ExpectedOutcome:
Project results are expected to contribute to all of the following expected outcomes:
- Accurate and precise energy yield prediction to ease investment decisions based on accurate simulations that take into account simultaneously predictions on Renewable Energy Production, Energy Consumption and Price Predictions.
- Enhanced digital transformation of wind energy sector by delivering the next generation of digital twins.
Scope:
The expected growth of both on-and offshore wind energy is enormous and many new wind parks are planned for the coming years. Experience from the existing wind farms shows the importance of a proper micrositing of the wind turbines as well their efficient interconnection within the farm. In addition, bringing wind farms together into clusters toward a wind power plant concept might induce long distance negative interaction between the farms, reducing their expected efficiency. This might happen both on- and offshore. The high amount of connected wind power and the expected increase during the coming years, requires that this technology has to be prepared to take a more important role as of its contribution to the reliability and security of the electricity system. The objective of this topic is to develop new digital twins to optimise the exploitation of individual wind farms (onshore, bottom-fixed offshore and floating offshore) as well as wind farm clusters, in view of transforming them into virtual power plants delivering a more reliable and secure electricity system. Such a digital twin is expected to integrate [at least three of the following bullet points]:
- 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).
- Spatial modelling: medium (within wind farms) to long distance (between/along wind farm clusters) wake effects.
- Interconnection optimisation via simulations to satisfy grid connection requirements and agility in grid reconfiguration and provide ancillary services.
- Include predictive maintenance, structural health and conditional monitoring, and
- 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. The projects is expected to build also on the digital twins developed under Destination Earth, which envisage to develop a high precision digital model of the Earth to model, monitor and simulate natural phenomena and related human activities.
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.
For onshore digital twin projects, the build environment and different landscapes should be considered, and cooperation is envisaged with the selected projects under topic HORIZON-CL5-2021-D3-03-05 Wind energy in the natural and social environment.
It is expected that one project on offshore digital twin will be funded and one on onshore digital twin.
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. Free licensing is also a possibility to consider to support rapid market uptake.
Selected projects will be required to share knowledge. 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. This data and relevant meta-data may be shared with other projects (not supported through Horizon Europe, including relevant projects supported through the Innovation Fund) on reciprocal terms, preferably leveraging on the tools and services provided by the European Open Science Cloud (EOSC) and exploring workflows that can provide “FAIR-by-design” data, i.e., data that is FAIR from its generation, and with EU-based researchers having a legitimate interest. The selected projects are expected to cooperate with the project selected under the call [CSA for data-sharing between renewable energy R&I project to advance innovation and competitiveness].
The selected projects are expected to contribute to the BRIDGE initiative[1], actively participate to its activities and allocate up to 2% of their budgets to that end. Additional contributions to the ‘Alliance for Internet of Things Innovation’ (AIOTI) and other relevant activities (e.g. clusters of digital projects and coordinating actions) might be considered, when relevant.
Specific Topic Conditions:
Activities are expected to achieve TRL 5 by the end of the project – see General Annex B.
[1]https://www.h2020-bridge.eu/
