Enhancing green mobility through vehicle-to-grid technology: potential, technological barriers, and policy implications

Abstract

Vehicle-to-grid technology accelerates the transition to renewable, low-carbon power systems by integrating electric vehicles. This study analyzes the 2023 US electric vehicle charging demand, variable renewable energy capacities, and charging infrastructure numbers in China, the US, and the EU. Moreover, an assessment of electric vehicle lifecycle carbon emissions using IEA data is conducted. Results indicate that V2G offers significant economic feasibility and environmental benefits by balancing grid supply and demand, absorbing renewable energy, conserving electricity, reducing CO₂ emissions, and supporting Sustainable Development Goals. Key underlying technologies are investigated to guide future V2G advancements. An orderly regulation framework for the EV-grid-aggregator system is provided, specifying market incentives and charging management measures to promote EV participation in V2G. Additionally, charging infrastructure planning strategies that integrate power and transportation networks are developed to facilitate decarbonization. By analyzing global policy contexts and market incentives, effective policies for advancing V2G implementation are emphasized. Finally, future development directions are proposed based on existing research, offering a roadmap for sustainable V2G development.

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Broader context

As adoption of electric vehicles (EVs) accelerates globally, integrating these dynamic and flexible loads into power grids represents a significant opportunity to enhance grid stability and accelerate the transition to renewable, low-carbon power systems. This study highlights the role of managed EV charging, particularly vehicle-to-grid (V2G) technology, in optimizing power system planning, operation, and reliability across bulk power and distribution systems. The analysis emphasizes the potential of EV–grid integration to balance supply and demand, absorb variable renewable energy, and reduce CO₂ emissions while enhancing grid resilience and sustainability. Key findings identify critical factors influencing EV–grid interaction, such as communication protocols, charging infrastructure, and market incentives, alongside challenges including battery degradation, implementation costs, and regulatory barriers. While existing research underscores the economic and environmental benefits of managed EV charging, comprehensive benefit–cost analysis and stakeholder-inclusive frameworks remain underexplored. This paper highlights the need for continued research and multi-stakeholder collaboration to unlock the full potential of managed EV charging and V2G technologies, benefiting utilities, EV users, and charging infrastructure operators.

1 Introduction

As the automotive industry continues to innovate, green mobility¹ will play a crucial role in the transition to a more sustainable and environmentally friendly transportation system.^2,3 Electric vehicles (EVs) represent some of the primary solutions for achieving green transportation.⁴ According to the IEA's net-zero emissions by 2050 scenario,⁵ the electrification of transport is a primary driver of decarbonization. Unlike traditional internal combustion engine vehicles (ICEVs), EVs are powered by electricity stored in high-capacity batteries. EVs are categorized based on their power source and motor type into pure electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), extended-range electric vehicles (EREVs), and fuel cell vehicles (FCVs) (Fig. 1). By eliminating tailpipe emissions, EVs will improve local air quality and enhance the health of cities and communities.⁶ Emissions could be reduced by about 94% if the number of EVs increases from the current 11 million to 2 billion by 2050.⁷

Fig. 1 Multi-type electric vehicle schematic. BEV: fully powered by electricity stored in a large battery and driven by an electric motor, emitting no tailpipe emissions; PHEV: combines an electric motor and an internal combustion engine (ICE), allowing short trips on electricity and longer trips using gasoline; HEV: primarily relies on an ICE with an integrated electric motor for assistance during acceleration and regenerative braking; no external charging is required as it recharges through braking; EREV: uses a fuel-powered generator to charge the battery, with an electric motor driving the vehicle, offering characteristics of an electric vehicle but with a smaller battery; and FCV: converts chemical energy from hydrogen into electricity via fuel cells to power the electric motor.

The energy crisis further accelerates the electrification of the transport sector. In recent years, the external market environment for oil imports has become increasingly complex,⁸ and the Russia–Ukraine conflict in February 2022 has driven international oil prices to a historic high of over $100 per barrel (Fig. 2(a)). Therefore, for many countries heavily reliant on oil imports, transport electrification could facilitate diversification and the use of domestic primary energy resources such as hydro, solar, and wind.⁹ With electric mobility playing an increasingly crucial role in addressing the energy crisis and achieving carbon neutrality goals,¹⁰ global electric vehicle sales reached a record high in 2023. This surge is driven by multiple incentives, including technological advancements, market dynamics, and national policies (Fig. 2(b)). Fig. 2(c) illustrates national electric vehicle ownership and global share among different countries.

Fig. 2 Summary and analysis of the data related to the EV sector under review. (a) International oil price trends in 2022. (b) Global electric vehicle stock. (c) The number and proportion of electric vehicles in different countries around the world (^adata from Google and ^bdata from the IEA). (d) V2G projects have been carried out around the world. (e) Participants in the process of V2G and the framework of V2G: an aggregator comprises a cluster of EVs, including independent operators such as fast charging stations and battery swapping stations that directly gather EV loads, as well as energy market players that integrate resources for geographically dispersed EVs.

The large-scale electrification of vehicles worldwide presents significant challenges. The higher power demands of EV charging simultaneously are expected to be a primary challenge due to local grid capacity limitations.¹¹ For example, in the Netherlands, approximately 3000 communities with at least 100 EVs are projected to exceed network capacity by 2025 due to faster-than-expected growth in EV usage. In California, local power distribution systems will need to upgrade five times more feeders than originally planned to accommodate EVs by 2030. Additionally, the simultaneous electrification of heating, access to air conditioning, and distributed photovoltaics could further strain network capacity, sometimes exacerbating or surpassing the impact of EVs.¹²

Despite these challenges, the flexibility provided by the storage capacity of EVs can be leveraged to benefit the power grid. With advancements in EV technology,¹³ EVs can function as flexible energy storage units within modern energy systems.¹⁴ EVs can charge during periods of low demand and discharge power back to the grid during peak load periods, thus aiding in load balancing and grid stability.¹⁵ The integration of EVs into the grid also facilitates the consumption of variable renewable energy (VRE) by allowing EVs to store excess energy from intermittent sources, such as wind and solar, thereby reducing the need for backup power plants. Consequently, the integration of EVs into the grid is receiving significant attention in the context of modern energy systems and sustainable development.¹⁶

To fully exploit this potential, improvements in EV charging technology, investments in communications and digital infrastructure,¹⁷ and changes in market design and regulation are essential. These advancements are necessary to effectively manage the increased demands on the power grid and optimize the integration of EVs.

In promoting the integrated development of EVs and renewable power, large-scale interconnection and interaction between EVs and the power grid is a key technology.¹⁸ The interaction between EVs and the power grid progresses through four stages: unordered charging (V0G), unidirectional charging (V1G), vehicle-to-grid (V2G), and vehicle-grid integration (VGI). Major countries worldwide have conducted extensive research and exploration on V2G.¹⁹

Currently, there are about 150 V2G pilot demonstration projects globally, with concentrations in the United States (18 projects), Western Europe (25 projects), and East Asia, including Japan,²⁰ South Korea, and Hong Kong, and China (7 projects in total). These projects encompass various types of technology verification, demonstration and promotion, and commercial operation programs (Fig. 2(d)).

V2G represents a key innovation in EV and power grid integration, enabling EVs to function as both consumers and contributors to the power grid.²¹ This bidirectional power flow capability allows EVs to not only draw power from the grid to charge their batteries but also release stored power back into the grid.²² V2G plays a vital role in grid management and stability, particularly during peak demand and emergency situations.²³ Acting as mobile power storage units, V2G-enabled EVs can provide valuable grid support and enhance reliability.²⁴ Additionally, V2G offers economic benefits to electric vehicle users (EVUs), who can earn income by selling excess electricity back to the grid, while also advancing environmental goals by increasing the utilization of renewable energy and reducing greenhouse gas emissions from burning fossil fuels.^25–27

This review provides a comprehensive analysis of V2G in promoting green mobility, emphasizing its role in optimizing demand-side resource allocation within evolving power systems. The study begins by analyzing the 2023 EV charging demand in the US, the capacity of VRE, and the number of charging infrastructures in China, the United States, and the European Union. The potential of V2G to balance grid supply and demand and to integrate cross-regional VRE is explored. Additionally, the lifecycle carbon emissions of EVs are assessed using data from the IEA. The ability of V2G to achieve energy savings and emission reduction by minimizing wind and solar curtailment, implementing peak shaving and valley filling, and preventing battery degradation is discussed. Based on the established EV cost–income model, the charging demand of EVs in the US in 2023 is studied. Data indicate that economic income is enhanced by approximately 27% to 50% when EVs are utilized compared to ICEVs. Nationwide in the US, the annual adoption of EVs generates income totaling $1.27 × 10⁷. Furthermore, at the same time, EVUs can profit from optimized charging and discharging of EVs via V2G, with profits ranging from $1.73 × 10⁹ to $5.2 × 10⁹. Through data analysis, this paper proves the economic feasibility of V2G. Specific pathways for implementing EVs and V2G to achieve SDGs are presented through various analytical methods. This paper also examines key technical challenges in V2G implementation, including communication protocols, electricity demand forecasting, bidirectional chargers, smart meters, and wireless communications, as well as regulatory and coordination frameworks for EV–grid aggregators. By analyzing global policy contexts and market incentives, strategies such as demand response, tax incentives, and smart grid development are highlighted to advance V2G adoption. These strategies support the promotion of green mobility and the energy transition. This article not only provides an in-depth technical and policy analysis of V2G's potential in green mobility but also guides future research and practice, underscoring the importance of technological innovation, policy support, and multi-stakeholder collaboration in achieving a low-carbon society.

2 The potential of V2G for the advancement of green mobility

The rapid development of EVs is also driving advancements in related technologies, where V2G represents a transformative innovation in power mobility and energy management. To explore the potential of V2G in advancing green transportation, this section examines the economic feasibility of V2G and the key role of V2G in balancing grid supply and demand, absorbing renewable energy,²⁸ and achieving power conservation, CO₂ emission reduction and the SDGs.²³

2.1 V2G for balancing the supply and demand of power grids

As an innovative power grid technology, V2G enables bidirectional, real-time, controllable, and high-speed power flow between EVs and the power grid. V2G effectively addresses challenges related to the large-scale integration of EVs into the power grid.²⁹ V2G technology manages the charging and discharging processes of EVs. During periods of low demand, EVs are scheduled by the power grid to charge, storing excess power generation. During peak demand periods, EVs discharge power back to the grid. This process reduces the negative impact of large-scale EV development on the power system, lowers total charging costs and system network losses, and contributes to peak shaving and valley filling. In the V2G mode, the orderly dispatch of large-scale EVs brings economic benefits to both the power grid and users, fostering a mutually beneficial and win–win situation.

In V2G, electric vehicle aggregators (EVAs) manage regional EVs and participate in power market trading (Fig. 2(e)). The upper level interacts with the power grid (aggregator-to-grid, A2G), while the lower level interacts with EVs (vehicle-to-aggregator, V2A), playing a crucial role in connecting EVs with the power grid. This creates a hierarchical interactive architecture of network-business-vehicles.

At the upper level of vehicle network interaction, EVAs provide dispatchable power from the vehicle fleet to the grid at different time scales and compete with various participants in the electricity power market and auxiliary service market to obtain benefits. At the lower level, EVAs develop individual charge and discharge guidance mechanisms based on scheduling strategies to encourage users to participate in the positive interaction with the power grid, achieving optimal charging and discharging schedules for EVs.

This dynamic interaction between EVs and the grid not only supports grid balance and improves grid reliability but also opens new revenue streams for EVUs through participation in demand response programs and ancillary services.³⁰ V2G technology represents a key step toward a more resilient and sustainable energy ecosystem, where EVs play an active role in the wider power network.³¹

2.2 V2G for absorbing variable renewable energy

Achieving net-zero carbon emissions has become a key global goal.³² Energy systems are becoming increasingly electrified, and the supply side is transitioning away from non-renewable fuels.³³ Meanwhile, technological advances are driving rapid changes in demand, with growing diversity in energy use strengthening the coupling between different energy types.³⁴ However, the rising electricity demand introduces greater operational risks for grids and energy networks. Traditional power sources like coal and natural gas are being phased out, with the future energy demand expected to rely heavily on VRE, particularly wind and solar.³⁵

As transportation advances the electrification transition, it also presents significant energy challenges. Globally, VRE capacity has grown rapidly, as an example, China's national VRE capacity is expected to double by 2030 and reach about 1.5 times the current VRE capacity by 2050 (Fig. 3). But the rising energy consumption of EVs has yet to receive comparable attention. Although VRE can theoretically meet EVs’ energy demand, both sectors face geographic imbalances, as illustrated in Fig. 4. At the local level, EVs consume more energy than expected, while regional VRE generation lags. A comparison of supply and demand reveals that VRE alone struggles to meet power needs in key areas, resulting in micro-level energy shortages and macro-level mismatches between resources and demand. Therefore, promoting VRE consumption by using EVs to adjust the energy demand has become valuable research.

Fig. 3 VRE capacity planning across China's provinces and regions.

Fig. 4 Geographic concentration of VRE and charging infrastructure distribution. (a) Distribution of power generation from major variable renewable energy (VRE), specifically wind and solar energy, across numerous countries worldwide in 2023.³⁶ The zoomed-in global map illustrates the distribution of VRE within charging infrastructure concentration areas, as shown in the following subfigures: (b) overview of Western European countries;³⁷ (c) state-by-state analysis in the United States;³⁸ and (d) examination of provinces in China.³⁷

One of the primary benefits of V2G technology is its ability to optimize the utilization of VRE. This capability is particularly crucial in scenarios where renewable energy generation is variable and unpredictable. For instance, studies have shown that V2G can facilitate peak shaving and valley filling, thereby enhancing the reliability of power supply from renewable sources.³⁹ Furthermore, the integration of V2G with smart grid technologies allows for improved management of energy resources,⁴⁰ leading to enhanced grid stability and efficiency.⁴¹

V2G enables EVs to function as mobile energy storage units, feeding stored energy back into the grid,⁴² thereby enhancing the grid's capacity to absorb intermittent VRE. This is particularly critical for the integration of high shares of VRE. At the local level, especially in regions with a high density of charging stations, V2G can better leverage the charging network to support the storage and absorption of renewable energy.⁴³ In regional power grids, despite the evident mismatch between the capacities of EVs and VRE, the interconnection of regional grids facilitates the transfer of electricity from low-density (high VRE proportion) areas to high-density (low VRE proportion) areas.⁴⁴ For example, in China, the “West-to-East Power Transmission” project allows VRE from the western regions to be transmitted to the energy-intensive eastern regions, while high EV penetration in the east can use V2G to absorb and store intermittent VRE, significantly reducing regional mismatches between resource availability and demand.⁴⁵

When VRE generation is high, EVs can be charged, and their battery storage capacity enables flexible absorption of intermittent VRE, reducing the need for additional local and regional storage. Conversely, when renewable generation is low, V2G can discharge energy from EVs to supplement grid demand, mitigating power supply fluctuations.⁴⁶ Furthermore, V2G can support flexible dispatch based on electricity prices, demand response, or grid load, helping to optimize the power system and reduce reliance on traditional coal-fired power plants, thereby facilitating the effective integration of renewable energy.⁴⁷

2.3 The potential of V2G to achieve power conservation and CO₂ emission reduction

Although EVs are not technically “zero-emission” vehicles evidence suggests that their emissions are likely to decrease further as the power sector undergoes decarbonization and industrial emissions are reduced.⁴⁸ For instance, in the UK, EVs have lower lifetime emissions compared to conventional internal combustion engine vehicles. Currently, the transportation sector is the largest emitter of greenhouse gases in the UK,⁴⁹ surpassing the energy sector in 2016.⁵⁰ In 2021, emissions from the energy sector accounted for 20% of the UK's total emissions, while transportation accounted for 26%. The decarbonization of the energy sector, particularly in power generation, will contribute to reducing the lifecycle emissions of EVs. Additionally, as EVs’ efficiency improves and emissions from other low-carbon fuels and manufacturing decrease, the increased adoption of EVs will further accelerate the decarbonization of the transportation sector.

Using the IEA's EV Life Cycle Assessment Calculator,⁵¹ this study compares the total CO₂ emissions and CO₂ emissions per kilometer at different stages of the vehicle life cycle for private cars, taxis, buses, and heavy-duty trucks, each powered by different powertrains (BEV, PHEV, and ICEV) over their life cycle (Fig. 5). The study also compares the cumulative carbon emissions for each vehicle type (Fig. 6). The results show that BEVs offer significantly greater CO₂ emission reduction as mileage or service life increases. For example, a typical private ICEV, with an average daily mileage of 76 km, will produce 94.8 tons of CO₂ equivalent emissions over its 15-year life cycle. The same private PHEV will generate 62.3 tons, reducing CO₂ emissions by 34%. In contrast, a typical private BEV will produce only 38.9 tons of CO₂, a 59% reduction compared to the same ICEV and a 38% reduction compared to the same PHEV. Although emissions associated with battery production are higher, the cumulative emissions of BEVs decrease over time, eventually becoming lower than those of comparable ICEVs and PHEVs with continued use.⁵²

Fig. 5 Total carbon emissions of vehicles of different types and power sources at multiple stages over their life cycle. The lifetime and average mileage of private vehicles in (a) and (b) are set at 15 years and 76 km,⁵³ respectively. The lifetime and average mileage of taxis in (c) and (d) are set at 8 years and 250 km, respectively.⁵⁴ The lifetime and average mileage of buses in (e) and (f) are set at 13 years and 150 km, respectively.⁵⁵ The lifetime and average mileage of trucks in (g) and (h) are set at 12 years and 275 km, respectively.⁵⁶

Fig. 6 Cumulative carbon emissions of vehicles of different types and power sources over their life cycle. The lifetime and average mileage of private vehicles in (a) are set at 15 years and 76 km,⁵³ respectively. The lifetime and average mileage of taxis in (b) are set at 8 years and 250 km, respectively.⁵⁴ The lifetime and average mileage of buses in (c) are set at 13 years and 150 km, respectively.⁵⁵ The lifetime and average mileage of trucks in (d) are set at 12 years and 275 km, respectively.⁵⁶

The CO₂ emissions in the “Energy Production” phase of EVs are primarily determined by the carbon intensity of the power grid.⁵⁷ V2G plays a pivotal role in optimizing the grid's energy structure and enhancing the utilization of renewable energy, significantly reducing CO₂ emissions during this phase.⁵⁸ V2G uses batteries as energy storage devices, helping to balance the temporal gap between renewable energy generation and power demand, thus reducing the curtailment of wind and solar power. This process indirectly lowers emissions by facilitating a higher proportion of renewable energy integration into the grid. Moreover, V2G reduces reliance on fossil fuel-based power generation during peak demand periods by flattening grid load curves. During peak periods, the grid is more reliant on fossil fuels, which increases CO₂ emissions during the BEV usage phase. However, V2G technology stores electricity in BEV batteries during off-peak periods (when renewable energy availability is higher) and feeds it back into the grid during peak hours, thereby reducing emissions during the BEV usage phase.⁵⁹ Research indicates that by 2025, V2G combined with multi-source optimization control could reduce carbon emissions by 138 783.799 tons, representing a total reduction of 1.101%.⁶⁰ Another forward-looking life cycle assessment study finds that by 2030, V2G charging strategies could reduce BEV operational emissions by 50% to nearly 200% compared to uncontrolled charging, depending on the level of greenhouse gas reduction achieved through BEVs serving as an energy storage option.⁶¹

Additionally, V2G optimizes the charging and discharging frequency of EV batteries, minimizing deep discharge events and thereby extending battery life. This optimization process reduces the aging rate of the battery's molecular structure, prevents performance degradation from overuse, and ultimately decreases the frequency of battery replacements. As a result, it not only significantly lowers the carbon emissions associated with battery production and recycling over the vehicle's life cycle but also enhances the overall economic and environmental benefits of EVs.⁶² A study integrating vehicles with smart grid systems developed a comprehensive battery degradation model that accounts for factors such as calendar age, capacity throughput, temperature, state of charge, depth of discharge, and current rate. The findings indicate that smart grid optimization can reduce battery capacity degradation by up to 9.1% and power degradation by up to 12.1%.⁶³

2.4 Economic feasibility of V2G

Compared to ICEVs, electric vehicles (EVs) are generally more affordable, especially in regions with low charging costs. The cost per kilometer for EVs is significantly lower than that of ICEVs. In recent years, fuel prices have experienced frequent fluctuations due to global factors (Fig. 7(b)),⁶⁴ and EVs are insulated from these price swings. Especially during periods of rising fuel costs, EVUs and national energy sectors can avoid the high expenses associated with fuel, resulting in long-term economic savings. Furthermore, to reduce global carbon dioxide emissions, low-carbon policies such as carbon trading and carbon taxes are being progressively implemented worldwide, which impose substantial implicit carbon costs on traditional fossil fuels.⁶⁵

Fig. 7 The economic benefits of electric vehicles and vehicle-to-grid. (a) Generation at utility scale facilities in the US (2023). Among them, the heat map shows the amount of electricity generated when the four primary energy sources are used for power generation, and the line chart shows the cost of the four primary energy sources used for power generation. (b) Trends of international crude oil prices from 2014 to 2023. (c) EV load (TW h) in the US by state (2023). (d) Increased energy use and carbon reduction benefits throughout the year using EVs (2023). (e) Price of electricity to ultimate customers in the US (2023). (f) Benefits of using V2G when the battery is maintained at different capacities (2023). (All data come from the Energy Information Administration (EIA).)

To more accurately assess the economic income of using EVs, this study utilizes 2023 electricity consumption data for EVs in the US (Fig. 7(c)) and, using a cost-income model for using EVs (outlined in eqn (1)–(7)), calculates the energy saving income and carbon reduction income for each of the four quarters of 2023 (Fig. 7(d)). As shown in Fig. 7(d), compared to ICEVs, the annual energy saving income from using EVs can reach $1.13 × 10⁸. Due to frequent fluctuations in oil prices and electricity generation costs, the energy saving income from EVs exhibits irregular patterns throughout the year. The data flow indicates that Q_III is the quarter with the highest energy saving income from using EVs, averaging $1.51 × 10⁷. This is because oil prices peaked during this quarter at approximately $0.5 per liter, while electricity generation costs were the lowest of the year, around $0.03 per kW h. In contrast, the carbon reduction income of using EVs remained steady throughout the year due to a relatively consistent increase in carbon penalty prices, totaling approximately $1.27 × 10⁷ in carbon reduction income. Detailed data show that simply replacing traditional coal-fired power with electricity for vehicle propulsion can improve the economic income of energy production and energy savings in the US by 27–50%.

The energy saving income from using EVs is determined by subtracting the vehicle electricity cost Cost_Electricity from the vehicle fuel cost Cost_Petroleum, as shown in eqn (1).

Income_Energy = Cost_Petroleum − Cost_Electricity (1)

Among them,

Cost_Petroleum = Energy_Petroleum × price_Petroleum (2)

Cost_Electricity = Energy_Electricity × price_Electricity (3)

Energy_Petroleum = Energy_Electricity × γ_Convert (4)

where Energy_Petroleum and Energy_Electricity are the fuel consumption (in L) and electricity consumption (in kW h) of EVs, respectively, based on the EV's electricity usage (Fig. 7(c)). γ_Convert is the conversion factor (in L kW h⁻¹) used to translate electricity into equivalent fuel consumption.⁶⁶ According to the US Environmental Protection Agency (EPA), the standard conversion is 33.7 kW h of electricity equal to 1 gallon of gasoline, which is approximately 0.1125 L kW h⁻¹. price_Petroleum and price_Electricity are the price of petroleum (in $ per L) and the calculated cost price of electricity (in $ per kW h) from the power generation sector, respectively.⁶⁷

To more clearly assess the economic differences between fossil fuel and electricity as vehicle power sources, the cost of electricity from the power generation sector is derived by dividing the energy purchase costs of coal, liquid petroleum, solid petroleum, and natural gas by the corresponding amount of electricity produced from each energy source (Fig. 7(a)).

The carbon reduction income from using EVs is determined by subtracting the carbon penalty cost of using electricity Cost_{Electricity,CO2} from the carbon penalty cost of using petroleum Cost_{Petroleum,CO2}, as shown in eqn (5).

Income_CO2 = Cost_{Petroleum,CO2} − Cost_{Electricity,CO2} (5)

Among them,

Cost_{Petroleum,CO2} = Energy_Petroleum × μ_Petroleum × price_CO2 (6)

Cost_{Electricity,CO2} = Energy_Electricity × μ_Electricity × price_CO2 (7)

where μ_Petroleum and μ_Electricity are the carbon emission coefficients for petroleum and electricity, respectively, which are set at 0.19 kgCO₂ L⁻¹⁶⁸ and 2.31 kgCO₂ kW h⁻¹⁶⁹ in this study. price_CO2 is the penalty price per unit of carbon emissions (in $ per kgCO₂),⁷⁰ used to evaluate carbon emissions’ economic cost.

V2G has been shown to create significant economic value for both the power generation sector and EVUs.⁷¹ By connecting EVs to the grid through V2G, EVUs can feed battery energy back into the grid during peak periods,⁷² generating economic returns while reducing the grid's dependence on costly and polluting peak power. This also leads to savings in investment costs for the power generation industry.⁷³ In addition, V2G enhances grid flexibility, alleviates congestion, and can partially replace other energy storage systems, thus enabling more cost-effective and sustainable grid management. Further studies have examined potential revenue streams for V2G services, such as frequency regulation⁷⁴ and renewable energy integration,⁷⁵ which can improve the economic income for both EVUs and grids.⁷⁶

The economic feasibility of V2G, however, faces several challenges. Battery degradation is a critical issue, as the frequent charge–discharge cycles associated with V2G can accelerate battery wear.⁷⁷ This could lead to higher replacement costs for EVUs, potentially deterring their participation in V2G.⁷⁸ Generally, maintaining battery capacity between 70% and 90% ensures long-term retention of high energy and power density, while also mitigating excessive aging. Capacities above 90% increase internal resistance, which negatively affects the battery's fast charge–discharge performance.⁷⁹ Moreover, the low grid input price during reverse discharging by EVs further diminishes the economic incentives for V2G. To overcome these challenges, some studies suggest that substantial subsidies from the grid are necessary to make V2G economically viable for EVUs.⁸⁰

To better assess the economic feasibility of V2G, this study examines the perspective of EVUs and further explores the economic viability of V2G across different industry scenarios, based on electricity pricing ranges provided by the grid to various ultimate customers (Fig. 7(e)). Assuming that EVUs will charge during low electricity price periods and discharge during peak price periods under V2G support, we compare the economic income achievable through controlled charge–discharge cycles, with battery capacities maintained at 70%, 80%, and 90%. For simplicity, we assume that the electricity price for reverse discharging to the grid is equal to the “price of electricity to various ultimate customers”. Fig. 7(f) illustrates the economic income gained through V2G by all EVUs across different industry scenarios. In all scenarios, the average peak-to-valley price difference in Q_III reaches approximately 12$ per kW h. During this quarter, if all EVUs in the US use V2G to control charging and discharging in an orderly manner, the economic income, with battery capacities at 70%, 80%, and 90%, would amount to 5.2 × 10⁹$, 3.47 × 10⁹$, and 1.73 × 10⁹$, respectively.

2.5 V2G for achieving the sustainable development goals

EVs play a crucial role in advancing the SDGs. By reducing greenhouse gas emissions,⁸¹ promoting the use of clean energy, and decreasing dependence on fossil fuels,⁸² EVs contribute to achieving SDG 13 (climate action).⁸³ Additionally, EVs drive technological innovation and the growth of green industries,⁸⁴ supporting SDG 9 (industry, innovation, and infrastructure). They also improve air quality and reduce noise pollution, thereby contributing to SDG 3 (good health and well-being).⁸⁵ Furthermore, EVs promote the development of sustainable transportation systems, help reduce urban pollution, and support SDG 11 (sustainable cities and communities). By fostering green consumption and production patterns, they align with SDG 12 (responsible consumption and production).⁸⁶ The widespread adoption of EVs also stimulates job creation,⁸⁷ drives economic growth, and contributes to SDG 8 (decent work and economic growth).⁸⁸

The development of V2G further advances EVs in achieving the SDGs. V2G significantly advances several SDGs by integrating EVs into the energy system, reducing carbon emissions, enhancing urban energy efficiency, and promoting sustainable resource management.⁸⁹Fig. 8 compares the specific paths for EV and V2G to achieve multiple SDGs in multiple methods.

Fig. 8 The specific paths for EVs and V2G to achieve multiple SDGs in multiple methods.

V2G plays a pivotal role in advancing SDGs 7, 11, 13, 9, and 12.⁹⁰ The deployment of V2G represents a critical step towards achieving a sustainable energy future and addressing global environmental challenges. EVs equipped with V2G function as mobile energy storage units capable of storing surplus power from renewable sources and releasing it back into the grid as needed. This capability enhances grid stability and supports the efficient utilization of renewable energy, directly contributing to SDG 7 (affordable and clean energy).⁹¹ Additionally, V2G supports SDG 13 by reducing carbon emissions associated with power generation and transportation. By optimizing renewable energy use and minimizing reliance on fossil fuels, V2G mitigates climate change impacts.

In urban settings, V2G contributes to SDG 11 by improving energy management and resilience. EVs with V2G capabilities provide ancillary grid services such as load balancing and peak shaving, thereby improving overall energy efficiency and reducing environmental impacts.

Moreover, V2G enhances SDG 9 by fostering technological innovation in energy storage and management systems. It also promotes efficient resource use and reduces energy waste through smart grid technologies, supporting SDG 12.

3 Technological challenges of V2G

In previous studies, the potential of V2G in achieving green mobility is demonstrated. This section will explore the challenges faced in advancing V2G from a technical perspective, analyzing them from the perspectives of key bottom technologies, the orderly regulation of the EV-grid-aggregator framework, and charging infrastructure planning strategies. These elements are crucial for the successful integration of V2G. Key technologies like communication protocols, electricity demand forecasting, bidirectional onboard chargers, smart meters, and wireless communications enable efficient interactions between EVs and the power grid. Effective regulation, including smart charging and discharging strategies and power market trading mechanisms, ensures EVs contribute to power grid stability and economic viability. Charging infrastructure planning that integrates power and transport networks supports large-scale V2G adoption, aiding decarbonization and sustainable development.

3.1 Intelligent bottom technology to realize vehicle-to-grid

Examining key bottom technologies, such as V2G communication protocols, electricity demand forecasting, bidirectional onboard chargers, smart meters, and wireless communications, is necessary to understand the technical foundations that support V2G integration. These technologies facilitate efficient communication, accurate demand prediction, and effective power exchange between EVs and the power grid.

3.1.1 Advanced intelligent communication technology and basic terminal devices in V2G. Once EVs equipped with bidirectional onboard chargers (Bi-OBCs) connect to the grid, the EVUs can interact with the grid via the EVAs to facilitate the bidirectional flow of electric energy.⁹² This process leverages real-time pricing and communication through smart meters and advanced wireless communication infrastructures, enabling intelligent scheduling of EV charging and discharging and reducing pressure on distribution system facilities.⁹³

Smart meters support bidirectional metering, remote and local communication, multiple tariff billing, remote power outage management, power quality monitoring, and user interaction.⁹⁴ Through real-time collection and automatic analysis of charging data, smart meters can promptly handle electricity bill settlements, monitor the status of electric vehicle supply equipment (EVSE), and analyze power grid line losses. Additionally, power line communication can transmit information and serve as a wireless hotspot in areas with poor signal, facilitating the intelligent networking of EVs and charging stations.

Vehicle wireless communications include cellular networks (3G, 4G, and 5G), dedicated short-range communications (DSRC), and WiFi. In-vehicle wireless communication infrastructures enable the exchange of information necessary for charging planning, coordination, vehicle routing, authentication, and billing.⁹⁵ For instance, an EV requesting a charging service sends a charging request containing details about the EV, such as speed, current location,⁹⁶ destination, maximum battery capacity, initial state-of-charge (SoC), and desired final SoC. This request also includes booking information and regular real-time updates,⁹⁷ such as arrival times, expected charging durations and locations, and charging prices.⁹⁸

Bi-OBCs consist of a filter, a bidirectional DC–DC converter, and a bidirectional AC–DC converter. In charging mode, the AC is first filtered to remove unwanted frequency components, then rectified to DC by the bidirectional AC–DC converter.⁹⁹ Since the output voltage of the bidirectional AC–DC converter may not match the voltage of the DC power storage unit,^100,101 a bidirectional DC–DC converter ensures an appropriate charging voltage. In discharging mode, the process is reversed (Fig. 9). Table 1 summarizes the types of EVs equipped with Bi-OBCs available in the market and their parameters.

Fig. 9 The process of converting grid power into the source for EVs: metering, power conversion and wireless communication. The electric power from the grid reaches the EVs through EVSE, and the electric vehicle battery and other loads are powered by bidirectional OBCs. Smart metering and wireless communication can better help EVs achieve V2G.

Table 1 The types of vehicles equipped with bidirectional OBCs in the existing market, plug, pin number, and charging class

Model	Year	Plug	Bidirectional	Number of pins	Charging level	Voltage (V)	Current (A)	Power (kW)
a 3 power pins: DC+, DC−, E7 control pins (CAN communication). b 3 power pins: DC+, DC−, E2 control pins (CP, PP (PLC over CP, PE).
Nissan Leaf	2013	CHAdeMO	V2G, V2H	3^a	DC Level 3	200–500	≤400	350
Hyundai Ioniq5	2021	CHAdeMO	V2L	3^a	DC Level 3	200–500	≤400	350
Kia EV6	2021	CCS	V2L	3^b	DC Level 3	200–1000	≤350	350
Genesis GV60	2022	CCS	V2L	3^b	DC Level 3	200–1000	≤350	350
BYD Atto 3	2022	CCS	V2L	3^b	DC Level 3	200–1000	≤350	350
Ford F150 Lightning	2022	CCS	V2H, V2L	3^b	DC Level 3	200–1000	≤350	350
MG ZS EV	2022	CCS	V2L	3^b	DC Level 3	200–1000	≤350	350
Sono Sion	2023	CCS	V2L	3^b	DC Level 3	200–1000	≤350	350
GMC Hummer EV	2024	CCS	V2L, V2V	3^b	DC Level 3	200–1000	≤350	350

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In the future, advancements in bidirectional on-board chargers (OBCs) will be crucial. Bidirectional OBCs are essential for facilitating energy exchange between EVs and the grid. To alleviate range anxiety, the capacity of power batteries will continue to increase, requiring ongoing improvements in bidirectional OBCs. Enhancing the power density of bidirectional OBCs for faster charging and designing more modular converter systems to accommodate multiple charging needs are necessary. Additionally, it is essential to analyze the potential impact of bidirectional OBCs on battery aging to determine whether they accelerate or affect battery lifespan.

3.1.2 Information exchange technology based on advanced V2G communication protocols. Experts in the power sector recognize that hardware alone is insufficient for implementing V2G; robust communication protocols are also essential. The V2G communication protocol standardizes communication and data exchange between EVs and power grids. Through the communication link between EVs and power grids, V2G protocols enable the collection of information about the SoC and other power management-related data. Some protocols can bypass EVSE and connect directly from the power grid to the EVs, while others route through EVSE. Selecting different communication protocols can support various business models for EV charging services. Fig. 10 illustrates several widely used V2G communication protocols, along with their communication domains and functions. These protocols utilize HTTP, XML, Web services, SOAP, and JSON technologies and rely on TCP/IP-level connections.

Fig. 10 The functions of various V2G communication protocols and the participants connected in the actual V2G communication process. The left side of Fig. 10 shows the body of the connection of the communication protocol, and the right side of Fig. 10 specifically explains the function and domain of the communication protocol.

When designing the communication protocol, the following aspects must be considered: bidirectional communication to allow EVs to send and receive grid instructions; high security, including data encryption, identity authentication, and access control to prevent attacks and information leaks;¹⁰² interoperability to accommodate different manufacturers and types of EVs; low latency and high real-time performance to meet power grid scheduling and energy management needs; and support for functions such as charging, discharging control, load scheduling, billing, and data acquisition. Standardization is also crucial to ensure interoperability and connectivity across different vendors and organizations.

V2G communication protocols must meet specific functional requirements. Typical protocols include seven categories: rule 21, Pricing, Load Control, Smart Charging, Monitoring, Restart, and Miscellaneous (e.g., GPS Location). Table 2 details the specific communication content of each functional requirement and evaluates the support of common communication protocols for these requirements.

Table 2 The specific communication content of each functional requirement and the support evaluation of common communication protocols for different functional requirements

Functional requirements	Communication content	Open ADR 2.0 b	IEEE 2030.5	OCPP v1.6	Telematics	SAE suite	IEEE 2030.1.1	ISO 15118
• Supported – means the protocol gets the message there, end to end. • Not supported – means the protocol doesn’t get the message there at all. • Supported in combination – means the protocol can transmit the message with some support from other protocols and/or implementation of specific programming.
Rule 21	Frequency, voltage, scheduling, dispatch location, and inverter type
Pricing	Different tariffs and variable price programs
Load control	Information needed to respond to demand response signals
Smart charging	Information needed to schedule charging sessions
Monitoring	Charging session: timing and electricity consumed and dispensed
Restart	Charging session: interruption time
Miscellaneous	Other information needed to achieve certain use cases (GPS location etc.)

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As the proportion of renewable power increases and EV adoption continues to rise, V2G communication protocols are constantly evolving and improving to meet growing demands and future challenges. In the future, standardized communication protocols are necessary for seamless EV-grid integration. Developing and implementing robust V2G communication standards will ensure interoperability between different EVs, charging infrastructures, and the grid. These protocols will facilitate efficient data exchange and real-time monitoring and control and improve V2G reliability and effectiveness. The advent of sixth-generation (6G) wireless communication technology offers significant opportunities for realizing VGI. With ultra-high data rates, ultra-low latency, and large-scale connectivity, 6G can enable seamless connections among EVUs, EVAs, and the power grid, fostering intelligent vehicle–network communication. Realizing the full potential of 6G in V2G will require the development of new V2G communication protocols.

3.1.3 Electricity demand forecasting of V2G. The factors influencing the distribution of electric vehicles’ charging demand are complex and diverse.¹⁰³ Varying the weights of these influencing factors will alter the distribution pattern of charging demand. Therefore, it is essential to analyze the interaction mechanisms between these factors.¹⁰⁴ Factors affecting load charging demand distribution can be categorized into objective and subjective factors, stemming from external environments and personal behaviors.¹⁰⁵ Objective factors primarily consider the impact of external conditions such as the scale and quantity of EVs, vehicle battery characteristics, and the travel environment on load distribution. Subjective factors mainly focus on the influence of personal factors, such as EVUs’ behaviors and psychologies, on the temporal and spatial distribution of charging demand.

Numerous studies on charging demand forecasting have been conducted,¹⁰⁶ which can be divided into long-term, medium-term, short-term, and ultra-short-term based on the forecasting period.¹⁰⁷ Charging demand forecasting generally falls under short-term load forecasting, primarily predicting the charging demand for the next 6–48 hours.¹⁰⁸ Forecasting methods can be classified into three types: mechanism model-driven methods, data-driven methods based on artificial intelligence,¹⁰⁹ and mechanism-data fusion-driven forecasting methods. The data-driven method can integrate historical charging demand, weather, temperature, and other multi-source datasets to simplify the forecasting model without assuming a large number of mechanism model parameters. However, the mechanism model-driven method relies less on historical data and is more broadly applicable, though its analysis process is more complex and its reliability is not as high as the data-driven method.

Operating characteristics of different EVs vary significantly, and the randomness and uncertainty of charging behaviour make it challenging to establish a unified forecasting model.¹¹⁰ By integrating mechanism models and data,¹¹¹ the advantages of both can be combined to effectively forecast the spatio-temporal distribution of charging demand and improve forecasting accuracy. The mechanism-data fusion-driven charging demand forecasting method is shown in Fig. 11. Currently, there is limited research on charging demand forecasting methods driven by the fusion of mechanism and data.¹¹² Additionally, integrating more practical influencing factors and reducing computational dimensions remain future research priorities.

Fig. 11 Forecasting electric vehicle charging demand driven by mechanism-data fusion. Firstly, the data layer integrates multi-source data such as road networks, weather, and charging infrastructure. Secondly, a road traffic model is established based on the road network,¹¹³ a charging model is built using charging and discharging information, and travel patterns are mined using weather, temperature, and travel information combined with the data-driven method to develop EVUs’ travel models.¹¹⁴ Finally, path optimization algorithms, Monte Carlo simulations, and deep learning algorithms are employed to explore the spatio-temporal distribution characteristics of charging.

Accurate forecasting of charging demand is crucial for maintaining grid stability and optimizing resource allocation. Given the regularity of EVU charging demand in time and space, future research should integrate multiple information sources such as TN, weather conditions, charging stations, and the power grid, while considering user decision-making randomness. Exploring the spatial distribution of charging demand using path optimization models and queuing theory and establishing time series forecasting models based on deep learning will further enhance forecasting accuracy. Comprehensive and accurate identification of influencing factors is vital for ensuring reliable charging demand forecasting methods. Utilizing advanced AI and machine learning models, alongside comprehensive EV mobility data, will help better manage charging plans, reduce peak loads, and enhance renewable energy integration.

3.2 Orderly regulation of the electric vehicle-grid-aggregator in vehicle-to-grid

The orderly regulation of the EV-grid-aggregator framework, including smart charging and discharging strategies and power market trading mechanisms, is critical. This regulation ensures that EVs can be seamlessly integrated into the power grid, optimizing their charging and discharging cycles and enabling their participation in power markets. Such measures help maintain power grid stability and maximize the benefits of V2G.

3.2.1 Electric vehicle management based on intelligent charging and discharging strategies. Charging and discharging strategies are critical for maximizing the efficiency and lifespan of electric vehicle batteries while optimizing the benefits of V2G systems.¹¹⁵ By managing charging, discharging, and reactive power,¹¹⁶ EVs can provide various services to charging infrastructures, distribution systems, and transmission systems,¹¹⁷ including peak load reduction, power quality improvement, line congestion alleviation, and ancillary services.¹¹⁸ Furthermore, these strategies can enhance the consumption of renewable energy within the power grid.

Power markets use these strategies to balance supply and demand,¹¹⁹ particularly during peak periods.¹²⁰ By intelligently managing charging and discharging cycles,¹²¹ V2G can optimize renewable power utilization, mitigate grid congestion, and provide grid services such as frequency regulation and load balancing.¹²² The strategies must also consider factors such as battery health, EVUs’ preferences, and market dynamics to create a sustainable and efficient V2G ecosystem.¹²³ Research on the charging and discharging strategies typically involves three stakeholders: power grids, EVAs, and EVUs (Fig. 12(a)). Without coordinated instructions for the EVUs, unordered charging fails to protect the interests of these stakeholders. Unordered EV charging can increase the peak-valley difference of the power grid, hindering the operation of the power grid.¹²⁴

Fig. 12 Orderly regulation of the electric vehicle-grid-aggregator in vehicle-to-grid. (a) The role of electric vehicle charging and discharging in the power system. Charging strategies involve determining the optimal time to charge EVs, considering factors such as power demand, grid stability, and renewable power availability. Smart charging prioritizes EV charging during periods of low power demand or high renewable power generation, reducing power grid's pressure and minimizing EV costs. Conversely, the discharging strategy focuses on determining when and how much power should be supplied back to the grid from EVs. (b) The process of electric vehicles participating in power market trading. (Demand side including: EVUs and EVAs). EVs participate in the clearing process of the power market. Through the coordination between the power grid and the power market, EVs first charge and store power during the period of low electricity price. When the price of electricity is high or the grid needs it, EVs put stored electricity back into the grid. Market operators develop charging and discharging plans based on supply and demand and charging and discharging quotes of EVs and monitor and adjust the operation of EVs in real time through smart meters and trading platforms. Ultimately, through data transmission and transaction settlement, EVs achieve economic gains while providing flexible regulatory capabilities for the power system.

Fig. 13 Decision sequence of charging infrastructure planning. The flow chart illustrates the atypical sequence of decisions and actions required to solve a charging infrastructure planning problem. Transportations and power distribution networks are closely linked to charging infrastructure. The bottom layer represents the transportation network, and the upper layer represents the power distribution network.

Additionally, the potential resources of EVs for the power grid are not fully utilized, leading to higher charging costs for EVUs compared to orderly charging and intelligent bidirectional V2G modes. Furthermore, disorderly charging limits the EVAs’ abilities to treat EVs as mobile energy storage devices and integrate resources to create value, thereby reducing profit opportunities. To address these issues, it is necessary to formulate reasonable charging and discharging strategies at different levels for various stakeholders to achieve orderly or intelligent bidirectional charging.

From the perspective of the EVAs, EVAs simultaneously address the charging demand (or discharge capacity) of EVUs and the power supply resources (or repurchase power demand) of the power grid.¹²⁵ As an intermediate power-hub for V2G, EVAs perform multiple functions. First, they serve as an electric power trading intermediary, coordinating with the power grid to purchase and sell electricity and establishing bidirectional electricity prices for EVUs. Second, they function as a resource scheduling center for power demand response, selecting or guiding the EV charging and discharging periods and power modes based on information such as the SoC of batteries and the load of distribution network nodes. Third, they act as an integrated analysis platform for the behavior characteristics of EVUs,¹²⁶ gathering information on EVUs’ tendencies for charging and discharging power, response to price changes, arrival and departure times, battery state of charge and health, and psychology and response characteristics of EVUs.

From the perspective of the power grid, due to the spatial and temporal volatility of EVs and renewable power, it is desirable for EVs (or reverse power supply) to complement renewable power. This approach can reduce the negative impacts of disorderly charging,¹²⁷ improve renewable power rates, and promote low-carbon power transformation. The key role of power grids in V2G is primarily reflected in providing price guidance for EVAs and EVUs.

From the perspective of EVUs, the spatio-temporal distribution characteristics of EVs,¹²⁸ the travel time-mileage model, and the mileage anxiety of EVUs significantly affect the overall mathematical expectation of charging demand. As the underlying resource for V2G, electric vehicle batteries belong to EVUs in charging mode (and to EVAs in the operating mode of power rental batteries). The extent to which they can participate in V2G, and for which specific power grid, depends on the psychological and behavioral characteristics of EVUs. EVUs’ travel characteristics and price response levels directly impact charging demand forecasting and indirectly influence the formulation of charging strategies, including charging and discharging periods and power selection.¹²⁹Table 3 summarizes representative research on EV charging and discharging strategies.

Table 3 Charging and discharging strategies in V2G

Strategy	Stakeholders	Objective	Ref.	Methods
• EVA, Electric vehicle: ^1charging station, ^2battery swapping station. • EVU, Electric vehicle user: ^3electric vehicle, ^4vehicle battery.
V1G (unidirectional)	Power grid	• Minimize the operation cost and peak-valley load difference	118 and 130–132	• Sequence operation theory and chance constrained programming.
	EVA^1,2	• Minimize power grid operating cost		• Weighted undirected graph model and queuing theory-based algorithm.
		• Minimize EVA^1,2 charging time and cost		• Markov decision.
		• Find the optimal depleted batteries
V2G (bidirectional)	Power grid	• Minimize peak-valley load difference	116, 120, 125, 126 and 133–135	• Two-stage optimization.
	EVA^1,2	• Minimize EVA^1,2 charging time and cost		• Improved optimization algorithm.
		• Minimize battery wear		• Receding horizon control.
		• Minimize power grid operating cost		• Mixed discrete programming.
				• Reinforcement and machine learning.
	Power grid	• Flatten the charging demand curve (minimize charging demand fluctuation).	117, 119, 121, 123 and 124	• Non-cooperative game.
	EVU^3,4	• Minimize the power grid operation cost		• Time-expanded V2G network graph and AI technology.
		• Minimize EVU^3,4 charging time and cost		• Shortest Paths scheduling.
		• Maximize EVU discharging benefits		• Queuing theory-based algorithm
		• Minimize battery wear		• Improved optimization algorithm
				• Fuzzy theory-based analysis of EVUs.
	EVA^1	• Charging demand allocation	136 and 137	• Hierarchical control and Dynamic programming.
	EVU^3	• Improve the efficiency and benefits of the charging and discharging processes		• Rolling optimization.
				• Multistage optimization.

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In the future, V2G will evolve towards cehicle-grid integration (VGI), with intelligent charging and discharging of EVs being key to this transition. On one hand, advanced intelligent charge and discharge control strategies are essential. Intelligent control can enhance grid operational flexibility, accelerate the utilization of renewable energy, and promote the decarbonization of electricity. Therefore, it is necessary to economically and effectively maintain the supply–demand balance of the power grid using optimized charging and discharging controls. Notably, as larger electric vehicles with varying capacities continue to integrate into the grid, developing reasonable and diversified charge and discharge control strategies remains a challenge.

3.2.2 Incentive power market trading mechanism in V2G. With the reform of the power market system, it is imperative to develop a friendly and interactive market trading mechanism for V2G, considering the interests of power grids, EVUs, EVAs, and other stakeholders. The process of EVUs participating in power market trading is illustrated in Fig. 12(b).¹³⁸ Establishing a power market trading mechanism is crucial for incentivizing large-scale EVUs to participate in V2G.¹³⁹ By clarifying the access conditions, incentive methods, and trading mechanisms for EVs in the power market and integrating the interests of all stakeholders,¹⁴⁰ a win–win situation can be achieved between EVs and the power grid, technology and business models, broadening the interaction at the lower level of V2G.

Currently, research on the power market trading mechanism and economic operation in V2G mainly focuses on the formulation of charge and discharge pricing strategies under V2G and the evaluation of economic benefits for all stakeholders.¹⁴¹ However, there is limited research on establishing a convenient, efficient, safe, and feasible power trading management platform. There is also a lack of technical means to ensure the openness, transparency, and data security of EVUs participating in power market transactions. Blockchain technology, with its characteristics of decentralization, information security, and openness, can address these issues.¹⁴² Therefore, integrating V2G with blockchain technology can advance research on electric vehicle power trading strategies and models, profit game strategies between EVAs, and optimization of EV charging guidance. This integration can further realize secure autonomous trading and encourage EVUs to actively participate in V2G services.

Meeting the driving needs of owners is a prerequisite for EVUs to participate in power grid scheduling. Therefore, the preferences of EVUs must be fully respected when scheduling EVUs. Formulating reasonable charge and discharge prices to promote positive interaction between EVs and the power grid is essential for encouraging EVUs to participate in V2G services. By assessing the needs of various real-world scenarios and setting corresponding charge and discharge prices,¹⁴³ EVUs can be guided to serve the power grid effectively while considering EVUs’ economics,¹⁴⁴ ensuring the maximization of common interests for both stakeholders.

Regarding the specific application scenario of EV entry bidding,¹⁴⁵ by deterministic or stochastic programming,¹⁴⁶ some research focuses on stabilizing load curve fluctuations or improving economic benefits between EVUs and EVAs and between EVAs and power grids, based on charging demand characteristics.¹⁴⁷ Other research, using game theory, explores demand-side discharging bidding mechanisms to maximize EVAs’ benefits while achieving load reduction and valley filling for the grid. Table 4 analyzes the needs of multiple stakeholders in the power market and summarizes existing research on EV grid bidding. However, most existing studies focus on deterministic models of EVUs’ power demand and electricity price. The proposed bidding mechanisms often achieve a win–win situation between EVUs and EVAs and between EVAs and the power grid.¹⁴⁸ A few studies fully explore the demand response of charging and discharging resources of EVs with high variability,¹⁴⁹ aiming for an economic win–win situation among EVUs, EVAs, and the power grid.

Table 4 Electric power market trading mechanisms considering the needs of multiple stakeholders in the process of V2G

Type of market		Optimal objectives	Ref.	Pricing method
Ancillary services markets	Peak shaving	• Minimizing load imbalance	140, 144, 150 and 151	• Deterministic optimization and stochastic planning of grid peaking demand response by EVs (or aggregators)
		• Maximizing the power grid profit
	Frequency regulation	• Maximizing profits of EV users, aggregators and the V2G system	76, 139 and 152–154	• Deterministic model considering frequency regulation demand response
		• Minimize EV battery degradation		• Game model that equalizes the cost-benefit of each participant
		• Maximizing the regulation revenue
	Spinning reserves	• Maximizing the operating profit of aggregators	155–158	• Separated and cooperative decision-making game models
		• Maximizing the profit of EV users and grid operators		• Uncertainty optimization model considering stochasticity of EVs and renewable power
Spot market	Day-ahead markets	• Minimizing the market costs of aggregators	143, 159 and 160	• Hierarchical blockchain-based power trading model considering the intelligent contracts
		• Maximizing the social welfare of V2G systems and the profit of participants		• Demand response models considering complex cost-benefit of multiple subjects
	Intraday markets	• Minimizing the operational cost based on real-time pricing	139 and 160–164	• Decentralized power trading model based on blockchain and smart contract
		• Maximizing EV users’ satisfaction and social welfare		• Deterministic game models that consider both day-ahead and real-time markets

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Effective incentives and trading mechanisms are essential for encouraging EV participation in V2G. Currently, V2G incentive and trading mechanisms are not mature enough to maximize EV flexibility and effectively coordinate stakeholder interests. Therefore, developing effective V2G market trading mechanisms is necessary. The power market should integrate V2G services, dynamic pricing, and demand response strategies to study EVU and EVA price sensitivity, develop incentive mechanisms, and enhance participation in power market regulation, achieving positive trading mechanisms. Economically utilizing V2G capacity to provide grid services remains a key issue. These mechanisms should offer economic benefits to EVUs as they contribute to grid stability and renewable energy consumption. An innovative power market will create a more flexible and resilient power system.

3.3 Charging infrastructure planning for integrated transportation and power distribution networks

Charging infrastructure is an essential component of smart grid systems, and its rapid deployment is critical to support the transition to EVs.¹⁶⁵ Globally, from 2022 to 2030, the total number of charging infrastructures in each country is projected to double (Fig. 14).¹⁶⁶ Planning for charging infrastructures involves identifying optimal locations and capacities within a transportation network (TN) while considering the constraints of the power distribution network (DN).¹⁶⁷ Given the interaction between the TN and DN, the placement and capacity of charging infrastructures influence not only the quality of power supply,¹⁶⁸ travel behavior, and ease of charging within the TN but also the power DN and associated costs. Consequently, planning for charging infrastructures may benefit one network at the expense of another.¹⁶⁹

Fig. 14 The growth trend of the total number of charging infrastructures in various countries.

Planning for charging infrastructure usually involves five steps (Fig. 13).¹⁷⁰ The first step is to determine the type of charging infrastructure being studied,¹⁷¹ as well as the target area and scope of the TN and the DN. The types of charging infrastructures involve the charging technologies to be deployed,¹⁷² and different charging technologies have varying charging speeds,¹⁷³ which affect the convenience for EVUs,¹⁷⁴ as well as the regional grid costs and the planning required for the charging infrastructure. To avoid non-optimal solutions,¹⁷⁵ it is typically necessary to consider a mix of multiple types of charging infrastructures (Table 5).

Table 5 Planning method of multi-type charging infrastructures

Types of infrastructures		Ref.	Optimization methodology	Application scenario	Charging characteristics
			Math models		Power range (kW)	Duration (h)	Efficiency (%)
Location-routing problem (LRP); optimal power flow (OPF); mixed integer linear programming (MILP).
Charging mode	Destination charging equipment	170 and 176–184	• Location-routing problem (LRP)	Residential	3–7	8–12	95
			• Mixed integer linear programming (MILP)	Commercial	10–60	1–6	95
	Fast charging stations	165, 168, 172, 175 and 185–190	• LRP.	Public station	30–300	0.25–2	95
			• MILP.	Specialized station	100–500	0.15–0.5	95
			• Optimal power flow (OPF) models considering traffic flow
			• User equilibrium (UE)
	Wireless charging lanes	171 and 191	• LRP.	Public station	3–15	4–8	95
			• OPF.	On-board charging	3–5	Rely on trip	<90
			• MILP (multi-class dynamic system optimal model)
	Mobile charging vehicle	192	• MILP	Emergency charging	15–30	2–4	<90
Swapping mode	Battery charging and swapping stations and dispatch vehicles	12, 174 and 193–196	• LRP	Private or commercial	3–500	0.15–0.25	−95^d
			• Time-space battery logistics model
			• Two-level planning model

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Secondly, model selection is crucial. The methods for modeling a TN and DN will determine the details and speed of modeling, affecting scalability in large-scale systems. The demand modeling approach for a TN involves choosing CI positioning methods, such as node-based, flow-based, and agent-based methods, as well as deciding whether to use queue systems.^176,185 Modeling a DN involves choosing a power flow model, selecting an algorithm, and determining the voltage level to be analyzed.

The third step involves selecting the planning objectives and constraints for CI optimization. The selection of relevant planning objectives is crucial for the results and applicability of practical planning methods. In general, goals can be classified as TN-related, DN-related, and CI-related. Considering that different goals often lead to trade-offs between different perspectives, which vary depending on the focus of the research, the goals should be carefully evaluated and selected^yy. Additionally, constraints must ensure an optimal feasible solution according to the actual conditions.¹⁷⁸

Finally, a suitable optimization algorithm is selected according to the established objective function and constraint conditions. The typical output of optimization includes the optimized location of CI under given conditions and the capacity of each location. It can also be a multi-objective problem with conflicting objectives. Furthermore, the performance of the TN and DN can be evaluated, and the overall cost of CI deployment can be determined. Popular optimization methods include traditional optimization methods, nature-inspired optimization methods, or hybrid methods. Traditional methods are optimization techniques for systems with linear or nonlinear constraints and objective functions, where all or some of the variables are constrained by integers.¹⁹²

The strategic location of charging infrastructure is critical for maximizing V2G efficiency. Considering factors such as EV density, mobility, and grid capacity, proper planning and deployment of charging infrastructure will ensure optimal accessibility and utilization. Using probabilistic methods to model congestion and available capacity is key to improving system adaptability. Additionally, site planning should consider geographical and temporal variations in charging demand to more accurately reflect real-world conditions. Site planning must also account for phased connectivity strategies, future growth in EV penetration, and increasing demand for charging services.

4 Policy implications in the promotion of V2G

Currently, EV policies primarily focus on advancing charging infrastructure, reducing EV purchase costs, and offering incentives for both EVUs and manufacturers. Governments stimulate the EV demand through measures such as purchase subsidies, tax exemptions, and preferential parking spaces.¹⁹⁷ In China, for example, subsidies, dual credit policies, and public procurement programs support the production and consumption of new energy vehicles.¹⁹⁸ In Europe and the US, policies emphasize the establishment of timelines for banning internal combustion engine vehicles, the expansion of charging networks, and the enforcement of stringent emission regulations, all of which facilitate the gradual transition to zero-emission vehicles.¹⁹⁹ Additionally, global policies stress the integration of smart charging systems with renewable energy sources. Countries like Germany and Japan promote the use of EVs alongside solar and wind energies, aiming to reduce carbon emissions during the usage phase.²⁰⁰ Moreover, efforts to strengthen charging infrastructure, foster collaboration between the private and public sectors, and provide public funding are driving the widespread and sustainable development of charging facilities. To further accelerate EV adoption, future policies must ensure the accessibility of charging stations and promote the development of intelligent charging systems, which will not only enhance energy efficiency but also support the integration of VRE.

The core of V2G policy focuses on technological development and market incentives. By enabling bidirectional charging, V2G facilitates deep interaction between EVs and the power grid, balancing grid loads and stabilizing grid operations.²⁰¹ Globally, policies mainly support the research and deployment of V2G technologies, the development of smart grids, and the integration of bidirectional charging capabilities into charging infrastructure. For example, Europe promotes the adoption of the ISO 15118 communication protocol, while the US encourages user participation through demand response programs and financial incentives for EVUs.²⁰² In China and Japan, V2G is integrated with smart grid technology, with an emphasis on its potential for peak load management and disaster resilience, particularly in renewable energy integration and regional power optimization.

Furthermore, policies should include incentives to encourage EVs to participate in V2G, optimizing grid loads and enhancing renewable energy utilization, ultimately achieving efficient energy use and reducing carbon emissions.²⁰³ Research shows that EVUs’ acceptance of smart charging is influenced by various factors, primarily economic benefits and control over the charging process.

To promote the adoption of EVs and V2G, it is essential to implement comprehensive policy measures.²⁰⁴Fig. 15 provides a detailed analysis of the future V2G policy promotion route. First, the tax credits for charging infrastructure should be expanded to replace those for traditional fuel refueling facilities. This policy aims to reduce upfront deployment costs, which is critical for enhancing investment willingness among consumers and businesses. Second, the establishment of unified technical standards is imperative. A national framework covering charging interfaces, communication protocols, and safety requirements should be developed to ensure seamless integration between vehicles and the grid, eliminating market barriers caused by technical incompatibilities.²⁰⁵

Fig. 15 Detailed analysis of the future V2G policy promotion route.

Future policies must also address the practical needs of EVUs by introducing flexible pricing models, including real-time and peak pricing, while ensuring sufficient charging speed and safety when required to increase user participation. Additionally, V2G functionality should be integrated into national and local emergency response plans. Priority should be given to deploying bidirectional charging facilities in critical infrastructures such as schools and hospitals, enhancing community energy resilience during disasters. Simultaneously, governments should strengthen public education on energy efficiency to improve awareness of the “prosumer” model and encourage broader adoption of V2G technologies.²⁰⁶

Finally, research funding is necessary to evaluate the impacts of bidirectional charging on battery performance, addressing consumer concerns about battery degradation. Such research should also assess the economic and environmental benefits of V2G to provide empirical evidence for optimizing future policies. The coordinated implementation of these measures will lay a solid foundation for the widespread adoption of V2G technology and facilitate the green, low-carbon transformation of transportation and energy systems.

5 Conclusion and outlook

V2G provides significant and valuable opportunities for the flexible scheduling and optimization management of EVs within modern energy systems. By achieving deep and intelligent connectivity between EVs and the power grid, V2G can effectively assist in balancing the supply and demand of electricity, particularly by regulating energy during peak and off-peak periods. Additionally, it facilitates the better absorption and integration of substantial amounts of renewable energy sources, such as solar and wind power, thereby significantly reducing overall carbon emissions and promoting environmental sustainability. Numerous studies and practical applications have demonstrated that V2G technology exhibits high feasibility and extensive application value in the coordinated optimization of power and transportation systems. This technology not only enhances energy utilization efficiency but also strengthens the stability and resilience of power systems.

However, despite its immense potential, the large-scale and widespread implementation of V2G still faces multiple barriers encompassing technical, market mechanism, and policy regulatory challenges. Currently, the lack of unified industry standards for communication protocols results in compatibility issues between different systems and devices. Moreover, bidirectional charging infrastructure has not yet been widely adopted, limiting the application scope of V2G. Additionally, real-time interaction strategies and regulatory mechanisms between the power grid and EVs require further refinement and optimization. These technical and infrastructural factors impede the rapid promotion and extensive application of V2G. Concurrently, insufficient market incentives fail to adequately motivate EV owners and utility companies, while fragmented and inconsistent regulatory policies create uncertainties and barriers for industry participants during investment and collaboration processes. This, in turn, affects investor confidence and the enthusiasm for multi-party cooperation.

As cross-sector collaboration deepens and more valuable practical experience with V2G is accumulated, V2G is expected to further advance the development of green transportation and the overall transformation of energy systems towards more environmentally friendly and sustainable directions. On one hand, continuous research targeting key technological bottlenecks—such as the optimization design of smart grid architectures, the enhancement of real-time load management systems, and the strengthening of data security and privacy protection measures—will provide a more robust and reliable technical foundation for the stable operation and widespread application of V2G. On the other hand, the establishment and improvement of appropriate policy incentive mechanisms, including tax incentives and subsidy policies, as well as the development of innovative commercialization models and market mechanisms, will help stimulate market vitality and attract more industrial investment and resources into the research and promotion of V2G technology. Through the concerted efforts and collaboration of governments, enterprises, research institutions, and the public, V2G has the potential to become a crucial pillar in constructing a green and low-carbon society. This will significantly enhance the sustainability of energy systems and provide stronger and more enduring support for global sustainable development goals.

Data availability statements

Data for this article, including the 2023 EV charging demand in the US, the capacity of VRE, the number of charging infrastructures in China, the US, and the European Union, the lifecycle carbon emission data of EVs from the IEA, the monthly power data for the US in 2023, and the carbon market price for the US in 2023, are available at the International Energy Agency (IEA), US Energy Information Administration (EIA), International Carbon Action Partnership (ICAP) and European Commission, China Association of Automobile Manufacturers (CAAM), and the International Renewable Energy Agency (IRENA)] at https://www.eia.gov/electricity/monthly/, https://icapcarbonaction.com/en/ets-prices, https://www.iea.org/data-and-statistics/data-tools/ev-life-cycle-assessment-calculator, https://www.eia.gov/electricity/monthly/, https://alternative-fuels-observatory.ec.europa.eu/transport-mode/road/european-union-eu27/country-comparison, https://en.caam.org.cn:9527/, and https://www.irena.org/Publications/2024/Jul/Renewable-energy-statistics-2024.

Conflicts of interest

The authors declare that they have no competing interests.

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (52107083, 52307072 and 62372039), the Guangxi Science and Technology Major Program (AA22068071 and 2024Z250), the Fujian Science and Technology Plan-STS Institute Provincial Cooperation project under grant (2022T3041, 2023T3015, and 2023T3046), and the Innovation Project of Guangxi Graduate Education (YCBZ2024033).

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