Data from Energy Systems Catapult’s Living Lab showed that at around 48% EV uptake, the largest daily electricity demand spike shifted from the early evening to the middle
Data from Energy Systems Catapult’s Living Lab showed that at around 48% EV uptake, the largest daily electricity demand spike shifted from the early evening to the middle of the night. It’s a good example of how more widespread adoption of new technologies will place challenges on networks in new and sometimes unanticipated ways. Here, Claire Rowland, senior manager for the Living Lab and Whole Energy Systems Accelerator at Energy Systems Catapult, explains why accommodating new technologies will mean both network reinforcement and greater flexibility.
According to recent data from the Society for Motor Manufacturers and Traders, there are more than two million battery electric cars in use. As the total number of electric vehicles continues to increase in the coming years, what demands will that place on our energy networks?
One way of finding out is creating a living laboratory, made up of real houses using EVs and EV chargers, and using data from those homes to create a virtual community, which can simulate what would happen if adoption of EVs was higher. That’s precisely what the Catapult’s Living Lab has done.
Analysis of 854 homes showed that at around 48% EV uptake, the highest peak in electricity demand shifted to the middle of the night, as a result of cheap overnight EV tariffs. At this level of adoption, the new peak is twice as large as the traditional early evening peak. This shows how a solution like overnight charging, which works well at lower rates of adoption, could create new constraints as more households adopt the same technology and tariff.
While this data comes from residential clusters, it represents the exact scenario fleet managers face when plugging in dozens of vans at a single depot overnight to catch cheap tariffs.
As electrification increases, technologies such as EVs and heat pumps will not only increase demand on networks, but also change where that demand will appear and when it will peak.
Adoption is unlikely to be evenly distributed. Several streets supplied by the same transformer may adopt new technologies much faster than the surrounding area. The impact will also vary because some parts of the distribution network have more spare capacity than others.
The challenge can also work in reverse. In an area with high solar uptake, many homes may export electricity simultaneously on a sunny day. This can raise local voltage and cause protective systems in equipment such as EV chargers to switch off.
Network operators could respond by upgrading infrastructure but while this will remain necessary in some locations, it is costly and could take years to deliver. Networks therefore also need to understand where flexibility in addition to reinforcement could release capacity from existing assets.
Flexibility involves changing when electricity is consumed or supplied. It could mean delaying EV charging, adjusting heat-pump operation or using batteries to absorb electricity when generation is plentiful and release it when demand is high.
However, national price signals do not always reflect local conditions. A supplier may offer cheap overnight charging because electricity is plentiful across the UK. However, dozens of EVs beginning to charge together could overload a local substation. The national signal is then encouraging precisely the behaviour that the local network needs to avoid.
For fleet managers, this means relying purely on a cheap overnight static tariff could inadvertently trigger local network restrictions or smart-charging curtailment just when your vehicles need to be charging.
To counter this, fleet operators can utilise these same flexibility services and smart charging software to spread their own depot demand. This avoids costly upgrades to their local grid connection while ensuring vehicles remain fully charged and ready for the morning shift.
The Whole Energy Systems Accelerator (WESA) is a safe testing environment that connects real household demand and controllable low-carbon technologies to a simulated local electricity network.
In a project with Scottish and Southern Electricity Networks (SSEN), WESA tested whether commercial flexibility services could spread household demand sufficiently to help retire some ‘load managed areas’ in northern Scotland.
These areas have historically allowed the network to control when some electric heating systems operate, preventing large numbers of devices from switching on simultaneously. SSEN wanted to know whether market-based flexibility could provide the same resilience without relying on that form of direct control.
The trial flexed EVs, heat pumps and storage heating in up to 200 Living Lab homes. Their real electricity demand was mapped onto the virtual network, allowing the project to test whether different control and pricing methods reduced peaks while still delivering acceptable outcomes for consumers.
One method, ‘dynamic congestion response’ using day-ahead forecasting, performed well enough for SSEN to propose the solution for commercial use. The project also indicated that flexibility could be delivered in social housing without compromising household comfort.
Where decisions about electricity network innovation depend on the interaction between technologies, market signals and real consumer behaviour, networks need evidence of what happens outside of modelling environments. Combining modelling with real-world testing like the example above can help operators identify where flexibility is viable, where market arrangements need to change and where physical reinforcement of the energy network remains unavoidable.
To read more about projects like the one above or find out how WESA could provide support through Energy Systems Catapult, visit: https://es.catapult.org.uk/tools-and-labs/wesa/
