Summary

Electric vehicles (EVs) are successfully decarbonising road transport. Global sales have increased year-on-year over the past decade, comprising more than 20% of new cars in 2024, and are set to rise rapidly in the decades to come. However, this surge in adoption will place new demands on the electricity system which, left unmanaged, threatens to strain existing grid infrastructure and increase system costs ultimately borne by consumers.

Demand flexibility can shift EV charging away from peak periods and toward times when electricity is cheap and abundant, while delivering direct bill savings for households. Managed charging – third-party control, particularly using AI-driven automation – can ensure that demand closely aligns with real time grid conditions.

The opportunity of managed charging is well-established, but there remains limited causal evidence on how consumers actually engage with such products and the subsequent impact on electricity demand. It also offers a compelling test case for the opportunities of AI to support grid decarbonisation by optimising demand with low-carbon supply.

Centre for Net Zero (CNZ), supported by the King Climate Action Initiative at the Abdul Latif Jameel Poverty Action Lab (J-PAL), created a randomised controlled trial with over 13,000 consumers to evaluate one of the world’s largest AI-managed EV charging tariffs. Once adopted, the tariff dynamically controls EV charging to optimise across real-time wholesale electricity prices and ancillary markets - while respecting user preferences for battery state-of-charge and desired departure time.

Key findings

The trial provides novel empirical evidence that AI-managed EV charging can reshape electricity demand at scale, with considerable benefits for consumers and the grid.

① Targeted consumer engagement can increase take-up of EV charging tariffs. Among a ‘harder-to-reach’ sample, simply an email raised tariff adoption by 3.4 percentage points, while an extra offer of £50/month for three months nearly doubled that effect. Customers that switched to the tariff largely remained on it for the 12 month trial period.

② Managed charging provides significant load-shifting. The tariff led to a 42% reduction in peak household electricity use, with the entirety of EV demand shifted to off-peak hours. This load-shifting did not change total household electricity consumption.

③ Automation is accepted by users and highly responsive to system conditions. We saw high user adherence to the automated schedule, with manual overrides only accounting for 2.3% of total electricity consumption and more than half of households never using this feature. The AI-driven managed charging tariff was more responsive to wholesale electricity prices than a static time-of-use tariff that does not use managed charging.

④ Significant benefits for society and the electricity system. Our results suggest that AI-managed charging generates a consumer surplus, including a reduction in electricity bills by £343 per year - or £650 when compared to a standard flat tariff. For the electricity grid, we found strong empirical evidence that managed charging can provide EV demand flexibility at scale to lower system costs.