This article looks at how the use of rooftop solar and exposure to price signals in tariffs can materially impact the average load profile of EV charging across the day. These very early data sets support our conviction that if smart charging controls are employed, EV charging will help rather than hinder the grid's transition to 100% renewables.
Charge HQ is a service which enables EV owners to dynamically charge according to their available excess rooftop solar energy, along with a wide range of features including advanced scheduling controls and the ability to respond to wholesale prices.
Provision of the service gives us access to granular charging profiles for a large number of electric vehicles which, in aggregate, form the basis of the charging profiles presented here.
It’s important to consider the source of the underlying data set before attempting to draw insights from these graphs. Let’s dive into some detail about the users, the data, and the app which enables the charging control.
The graphs are based on a sample of hundreds of EV charging profiles from Australian users of the Charge HQ app.
The app is currently in beta mode, most users have found it via word of mouth, and we believe there’s a skew towards more tech-savvy early adopters. It is likely also skewed to users with a greater focus on sustainability and the use of renewable energy.
There is a significant skew towards Tesla vehicles due to the integrations currently offered and the make up of the Australian EV fleet.
There is a notably higher-than-average level of home battery ownership amongst our users.
The predominant use case is enabling charging from rooftop solar, so our user base is skewed to EV owners living in detached houses with rooftop PV systems.
On the back of COVID, we expect there is still an historically high number of users working from home, who are able to charge their car from solar more often. It is unknown if this trend will remain or whether more workers will return to offices which may alter charging profiles.
Users will be on a mix of electricity tariffs including flat-rate, time-of-use and wholesale price. The different tariffs provide different price signals to users.
The graphs are based on a one week sample starting 27 August 2022, circa two months out from the winter solstice. There will be a below-average level of solar energy generation. Annual variation in weather may be further skewing the solar generation from averages for this time of year.
All values are based on energy consumed by EV charging regardless of source, which will be a mix of grid, solar and home battery.
The graphs are presented as average power consumption at 15 minute intervals. To calculate the average we sum the power readings for EV charging for all users in the cohort for each interval and divide by a count of the total number of users in the cohort.
The user count includes any user who has charged at home at some point during the period being considered.
We count only charging at home, noting that there will be a spread of users that charge only at home through to those that mostly charge away from home. This will impact the average.
Considering how the average is calculated, it may be more informative to focus on the shape of the charging profile, and the relative charging power level at different times of day, than the absolute figures presented.
The user cohorts vary in size: more than 50% of users have solar tracking enabled whilst less than 10% of users have price limits set.
The app is highly configurable but is provided to the user with no features enabled by default. All decisions about what features to enable and when to schedule charging are currently controlled by the user.
The app currently only performs a limited level of optimisation of the timing of when charging occurs. This optimisation is in response to available solar generation, user defined wholesale prices, and levels of renewable generation on the grid. For the majority of users, charging occurs either in response to their rooftop solar generation or when they’ve chosen to set scheduled charging periods.
Taking to account the caveats on the data above:
The Charge HQ peak EV charging loads are roughly inversely correlated with peak residential demand periods. In other words, the lowest average charging power occurs during the morning and evening peak periods. If this trend continues it’s a good thing for managing peak demand on energy networks.
Taking into consideration the limitations of the averaging mechanism, the average load from EV charging isn’t that high. Considering the common maximum charge rates of 2.4 to 11 kW found on EV chargers, average observed loads of 0.11 to 0.33 kW during early evening peak demand periods is lower than expected.
This occurs since the average charging power considers all vehicles which are actively using the Charge HQ system. When a vehicle is not plugged in or not charging, it still contributes a power of zero kW to the average.
From a grid-integration perspective, it is this average charging power across a number of vehicles that is most important. To illustrate: the data shows that the peak average charging power is about 1 kW per vehicle. This means that if there are 1,000 EVs in a region, the peak total draw from those EVs would be around 1,000 kW at any given time. This finding may be counter-intuitive given that many EVs have a peak charging rate of around 7 kW. The reason that the average is much lower than the typical charge rate of a vehicle is that at any given moment most vehicles are not charging.
Notably, this observed trend is consistent with work undertaken on independent data sets by the Australian Electric Vehicle Council.
Users appear to be responding to price signals on the existing electricity tariffs.
For users charging from their home solar, it is usually cheaper to charge from solar than at any other time from the grid. Accordingly, we see maximum demand in the middle of the day, with a peak value close to that of overnight off-peak periods. The relatively high overnight off-peak charging most likely reflects the need to “top up” when solar charging was either not possible or did not deliver enough energy for the next day’s driving needs.
For users not charging from rooftop solar, we see a much flatter load profile with more substantial loads carrying through the evening peak. Users who are not using the solar tracking feature have an evening peak load approximately three times greater than those who have the solar tracking feature enabled.
Users who have set a price limit are predominantly those who are exposed to wholesale prices via the Amber Electric energy retailer. This cohort is more universally incentivised to avoid charging during peak demand periods, and they’re also more highly-engaged energy users. The data appears to show a very powerful impact of the price signal with extremely low levels of EV charging occurring during the evening peak. More widespread adoption of Time of Use tariffs may achieve a similar outcome.
Evidence exists of significant concentration of scheduled charging with midnight start times and to a lesser extent at 9, 10 and 11pm. This highlights the need for some form of randomisation as EV adoption grows - and a feature on our roadmap.
As we gather more users and more data we’ll continue to release more insights. Check back here or follow us on LinkedIn.