Current electric vehicle (EV) time of use tariffs incentivise consumers to charge their cars overnight, when electricity demand is low. This works well at current levels of EV adoption. However, real world data from homes in our Living Lab shows that, as more consumers switch to EVs, herding behaviours could create a dramatic new overnight peak in electricity demand.
For some time now, the smart meter data we collect in the Lab has been showing high electricity demand in the middle of the night. Although we have a broad demographic mix of homes in the Lab, we have a higher proportion of homes with EVs and other low carbon technologies, compared to the general UK population.
To understand the scale of this effect, we analysed smart meter data from 854 Living Lab homes for a typical week in November 2024.
Figure one shows half hourly power demand from those homes during that week
The pink profile represents 446 homes with no EVs/chargers. Their electricity usage follows the familiar ‘duck curve’ pattern: a clear peak between 17:00-17:30 as people arrive home from work, with low overnight usage that gradually rises from about 05:30-06:00 as households begin their morning routines.
The blue profile on this graph represents 408 homes – about 48% of our sample – who have told us they own an EV and home charger. Right now, the number of UK homes with an EV charger is around 3%, but this is rising. Living Lab homes show us what future demand could look like and the difference is dramatic.
In homes with an EV, the biggest daily spike in electricity demand isn’t during the traditional early evening peak. Instead, it’s happening in the middle of the night when EVs are charging. And at around 50% EV uptake, the new peak is twice the size of the early evening peak. We see this pattern consistently in the Lab. Amongst groups of homes who all have EVs, for instance those taking part in flex trials using our WESA facility, (pre-trial) smart meter data shows an overnight demand peak around four times the early evening peak.
This doesn’t even represent the maximum potential EV demand peak that could occur. EV owners typically charge their vehicles two to three times per week at home (though some homes have multiple EVs and may charge more frequently). So, our sample might represent only a third to half of potential maximum consumption. At times, perhaps ahead of a bank holiday weekend when many people plan to travel, a higher proportion of EVs could be charging at any one time.
Consumers often charge overnight because it’s convenient, but also because many of them are incentivised to do so by current EV tariff structures.
On our graph, we see a notable spike at 00:30, when Octopus Go’s cheaper overnight rate begins, followed by a significant drop-off at 05:30 when the discounted period ends. The influence of other EV-specific tariffs is also visible in the consumption patterns, though to a lesser extent.
Cheap overnight EV tariffs work when relatively few homes have EVs. But, as EV adoption rises, we’ll eventually hit a critical mass at which conventional time of use tariffs create problematic new peaks through herding behaviours – consumers all flocking to charge en masse during the cheapest periods. (Previous Catapult research and modelling has already predicted this, for instance our Consumers, Vehicles and Energy Integration, EV Energy Taskforce projects, and our ESME Transport model).
At that point, we’ll need charging systems that smooth demand across the day, instead of crowding it all into fixed, cheap periods that are the same for everyone. This might involve smart and bidirectional charging technologies that respond to real-time grid conditions, potentially with incentives based on local and not just national conditions. It will also require more complex and dynamic tariff structures which vary the cheapest times from home to home to avoid herding.
While 48% EV ownership might seem distant in the real world, the growth in EV sales suggests this scenario could become reality within the next few years. As EV adoption is rising faster in some areas than others, this could happen quite quickly in some locations.
Understanding these emerging patterns gives us time to develop solutions before they become critical problems. Success will require technology and market innovations as well as new types of tariffs.
Insights like these from our Living Lab highlight the value of studying actual consumer behaviour rather than relying solely on theoretical models. Real households don’t always behave as predicted, and their collective actions can have unexpected consequences for the energy system.
The Lab comprises over 5,000 diverse UK households who help us understand changing energy demand, consumer needs, and system impact through sharing their energy data and trialling new product, service, and policy innovations. Contact the Lab team to discuss how we can help you access home energy data or derisk new innovations.
The Living Lab currently has 2300 homes sharing smart meter data with us, but some of those are involved in other trials that ruled out the use of their data for this analysis.
The November data we analysed was selected specifically because we weren’t running demand-side response trials during that period, ensuring the consumption patterns reflect natural behaviour rather than experimental interventions. The weather was also typical for the season during that week, to avoid any effects of increased electric heating from cold snaps.
Test, trial and refine your clean energy products, services, and policies with real people in real homes.
Find out moreFind out more about how Energy Systems Catapult can help you and your teams
Find out more about how Energy Systems Catapult can help you and your teams