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Predictively avoiding grid bottlenecks

Electric mobility is becoming increasingly popular. According to Statista, 63,000 purely electric vehicles were registered in Germany in 2019. In 2020, the number of registrations has already exceeded 77,000 (as of August). Growing model diversity, greater ranges and government subsidies are encouraging more and more customers to switch to an emission-free e-drive. To ensure that these vehicles can also be charged, the demand for electricity is growing. And this should, at best, come from renewable energies for the ecobalance of the e-vehicles.

This is particularly tricky when entire fleets of vehicles have to be charged simultaneously overnight, for example by delivery services. This poses a growing challenge for distribution grid operators. Because if an unexpectedly large number of vehicles are connected to the grid, this can lead to grid bottlenecks in the supply. Especially since sun and wind are also not constantly available to meet the regenerative power demand. The danger: Without intelligent planning, the supply and demand of energy can become unbalanced at peak times – and the vehicle fleet might not be fully charged come the following morning.

To prevent this, IAV, the Reiner Lemoine Institute and E.DIS Netz have joined forces in the “Intelligent grid integration of electrified logistics” research project (Netz_eLOG). At the Deutsche Post DHL distribution center (DPDHL) in Kleinmachnow, Brandenburg, the consortium is investigating the flexibility potential of an electric fleet and the possibilities of grid integration. Specifically, the aim is to develop a procedure for automated load and charge management so that the entire vehicle fleet can be operated in a grid-compatible manner and thus make its own contribution towards grid stabilization. For the first time, data from both the grid operator and the fleet operator will flow together in one system. In the practical test, 63 Work XL street scooters from DPDHL will be used. The idea is to use targeted, time-controlled load shifts to predictively avoid possible grid bottlenecks on the one hand and thus ensure that the fleet is charged, and on the other hand to compensate for the peaks in the feed-in of renewable energies into the grid. These interventions for grid stabilization are achieved by ad-hoc control and regulation and a high data transparency between distribution grid and loading fleet.