According to the Department for Business, Energy & Industrial Strategy (BEIS) [1
], the transport sector is the largest emitter of greenhouse gases (GHG) in the UK, responsible for 27% of emissions in 2019. The energy supply sector was the second largest emitter, responsible for 21% of UK GHG emissions. The increase in renewable energy generation, the reduction in the use of coal for electricity generation, and the closure of the largest deep coal mines were some of the factors that influenced the reduction in emissions from energy, as this was the largest emitter until 2016, when transport took over [1
The Intergovernmental Panel on Climate Change (IPCC) reaffirmed the near-linear relationship between carbon (CO2
) emissions and global warming, as well as the need to reach net zero to stabilise the increase in global temperature [2
]. According to the IPCC [3
], net-zero emissions are achieved when GHG emissions caused by human activities are balanced by anthropogenic removals [2
]. The UK has established a pathway to achieve net zero in the transport sector and the energy sector. The goal is to decarbonise all forms of transport [4
] by increasing cycling and walking, supporting the adoption of zero-emission buses, decarbonising railways, phasing out petrol and diesel cars, and accelerating maritime and aviation decarbonisation. On the other hand, the UK energy sector aims to support renewable energy generation using smart and flexible energy systems [5
], defined as “one which uses smart technologies to provide flexibility to the system, to balance supply and demand and manage constrains on the network” [6
] (p. 9). It has been highlighted that this flexibility can be provided by electricity storage, flexible demand, and flexible generation [5
Integral to both transport and energy plans is the transition to electric mobility. For instance, from the transport perspective, in the UK, plans are in place to stop the sale of diesel and petrol cars by 2030 [6
] and to support the deployment of charging infrastructure to fulfil drivers’ needs and establish a circular economy for the batteries of electric vehicles (EVs) [7
]. The plan for the decarbonisation of transport recognises the need to expand the electricity system to absorb the load from EVs, which may increase energy demand by up to 20% by 2050, compared to a system without EVs [7
From the energy system’s perspective, it is recognised that EVs will represent a significant increase in energy demand; however, it also represents an opportunity to add flexibility to the energy system [8
]. For instance, it is expected that technologies such as smart charging could support the grid by avoiding charging during peak time, and vehicle-to-grid (V2G) could be used to send the energy stored in EVs back to the grid on demand (e.g., at peak electricity demand) [8
The concept of using electric vehicles as energy storage and support for the energy grid was first published by Kempton and Letendre in 1997 [9
]. According to the authors, some of the benefits of this new technology are the capacity to support the grid as a backup in case of power outages and the low cost that EVs would represent in comparison to electric generation infrastructure. In 2001, Kempton et al. [10
] defined vehicle-to-grid (V2G) as “…using the electric storage and/or generation capacity of battery, hybrid and fuel cell vehicles to send power to the grid” (p. 1). The authors also described that this technology would provide air-pollution benefits in three ways: (i) incentivising drivers to switch to EVs by generating an economic revenue from selling the energy stored in their vehicles; (ii) supporting the energy grid during peak times and emergency generation, conditions that are usually covered by fossil fuel plants; and (iii) storage of renewable sources, such as wind and solar [10
According to Waldron et al. [11
], despite V2G technology being developed more than 20 years ago, studies and publications have been ramping up over the last few years. This recent deployment of the technology is explained by the increasing uptake of EVs, the increased capacity of renewable energy generation, technology development (e.g., battery capacity), and the need to reduce carbon emissions from the transport and energy sectors [11
There are currently multiple V2G trials in the UK, developing different business cases. For instance, EDF Energy proposed V2G for business by providing the charger, installation, and maintenance [12
]. The benefits for the end user are estimated to be around 9000 miles per year of electric fuel to run their vehicle for free if they connect every day from 4:30 p.m. to 8:00 a.m. of the next day. Octopus Energy, with the project Powerloop [13
], offered a bundle for domestic users, including leasing of a vehicle, providing a charger and the option to connect the vehicle 12 times per month between 6:00 p.m. and 5:00 a.m. to obtain a cashback benefit of GBP 30 on the user’s energy bill. Project Sciurus [14
] offered a charger and an app to control and schedule the charging sessions. The energy from the vehicle would be used to power the user’s home, and any excess would go back to the grid. The economic incentive was 30 pence per kilowatt hour discharged [15
]. The Electric Nation V2G trial [16
] offered users a minimum reward of GBP 120 per year and the possibility to keep the V2G charger for GBP 250 at the end of the trial.
However, according to Sovacool et al. [17
], environmental performance is one of the underexamined topics of vehicle-to-grid. In a review of 197 publications between 2015 and 2017, the authors observed that only 10% of studies reviewed carbon emissions [17
]. The carbon emissions generated during the operation of an EV come from the carbon intensity of the electricity used to charge them [17
]. It is estimated that whereas an internal combustion engine light car emits between 95 and 147 gCO2
/km, an EV with unmanaged charging and smart charging would emit between 35 and 52 gCO2
]; however, these figures would be reduced as the grid is decarbonised. Moreover, V2G-enabled vehicles would allow for a reduction in overall system emissions through offset of grid demand, with studies suggesting they can have effective negative carbon emissions between −116 and −243 gCO2
]. In the report Future Energy Scenarios for the UK [19
], it is forecasted that unmanaged EV charging could increase peak electricity demand by more than 25 GW by 2040, implementing smart charging would result in a peak demand of around 10 GW, whereas V2G could reduce the peak demand by nearly −8 GW by 2040. Additionally, O’Malley et al. [20
] estimated that V2G-enabled fleets can reduce system operation costs by up to GBP 12,000 and CO2
emissions by up to 60 tonnes per year.
The carbon reductions from optimally managed vehicle-to-grid come from several sources:
(i) Managing charging demand of the vehicle: as with one-directional (V1G) smart charging, recharging the vehicle battery after use of the vehicle itself is timed to offset charging to when the grid carbon intensity is low. This reduces carbon emissions relative to unmanaged charging of the same vehicle.
(ii) EVs as battery storage: using any available battery capacity from EVs to store energy at times when system carbon intensity is low allows for strategic discharging of this stored energy when carbon intensity is high. In this way, the EV battery can be used to effectively offset carbon emissions from its surrounding system (applicable at the scale of local buildings, the local energy grid, or the wider national grid).
(iii) EVs to support renewable energy application: as renewable energy is an intermittent source, using EVs as storage when renewable generation is high can facilitate the most effective use of low-carbon generated energy. This method is particularly important in localised energy systems where renewable generation can exceed local demand. V2G can increase local self-consumption of renewables and potentially offset the need for dedicated local battery storage.
(iv) Optimising the battery ageing process: it has been shown that managed V2G charging schedules could reduce EV battery ageing [21
], potentially reducing the embedded carbon associated with EV battery production across a vehicle’s lifetime. Given the complexity of modelling required to determine effects of charging on the vehicle battery, this source is beyond the scope of this paper and is not considered further in the methods presented.
In this paper, the authors present a scenario of integrating vehicle-to-grid in an existing fleet without affecting current service provision, with the aim of reducing carbon emissions from the building energy demand and vehicle operation. To achieve this, the following method was developed: (1) assessment of the strategic location of charging infrastructure based on the vehicles’ behaviour; (2) definition of the charging/discharging profile, prioritising CO2 emission reduction according to the building’s energy demand, renewable energy generation, and grid carbon intensity; and (3) calculation of the carbon savings of the system through simulation. The outcomes of this paper indicate that vehicle-to-grid can reduce the average CO2 intensity per kilowatt in all the scenarios tested. Additionally, it was found that the vehicles analysed can easily make the transition to electric and integrate V2G, as 99.5% of the time, the vehicles fulfilled the charge required for their journeys.
This research presented the potential contribution that charging infrastructure can make to reduce carbon emissions when integrating the energy storage capacity of EVs with the fluctuations of local energy demand and renewable energy generation. The vehicle behaviour analysis introduced the potential of using behaviour data to optimise the location of charging infrastructure and vehicle availability to store and discharge energy. Multiple different approaches to plan electric vehicle charging infrastructure are reported in the literature, such as the node-based approach to minimise the number of chargers while fulfilling the demand of all users; the path-based approach, which aims to provide charging services in common pathways; and the tour-based approach, which estimates charging demand based on parking demand [29
]. For the case of vehicle-to-grid, it was found that only 10% of the stops of the vehicles were longer than one hour, which means that not every stop represents an opportunity to connect to a charger. In this way, a tour-based approach to parking demand considering vehicles energy storage assets would be the most suitable pathway to identify strategic locations for V2G infrastructure.
It was estimated that the five vehicles would be simultaneously available to connect to a V2G charger 55% of the time during the year. This means an equivalent of up to 200 kWh battery storage capacity without requiring additional investment in battery storage. According to Comello and Reichelstein [30
], the U.S. market price of lithium-ion batteries is around USD 171 per kWh and USD 970 per kW. If we consider the entire fleet of 50 vehicles, the battery storage capacity would theoretically be more than 2 MWh, which would correspond to USD 1.94M in traditional battery storage. To provide a scale of battery storage capacity for 50 vehicles, it would be almost the equivalent capacity of the battery installed at the Community Energy Scheme Project SCENe (2.1 MWh), which, over the course of one year, generated 152.8 MWh, which is enough to cover the annual electricity consumption of 64 average properties in the community energy scheme [31
According to the presented carbon emissions and energy demand calculations, it was observed that although the total energy demand of the system was increased by the introduction of vehicle charging, the way that this charging was managed had a significant impact on carbon intensity. The aim of reducing the average carbon emissions was achieved, and the model with the increased fleet (×10) allowed further understanding of how V2G could support a reduction in CO2
emissions. In agreement with Alsharif et al. [32
], the V2G scenarios with an increased fleet (10 vehicles) showed a load shifting, valley filling, and peak shaving (Figure 12
); these benefits of V2G are presented in Table 14
As presented in the introduction, V2G programmes usually require the end user to commit connecting to a charger for a certain amount of time to make the business case feasible. Nevertheless, these clauses may add complexity to the relationship with the end user and disincentivise the adoption of V2G. The analysis presented in this paper suggests that by determining a minimum state of charge required per day based on vehicle usage and assuming a connection of the vehicles every time they are near a charger and remaining over 30 min, it is possible for the diesel and non-V2G compatible electric vehicles to make the transition to EVs and integrate V2G without affecting most of their current operation. However, there were a few events of battery depletion, representing 0.5% of the entire year in the simulated V2G scenarios. These events can be addressed in advance by allowing the end user to manually opt out of the V2G scheme and charge their vehicle to fulfil their travelling requirements. It is also important to note that the simulated scenarios only included the V2G chargers proposed to cover the fleet demand; however, it is expected that V2G infrastructure will be part of a wider charging ecosystem that may offer other types of charging technologies to fulfil charging demand (e.g., smart, fast, rapid, and wireless charging).
In comparison to the results from the existing five-vehicle fleet, the increased impact on carbon intensity of the larger simulated fleet demonstrates that V2G will provide the most benefit in application over larger populations. Whereas the impact of an individual vehicle may be relatively small, there is significant potential for larger aggregated groups of vehicles to act as a single energy storage unit while maintaining their existing use patterns.
Therefore, analysing a wider group of vehicles and buildings would provide a further understanding of how other type of fleets [4
] can support energy storage based on their proximity to charging infrastructure.
This research presented the integration of different sources of data to address the best possible scenarios for vehicle-to-grid. However, this system will need to be improved in terms of capturing and centralising data to allow for larger-scale calculation of the most suitable scenarios according to user needs, building energy demand, renewable energy generation, and grid carbon intensity. According to Alsharif et al. [32
], the integration of electric vehicles into the energy grid may cause regulation issues and peak load rising, but this can be addressed by integrating an advanced power management system and using machine learning to predict the charging scheme 24 h in advance. The findings of this research show how a system can support the transition of integrating EVs without affecting the current operation of vehicles and addressing the issues of peak load demand.
The V2G strategy presented is a scheme based on fixed thresholds calculated from historical data. This scheme can be further optimised in several ways:
Defining thresholds per season to allow for maximisation of the benefits of vehicle-to-grid. This will allow for better capture of vehicle and energy usage per season.
Calculating the V2G scenarios 24h to 48h in advance. This will require integration of grid carbon intensity and building energy demand forecasts. This will also require integration of prediction of vehicle availability to connect to V2G chargers, as developed by Shipman et al. using machine learning techniques [23
The analysis was focused on the use of historical datasets. However, the same parameters could be used in real-time applications if V2G chargers run as ‘data aggregators’ to optimise the operation of the system. This system would require predictions of grid carbon intensity, such as the carbon intensity API [25
], which provides a forecast 96 h in advance for each region in Great Britain, and predictions of energy demand, local renewable energy generation, and vehicle availability, which could be integrated using machine learning (e.g., [23
According to the report “Understanding the true value of V2G” [35
], a user can achieve around GBP 436 of annual revenue with smart charging (with a plug-in rate of 75%). This economic incentive represents the feasibility of the business model. However, the capacity of vehicle-to-grid to reduce carbon emission from the grid seems to be the only charging alternative to decarbonise the transport system in the pathway to achieve net zero and support the energy grid as presented in the Future Energy Scenarios Report [19
This paper presents an overview of the role of V2G to support decarbonisation of the transport and energy sectors in the UK. The authors presented a method to assess V2G scenarios using real-world behaviour data from non-V2G-compatible vehicles, building electricity demand, local renewable energy generation data, and grid carbon intensity.
The outcomes of the simulated scenarios suggest that unmanaged charging increased the net energy demand and CO2 imports of the system, whereas V2G allowed for a reduction in average CO2 imports despite adding the energy demand of charging EVs.
It was possible for the diesel and non-V2G-compatible electric vehicles to operate in a simulated V2G scenario; therefore, it is expected that this fleet would be EV-V2G compatible in the real application without affecting most of its operation. However, instances of battery depletion were observed for Buildings B and C; this occurred two times in a year for the V2G scenarios, which is equivalent to 0.2–0.5% of the time. Therefore, an opt-out mechanism is suggested for V2G schemes to allow users to charge on demand on busy days.
The impact of unmanaged charging was further increased with a larger fleet (10 vehicles). Conversely, the V2G scheme was able to significantly flatten the average energy import load throughout the day by the combined mechanisms of diverting vehicle charging demand to times of lower carbon intensity.
The location analysis suggested that all vehicles were at the proposed V2G locations more than 55% of the time. These stationary vehicles equate to a 200 kWh battery storage capacity, which would be available without requiring further infrastructure investment. Although the impact of an individual vehicle may be relatively small, there is a significant potential for larger aggregated groups of vehicles to act as a single energy storage unit while maintaining their existing use patterns.
The proposed optimisation of the system would rely on access to different sources of data from vehicles, building energy demand, renewable energy generation, and grid carbon intensity with 24–48 h forecasts and the implementation of optimisation models using machine learning and artificial intelligence techniques.
Future charging schemes aiming to optimise carbon emissions should take battery degradation parameters into account; for instance, Truong M. N. Bui et al. [21
] propose a VxG charging model that can optimise battery ageing.