Our project partner, Trialog, is delighted to announce that its solution passed both integration and functional tests!

The Trialog Electric Vehicle (EV) charging management system is now experiencing the final preparations in order to be used for the SENDER demonstration pilots.

This solution aims to show how private EV chargers can be integrated in use cases involving flexibility aggregators, price signals and possible PV consumption in order to minimize the end-user bills or be integrated into a more global load management.

Furthermore, Trialog combined its efforts with the other partners in order to define and provide the rules to protect the user’s privacy.

Discover the latest study conducted by our partner VTT on the communicational aspects of real-time hierarchical control of electric vehicle charging…

Smart electric vehicle (EV) charging is a widely studied topic, and multiple different solutions have been proposed in the scientific literature to execute various demand response objectives. However, most studies use assumptions and simplifications that may not hold in real-life situations, and thus, the proposed solutions may have a diminished practical value. Here, communicational aspects of hierarchical EV charging control are studied.

The study considers the availability, acquisition, and value of certain information in case of real-time control. The paper illustrates potential pitfalls when using certain assumptions or simplifications in the simulations and proposes a novel algorithm to deal with the lack of preliminary information that exists in real-life situations.

The objective of the proposed algorithm is to allow a lower-layer controller to track the real-time controllability of the EVs. This information is necessary in hierarchical control solutions with multiple lower-layer controllers controlled by an upper-layer controller to ensure efficient operation.

The simulation results shows that a lower layer controller can estimate the controllability of the EV charging in realistic conditions within 1%–4 % accuracy despite the initially unknown characteristics of EVs.