Darvish, Sahar

The large-scale deployment of grid-friendly controllable charging infrastructure or electric vehicle supply equipment for e.g. congestion management does pose the necessity for dedicated testing equipment. This work presents the design requirements and test device concepts for such a test device. The proposed test device combines test capabilities for both Mode 3/AC and Mode 4/DC charging infrastructure. The norm-conforming communication protocols are implemented and available for monitoring, while also testing under real load conditions is available while being independent of a mains connection. Further, the test device was validated using extensive testing with different EVSE. The applicability for in-field usage is shown.

The large-scale deployment of charging infrastructure poses challenges to the distribution grid stability. In Hamburg, the project Electrify Buildings for Electric Vehicles (ELBE) implements and tests a demand side management communication protocol that allows the distribution grid operator to reduce the charging power of electric vehicle charging stations if necessary. For this purpose, a test device capable of testing the signal chain as well as a real load reduction is presented for DC charging infrastructure. The standardized high-level communication protocols are described. With the charger’s power electronics located in the DC charging station, the need for a battery emulation arises, which is also discussed. Finally, a novel concept for a mobile and modular test setup for combined testing signal and real load reduction of AC and DC charging infrastructure is presented.

High penetration of electric vehicles into the electrical grid requires a grid-friendly management method to decrease possible congestion issues. In this work, a congestion management concept was modeled and simulated. The concept can be implemented by low voltage distribution system operators and charge point operators through a communication protocol. This paper aims to investigate the effectiveness and efficiency of this management method on different penetration scenarios of electric vehicles by using Python and quasi dynamic simulation method in DIgSILENT Power Factory. The modeling of the electric vehicles, as well as other loads and small generators, was conducted with the Monte Carlo simulation and distribution data based on real measurements.