Author: Amber Woodward, International Public Affairs Manager, Centre for Net Zero

Electric vehicles (EVs) present both a challenge and an opportunity for electrification - either overwhelming grids with new large loads, or acting as distributed battery assets which can help the grid. AI-managed, smart scheduling of EV demand can provide a solution. Instead of relying on drivers to track price changes throughout the day, or setting tariffs with static periods of cheap energy that can cause secondary demand peaks, managed charging can use machine learning algorithms to spread demand more efficiently. This can lower running costs for consumers and the grid. Consumers with EVs can benefit directly from having their vehicle charged at times of low prices, whilst grid operators and all consumers can benefit from a more efficient system with lower running costs and reduced infrastructure investments, paid for by everyone’s bills.

With the rapid growth of EV sales, expansion of managed charging tariffs to more markets, and customers' increased adoption of these tariffs, we wanted to investigate the data on how customers engage with managed charging across different regions. We analysed anonymised data from customers on Octopus Energy’s ‘Intelligent Octopus Go’ (IO Go) managed charging tariff in the UK, Spain, Germany, and Texas (US), from January – December 2024*. On this tariff, managed charging typically occurs overnight, though daytime charging may be scheduled based on forecasts, with customers receiving a cheap rate of electricity during periods when EV charging is remotely managed.

Quick summary

Customers on managed tariffs in the UK, Germany, Spain and Texas share more similarities than differences in charging patterns, explored in the following ten charts. They have similar plug in and out times, quickly get used to automated charging, and typically leave vehicles idle for long periods. All four regions exhibit a similar 0.4 kWh overnight spike in EV demand, indicating comparable volumes of flexible load. However, subtle differences in when EVs are plugged in, for how long, and how often, suggest that tariff design, automation strategies, and network modelling should adapt to local contexts.

10 Charging Charts

1. Customers tend to plug in their vehicles towards the end of the day, aligned with the start of overnight managed charging windows

Customers in all markets are most likely to plug in after 18:00, with the biggest spike in plug-ins after 22:00, just before the window of cheap, overnight managed charging begins. There are some visible differences in the most likely period for customers to plug in, but there are broad similarities across regions.

2. Customers tend to unplug their EVs at the start of the day fairly consistently across regions

Plug out behaviour is relatively consistent between regions, with most customers plugging out between 07:00 – 09:00 in the morning.

3. Plug-in duration is consistent across regions, with customers opting to plug in for short bursts, or overnight

Analysis of the length of time EVs are plugged in shows two distinct peaks – one at less than 2 hours, and one peaking around 12 hours.

4. Cars are typically left idle for 6-10 hours during each plug event

The median time spent idle (the total time plugged in minus the time spent charging) is between 6 hours in Spain and nearly 10 hours in Texas, with a quarter of all events being idle for more than 11 hours across all markets.

5. Customers quickly get used to automated charging, with charging overrides decreasing over time

Customers have the ability to override managed charging, using a ‘bump charge’ feature on their app. Customers are most likely to utilise this feature when they first join the tariff. But over time, bump charges become less common. Most customers across all markets have never used the bump charge feature.

6. In Germany, customers plug in just as much in the middle of the day as in the evening, particularly at weekends

So-called “solar charging” is popular in Germany, with the effect most pronounced at weekends. As the take up of smart meters is small in Germany, IO Go customers tend to be early adopters, with multiple low carbon technologies. These customers will often charge their cars directly from PVs in the middle of the day, hence the early plug in times. Spanish customers also appear to exhibit some of these daytime charging behaviours.

7. ‘Solar charging’ behaviour is highly seasonal – most prevalent in the middle of the day, during the summer months

In figure 7, values above zero indicate that the consumption measured at EVs is higher than the smart meter consumption, suggesting that there is local generation. The hotspots in the middle of the day between 10:00 – 16:00, particularly during spring and summer months of April to July, are well-aligned with periods of high solar generation.

8. Customers in Texas plug in their vehicles when the battery is at a relatively high State of Charge (SoC)

For the UK, Spain and Germany, SoC at plug in follows a normal distribution, with a mean of around 40 – 50%. In contrast, in Texas the average SoC is slightly higher, at c.60%. Texan customers are nearly 70% more likely to plug in at 75% SoC vs in the UK.

9. Customers in Texas and Spain plug in more times per week than the UK and Germany

More UK and German customers plug in once or twice a week and more Texan and Spanish customers tend to plug in 5 or more times a week. The average plug-ins per week hovers around three across all markets, ranging from 2.8 times in the UK and DE, to 3.2 in Spain, and 3.4 in Texas.

10. Intelligent managed charging provides significant load-shifting across all markets

The final figure plots hourly electricity consumption for a random sample of customers across the four regions discussed. The charts show EV charging consumption, based on telemetry data from IO Go users (i.e. a portion of total household consumption). All four regions observe an average 0.4 kWh spike in EV charging demand overnight, implying a similar volume of flexible load from EV charging. For more detail on how intelligent managed charging impacts consumption in the UK, check out our recent working paper: AI in Charge.

Limitations

These ten charts provide some insights into consumer behaviour across regions – but there are limitations to the data and analysis. For example, IO Go launched in Spain in September 2024, so the data for this market may be seasonally biased. This perhaps explains why solar charging appears less prevalent in Spain than in Germany, despite the high proportion of customers with solar and EVs in this region.

Further, as this is descriptive analysis, we are unable to say what is driving consumer preferences and behaviour – whether it’s the tariff design that affects behaviour or whether much of the behaviour we can see is tariff agnostic. Understanding how adaptable consumer behaviour is in response to incentives will be an important factor in tariff innovation – whether that be designing tariffs that optimise charging for a higher penetration of vehicles, across multiple days, or for households with many low carbon technologies.

What this means for the future of automated, managed charging

Managed charging provides an easy, seamless way for EV drivers to charge cheaply, and not overwhelm the grid. In summary:

1. AI-managed charging works everywhere, with customers regularly plugging in in the evening, and plugging out in the morning, to take advantage of cheap periods of electricity.
2. Idle time provides major opportunities for flexibility. With vehicles commonly idle for 6 – 10 hours, there’s plenty of optionality for EVs to be charged at the cheapest times. As vehicle-to-grid (V2G) becomes more prevalent, and EVs can both charge and discharge smartly, these idle periods become even more compelling.
3. Customers adapt quickly to managed charging, with charging overrides decreasing over time – indicating that automation strategies are successful in meeting customer needs.
4. Local factors matter for tariff and network design. Solar-rich markets, like Germany and Spain, may benefit from explicit daytime charging windows to take advantage of local generation, rather than always charging overnight. High-frequency plug-in markets, such as Texas and Spain, could potentially leverage EVs as flexible grid assets more than other markets, with greater availability and optionality for when to charge the vehicle.
5. Managed charging provides significant load shifting across all markets, with EV charging demand spiking by an average of 0.4 kWh overnight. Incentivised correctly, EVs have great potential to act as flexible grid assets, spreading demand more efficiently and reducing system costs.

While we find the opportunities for managed charging at home are consistent across markets, the data suggests that tariff structures, automation strategies, and market design will likely need to adapt to local contexts. This is particularly important for grid operators, which will need to factor regional and cultural charging habits into flexibility strategies – and provide the right market incentives to leverage the flexibility EVs provide. Consistently, policymakers and regulators can unlock the full potential of AI-managed charging by sharpening price signals on national and local markets, such that EV charging can respond dynamically to system needs. Equally, interoperability across devices and software will be necessary such that all customers, regardless of car, charger or energy supplier, can benefit from the flexibility their assets can offer.

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*IO Go launched in Spain in September 2024, so data for this market covers September - December 2024.