A digital twin is expected to integrate [at least three of the following bullet points:

1. Wind and weather forecast models relevant for the full wind power production system (turbines, grid, transmission) (including the effects of external physical conditions such as temperatures, rain, turbulences, waves, and currents).
2. Spatial modelling: medium (within wind farms) to long distance (between/along wind farm clusters) wake effects.
3. Interconnection optimisation via simulations to satisfy grid connection requirements and agility in grid reconfiguration and provide ancillary services.
4. Include predictive maintenance, structural health and conditional monitoring, and
5. End user location and needs.

The digital twin will improve accurate energy yield prediction and will balance supply and demand side needs and will help to ease investment decisions based on accurate simulations. The models should incorporate other relevant parameters influencing the siting of wind farms, such as ground conditions, noise impacts and environmental impacts as well as representing the complex system in a map view format while considering time series data of each and every asset. Infrastructure modelling of each and every asset should be executed via independent profiling based on past performance data and contextual data in view to deliver prediction at the level of each and every asset with as much accuracy as possible”.

The project should focus on offshore or on onshore wind power systems and make optimal use of previously developed models. Validation should be carried out with data of existing wind farms. Cooperation with wind energy suppliers, OEM’s, developers and O&M services can make the available data more accurate, resulting in better, more sustainable and eventually circular products and sector. The project should also sufficiently invest in delivering a cyber-secure system.

For the offshore digital twin projects the impact of other blue economy sectors, islands, different land-sea interactions for near shore wind farms should be considered.

To support rapid market uptake, widespread application and further innovation based on the developed solutions, projects are invited to use Open-Source solutions when appropriate and clarify in case they choose not to use Open Source, so that they can support the planning of future large scale offshore wind installations.

Projects will acquire performance-related data in a standard format to support advancement and validation of R&I for the benefit of all projects through Artificial Intelligence methods.