Fraunhofer IOSB-AST is contributing its long-standing expertise in the fields of energy systems engineering, control system development and real-time simulation to the project. The work at IOSB-AST focuses on the following key areas:
Modular DSA system architecture and prototype development – The overall system is divided into three interconnected subsystems:
- LI-SA-RD – a flexible research and development system for testing new DSA algorithms
- LI-SA-VT – a validation and testing environment for the systematic verification of developed modules
- LI-SA-RT – a real-time environment for dynamic simulation under conditions close to those encountered in actual operation
Thanks to its modular design, individual DSA components can be flexibly combined and tailored to specific stability requirements – at both the hardware and software levels. The platform is suitable for testing on reference and customer networks and can, in the future, be linked to real operational data from network control systems.
Online assessment of system inertia At the IOSB-AST, research is being carried out into methods for continuously monitoring and forecasting system inertia in interconnected grids with a high proportion of renewable generation. Based on measurement data and model-based estimation methods, this enables the ongoing assessment of frequency stability.
Taking active distribution networks into account As an increasing proportion of electricity generation takes place at distribution network level, their dynamic characteristics must be factored into the operational management of transmission networks. To this end, methods are being developed to model distribution network dynamics – including through dynamic network equivalents based on detailed models that are updated during operation.
Integration of EMT models and digital twins Together with the project partners, work is underway to integrate electromagnetic transient (EMT) simulation into real-time grid monitoring. With the aid of digital twins, simulation results are continuously compared with real-world grid measurements. Artificial intelligence techniques are being used to automatically adapt dynamic model parameters to changing operating conditions.