Description of the Workpackages

Workpackage 1: Impact of EV on low-voltage network
Analyses of Uncontrolled versus Smart EV charging on the different international network-structures.

Workpackage 2: Scenario and simulation definitions
The analysis which has been done in WP1 forms the basis for the scenario development and simulation definitions in WP2.
• Scenario’s:
– Public, semi-public, private
– Rural, urban
– EV uptake
– Uptake sustainable energy generation
• Simulation’s focussing on EV growth and grid impact (congestion, voltage, probably also PQ aspects as reactive current, harmonic distortions, phase imbalance) :
– Individual scenario’s for DSO, TSO, BRP, etc.
– Multi-actor optimization scenario’s

Workpackage 3: Protocol architectural study and development of DSO smart charging services
- Analysis of protocols for specific situations, including the alignment for different ‘sequential’ interfaces. The best fitting architecture for each situation will be investigated.

- Addresses the development of the mechanisms, incentives and organization of flexible demand to realize a solid technical chain for smart charging.

- Organize that all the (future) flexibility actors can do this in a way that actions from one actor do not disturb those from another by realizing multi-actor-optimisation-algorithms.

Workpackage 4: Development of the integrated demonstration environment
- Implementing the protocols and algorithms in a software platform that automatically manages demand response balancing with simulation and dashboard views in mass scale.

- Developing different hardware prototype concepts. These concept charging stations with different technologies will be developed and tested with relevant protocols.

Workpackage 5: Demonstration including visualization and simulation (HW-in-the-loop)
- In multiple open demonstrations it is shown how the improved hardware and software performs better in the total chain.

- Demonstrate how the charging process is going to be influenced by the EV uptake and the related power demand increase using hardware-in-the-loop simulation.