Result description
The developed software platform is able to apply different policies for the management of EV charging, depending on current and forecasted states by considering several criteria (e.g. electricity cost, grid state). It utilizes clustering and machine learning techniques to forecast parameters (e.g. departure time) that are considered as constraints in the optimization algorithms.
Missions
Addressing target audiences and expressing needs
Our needs are
- Collaboration
We specifically need / are looking for
We are targeting EV charging station owners and EV owners that would like to benefit from data-driven EV charging management.
Target audiences
- Public or private funding institutions
- Research and Technology Organisations
- Academia/ Universities
R&D, Technology and Innovation aspects
Current stage description
The platform was validated in simulation mode. It is under deployment to be tested in real-life environment.
Replicability
Replicability is possible since open communication protocols and standards are used, as well as common formats for data representation.
Result submitted to Horizon Results Platform by ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS