What effect will electrifying the car fleet have on the power industry? How often will cars be recharged, what additional load will this place on the grid, and where might the need for charging expose capacity restrictions on the network? Dr Laurence Chittock, Transport Modeller at PTV Group, considers the issues
In 2019 the UK government declared a climate emergency and committed to reducing carbon emissions to net zero by 2050. With transport accounting for more emissions than any other UK sector, the government’s Road to Zero strategy provides a pathway for the phasing out of combustion engines and a transition to electrified mobility. This is underpinned by a legislated ban on new petrol and diesel cars, previously set at 2040 but brought forward to 2030 in a recent announcement.
Such a ban will create a major challenge for the auto industry, with massive changes required to build enough batteries and shift manufacturing and supply chains. But what effect will electrifying the car fleet have on the power industry?
A single EV can double a household’s electricity demand, so if uptake is initially clustered in certain neighbourhoods, then smart planning is required to mitigate the impacts on the local electricity network. And what level of public charging is required? For those that can’t charge at home their reliance on a public network is critical, so where might they charge and how often?
Balancing supply and demand
These sorts of questions raise issues for investors, public bodies, transport planners, and electricity network planners alike. If infrastructure rollout isn’t planned with both demand and supply in mind, we risk creating an imbalance.
This might create inequalities in who can and can’t drive EVs, for instance between those who have a driveway and those who don’t. By integrating transport models with electricity network models, we can explore these challenges and questions.
Transport models are used widely across the planning industry to support important policy and investment decisions, including for public transport upgrades, road and junction modifications, cycling infrastructure, and pedestrian access.
Inherent in these models is an understanding and representation of the movement of people and vehicles, based on what we know about where people live, and where and how they might choose to travel. This information is vital to understanding future charging infrastructure requirements.
Car distances and patterns are a key requisite for EV energy calculations and the detailed spatial and temporal data in a transport model can help us understand where infrastructure might be needed, and who is likely to be reliant on it.
A set of plausible scenarios have been developed for the project and are centred on two critical uncertainties: EV uptake and public charging provision. The four scenarios explore these uncertainties, helping to understand what might happen if strong EV uptake does or doesn’t materialise, and if widespread public infrastructure is or isn’t installed ahead of need
Pace of change
Although electric vehicle sales are growing, the pace of this change is far from certain and will be influenced by a range of factors, including automotive capacity and costs, consumer choice, political factors such as Brexit, and underlying economic conditions.
With so much uncertainty, scenario planning is crucial to determine what sorts of futures might play out or to help guide progression to a desired future. Such scenarios can then be tested in a model to highlight potential impacts, understand where and how infrastructure can be planned, and enable decisions to be made with the least regret.
These issues and more are being explored in an ongoing project called Charge, involving a consortium of companies led by SP Energy Networks; with EA Technology, Smarter Grid Solutions and transport modelling software company PTV Group. The project aims to facilitate the provision of public infrastructure ahead of time by marrying transport demand patterns from a set of scenario-based models and local electricity network capacity.
Plausible scenarios
A series of plausible scenarios have been developed for the project and represent the collective view from a range of experts across the transport and energy industries. In each one, factors were collectively assessed to anticipate EV uptake, future vehicle range, battery efficiency, and infrastructure options. These scenarios are currently being tested in a transport model covering the Manweb region, providing a tool to understand the potential needs of future electric vehicles.
Level of infrastructure required
The vision-based scenario ‘On Course for Net Zero’ allows us to explore the level of infrastructure required to reach staged carbon reduction targets from 2025 onwards. The ‘Driveway to Electrification’ scenario suggests the requirement for public infrastructure given most people can charge at home.
Conversely, if we wish to avoid this future, we can see what level of infrastructure is needed to support those without a driveway. The detail in these models can thus help both investors, who wish to maximise their utilisation, and local authorities, who want to understand how to support a rapid, yet equitable transition.
Given EVs will significantly increase our demand for electricity, understanding what this will mean for the electricity network is vital for the operators. If they understand where additional demand may manifest, they can see if their existing capacity and supply is sufficient.
In areas where capacity is lacking, they need to identify the business case for investing in network upgrades ahead of time. By understanding future requirements for charging infrastructure today, the electricity network can be planned to facilitate EV usage in the coming years. By understanding the uncertainty the future holds, these decisions can also be made with future-proofing in mind.
The results from this study will be made available on a free-to-use online tool called ConnectMore, due to be released next year. Key insights about charging demand from the transport model and scenarios can be explored alongside detailed data on electricity network capacity. Combined, this will show where connections to the grid can be made at the lowest cost, guiding investment and planning decisions, and helping speed up the necessary transition to a decarbonised future.
This image shows an early development view of the ConnectMore tool with substation capacity overlayed with zonal EV charging demand. Future versions will show capacity data down to feeder level and charging demand broken down by location type, dwell time metrics, and energy demand.
This image shows an early development view of the ConnectMore tool with substation capacity overlayed with zonal EV charging demand. Future versions will show capacity data down to feeder level and charging demand broken down by location type, dwell time metrics, and energy demand.