The large-scale integration of EVs into the grid will significantly affect the grid voltage [17]. The influence of the integration of EVs to the UK’s grid was analyzed under different EV aggregation levels and penetration scenarios [18]. Integrating 24 users at the low voltage segment, a power systems computer-aided design (PSCAD/EMDTC) model was used whose simulation results show that the low-voltage limit was exceeded for the minimum load and 50–100% for the penetration extreme conditions. Another study by Muhammad et al. gives an overview of the energy management system to manage and control the transient voltage stability through V2G [19]. It is shown that EVs can provide the baseload for a short period of time to improve the grid stability. Since the charging voltage of EVs is typically the lowest in the system and EV charging loads will account for a large part of electric loads in the future, it is essential for EVs to participate in under-voltage load shedding, especially in the AC electric vehicle charging stations, to avoid a voltage collapse [20].

2.1.4. Peak Shaving and Load Leveling

Peak shaving, apart from the apparent economic advantage, also benefits the grid operator and end-user and helps in the reduction of carbon emissions. When the demand (load) increases, the stress on the overall grid system increases. This could lead to a blackout in worse scenarios. Peak shaving techniques generate an efficient load demand profile, ultimately improving the power quality while reducing costs [21]. Since the generated load demand profile is sustainable, the overall load on the transmission and distribution system also decreases, which helps to increase the system’s efficiency. For the end-user participating in the V2G, incentives and financial compensation, in terms of electricity cost reduction, are considered crucial to encourage their participation. A comparison of both peak shaving and load leveling is shown in Figure 5. While peak shaving (shown in Figure 5a) is a strategic way of load shedding, load shifting/leveling (shown in Figure 5b) refers to a short-term reduction in consumption followed by an increase in the demand load when power prices or grid demand is lower.
However, the number of EVs participating in the grid integration process becomes a crucial factor for the effective performance of peak shaving and load leveling algorithms. In addition, synchronizing the charging and discharging of a large number of EVs can also be a problem that will influence the efficient management and optimization of the systems [22].

2.1.5. Spinning Reserve

Another ancillary service that can be offered by V2G is a spinning reserve. Spinning reserves have a current market value of about 10 USD/MWh [23]. A spinning reserve is typically provided by online generators which can immediately adapt their output power in response to major transmission outages. These units are equipped with automatic gain control (AGC) telecommunication systems. They can attain their full potential in 10 min while being able to sustain this response for up to 2 h to comply with North American Electric Reliability Corporation (NERC) guidelines. To put it simply, a spinning reserve requires lower total energy compared to active power generating units, but has a quicker response time, which is well suited for batteries. Another criterion is that the active power supplied must be electrically synchronized with the grid, which is the case for an EV connected to a charger with a phase lock loop. Once again, the challenge here is that a sufficient number of EVs need to be available and connected to the grid with enough electricity stored in the battery to serve as a spinning reserve [24]. Another study that took into account three scenarios—with V2G, without V2G, and with V2G and a wind farm—has summarized that the reserve potential of the grid increases significantly with the integration of V2G and a wind farm, as shown in Figure 6 [25].

2.1.6. Congestion Mitigation

When the power consumption/load increases, it can overload the grid and subsequently increase power consumption bills due to peak grid prices. Looking at this issue, even without the utilization of communication strategies and a VPP, it should be noted that a building’s ability to balance its electricity demand with V2G charging stations on a small scale also helps out the power grid on a larger scale. This also improves with an increase in electricity production from solar and wind power, which are intermittent by nature.
These circumstances can give rise to what is popularly termed as “grid congestion” [26,27] or bottlenecks that impede electricity from reaching its destination. V2G can offer a solution by utilizing the energy from standby EVs. This does not only serve as a solution to grid congestion but also circumvents the need for expensive grid infrastructure upgrades. In the absence of V2G, the additional demand for energy would need to be supplied from the reserve power plant, leading to increased costs and even more demand during peak hours, making it an economically unviable option [28].

2.1.7. Renewable Energy Storage and Reduction of Intermittence and Curtailment

When the electricity generated from renewable energy is higher, especially when surplus electricity is obtained, this electricity could be used to charge electric vehicles—otherwise, the surplus electricity results in grid imbalance. Since renewable energy is volatile and intermittent by nature, and it is hard to incorporate such an energy source into the conventional electric grids that typically involve controllable fixed power generation units. However, if this intermittent energy source is tapped to charge the EVs, which are integrated with the power grids, this would form a very efficient way of renewable energy storage while also tackling the problem of energy intermittency.
Intermittent renewable energy production sources are particularly challenging because they disrupt the conventional methods for planning the daily operation of the electric grid. Moreover, their power fluctuates over multiple time horizons, forcing the grid operator to adapt its operating procedures.
V2G policies that bring down RE curtailment are looked upon favorably because the curtailment—although it serves to balance grid stability—raises the operating cost and is an ineffective way of utilizing renewable energy resources [29]. Curtailment is often used because of grid congestion and unmatched supply and demand [30], and lowering curtailment can improve confidence amidst investors in developing future renewable energy projects [31]. Assuming that 0.95 to 5 million plug-in electric vehicles [32] (PEVs) are placed in a smart charging environment instead of unmanaged vehicle charging, smart charging brings down the annual renewable energy curtailment by 9–40%, more than the original value, which is around 120–410 GWh, respectively. It is to be noted that PEVs are not to be confused with plug-in hybrid electric vehicles (PHEVs), which can charge their battery through external power sources and using their on-board ICE. Even though annual curtailment with unmanaged charging is only 1.1–1.4% of renewable energy generation, more study is needed in this matter to understand renewable energy curtailment with future renewable energy levels [33]. The EV drivers/owners require sufficient stored electricity in the battery before departure for their travel. On the other hand, the grid operator and aggregator demand accurate availability and power capacity during the service.

2.2. Grid Ancillary Potential

The potential of V2G is influenced by EVs availability, which depends on the owner acceptance, driving behavior, willingness to participate, system readiness (e.g., charger availability), technical constraints (e.g., battery degradation), market readiness, and regulations. This availability of EVs has been simulated using multiple probabilistic algorithms. The available V2G power modeling (AVPM) was designed to calculate the available power for EVs that commute to the office in the mornings and back home in the evening. The model used fundamental parameters that were estimated using the fundamental parameters estimation (FPE) block, which contains SOC on arrival, V2G and G2V energy, and plug-in interval. These indirect parameters were calculated by utilizing the output parameters from the multivariate modeling of stochastic variables (MMSV) block and averaged PEV characteristic parameters, such as driving, charging, and discharging efficiencies. Alternatively, probabilistic availability uncertainty modeling (PAUM) block can also be utilized to calculate the V2G power, which uses data of daily commuting in order to associate a probability density function to the availability uncertainty phenomena [34].
In order to reduce the load stress due to the fast charging of EVs, especially in charging stations, Aziz et al. [35] have developed a charging system equipped with a battery. Their system consists of AC-to-DC inverter, DC-to-DC converter, stationary battery, and charger. The developed system has the potential to provide fast charging while keeping the contracted power capacity. Furthermore, Huda et al. [36] have conducted a techno-economic analysis of V2G in the Indonesian grid. They found that by adopting V2G, the electricity supply by both coal and natural gas during peak hours can be reduced by about 2.8% and 8.8%, respectively. In addition, the adoption of V2G by consumers can reduce the power generation cost and increase the power company’s revenue by approximately 3.65% as the peaking power generator can be reduced. Table 1 lists selected V2G projects in different countries and services [37].

3. V2G System and Infrastructure

3.1. System Architecture

The system architecture associated with V2Gs can be classified into centralized and decentralized architectures. In a centralized architecture, the aggregator is the primary component for handling all the charging and discharging phenomena of EVs. In addition, the aggregator can also perform optimization for smart charging of the EVs: hence, it may have access to the system data whenever necessary. These features serve to organize the distribution, increase the system capacity, and provide ancillary services. However, this also means that the system has a huge quantum of data to process and optimize, such as the preferred level of SOC, available battery size, charging time, and many more to arrive at the most optimum solution [38]. Frequency control also becomes complicated with a centralized control architecture, since controlling is difficult when different vehicles are at different states of charge, and this could often be coupled with the uncertainty of EVs at the charging stations. Most literature, for this reason, dives deeper into the prospects of a decentralized or a local control architecture [39].
On the other hand, in the local/decentralized control architecture [40], the local systems, such as office, factory, and apartment, autonomously pursue their own way to optimize the charging cost and other parameters associated with V2G. The local systems are equipped with a server that has real-time communication with the EVs that belong to the local systems (such as employees and residents). However, this would tilt the scale in favor of probabilistic individual-made decisions [41]. This unpredictability factor can also snowball into increasing or decreasing the electricity cost when a large fleet of individual vehicles chooses to vary their charging rate. This problem is expected to be less of a concern if the sample space of vehicles participating in the decentralized/local control architecture is high enough. The advantages and disadvantages of these control architectures are shown in Table 2 below [39].

3.2. Charging System

V2G involves two main types of charging systems: AC and DC charging systems. While the AC charger charges the battery via the vehicle’s on-board charger, the DC charger directly charges the vehicle’s battery using an AC-DC converter on the charger side, as shown in Figure 7a. Before diving into the details of how the AC and DC charging works and why it is used, it is important to understand what an on-board and off-board charger are all about. An on-board charger is primarily responsible for charging the battery pack during its final stage. It utilizes the AC power source from the electric vehicle supply equipment and converts this power into the required battery-charging profile (typically in high-voltage DC). While the on-board charger’s primary role is to transform power from the off-board charger before supplying to the battery management system (which often is abbreviated as BMS—an electronic system that manages/protects the battery by operating it within a safe operating region and controlling its environment), the off-board charger has the ability to work without an on-board charger and is interfaced directly with the battery management system [43].
With AC charging stations, the Society of Automotive Engineers (SAE) has characterized these charging stations into two standard levels: Level 1 and Level 2. A Level 1 electric vehicle supply equipment (EVSE) usually used in a residential charger utilizes the commonly available 220 V AC power from the grid in the current range of 12–16 A. Usually, a Level 1 charger requires about 11–20 h to completely charge an EV with a 16 kWh battery. On the other hand, a Level 2 EVSE, which is primarily used in commercial spaces, such as malls and offices, uses three-phase 440 V AC power off the grid to power up to an electric current of 32 A and would require 3–8 h to fully charge an EV with a 16 kWh battery [38,43].
At the same time, DC charging stations (also known as Level 3 fast-charging stations) take AC power from the grid and utilize a power converter to supply high-voltage DC power at a voltage of 300–750 V and a current of up to 400 A to charge the battery directly. This type of equipment circumvents the need for an on-board charger (OBC). Because high voltage power is directly used to charge the vehicle, the time needed to charge is much lower (less than 30 min) to completely charge an EV with a 16 kWh battery [38].

3.2.1. Uni-Directional Charger

When EVs are integrated with a grid, they serve as either a load or a distributed storage device to power and support the grid. Uni-directional chargers can only charge the EVs from the grid but cannot redirect the power to the grid when needed. Various studies are currently in progress on optimizing uni-directional charging to yield the most benefits for EV owners, aggregators, and grid operators [45,46]. Most of the utility objectives that arise with EVs can be addressed with uni-directional charging, even if there is a higher level of EV penetrations in the market while avoiding many major issues, such as equipment cost, system performance, and safety associated with bi-directional chargers [47,48]. This way of charging adds to no additional cost for implementation while also preventing the degradation of the battery life due to high charging and discharging cycles [49]. Countries with higher EV penetration require no additional investment for uni-directional charging. For example, the power grid in Ontario, Canada, can charge up to 500,000 EVs at no extra infrastructure cost [48,50]. In addition, time-sensitive energy pricing can be adopted to manage uni-directional charging. Integration with the grid and other complexities that arise due to uni-directional charging are detailed in standard IEEE −1547.8 [45]. Thus, uni-directional charging offers financial incentives for early adopters of EVs in the market [49,51].

3.2.2. Bi-Directional Charger

V2G requires a bi-directional system to deliver electricity from the grid to EVs’ batteries, and vice versa. This bi-directional system can be facilitated using double uni-directional or single bi-directional converters [52,53]. However, utilization of double uni-directional converters (chargers) means a higher total cost, heavier weight, and larger dimensions. Therefore, bi-directional converters (chargers) and the advanced development of solid-state technology lead to optimum techno-economic benefits.
A bi-directional AC-DC converter facilitates both AC-DC power conversion and power factor correction. EVs with bi-directional chargers can achieve various features due to the nature of the power flow both off and to the grid, which is popularly termed V2G. When the batteries of EVs are idle but still connected to the grid, they can provide energy to the grid when the demand is high, enhancing the grid efficiency [54,55,56]. Also, bi-directional charging plays a key part in integrating RES with the grid [57,58]. While bi-directional charging aids in voltage regulation, recurrent charging and discharging (cycling) of the battery causes battery degradation, which finally affects the battery life. Another issue with bi-directional charging is the additional cost involved with its infrastructure. Additionally, customer acceptance and secure two-way communication networks impede the market penetration of bidirectional chargers [51,59,60,61].

3.3. Communication System

Communication between the grid and the EVs to transfer the data (e.g., travel, battery, EVs conditions) and decide the charging mode results in a complex communication structure [62]. Seamless communication is a prerequisite to designing a charging station network [63]. Various communication schemes and strategies have been proposed to avoid compatibility issues within the charging station network. [64,65,66,67]. Also, to avoid this scenario, specific standards have been established that must be complied with by the manufacturing companies. Standards have been set for EVs in four levels of the V2G technology, which are the plug, communication network scheme, charging topology, and safety standards.
In V2G technology, both the data and the energy flow are bi-directional amidst the vehicles, charging stations, and power networks. As summarized in Table 3, ISO/IEC 15110 standard establishes the standard for EV charging station communication, while the IEC 61850 standard establishes the standard charging station-grid communication as a result of which tariffs and charging are carried out effectively [68,69,70,71].

3.4. Aggregator

An aggregator must be able to participate in the electricity market through different ancillary services of the grid by organizing and optimizing the EVs charging and managing the load profile [72]. A simplified architecture of the V2G system highlighting the role of an aggregator is shown in Figure 8. A little consideration will show that the aggregator plays as an interface between EV fleets and grid operators. In the first step of the process, the aggregator will establish a connection to each vehicle in the EV fleet, which has a service contract with the aggregator to utilize its battery, based on its current SOC to participate in ancillary services to the grid. Data from the EV will pass on the parameters required by the aggregator, with the condition for participation in this V2G system being that the EV sufficiently charges during the plug-out time. However, it should be noted that if the EV driver does not abide by the contract and drives away before the pre-notified departure time, the battery may not be sufficiently charged at time of the plug-out. Since the aggregator deals with thousands of vehicles at a time, the fraction of vehicles departing before the pre-notified time will remain constant and is negligible when considering the regulation process [72].
As a final step, the aggregator makes another contract, this time with the grid operator, and communicates to decide the type of the service and regulation capacity to be provided to the grid or the power required by the aggregator to charge the EVs in hand, thus simplifying the task of the grid operator significantly [73].

3.5. System Operation and Optimization

Power grid optimization has multiple objectives that must be achieved, but these objectives are riddled with many uncertainties and non-linearities while being limited by multiple constraints [74]. Furthermore, the dynamic and unpredictable nature of EVs could also increase the system complexity. This further complexity demands an optimization algorithm to utilize EV mobility to achieve V2G services. Since the integration of EVs and the grid will create a complex system that will increase a large number of non-linear variables, unit commitment becomes necessary to determine the optimal dispatch schedule, and various optimization approaches are usually applied to such unit commitment problems. Optimization approaches, such as genetic algorithm (GA) and particle swarm optimization (PSA), were applied to this unit commitment problem. This has been analyzed by Tan et al. [69]. In addition, many of the V2G objectives or ancillary services can be optimized to maximize benefits for the consumers. Figure 9 presents the summary of various types of V2G, services offered, and the associated optimization objectives and constraints.
Another factor for consideration/optimization when it comes to charging stations is the location of the substations. Planning the location of these charging stations, when approached from an electricity sector point of view, the only factors that need to be optimized include minimizing the investment and reducing the operations and maintenance costs. However, it is essential to understand that the planning of the location of a charging station is of particular interest to more sectors than one, thus making the problem of location planning, a problem of a multi-disciplinary nature. For instance, the considerations when planning the location of the charging stations from a traffic flow perspective and electric grid perspective is quite conflicting in general. Moving the location of a charging station away from an existing load center is beneficial from a grid perspective but is particularly undesirable from the consumer’s perspective, making it a sub-optimal solution. Some of the primary considerations in location planning of charging stations are planning to locate the station optimally in such a way that the PEV drivers do not exceed their driving range in traveling from origin to destination and planning the station at a desirable location to facilitate the adoption of the technology with only minor changes to their driving habit [75,76]. Location planning is also done to reduce the burden to cost payers with regards to cost targets by reducing the investment and construction costs, operation and maintenance costs, and also the wastage cost to the user. Many algorithms, such as the cross-entropy (CE) algorithm and GA, have been utilized by defining specific objective functions based on these criteria mentioned above [77]. Table 4 summarizing the utilization of such algorithms across various literature has been shown below.

4. Business Model and Power Market

From a power grid point of view, the charging of EVs can be considered an additional load to the grid. The increase and concentrated charging will need an additional generation of grid power, which is bound to increase the system cost and the cost of the power consumed. Furthermore, the current grid infrastructure will suffer losses through energy transmission with the increased EV penetration. Only through smart controlled charging can the cost of power consumed be reduced to about 60% [91,92].
When comparing the revenues generated by the V2G services with the investment costs, we find that the investment costs far outweigh the revenues generated from the V2G. Even if one tends to ignore the substantial investment costs, the opportunity costs that arise from not charging the vehicles at the parking facilities will outweigh possible revenues obtained by V2G ancillary services, especially frequency regulation.
A study conducted by Brandt et al. [93] discusses that even if a market environment appropriate for the V2G were present, the technology would not be feasible since high market prices for regulation are required to make up a viable model for business. The study goes on to suggest that just because V2G is a technically feasible concept does not make it an economically viable option. If this were the case, the increasing market penetration of EVs needs to be given more careful consideration with respect to the grid infrastructure that we currently have today. Be that as it may, it is also projected that the global V2G market size is projected to reach 28.12 billion USD by 2026, with a compounded annual growth rate of around 4.28% between 2021 to 2026 [94]. The V2G market size across various regions of the world has been summarized in Figure 10a. V2G could accommodate BEVs, plug-in hybrid electric vehicles (PHEVs), and fuel cell vehicles (FCVs) to support the grid infrastructure. The increasing adoption of electric vehicles across the world, which can be realized from Figure 10b, affects the demand for its associated infrastructures, including uni-directional and bi-directional charging infrastructures.
Furthermore, it is also crucial to note the effect policies and state fundings can have on improving technology growth. Figure 10c shows the investment trends on power grids in Europe. It is evident that there has been increased funding towards digital and smart grid technologies in the recent past, hinting at a favorable climate for V2G adoption in this region [95].
EVs are emerging as a promising alternative to conventional modes of mobility, both in terms of cost competitiveness and range. With the cost of the batteries taking up a major chunk of the EV cost, the recent trend of a fall in battery prices seems to suggest EV feasibility. However, this cannot be translated directly to the adoption of the V2G services on a larger scale. This possibly could be due to the uncertainties regarding battery degradation, efficiency, communication, and security associated with the V2G, which have developed only partially in the current scenario [97,98]. V2G is currently only implemented in test projects. Most EVs on the market today and the ones announced to be launched in the upcoming 3–5 years lack V2G capability, except for a few vehicles, such as the Nissan Leaf and eNV200 van [99].
That aside, another market gap arises from an infrastructure standpoint since new charging stations can demand more electricity, creating capacity issues for local grids. It is also understood that fleet managers wanting to operate EVs faced two issues: a strict upper limit to the amount of power that can be drawn from the grid and the high infrastructural costs that might be needed to handle the additional capacity issues. An increase in problems such as this that are related to EV infrastructure could push for large-scale adoption of V2G since it promotes a more efficient way of utilizing resources and also taps into the energy that lays idle in the mobile vehicle batteries [100]. However, the charging infrastructure that we have today does not facilitate V2G. Since charging infrastructures are generally installed for a period of no less than five years, moving towards a V2G compatible infrastructure could inflict higher infrastructure costs or worse, leading to a situation where the entire infrastructure is made without V2G capabilities.

5. Impacts and Challenges

EVs have a very positive impact on the environment, with ground EVs being one of the cleanest transportation options available today. The well-to-wheel emissions of EVs (including both emissions from power plants and straight-line pollution) are concluded to be the least, according to [38,101]. However, if EVs are continued to be powered by electricity that is generated by burning fossil fuels, the resulting emissions might be higher than conventional ICE-powered vehicle. While that is true, it is also shown that deploying EVs and photovoltaics can reduce CO2 emissions by around 80%. The impacts of V2G technology on power grid regulation, line losses, distribution components, and load profiles are also significant. Some of the biggest challenges that impede the growth potential of V2G, apart from the apparent economic challenges, are the social barrier, network security, and battery life degradation.
Consumers generally tend to resist new, unaccustomed methods, but this could be best overcome by appropriate policy decisions that encourage and incentivize citizens to opt for a technology change and nurture it in its initial stages. Since taking part in V2G means that the batteries of EVs will be used to support the grid, it creates anxiety and uneasiness among ground EV owners [38]. The lack of a fast-charging infrastructure does not make the situation any better. One possible solution that has been suggested to overcome this issue of reducing the downtime that comes with charging an EV’s battery is battery swapping technology. A major issue when it comes to market penetration of EVs is their cost, of which 25–50% can be attributed to the battery packs in EVs. Battery swapping can also overcome this hurdle if an ideal scenario of a pay-as-you-go model is adopted, where a third party holds ownership of the batteries while managing their charging conditions. Battery swapping stations (BSSs) are needed in this case which adds to the infrastructural costs. A topology of BSS, along with a battery sharing network, is suggested to interact with each other using internet-of-things (IoT), thereby acting as an aggregator and providing services as a whole to the grid, such as enhancing grid stability and reliability in the process. Even if the infrastructural cost associated with this topology can be put aside for a moment, the idea of owning a car without the battery and having no guarantee for the state of health of the battery that is swapped can operate as a social barrier from the consumer’s viewpoint. Standardization of battery packs, though unlikely, is also a necessary change that will need to be adopted globally for this technology to be widely accepted [102]. Hence, technological and policy developments are immensely important to overcome this social barrier.
Cybersecurity for V2G technology has garnered some attention from the research community [38]. V2G technology requires a certain level of cybersecurity for seamless operation and to ensure grid safety, since the grid going digital handles massive amounts of data, making V2G a perfect target for cyber-attacks. Thus, network security and integrity with data transmission in the grid becomes essential for the seamless and safe data transfer from EVs to the grid.
Finally, battery degradation might be an issue with V2G technology, as the recurrent charging and discharging cycles of the battery induced by the nature of the V2G infrastructure might degrade the battery life span. This will have a huge impact on the viability of the business models that pin on the V2G technology and affect the social acceptance of the technology [103]. Battery degradation is primarily dependent on two factors: calendar aging and cycling aging. While the former is dependent on temperature and SOC, the latter is dependent on the depth of discharge and power throughput [104]. Recent research shows that V2G, if used without proper management, may lead to significant battery life reduction, which will be the case when, for example, peak shaving services are used daily. However, the effects tend to be minor if the utilization for energy-intensive services, like peak shaving, is restricted to less than 20 times a year [105,106,107]. Furthermore, according to Krein [108], utilizing the battery at its middle range of SOC can reduce the rate of battery degradation because of a lower equivalent series resistance at this SOC range [109]. Therefore, Quinn et al. [110] have developed a control and optimization method for V2G conducted in this middle range of SOC. Both the optimization and smart control of charging time and energy flows have been proven to reduce the level of battery degradation [111].
Table 5 shows summarized potential challenges faced during V2G implementation and deployment. In order to measure the feasibility and improve the social acceptance of V2G, further massive demonstration projects are required, in which not only technological aspects are tested, but also other aspects (including regulation, social, and market creation).
V2G-enabled EVs are more expensive than ordinary PEVs (except for a few V2G-enabled EVs, such as the Nissan Leaf), which are already considered expensive compared to their conventional counterparts. Another important aspect to note is the consumer mentality. Since most consumers seem not to consider the cost-saving in the longer run, the potential revenue of V2G is undervalued, which is the opposite of what a rational actor model would predict. A survey result shows that none of the surveyed Californian households had factored in the estimated fuel savings as a part of their decision-making process in purchasing a new vehicle [112]. While this does not necessarily tend to suggest a global pattern in the decision-making process when purchasing a new vehicle, this survey shows that it is possible for a community not to factor in pressing environmental concerns in their day-to-day decision-making process. In addition, it also suggests a global need for awareness regarding the environmental and economic advantages that this technology offers over conventionally available ones.
Another aspect of concern is the environmental impacts due to EVs and V2G. Even with the integration of EVs to the grid presenting many environmental advantages, this does not preclude the possibility of an environmental impact that is primarily suspected to arise in terms of water availability. Since a transition from conventional ICEs to electric power increases the overall electricity consumption, water is needed to cool the power plants that are primarily powered by fossil fuels and nuclear plants in today’s energy landscape [113].
Another research gap that causes concern is the lack of modeling of the consumer/EV buyers who are typically always assumed to behave in an optimal way for the entire system or financially profitable for the self. However, consumer modeling could be more complicated, since perceptions and motivations behind a decision made by a consumer are more sophisticated than an optimizing agent. Such complex dynamics make a path for considering passenger vehicles as goods with private and public dimensions, and even more so for vehicles whose primary development motivation is reduced environmental impact [114].

6. Conclusions

Since EV technology is still developing, policies play a huge role in taking this technology forward to its next steps in terms of market and social acceptability. In countries where EVs are still in the early stages of adoption, incentives, supporting infrastructure, and electricity, for both supply capacity and balancing capability, are considered fundamental problems to be solved initially. Fiscal incentives, including subsidy and tax reduction, and complementary treatments, e.g., free road parking, toll rebates, priority lanes, are considered some of the policy incentives that will ramp up the adoption of EVs by the public.
While that happens, equal care should be directed at the infrastructural developments to support the growing fleet of EVs, including the deployment of public and private charging stations and provisions in building and codes to support charger installation.
However, the government support in EV deployment is transitional, only in its early adoption period. These supports are arranged in order to shift the market transition from a predominantly oil-dependent mobility market to a renewable carbon-free market. As the benefits of EVs are experienced by the community and the total cost of EVs can be reduced, mass-market adoption is expected with government support wearing out step-by-step. Therefore, a mutual correlation among the government, EV industries, and community is required to facilitate a gradual shift towards emerging technologies, such as EVs and an associated V2G technology.
Currently, it is quite difficult to quantify the economic feasibility of V2G due to market and technical conditions, objectives, unestablished regulations, and the lack of a massive demonstration test [3]. Previous studies, which were predominantly simulation-based studies, tend to suggest technical feasibility; however, if the technology will serve to be an economically lucrative one when adopted is yet unclear. However, it will be fair enough to say that diversifying the mobility sector in terms of clean fuels, such as clean electricity, hydrogen, and carbon-neutral fuels (e.g., e-fuels), will help to combat the bigger issue of climate change and carbon emissions in more ways than a conventional one.
To summarize briefly,
  • Huge steps towards infrastructural developments in terms of charging stations, charging and discharging protocols, security protocols, and standardization become quintessential. They need to be developed alongside EV technology to avoid overwhelming the current unprepared grid infrastructure.
  • Government policies, incentives, and support that are provided initially to boost a transition towards EVs might not be sustainable. In addition, a collective increase in acceptance of the technology leading to mass production might make the technology more economically viable to the consumer.
  • The social and market acceptability of a technology that is different from a conventional way is an issue that needs to be addressed.
  • Since most vehicle grid integration-based studies are simulation-based and the lack of large-scale demonstration of the technology, it is quite uncertain to predict/forecast the economic feasibility of this technology at this point with the current market conditions and current technological developments.
  • Small-scale demonstration and simulation-based studies suggest technical viability, which need not necessarily translate into economic viability.

Author Contributions

Conceptualization, S.S.R. and M.A.; methodology, S.S.R. and M.A.; formal analysis, S.S.R. and M.A.; investigation, S.S.R. and M.A.; resources, S.S.R. and M.A.; writing—original draft preparation, S.S.R. and M.A.; writing—review and editing, S.S.R. and M.A.; visualization, S.S.R.; supervision, M.A.; project administration, M.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Dawood, F.; Anda, M.; Shafiullah, G.M. Hydrogen Production for Energy: An Overview. Int. J. Hydrogen Energy 2020, 45, 3847–3869. [Google Scholar] [CrossRef]
  2. Global EV Outlook 2020—Analysis—IEA. Available online: https://www.iea.org/reports/global-ev-outlook-2020 (accessed on 6 December 2021).
  3. Heilmann, C.; Friedl, G. Factors Influencing the Economic Success of Grid-to-Vehicle and Vehicle-to-Grid Applications—A Review and Meta-Analysis. Renew. Sustain. Energy Rev. 2021, 145, 111115. [Google Scholar] [CrossRef]
  4. A Behind the Scenes Take on Lithium-Ion Battery Prices BloombergNEF. Available online: https://about.bnef.com/blog/behind-scenes-take-lithium-ion-battery-prices/ (accessed on 6 December 2021).
  5. Kempton, W.; Tomić, J. Vehicle-to-Grid Power Implementation: From Stabilizing the Grid to Supporting Large-Scale Renewable Energy. J. Power Sources 2005, 144, 280–294. [Google Scholar] [CrossRef]
  6. Aziz, M.; Oda, T.; Mitani, T.; Watanabe, Y.; Kashiwagi, T. Utilization of Electric Vehicles and Their Used Batteries for Peak-Load Shifting. Energies 2015, 8, 3720–3738. [Google Scholar] [CrossRef][Green Version]
  7. Power to the People: Nissan and ENEL Launch First Smart Grid Trials. Available online: https://europe.nissannews.com/en-GB/releases/release-140287-power-to-the-people-nissan-and-enel-launch-first-smart-grid-trials (accessed on 6 December 2021).
  8. Sortomme, E.; El-Sharkawi, M.A. Optimal Scheduling of Vehicle-to-Grid Energy and Ancillary Services. IEEE Trans. Smart Grid 2012, 3, 351–359. [Google Scholar] [CrossRef]
  9. Tan, K.M.; Ramachandaramurthy, V.K.; Yong, J.Y. Integration of Electric Vehicles in Smart Grid: A Review on Vehicle to Grid Technologies and Optimization Techniques. Renew. Sustain. Energy Rev. 2016, 53, 720–732. [Google Scholar] [CrossRef]
  10. Raveendran, V.; Nair, M.G. Power Factor Corrected Level-1 DC Public Green-Charging Infrastructure to Promote e-Mobility in India. IET Power Electron. 2020, 13, 221–232. [Google Scholar] [CrossRef]
  11. Knezovic, K.; Martinenas, S.; Andersen, P.B.; Zecchino, A.; Marinelli, M. Enhancing the Role of Electric Vehicles in the Power Grid: Field Validation of Multiple Ancillary Services. IEEE Trans. Transp. Electrif. 2017, 3, 201–209. [Google Scholar] [CrossRef][Green Version]
  12. Peng, C.; Zou, J.; Lian, L. Dispatching Strategies of Electric Vehicles Participating in Frequency Regulation on Power Grid: A Review. Renew. Sustain. Energy Rev. 2017, 68, 147–152. [Google Scholar] [CrossRef]
  13. Payne, G.; Cox, C. Understanding the True Value of V2G; Cenex: Leicestershire, UK, 2019. [Google Scholar]
  14. Mu, Y.; Wu, J.; Ekanayake, J.; Jenkins, N.; Jia, H. Primary Frequency Response from Electric Vehicles in the Great Britain Power System. IEEE Trans. Smart Grid 2013, 4, 1142–1150. [Google Scholar] [CrossRef]
  15. Huang, H.; Chung, C.Y.; Chan, K.W.; Chen, H. Quasi-Monte Carlo Based Probabilistic Small Signal Stability Analysis for Power Systems with Plug-in Electric Vehicle and Wind Power Integration. IEEE Trans. Power Syst. 2013, 28, 3335–3343. [Google Scholar] [CrossRef]
  16. Neofytou, N.; Blazakis, K.; Katsigiannis, Y.; Stavrakakis, G. Modeling Vehicles to Grid as a Source of Distributed Frequency Regulation in Isolated Grids with Significant RES Penetration. Energies 2019, 12, 720. [Google Scholar] [CrossRef][Green Version]
  17. Zhu, X.; Xia, M.; Chiang, H.D. Coordinated Sectional Droop Charging Control for EV Aggregator Enhancing Frequency Stability of Microgrid with High Penetration of Renewable Energy Sources. Appl. Energy 2018, 210, 936–943. [Google Scholar] [CrossRef]
  18. Papadopoulos, P.; Cipcigan, L.M.; Jenkins, N.; Grau, I. Distribution networks with electric vehicles. In Proceedings of the 2009 44th International Universities Power Engineering Conference, Glasgow, UK, 1–4 September 2009; pp. 1–5. [Google Scholar]
  19. Khalid, M.S.; Lin, X.; Zhuo, Y.; Kumar, R.; Rafique, M.K. Impact of Energy Management of Electric Vehicles on Transient Voltage Stability of Microgrid. World Electr. Veh. J. 2015, 7, 577–588. [Google Scholar] [CrossRef][Green Version]
  20. Li, C.; Cao, Y.; Kuang, Y.; Zhou, B. Influences of Electric Vehicles on Power System and Key Technologies of Vehicle-to-Grid; Springer: Berlin/Heidelberg, Germany, 2016. [Google Scholar] [CrossRef]
  21. Peak Shaving. What It Is & How It Works. Available online: https://www.next-kraftwerke.com/knowledge/what-is-peak-shaving (accessed on 6 December 2021).
  22. View of Vehicle to Grid (V2G) for Peak Shaving: New Trend, Benefits, and Issues. Available online: https://ijccn.com/index.php/IJCCN/article/view/21/15 (accessed on 6 December 2021).
  23. Kempton, W.; Udo, V.; Huber, K.; Komara, K.; Letendre, S.; Baker, S.; Brunner, D.; Pearre, N. A Test of Vehicle-to-Grid (V2G) for Energy Storage and Frequency Regulation in the PJM System. Results Ind. Univ. Res. Partnership 2008, 32, 1–32. [Google Scholar]
  24. Ehsani, M.; Falahi, M.; Lotfifard, S. Undefined Vehicle to Grid Services: Potential and Applications. Energies 2012, 5, 4076–4090. [Google Scholar] [CrossRef][Green Version]
  25. Imani, M.H.; Yousefpour, K.; Ghadi, M.J.; Andani, M.T. Simultaneous Presence of Wind Farm and V2G in Security Constrained Unit Commitment Problem Considering Uncertainty of Wind Generation. In Proceedings of the 2018 IEEE Texas Power and Energy Conference, College Station, TX, USA, 1–6 February 2018. [Google Scholar] [CrossRef]
  26. López, M.A.; Martín, S.; Aguado, J.A.; de La Torre, S. V2G Strategies for Congestion Management in Microgrids with High Penetration of Electric Vehicles. Electr. Power Syst. Res. 2013, 104, 28–34. [Google Scholar] [CrossRef]
  27. Deb, S.; Goswami, A.K.; Chetri, R.L.; Roy, R. Congestion Management Considering Plug-in Electric Vehicle Charging Coordination in Distribution System. In Proceedings of the 2021 International Conference on Nascent Technologies in Engineering, ICNET 2021, Navi Mumbai, India, 15–16 January 2021. [Google Scholar] [CrossRef]
  28. Vehicle-to-Grid (V2G): Everything You Need to Know. Available online: https://www.virta.global/vehicle-to-grid-v2g (accessed on 6 December 2021).
  29. Bird, L.; Cochran, J.; Wang, X. Wind and Solar Energy Curtailment: Experience and Practices in the United States; Technical Report No. NREL/TP-6A20-60983; NREL: Golden, CO, USA, 2014.
  30. Golden, R.; Paulos, B. Curtailment of Renewable Energy in California and Beyond. Electr. J. 2015, 28, 36–50. [Google Scholar] [CrossRef]
  31. Cochran, J.; Miller, M.; Zinaman, O.; Milligan, M.; Arent, D. Flexibility in 21st Century Power Systems; Technical Report No. NREL/TP-6A20-61721; NREL: Golden, CO, USA, 2014.
  32. Alternative Fuels Data Center: Plug-In Electric Vehicle (PEV) Definition. Available online: https://afdc.energy.gov/laws/9355 (accessed on 4 January 2022).
  33. Szinai, J.; Sheppard, C.; Abhyankar, N.; Policy, A.G.-E. Reduced Grid Operating Costs and Renewable Energy Curtailment with Electric Vehicle Charge Management. Energy Policy 2020, 136, 111051. [Google Scholar] [CrossRef]
  34. Sarabi, S.; Davigny, A.; Courtecuisse, V.; Energy, Y.R.-A. Potential of Vehicle-to-Grid Ancillary Services Considering the Uncertainties in Plug-in Electric Vehicle Availability and Service/Localization Limitations in distribution grids. Appl. Energy 2016, 171, 523–540. [Google Scholar] [CrossRef]
  35. Aziz, M.; Oda, T.; Ito, M. Battery-Assisted Charging System for Simultaneous Charging of Electric Vehicles. Energy 2016, 100, 82–90. [Google Scholar] [CrossRef]
  36. Huda, M.; Koji, T.; Aziz, M. Techno Economic Analysis of Vehicle to Grid (V2G) Integration as Distributed Energy Resources in Indonesia Power System. Energies 2020, 13, 1162. [Google Scholar] [CrossRef][Green Version]
  37. V2G Hub Insights. Available online: https://www.v2g-hub.com/insights/ (accessed on 17 August 2021).
  38. Arfeen, Z.A.; Khairuddin, A.B.; Munir, A.; Azam, M.K.; Faisal, M.; Arif, M.S.B. En Route of Electric Vehicles with the Vehicle to Grid Technique in Distribution Networks: Status and Technological Review. Energy Storage 2020, 2, e115. [Google Scholar] [CrossRef]
  39. Bhatti, A.R.; Salam, Z.; Aziz, M.J.B.A.; Yee, K.P.; Ashique, R.H. Electric Vehicles Charging Using Photovoltaic: Status and Technological Review. Renew. Sustain. Energy Rev. 2016, 54, 34–47. [Google Scholar] [CrossRef]
  40. Hammer, D. Evaluating the Transition from V2G to AV2G: The Autonomous Battery Electric Vehicle as Decentralised Bidirectional Electricity Storage System. Master’s Thesis, Delft University of Technology, Delft, The Netherlands, March 2019. [Google Scholar]
  41. Gamit, M.; Shukla, A.; Kumar, R.; Verma, K. Supplementary Frequency Control in Power Systems via Decentralised V2G/G2V Support. J. Eng. 2019, 18, 5287–5291. [Google Scholar] [CrossRef]
  42. The Difference between Level 1 & 2 EV Chargers. EVOCHARGE. Available online: https://evocharge.com/resources/the-difference-between-level-1-2-ev-chargers/ (accessed on 6 December 2021).
  43. Rangaraju, J. Taking Charge of Electric Vehicles-Both in the Vehicle and on the Grid Xun Gong Powertrain Systems Texas Instruments; Texas Instruments: Dallas, TX, USA, 2018; pp. 1–13. [Google Scholar]
  44. Taking Charge of Electric Vehicle Battery Charging—Electronics Maker. Available online: https://electronicsmaker.com/taking-charge-of-electric-vehicle-battery-charging (accessed on 6 December 2021).
  45. Electric Vehicle and Plug-in Hybrid Electric Vehicle. Google Scholar. Available online: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Electric+Vehicle+and+Plug%E2%80%90in+Hybrid+Electric+Vehicle+Conductive+Charge+Coupler&btnG= (accessed on 6 December 2021).
  46. MiEV Electric Car History, Specs & Future. Mitsubishi Motors. Available online: https://www.mitsubishicars.com/i-miev-electric-car (accessed on 6 December 2021).
  47. Zhou, X.; Lukic, S.; Bhattacharya, S.; Huang, A. Design and control of grid-connected converter in bi-directional battery charger for plug-in hybrid electric vehicle application. In Proceedings of the 2009 IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, USA, 7–11 September 2009; pp. 1716–1721. [Google Scholar]
  48. Sortomme, E.; El-Sharkawi, M.A. Optimal Charging Strategies for Unidirectional Vehicle-to-Grid. IEEE Trans. Smart Grid 2011, 2, 131–138. [Google Scholar] [CrossRef]
  49. Sortomme, E.; El-Sharkawi, M.A. Optimal combined bidding of vehicle-to-grid ancillary services. IEEE Trans. Smart Grid 2011, 3, 70–79. [Google Scholar] [CrossRef]
  50. Hajimiragha, A.; Canizares, C.A.; Fowler, M.W.; Elkamel, A. Optimal transition to plug-in hybrid electric vehicles in Ontario, Canada, considering the electricity-grid limitations. IEEE Trans. Ind. Electron. 2009, 57, 690–701. [Google Scholar] [CrossRef]
  51. Habib, S.; Khan, M.M.; Abbas, F.; Tang, H. Assessment of Electric Vehicles Concerning Impacts, Charging Infrastructure with Unidirectional and Bidirectional Chargers, and Power Flow Comparisons. Int. J. Energy Res. 2018, 42, 3416–3441. [Google Scholar] [CrossRef]
  52. Geske, M.; Winkler, T.; Komarnicki, P.; Heideck, G. Controlled Battery Charger for Electric Vehicles. PIERS Online 2010, 6, 532–536. [Google Scholar] [CrossRef]
  53. Sharma, A.; Sharma, S. Review of Power Electronics in Vehicle-to-Grid Systems. J. Energy Storage 2019, 21, 337–361. [Google Scholar] [CrossRef]
  54. Fasugba, M.A.; Krein, P.T. Cost benefits and vehicle-to-grid regulation services of unidirectional charging of electric vehicles. In Proceedings of the 2011 IEEE Energy Conversion Congress and Exposition, Phoenix, AZ, USA, 17–22 September 2011. [Google Scholar]
  55. Schwenk, K.; Meisenbacher, S.; Briegel, B.; Harr, T.; Hagenmeyer, V.; Mikut, R. Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles. IEEE Trans. Smart Grid 2021, 12, 5135–5145. [Google Scholar] [CrossRef]
  56. Ma, Y.; Houghton, T.; Cruden, A.; Infield, D. Modeling the Benefits of Vehicle-to-Grid Technology to a Power System. IEEE Trans. Power Syst. 2012, 27, 1012–1020. [Google Scholar] [CrossRef]
  57. Pillai, J.R.; Bak-Jensen, B. Integration of vehicle-to-grid in the western Danish power system. IEEE Trans. Sustain. Energy 2010, 2, 12–19. [Google Scholar] [CrossRef]
  58. Monteiro, V.; Pinto, J.G.; Exposto, B.; Gonçalves, H.; Ferreira, J.C.; Couto, C.; Afonso, J.L. Assessment of a battery charger for electric vehicles with reactive power control. In Proceedings of the IECON 2012 38th Annual Conference on IEEE Industrial Electronics Society, Monetreal, QC, Canada, 25–28 October 2012; pp. 5142–5147. [Google Scholar]
  59. Habib, S.; Kamran, M. A novel vehicle-to-grid technology with constraint analysis-A review. In Proceedings of the 2014 International Conference on Emerging Technologies (ICET), Islamabad, Pakistan, 8–9 December 2014; pp. 69–74. [Google Scholar]
  60. Habib, S.; Kamran, M.; Rashid, U. Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks—A review. J. Power Sources 2015, 277, 205–214. [Google Scholar] [CrossRef]
  61. Yilmaz, M.; Krein, P.T. Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces. IEEE Trans. Power Electron. 2012, 28, 5673–5689. [Google Scholar] [CrossRef]
  62. Lu, L.; Han, X.; Li, J.; Hua, J.; Ouyang, M. A Review on the Key Issues for Lithium-Ion Battery Management in Electric Vehicles. J. Power Sources 2013, 226, 272–288. [Google Scholar] [CrossRef]
  63. He, Y.; Venkatesh, B.; Guan, L. Optimal Scheduling for Charging and Discharging of Electric Vehicles. IEEE Trans. Smart Grid 2012, 3, 1095–1105. [Google Scholar] [CrossRef]
  64. Du, Y.; Lukic, S.; Jacobson, B.; Huang, A. Review of High Power Isolated Bi-Directional DC-DC Converters for PHEV/EV DC Charging Infrastructure. In Proceedings of the IEEE Energy Conversion Congress and Exposition: Energy Conversion Innovation for a Clean Energy Future, Phoenix, AZ, USA, 17–22 September 2011; pp. 553–560. [Google Scholar] [CrossRef]
  65. Saltanovs, R.; Krivchenkov, A.; Krainyukov, A. Analysis of Effective Wireless Communications for V2G Applications and Mobile Objects. In Proceedings of the 58th Annual International Scientific Confererence on Power and Electrical Engineering of Riga Technical University, RTUCON 2017, Riga, Latvia, 1–5 November 2017. [Google Scholar] [CrossRef]
  66. Mahmud, K.; Town, G.E.; Morsalin, S.; Hossain, M.J. Integration of Electric Vehicles and Management in the Internet of Energy. Renew. Sustain. Energy Rev. 2018, 82, 4179–4203. [Google Scholar] [CrossRef]
  67. Wei, Z.; Li, Y.; Zhang, Y.; Cai, L. Intelligent Parking Garage EV Charging Scheduling Considering Battery Charging Characteristic. IEEE Trans. Ind. Electron. 2018, 65, 2806–2816. [Google Scholar] [CrossRef]
  68. Schmutzler, J.; Gröning, S.; Wietfeld, C. Management of Distributed Energy Resources in IEC 61850 Using Web Services on Devices. In Proceedings of the 2011 IEEE International Conference on Smart Grid Communications, SmartGridComm, Brussels, Belgium, 17–20 October 2011; pp. 315–320. [Google Scholar] [CrossRef]
  69. Kiokes, G.; Zountouridou, E.; Papadimitriou, C.; Dimeas, A.; Hatziargyriou, N. Development of an integrated wireless communication system for connecting electric vehicles to the power grid. In Proceedings of the 2015 International Symposium on Smart Electric Distribution Systems and Technologies (EDST), Vienna, Austria, 8–11 September 2011; pp. 296–301. [Google Scholar]
  70. Schmutzler, J.; Wietfeld, C.; Jundel, S.; Voit, S. A Mutual Charge Schedule Information Model for the Vehicle-to-Grid Communication Interface. In Proceedings of the 2011 IEEE Vehicle Power and Propulsion Conference, VPPC 2011, Chicago, IL, USA, 6–9 September 2011. [Google Scholar] [CrossRef]
  71. Vadi, S.; Bayindir, R.; Colak, A.M.; Hossain, E. A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies. Energies 2019, 12, 3748. [Google Scholar] [CrossRef][Green Version]
  72. Han, S.; Han, S.; Sezaki, K. Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation. IEEE Trans. Smart Grid 2010, 1, 65–72. [Google Scholar] [CrossRef]
  73. Wang, R.; Li, Y.; Wang, P.; Niyato, D. Design of a V2G Aggregator to Optimize PHEV Charging and Frequency Regulation Control. In Proceedings of the 2013 IEEE International Conference on Smart Grid Communications, SmartGridComm 2013, Vancouver, BC, Canada, 21–24 October 2013; pp. 127–132. [Google Scholar] [CrossRef]
  74. Soares, J.; Vale, Z.; Canizes, B.; Morais, H. Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling. In Proceedings of the 2013 IEEE Computational Intelligence Applications in Smart Grid (CIASG), Singapore, 16–19 April 2013; pp. 138–145. [Google Scholar]
  75. Mao, D.; Tan, J.; Wang, J. Location Planning of PEV Fast Charging Station: An Integrated Approach under Traffic and Power Grid Requirements. IEEE Trans. Intell. Transp. Syst. 2021, 22, 483–492. [Google Scholar] [CrossRef]
  76. Kong, W.; Luo, Y.; Feng, G.; Li, K.; Peng, H. Optimal Location Planning Method of Fast Charging Station for Electric Vehicles Considering Operators, Drivers, Vehicles, Traffic Flow and Power Grid. Energy 2019, 186, 115826. [Google Scholar] [CrossRef]
  77. Ren, X.; Zhang, H.; Hu, R.; Qiu, Y. Location of electric vehicle charging stations: A perspective using the grey decision-making model. Energy 2019, 173, 548–553. [Google Scholar] [CrossRef]
  78. Ravichandran, A.; Sirouspour, S.; Malysz, P.; Emadi, A. A Chance-Constraints-Based Control Strategy for Microgrids with Energy Storage and Integrated Electric Vehicles. IEEE Trans. Smart Grid 2018, 9, 346–359. [Google Scholar] [CrossRef]
  79. Sayed, K.; Gabbar, H.A.; Pecht, M.G.; Cheng, X. Electric Vehicle to Power Grid Integration Using Three-Phase Three-Level AC/DC Converter and PI-Fuzzy Controller. Energies 2016, 9, 532. [Google Scholar] [CrossRef]
  80. Chaudhari, K.; Ukil, A.; Kumar, K.N.; Manandhar, U.; Kollimalla, S.K. Hybrid Optimization for Economic Deployment of ESS in PV-Integrated EV Charging Stations. IEEE Trans. Ind. Inform. 2018, 14, 106–116. [Google Scholar] [CrossRef]
  81. Alonso, M.; Amaris, H.; Germain, J.G.; Galan, J.M. Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms. Energies 2014, 7, 2449–2475. [Google Scholar] [CrossRef]
  82. 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. [Google Scholar] [CrossRef]
  83. Mesarić, P.; Krajcar, S. Home Demand Side Management Integrated with Electric Vehicles and Renewable Energy Sources. Energy Build. 2015, 108, 1–9. [Google Scholar] [CrossRef]
  84. Amini, M.H.; Moghaddam, M.P.; Karabasoglu, O. Simultaneous Allocation of Electric Vehicles’ Parking Lots and Distributed Renewable Resources in Smart Power Distribution Networks. Sustain. Cities Soc. 2017, 28, 332–342. [Google Scholar] [CrossRef]
  85. Jiang, T.; Putrus, G.; Gao, Z.; Conti, M.; McDonald, S.; Lacey, G. Development of a Decentralized Smart Charge Controller for Electric Vehicles. Int. J. Electr. Power Energy Syst. 2014, 61, 355–370. [Google Scholar] [CrossRef]
  86. Anastasiadis, A.G.; Konstantinopoulos, S.; Kondylis, G.P.; Vokas, G.A. Electric Vehicle Charging in Stochastic Smart Microgrid Operation with Fuel Cell and RES Units. Int. J. Hydrogen Energy 2017, 42, 8242–8254. [Google Scholar] [CrossRef]
  87. Yang, J.; He, L.; Fu, S. An Improved PSO-Based Charging Strategy of Electric Vehicles in Electrical Distribution Grid. Appl. Energy 2014, 128, 82–92. [Google Scholar] [CrossRef]
  88. Luo, Y.; Zhu, T.; Wan, S.; Zhang, S.; Li, K. Optimal Charging Scheduling for Large-Scale EV (Electric Vehicle) Deployment Based on the Interaction of the Smart-Grid and Intelligent-Transport Systems. Energy 2016, 97, 359–368. [Google Scholar] [CrossRef]
  89. Betancourt-Torcat, A.; Poddar, T.; Almansoori, A. A Realistic Framework to a Greener Supply Chain for Electric Vehicles. Int. J. Energy Res. 2019, 43, 2369–2390. [Google Scholar] [CrossRef]
  90. Talebizadeh, E.; Rashidinejad, M.; Abdollahi, A. Evaluation of Plug-in Electric Vehicles Impact on Cost-Based Unit Commitment. J. Power Sources 2014, 248, 545–552. [Google Scholar] [CrossRef]
  91. Lyon, T.P.; Michelin, M.; Jongejan, A.; Leahy, T. Is “Smart Charging” Policy for Electric Vehicles Worthwhile? Energy Policy 2012, 41, 259–268. [Google Scholar] [CrossRef]
  92. Weis, A.; Jaramillo, P.; Michalek, J. Estimating the Potential of Controlled Plug-in Hybrid Electric Vehicle Charging to Reduce Operational and Capacity Expansion Costs for Electric Power Systems with High Wind Penetration. Appl. Energy 2014, 115, 190–204. [Google Scholar] [CrossRef]
  93. Brandt, T.; Wagner, S.; Neumann, D. Evaluating a business model for vehicle-grid integration: Evidence from Germany. Transp. Res. Part D Transp. Environ. 2017, 50, 488–504. [Google Scholar] [CrossRef]
  94. Vehicle to Grid (V2G) Market Size, Industry Outlook, Market Forecast, Demand Analysis, Market Share, Market Report 2021–2026. Available online: https://www.industryarc.com/Report/19376/vehicle-to-grid-market.html (accessed on 7 December 2021).
  95. Power Grid Investment Trends in Europe, 2015–2019—Charts, Data & Statistics—IEA. Available online: https://www.iea.org/data-and-statistics/charts/power-grid-investment-trends-in-europe-2015-2019 (accessed on 6 December 2021).
  96. Global EV Stock by Mode in the Sustainable Development Scenario, 2020–2030—Charts, Data & Statistics—IEA. Available online: https://www.iea.org/data-and-statistics/charts/global-ev-stock-by-mode-in-the-sustainable-development-scenario-2020-2030 (accessed on 6 December 2021).
  97. Noel, L.; Rubens, G.; de Kester, J.; Sovacool, B. Vehicle-to-Grid; Springer: Cham, Switzerland, 2019. [Google Scholar]
  98. Kester, J.; Noel, L.; Lin, X.; Zarazua de Rubens, G.; Sovacool, B.K. The Coproduction of Electric Mobility: Selectivity, Conformity and Fragmentation in the Sociotechnical Acceptance of Vehicle-to-Grid (V2G) Standards. J. Clean. Prod. 2019, 207, 400–410. [Google Scholar] [CrossRef]
  99. Whitepaper: V2G: The Journey to Commercialisation. Available online: https://www.delta-ee.com/downloads/1823-v2g-the-journey-to-commercialisation.html#form-content (accessed on 10 December 2021).
  100. Noel, L.; Zarazua de Rubens, G.; Kester, J.; Sovacool, B.K. Beyond Emissions and Economics: Rethinking the Co-Benefits of Electric Vehicles (EVs) and Vehicle-to-Grid (V2G). Transp. Policy 2018, 71, 130–137. [Google Scholar] [CrossRef]
  101. Windecker, A.; Ruder, A. Fuel Economy, Cost, and Greenhouse Gas Results for Alternative Fuel Vehicles in 2011. Transp. Res. Part D Transp. Environ. 2013, 23, 34–40. [Google Scholar] [CrossRef]
  102. Adegbohun, F.; von Jouanne, A.; Lee, K.Y. Autonomous Battery Swapping System and Methodologies of Electric Vehicles. Energies 2019, 12, 667. [Google Scholar] [CrossRef][Green Version]
  103. Jafari, M.; Gauchia, A.; Zhao, S.; Zhang, K.; Gauchia, L. Electric Vehicle Battery Cycle Aging Evaluation in Real-World Daily Driving and Vehicle-to-Grid Services. IEEE Trans. Transp. Electrif. 2017, 4, 122–134. [Google Scholar] [CrossRef]
  104. Wang, D.; Coignard, J.; Zeng, T.; Zhang, C.; Saxena, S. Quantifying Electric Vehicle Battery Degradation from Driving vs. Vehicle-to-Grid Services. J. Power Sources 2016, 332, 193–203. [Google Scholar] [CrossRef][Green Version]
  105. Dubarry, M.; Devie, A.; McKenzie, K. Durability and Reliability of Electric Vehicle Batteries under Electric Utility Grid Operations: Bidirectional Charging Impact Analysis. J. Power Sources 2017, 358, 39–49. [Google Scholar] [CrossRef]
  106. Thompson, A.W. Economic Implications of Lithium Ion Battery Degradation for Vehicle-to-Grid (V2X) Services. J. Power Sources 2018, 396, 691–709. [Google Scholar] [CrossRef]
  107. Kester, J.; Noel, L.; Zarazua de Rubens, G.; Sovacool, B.K. Promoting Vehicle to Grid (V2G) in the Nordic Region: Expert Advice on Policy Mechanisms for Accelerated Diffusion. Energy Policy 2018, 116, 422–432. [Google Scholar] [CrossRef]
  108. Krein, P.T. Battery Management for Maximum Performance in Plug-in Electric and Hybrid Vehicles. In Proceedings of the 2007 IEEE Vehicle Power and Propulsion Conference, Arlington, TX, USA, 9–12 September 2007; pp. 2–5. [Google Scholar] [CrossRef]
  109. Dogger, J.D.; Roossien, B.; Nieuwenhout, F.D.J. Characterization of Li-Ion Batteries for Intelligent Management of Distributed Grid-Connected Storage. IEEE Trans. Energy Convers. 2011, 26, 256–263. [Google Scholar] [CrossRef][Green Version]
  110. Quinn, C.; Zimmerle, D.; Bradley, T.H. An Evaluation of State-of-Charge Limitations and Actuation Signal Energy Content on Plug-in Hybrid Electric Vehicle, Vehicle-to-Grid Reliability, and Economics. IEEE Trans. Smart Grid 2012, 3, 483–491. [Google Scholar] [CrossRef]
  111. Guille, C.; Gross, G. A Conceptual Framework for the Vehicle-to-Grid (V2G) Implementation. Energy Policy 2009, 37, 4379–4390. [Google Scholar] [CrossRef]
  112. Greene, D.L.; German, J.; Delucchi, M.A. Fuel Economy: The Case for Market Failure. Reducing Clim. Impacts Transp. Sect. 2008, 181–205. [Google Scholar] [CrossRef]
  113. King, C.W.; Webber, M.E. The Water Intensity of the Plugged-in Automotive Economy. Environ. Sci. Technol. 2008, 42, 4305–4311. [Google Scholar] [CrossRef] [PubMed]
  114. Brown, M.B. The Civic Shaping of Technology: California’s Electric Vehicle Program. Sci. Technol. Hum. Values 2016, 26, 56–81. [Google Scholar] [CrossRef][Green Version]
Figure 1. An illustration of the V2G concept during different times of the day. The graph shown in the figure is only for illustrative purposes (redrawn from [7]).
Figure 1. An illustration of the V2G concept during different times of the day. The graph shown in the figure is only for illustrative purposes (redrawn from [7]).
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Figure 2. Potential services of V2G for different levels of the grid.
Figure 2. Potential services of V2G for different levels of the grid.
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Figure 3. An illustration of V2G units trading energy using VPPs (redrawn from [13]).
Figure 3. An illustration of V2G units trading energy using VPPs (redrawn from [13]).
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Figure 4. Frequency response signals: (a) with negative deviation, (b) with positive deviation (redrawn from [16]).
Figure 4. Frequency response signals: (a) with negative deviation, (b) with positive deviation (redrawn from [16]).
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Figure 5. Load leveling and peak shaving: (a) load leveling: overall consumption remains the same during load leveling, (b) peak shaving: overall consumption is reduced during peak shaving.
Figure 5. Load leveling and peak shaving: (a) load leveling: overall consumption remains the same during load leveling, (b) peak shaving: overall consumption is reduced during peak shaving.
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Figure 6. Spinning reserve of generation system for each hour during different scenarios (redrawn from [25]).
Figure 6. Spinning reserve of generation system for each hour during different scenarios (redrawn from [25]).
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Figure 7. Conceptual diagram of V2G enabled charging system: (a) Differences between AC, DC charging and on-board charger (b) Level 1 and Level 2 AC charging stations and (c) Level 3 DC charging station (fast charging) (redrawn from [44]).
Figure 7. Conceptual diagram of V2G enabled charging system: (a) Differences between AC, DC charging and on-board charger (b) Level 1 and Level 2 AC charging stations and (c) Level 3 DC charging station (fast charging) (redrawn from [44]).
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Figure 8. A simplified data flow and architecture of the V2G system highlighting the role of an aggregator.
Figure 8. A simplified data flow and architecture of the V2G system highlighting the role of an aggregator.
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Figure 9. Relation diagram for V2G types, V2G services, optimization objectives, and constraints (redrawn from [9]).
Figure 9. Relation diagram for V2G types, V2G services, optimization objectives, and constraints (redrawn from [9]).
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Figure 10. V2G market potential: (a) V2G market size across various countries globally (data from [94]), (b) projection of the number of EVs across different vehicle types till 2030 (data from [96]), and (c) power grid investments in Europe (redrawn from [95]).
Figure 10. V2G market potential: (a) V2G market size across various countries globally (data from [94]), (b) projection of the number of EVs across different vehicle types till 2030 (data from [96]), and (c) power grid investments in Europe (redrawn from [95]).
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Table 1. Representative V2G projects for different services and locations.
Table 1. Representative V2G projects for different services and locations.
Project NameCountryFromToNo. EVs/ChargersTested Services
M-tech LaboJapan201020135Peak shaving, load shifting
Grid on wheelsUS2012201415Freq. regulation
Smart MAUIHawaii2012201580Load shifting
INEES VolkswagenGermany2012201520Freq. regulation
Zem2AllSpain201220166Freq. regulation, load shifting, emergency backup, arbitrage, reserve, distribution
US Air ForceUS2012ongoing13Freq. regulation, load shifting, backup, reserve
Cenex EFESUK201320131Freq. regulation, reserve, load shifting
US DoD, Smith TrucksUS201320145Load shifting, backup
Amsterdam Vehicle2GridNetherlands201420172Load shifting
Torrance V2G School BusUS201420172Freq. regulation, load shifting
City-Zen Smart CityNetherlands201420194Arbitrage, distribution
Clinton Global Initiative School Bus DemoUS2014ongoing6Freq. regulation, load shifting, backup
ITHECAUK201520171Freq. regulation, load shifting
Solar-powered bidirectional EV charging stationNetherlands201520171Load shifting
Distribution System V2G for Improved Grid Stability for ReliabilityUS201520182Load shifting, distribution
Vehicle-to-coffee—The Mobility HouseGermany2015ongoing1Load shifting
Smart Solar ChargingNetherlands2015ongoing22Distribution, load shifting
NewMotion V2GNetherlands2016201810Freq. regulation
ParkerDenmark2016201850Freq. regulation, arbitrage, distribution
Parker DenmarkDenmark2016201915Freq. regulation, distribution service
SEEV4CityUK201620206Freq. regulation, arbitrage, load shifting
Denmark V2GDenmark2016ongoing10Freq. regulation
UK Vehicle-2-Grid (V2G)UK2016ongoing100
KIA Motors, Hyundai Technical Center Inc., UCIUS2016unknown6Load shifting
Grid MotionFrance2017201915Freq. regulation, arbitrage, load shifting
INVENT—UCSD/Nissan/NuvveUS2017202050Freq. regulation, distribution, load shifting
BlueBird School Bus V2GUS201720208Freq. regulation, load shifting, backup
Static and Mobile Distributed Energy Storage (SaMDES)UK201720212Load shifting, back up
Elia V2GBelgium2018201940Freq. regulation
V2StreetGB201820202Arbitrage, distribution, load shifting
E-REGIO with Power2U and ÖBOSweden201820202Freq. regulation, arbitrage, distribution, load shifting
SOLARCAMPFrance201820201Freq. regulation, arbitrage, distribution, load shifting, backup
OVO Energy V2G (Project Sciurus)UK20182021320Arbitrage
e4FutureUK20182022unknownFreq. Response, Arbitrage, Dist. Services, Time shifting
FlexGridNetherlands201820221Freq. regulation, load shifting, backup
EV-elocityUK2018202235Arbitrage, load shifting
uYilo eMobility Programme—Smart Grid EcoSystem for EV-Grid InteroperabilitySouth Africa201820231Freq. regulation, distribution, load shifting
Powerloop: Domestic V2G Demonstrator ProjectUK2018 ongoing135Arbitrage, distribution, load shifting, backup
Utrecht V2G charge hubs (We Drive Solar)Netherlands2018 ongoing80Arbitrage
Bus2GridUK2018 ongoingunknownFreq. regulation, arbitrage, load shifting
E-FLEX -Real-world Energy Flexibility through Electric Vehicle Energy TradingUK2018 ongoingunknownFreq. regulation, distribution, load shifting
V2GOUK2018 ongoingunknownFreq.regulation, arbitrage, load shifting
Share the Sun/Deeldezon ProjectNetherlands2019 202180Freq. regulation, distribution, load shifting
BloRinItaly2019 20221Freq. regulation, load shifting
Peak DriveCanada2019 202521Distribution, load shifting
Piha vehicle-to-home (V2H) trialNew Zealand2019 ongoing2Load shifting
Smart micro grid EMSChina2019 ongoing5Freq. regulation, load shifting, backup
UNDP Windhoek (Namibia) V2GNamibia2019 ongoing2Load shifting
V2G EVSE Living LabUK2019 ongoing2Load shifting, back up
Realising Electric Vehicle to Grid ServicesAustralia2020 202251Freq. regulation, reserve
Electric Nation Vehicle to GridUK2020 2022100Reserve, distribution, load shifting
Optimized HF isolated DC/DC converterSpain202020222Reserve, load shifting, back up
Milton Keynes Council—Domestic Energy Balancing EV Charging TrialUK202020224Load shifting
Direct Solar DC V2G Hub @LelystadNetherlands2020 202314Freq. regulation, distribution, load shifting, backup
V2G ZelzateBelgium2020 202322Freq. regulation, reserve, load shifting
VIGIL (VehIcle to Grid Intelligent controL)UK2020 ongoing4Reserve, distribution, load shifting
V2G @ homeNetherlands202120221Load shifting, back up
Bidirektionales Lademanagement—BDLGermany2021202250Freq. regulation, arbitrage, load shifting
Table 2. Advantages and disadvantages of centralized and decentralized control architecture [39,42].
Table 2. Advantages and disadvantages of centralized and decentralized control architecture [39,42].
Control TypeAdvantagesDisadvantages
Centralized control
  • Larger scale, number of EV, and coverage
  • Various possible ancillary services
  • Possible different connections to transmission, distribution, and renewable energy
  • Smart manipulation of network capacity
  • Possible real-time implementation
  • Flexible and wider geographical accessibility
  • Possible wider and larger-scale electricity market and higher possible revenue
  • Extensive and expensive central control system, as well as the backup and storage sources
  • Complex and expensive communication architecture and infrastructure
  • Big data to process
  • Demand for higher connection security (risk for privacy defilements)
  • Possible full control of EV (the anxiety of the user that EV charging process can be interrupted at any time)
Decentralized control
  • Smaller and simple communication infrastructure
  • Higher control flexibility/autonomy (charging control in the hand of the local system, resulting in faster and convenient service)
  • High data security as the data are stored locally
  • Higher consumer trust and acceptance (especially during initial adoption)
  • Scalable and adaptable to EVs fleet and energy management system
  • Better fault tolerance
  • Limited types of ancillary services, electricity market, and connections
  • Smaller revenue due to limited services
  • Uncertainty in the end-result
  • Accurate forecast and prediction of the user behavior of users are necessary
  • Possibility for avalanche effects or concurrent reactions
Table 3. Communication/safety standards associated with V2G technology [71].
Table 3. Communication/safety standards associated with V2G technology [71].
Communication/Safety StandardsOperation Procedures
IEC 62196-1Plugs, socket-outlets, vehicle couplers, and vehicle inlets—conductive charging of electric vehicles, charging of electric vehicles up to 250 A AC and 400 A DC.
IEC 62196-2Plugs, socket-outlets, vehicle connectors, vehicle inlets—conductive charging of EVs, dimensional compatibility, and interchangeability requirements for AC pin and contact-tube accessories.
IEC 62196-3Plugs, socket-outlets, and vehicle couplers—conductive charging of EVs, dimensional interchangeability requirements for pin, and contact-tube coupler with rated operating voltage up to 1000 V DC and rated current up to 400 A for dedicated DC charging.
IEC 61850-xCommunication networks and systems in substations.
ISO/IEC 15118V2G communication interface.
IEC 61439-5Low-voltage switchgear and control gear assemblies, and assemblies for power distribution in public networks.
IEC 61851-1EV conductive charging system—general requirements.
IEC 61851-21EV conductive charging system—EV requirements for conductive connection to an AC/DC supply.
IEC 61851-22EV conductive charging system—AC EV charging station.
IEC 61851-23 EV conductive charging system—DC EV charging station.
IEC 61851-24EV conductive charging system—control communication protocol between off-board DC charger and EVs.
IEC 61140 Protection against electric shock—common aspects for installation and equipment.
IEC 62040Uninterruptible power systems (UPS).
IEC 60529Degrees of protection provided by enclosures (IP code).
IEC 60364-7-722Low voltage electrical installations, requirements for special installations, or locations—supply of EVs.
ISO 6469-3Electrically propelled road vehicles, safety specification, and protection of persons against electric shock.
Table 4. A list of various optimization algorithms used for various objective functions across literature.
Table 4. A list of various optimization algorithms used for various objective functions across literature.
Journal with YearDiligenceController/Optimization Techniques
IEEE Transactions on the smart grid, 2018.Chance constraints-based rolling horizon controller used for minimizing cost and fulfilling end-user expected EV charge level during disconnection from the grid, though in the occurrence of uncertainty [78]Mixed-integer linear program
(MILP)
Energies, 2016The charging station control schemes to control the grid side converter. The hybrid PI-Fuzzy controller reduced the settling period and peak over-shoot [79]Hybrid PI-Fuzzy
IEEE Transactions on Industrial Informatics, 2018A self-adaptive hybrid optimization algorithm, Hybrid of deterministic and rule-based approaches for reducing the running price of an EV facility integrated with solar and battery storage [80]Deterministic-Rule based algorithm
Energies, 2014Genetic Algorithm applied to harmonize the charging behavior of EVs. Also, to establish an optimum load pattern for vehicle charging reliability [81]Genetic Algorithm (GA)
Energy, 2017Stochastic optimization Bat algorithm is devised to control the power generators and charging pattern of PHEVs [82]Bat algorithm (BA)
Energy and Buildings, 2015The mixed-integer LP method is applied for the optimization of the model with appropriate home DSM to enhance microgrid stability with less grid domination [83]Mixed integer linear programming (MILP)
Sustainable cities and society,2016The genetic algorithm and PSO algorithm are used in the distribution system for loss minimization drive [84]Genetic algorithm (GA) and particle swarm optimization (PSO)
International Journal of Electrical Power & Energy Systems, 2014A smart Fuzzy logic controller is used which determines the optimal charging current based on grid voltage, battery state of health and user’s trip requirement [85]Fuzzy logic controller (FLC)
International Journal of Hydrogen Energy, 2017A meta-heuristic algorithm HS-harmony search method is excelled for charge scheduling [86]Harmony search algorithm (HSA)
Applied Energy, 2014An improved PSO algorithm is proposed for the optimum energy flow, statistic features of EVs, owners’ degree of satisfaction (DoS), and grid cost [87]Improved particle swarm optimization (IPSO)
Energy, 2016The Dijkstra’s algorithm is selected to balancing load; the small node-voltage offset; and reduced power loss [88]Dijkstra’s algorithm
International Journal of Energy Research, 2018General algebraic modeling system (GAMS) for optimal strategy and firm decision to EVs supply chain demand has been employed [89]Mixed integer linear programming (MILP)
IEEE Transactions on Power Systems, 2015Charging load models and selection for EV charging stations [90]Ant colony (AC) optimization
Mathematical Problems in Engineering, 2015Smart power allocation plan for charging stations EVs [91]Gravitational search algorithm (GSA)
Table 5. Summarized potential challenges toward V2G implementation in different dimensions.
Table 5. Summarized potential challenges toward V2G implementation in different dimensions.
TechnicalRegulatory and Policies SocialMarket/Economy
Battery degradation (lifetime)Taxation system (double tax system)Distrust in V2G benefits, V2g technologies, and lack of motivationsCapital cost of charging system (needs for investment and subsidies)
Charging infrastructureIntegration policies and standards of chargers with distribution and transmission linesInconveniences (charging, maintenance, etc.)Vehicle cost (user upfront investment)
Charging protocolsEVs purchase subsidies and incentivesSystematic confusions (hardware, software, and regulation/policies) EV, especially battery, maintenance and replacement costs
Energy loss during charging and dischargingInfrastructure subsidiesRange anxiety (low interest for purchasing new EVs)Interconnection cost
Risk of imbalances, overload, and limited energy bufferIndependent, open, and accessible aggregator Remaining SOC anxiety Communication cost
Grid connection, limited existing grid designLack of communication with all stakeholdersConventional behavior (difficult-to-change behaviors)Unclear revenues
Integration with renewable energy sourcesOwnership issues (for chargers and other instruments)Unclear environmental impacts Market creation/reformation (emerging market)
Communication networkData security and handling procedures and protectionLack of early adopters, lack of public interestHigh charging cost and limited distribution of chargers
Communication and data securityPolicies for facilitative and accessible markets Industry dependency (in the established conventional vehicle industries)
Battery self-dischargingMutual communication among the stakeholders Market decentralization
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Ravi, S.S.; Aziz, M. Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives. Energies 2022, 15, 589. https://doi.org/10.3390/en15020589

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Ravi SS, Aziz M. Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives. Energies. 2022; 15(2):589. https://doi.org/10.3390/en15020589

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Ravi, Sai Sudharshan, and Muhammad Aziz. 2022. "Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives" Energies 15, no. 2: 589. https://doi.org/10.3390/en15020589

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