Those who plan loading points make uncertain decisions and need a lot of time and money. The search for the right location is particularly difficult: it is basically about the locations with the highest charging demand and at the same time having a sensible effect on the overall charging infrastructure.
So there is a lot to consider for all actors who want to push ahead with the development of charging infrastructure within their own sphere of influence. Luckily there is new help in sight: automated charging infrastructure planning.
Two approaches to charging infrastructure planning
First approach: maximum area coverage as a goal
Basically, two approaches are pursued here. In the first case, the objective is to achieve the widest possible coverage in a given area. Algorithms are used which keep the distances between the charging points as small as possible and at the same time supply the entire planning area optimally with charging infrastructure. In a model region, this could look like as seen in the figure below.
Figure 1: exemplary calculation for maximum area coverage with 20 loading locations (source: Localiser RLI Webtool)
In this planning area shown as an example, it can be seen that the greatest possible supply of space has been achieved. The existing charging point (coloured blue) was also taken into account. The set circles indicate the supply area of each charging location.
This planning approach ensures the best possible coverage of the supply area, but does not take into account the fact that the charging requirements are probably distributed differently. Especially in planning areas with a very heterogeneous distribution of electric vehicles or agglomeration zones of the population, this approach can lead to bad investments.
Second approach: demand-oriented location calculation of the charging infrastructure
To achieve this, there is a second calculation approach, which aims at a demand-oriented location calculation of the charging infrastructure. A variety of factors, such as demographic data, existing points-of-interest (POI) and the population’s affinity for sustainability or new forms of mobility, can be taken into account in the calculation. The algorithm then determines the electromobility potential and, on this basis, the optimal distribution of the charging locations.
Figure 2: example of demand-oriented calculation with 20 charging locations (source: Localiser RLI Webtool)
Figure 2 shows the difference compared to the area coverage. The urban agglomerations are now strongly preferred in charging infrastructure planning, as the forecasted demand is at its highest here. However, the automatically set charging points also show attractive charging points away from urban agglomerations, as POIs relevant for planning are available there.
Demand-oriented planning therefore makes sense, particularly with regard to amortization of the charging locations to be set up. This approach makes it possible to achieve increased capacity utilisation and to incorporate future charging requirements through forecasts. A combined approach of both methods can ensure an approximate supply of land without disregarding actual needs.
Companies such as Localiser from Berlin offer software solutions that combine the described approaches. Localiser is a web-based tool that automatically takes over spatial planning and delivers location proposals within seconds.
Main criteria for good locations
If you still want to find your own loading point locations, you can also orient yourself on the following main criteria for good locations:
Existing charging infrastructure
Where are the existing charging points in the search area and what charging needs do they already cover? Accordingly, further loading points should be planned, i.e. as a sensible supplement. Information about existing charging points can be found via databases on the Internet, websites or maps of providers.
Electromobility potential
Where exactly are the hotspots of electromobility, i.e. where will charging points be needed in the future? Research projects have shown that a number of sociodemographic parameters are decisive for this. The core questions are as follows. How is the distribution of single-family houses compared to multi-family houses in the search area, what is the age structure in the surrounding area, what is the purchasing power?
If one evaluates these data for the possible charging points or the region under consideration, one identifies the areas with the greatest growth potential.
Needs
Today, the majority of loading processes still take place at home. As the number of E-vehicles increases, drivers will also want to load at work, shopping at the supermarket or in other situations where their car is parked for more than 15 minutes.
The distribution algorithms described above have also addressed this question. In the planning process I can check which POIs are available and what the traffic levels of the roads are near the loading points. Or also: where are parking spaces that could be converted into charging points? I can see directly where charging infrastructure could be set up in cooperation with car park operators and where the remaining public demand for charging points will arise.
Whatever you’re looking for: the battle for the best locations has begun! Find the most economical first, then get yourself in an optimal position for the run-up of electromobility.
Der Beitrag Electromobility and the search for the best locations for charging stations erschien zuerst auf Energy BrainBlog.
