The Hybrid Labs consortium, supported by the Dutch National Science Agenda, brings together an unprecedented network of hybrid experimental facilities, simulators, and offshore demonstration sites to accelerate innovations in offshore renewable energy through data- and physics-driven approaches.

Currently, the existing aggregated static and myopic models (lacking foresight) do not allow a proper study of steady -state and dynamic disturbances. Addressing these limitations and aiming to enhance the resilience and stability of offshore systems, this project aims to develop a data-driven, self-calibrating and unsupervised digital twin network of offshore energy systems. By integrating wind power plant models with a digital synthetic Dutch power system and using time-series data from SWITCH field labs, the study will enable dynamic model calibration under varying grid conditions by employing a combination of computationally efficient digital simulation, hybrid AI signal processing techniques, multivariable control theory, and probabilistic stability assessment and improvement. The project will create adaptive equivalent models that improve situational awareness and robust control of offshore and hybrid renewable systems.

Outcomes of the project:

1. Develop a self-calibrating and unsupervised digital twin model of the offshore network.
2. Integrate a wind farm model into the digital twin network model and analyze the system dynamic performance through various experiments.
3. Couple the digital twin model with a SWITCH field lab in real time to enable adaptive control settings that enhance performance and resilience at both the wind farm and power plant levels.
4. This real-time coupling will improve situational awareness and the robustness of control design in integrated offshore–onshore systems. Additionally, it will enable wind farm operators (integrated with solar, conversion, and storage systems) to take proactive actions such as adjusting dispatch and control settings.

Contact: H.N.Nayak@tudelft.nl