By Laurence Chittock, Transport Modelling Project Manager for the Charge Project
The Business of Mobility is a series of articles penned by business leaders in sustainable mobility.
The Charge Project is an innovation project combining transport planning and electricity network planning to understand where charge points are needed, and how they can be connected to the electricity grid. It is a collaboration between German software company PTV Group, Smarter Grid Solutions and EA Technology. The project was funded by UK’s energy regulator Ofgem and led by SP Energy Networks. Laurence Chittock, of PTV Group, managed the team responsible for the development of the transport model. Here he discusses their approach in developing a model to predict demand for charging stations in the Manweb region (Merseyside, Cheshire, Shropshire, and North and Mid-Wales).
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Much has been said about range anxiety for EV owners, but I don’t think that’s much of an issue anymore. I think the real problem is charge anxiety – can I rely on the public network?, will the points be working and will they be available? As more people switch to EVs – especially those without driveways – we’re going to face increasing pressure to build out a network of charging stations to serve them. This was what prompted Ofgem and Scottish Power to get behind The Charge Project, which is about modelling future requirements for charging stations.
Extra pressure on the grid
An electric car adds the equivalent of a house to the grid, so the power requirement is not insubstantial. And electricity grids are constrained by the capacity of cables and substations. This means that in some locations, it won’t be possible to install new charging stations without expensive upgrades. So what we’re trying to do is help the electricity network understand the potential demand that is coming to that network, how that might change over time, and how the requirement for charging might impact their network.
Modelling different scenarios
It’s tricky to predict exactly how EV uptake will play out from between now and 2050. Fortunately, our model need not accurately predict EV sales for it to be useful. We simply need to show how different uptake scenarios will affect demand across different areas. And so the base of our model is really current vehicle (all-types) use.
We bought a commercial road network from TomTom and we used census data, national travel surveys, and detailed surveys that ask people specific questions about car use. And we segmented people by income, whether they have a driveway, location of jobs, shops and attractions, etc. And then all this data was calibrated against mobile phone data and traffic count data.
But we weren’t working from scratch in terms of our model. We used PTV’s well-known transport planning software, Visum, and expanded some of its functionalities to represent electric vehicles. Using this software, we were able to represent very detailed trip patterns, in the form of a tour-based model that represents trip chaining as opposed to simple trip only models.
CONNECTMORE Interactive Map
The main output of the Charge Project is the CONNECTMORE Interactive Map. This is a unique tool in that it combines a transport model with an electricity network capacity model. In other words you can see expected EV uptake for certain areas overlayed with the network capacity. In the UK if you want to install charging points and these necessitate a new substation or a network upgrade, then currently you are required to pay for it. So, it’s really useful to have this information in the public domain so installers and authorities can optimize how many charging points are needed, and where they can be connected to the electricity grid at the minimum cost. The next step is to share the approach and findings so our method can be used as a blueprint for other regions. As someone who works in the transport industry, it’s great to be able to work with electricity sector colleagues and combine skills and knowledge to address the need for future public charging.
From a software development perspective there were some interesting challenges we needed to work with. PTV’s Visum tool does not currently have functionality to assess energy demands from electric vehicles. And so we needed to do some scripting in Python to add this functionality. Now that we’ve done so, this functionality can be shared with others to apply to their models.
Future of charging
I have a background in EV research, in fact I did my PhD in assessing infrastructure needs for EVs so have seen how the market has developed over the last 10 years. From a small base, sales of EVs have really grown in the last year or so, and the transition is definitely underway. My sense is that perhaps we’re underestimating the demand EVs will put on the grid. I see the market growing five-fold to 2030, and in some areas this could equate to a doubling of general household electricity consumption. There’s a clear case to be made for smart charging, where the car only draws power during off-peak hours, and it’s likely this will be common in residential settings. In public locations however, we’ve found that charge point operators are currently focused on providing a fast and efficient service and therefore are not that keen on compromising customers with smart charging options. We also see a clear line between those who can charge at home and those who can’t. EV uptake has so far been dominated by those who can charge at home, but this will change as uptake increases. Although those without driveways will be in the minority, our models suggest these drivers will generate most of the demand for public charging. It’s therefore vital that we design the future of public charging for this section of the population.