Vattenfall turns to predictive EV charge point placement in Netherlands

Vattenfall is to install at least 4,000 electric vehicle (EV) charge points over the next three years in the Dutch provinces Gelderland and Overijssel.

To assist with the charge point placement, Vattenfall InCharge is using an algorithm based on a model from the international engineering consultancy Royal Haskoning, which determines the load requirement in areas with a radius of 250m.

The aim is to adopt a proactive approach to the placement of the charge points, ensuring that they are in pace before the need arises for them.

In addition, Vattenfall InCharge will provide the charging solutions on request and on the basis of usage data. If an EV driver does not have access to a charge point or only to a very busy charging station, a nearby charge point can be requested.

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“In this way, the charging infrastructure becomes accessible to all electric drivers. There is a good chance that the charging point is already there before the electric driver has picked up the EV from the car dealer,” says Alied Wessels Boer, Manager Public Sector InCharge at Vattenfall.

“Predicting the required number of charging points in the future will also help network administrators manage network capacity.”

Vattenfall introduced the InCharge network in 2016 band counts more than 30,000 connected charging points in northern Europe providing EV drivers access through roaming agreements.

With the new agreement, Vattenfall InCharge is now installing charge points in four of the twelve provinces in the Netherlands.

Under a January agreement, the company is to instal 8,000 charge point in North Brabant and Limburg, with about 4,500 to be proactive.

The model developed in collaboration with the Amsterdam University of Applied Sciences and Eindhoven University of Technology utilises a variety of data, including the locations and consumption of existing charging points, mobility data, vehicle and parking data and the locations of roads and parking spaces, as well as national demographic statistics.