ExpectedOutcome:
Project results are expected to contribute to all of the following expected outcomes:
- Large-scale reference testing and experimentation facilities (TEFs) will offer a combination of physical and virtual facilities, in which technology providers can get support to test their latest AI-based software and hardware technologies in operational environments.
- This will include support for full integration, testing and experimentation of latest AI-based technologies to solve issues/improve solutions in the energy sector, at national as well as at local level, including validation and demonstration.
- The TEF is open to all the sites in Europe and equipped with the right equipment (Infrastructure, computing capacity & latest AI innovations).
- The TEF is a “long term investment”. There should be a business model to guarantee self-sustainability.
Scope:
The TEF is a technology infrastructure that has specific expertise and experience with testing in real conditions in the energy sector. They should build on existing infrastructures, facilities.
TEF should become common resources open to all the players, especially end users who should closely be involved. TEFs seek to support technology providers, but we also expect TEFs to include end-users of the technologies to ensure co-creation (in particular end-users can be involved in defining testing scenarios, protocols and metrics.
The TEF has the scope to then bridge the gap between lab and market due to lack of in-depth testing of AI technology in the real environment to fully validate them before the deployment.
Energy AI TEF will aim at testing AI–based technologies and solutions that have already been tested in the labs and have to be tested in operational environments.
Energy AI TEF will aim at optimising the deployment of AI-based solutions for a greener, smarter, more resilient, and more flexible energy system. For instance, it can investigate, how electricity grids respond to stimuli or shocks (e.g. RES integration, cyber-attacks, micro-grids development), making use of digital twins of the electricity grid at local level. Energy AI TEF can also target distribution grid optimisation, integrating both (decentralised) supply and demand-side, taking into account energy data coming from buildings, local storage, DER, electrical vehicles
TEFs can also support regulatory sandboxes by setting up a dialogue with competent national authorities for supervised testing and experimentation under real or close to real conditions.
The TEF can also support the development of new standards and ontologies for AI-Software for energy sector and common interoperability framework.
Energy AI TEF should give regions a further boost in attracting funding to upgrade its facilities and also attracting innovative players to collaborate with its own champions. In addition, TEF will contribute to more trustworthy AI made in Europe.
Specific Topic Conditions:
Activities are expected to achieve TRL 6-8 by the end of the project – see General Annex B.
