This video is intended to help you interpret the EV Charging Demand graphs in the ConnectMore software tool. The tool covers Merseyside, Cheshire, North Shropshire, North & Mid Wales – the MANWEB license area covered by SP Energy Networks.
The ConnectMore tool provides predictions of future demand for public charging infrastructure. The tool provides a forecast rather than a known outcome so it should only be used as a guide to inform decisions. It includes public destination, public residential, private workplace and en-route chargepoints but excludes residential off-road chargepoints.
If you would like more information about the EV charging demand heatmaps and the data included in it, please watch the ‘How to use the EV charging demand heatmaps’ video which explains some of the terminology used in this video
I will firstly explain the charts available for public residential, public destination and private workplace charging heatmap before moving on to those available about en-route charging.
To demonstrate the information available in the graphs in the EV Charging Demand heatmaps we have set the tool to look at Public Residential – that is on street, and Public Destination charging infrastructure in 2030 with a High EV uptake and plentiful infrastructure. We are looking at the Lower-layer Super Output Area or LSOA that covers Capenhurst and Backford – an area between Chester and the Wirral. Most houses in this area have off-road parking so can install their own chargepoints.
(Click through setting this and zoom in on this area)
You will see from the map that this LSOA, which is a mix of residential, industrial and commercial premises, is shaded darker purple indicating it has a medium to high daily energy consumption – for the scenario selected.
Here it is detailed that the energy consumption per day is 60kilo-Watt hours per square kilometre and there are 4 charging events per day per km2. The data used to colour the heat map has been divided by the area of each LSOA, so LSOAs of different sizes can be more easily compared. The graphs we’ll describe next show the data in terms of total killowatt hours, or the number of charging events per day.
(Zoom and circle these stats)
The top chart shows the predicted proportion of public charging daily energy demand in the LSOA per charger type. For this LSOA, most of the demand is from destination chargepoints – so , restaurants, shops, leisure centres, tourist attractions and city centre car parks, followed by workplace charging, then off-road residential charging. The total amount of energy consumption for each category are shown below the pie chart.
(Circle the energy total)
The next chart shows the predicted distribution of dwell times for the two selected charger types for this LSOA. It displays how long electric vehicles are typically at a chargepoint for. The columns are a percentage of the total for each charger type and in this example 80% of residential charging sessions last over 6 hours – maybe corresponding with residents charging overnight. Charging sessions at destination chargers are much shorter – 40% last between 1 and 3 hours – so potentially a weekly shop.
The next chart shows the predicted distribution of energy per charging session for the charger types selected. This graph provides an estimate of energy that might be required per charging session. Energy used is in five categories. For both charger types, most charging events are expected to use between 5 and 15 kilowatt hour of energy although both charger types also have a high proportion of charging sessions under 5 kilowatt hours.
The next chart shows the total number of sessions predicted per day per charger type in this LSOA. The aim is to help you determine what type of infrastructure is needed to meet your user requirements. If lots of energy needs to be supplied in a short amount of time, then faster chargers are needed. These are more expensive to buy and might cost more to connect to the network because they need more power from the network. If cars are plugged in for a longer period of time, then a slower charger might be sufficient. The total for each charger type is broken down by charging speed. For this example, you can see that there are expected to be around 10 public residential charging sessions per day, which could utilise a 3.5 kilowatt chargepoint. This means that in most cases installing chargers which could supply 3.5 kilowatt would provide fast enough charging to meet your users’ needs. By contrast there are predicted to be around 100 sessions at public destination charge points, split across different speeds – so about 40 sessions could be satisfied using 3.5 kilowatt chargers, about 20 by using 7 kilowatt chargers and about 30 using 22 kilowatt chargers, with the rest needing even quicker infrastructure. This chart should be interpreted as cumulative, so. all the sessions that could be satisfied by a 3.5 kilowatt charger would also be satisfied by a 7 kilowatt, and so on.
The next chart shows the predicted number of charging events beginning in each hour of the day within the LSOA for the selected type of charging infrastructure. From this graph we can see that the highest number of charging sessions correspond to those with the highest bars. When you use this data source it is worth bearing in mind the results from the dwelling time chart above – so, if for example, you were trying to work out how many chargepoints you needed in a LSOA and the usual dwell time was between 1 and 3 hours then you may want to ensure that you have enough charge points to accommodate all the electric vehicles that want to plug in between at specific times..
And the final chart shows the predicted daily energy demand for the different charger types, and the total demand, for an average day in five-year increments from 2025 to 2050.
Beneath this graph you will see some summary data which shows the Total predicted energy demand, number of car trips and number of charging sessions per day for the charger types selected.
If we now select to see the en-route charging infrastructure heatmap layer, the network of motorway, A and B roads in the area becomes visible. This data predicting the total number of EV’s requiring charging somewhere during their journey and the energy demand that this creates. These drivers’ main purpose when they pause their journey is to charge their vehicle.
Near to the site already selected, there is a busy motorway intersection. Clicking on this stretch of this carriageway provides data such as the road name and whether it is single or bi-directional. There is also forecast data such as the number of EV’s predicted to travel along that stretch of carriageway.
The first chart shows the trend for the number of EV’s and the number of vehicles (so EV’s and non-EV’s) using that span of carriageway on an average day between 2025 and 2050
The next chart shows the trend for daily EV charging demand between 2025 and 2050. This forecasts the number of EV’s that travel along this stretch of carriageway that will need to stop to charge at some point in their journey. The model does not predict where they will stop, just that at some point they are likely to do so.
The final chart the daily EV charging energy demand trend between 2025 and 2050 in kilowatt hours. This is the amount of energy that will satisfy the charging demand of all the EV’s that travel along this stretch of carriage way and stop to charge at some point in their journey to charge.