Data sharing to support the training and development of AI foundation models in the energy sector

This Destination includes activities targeting a sustainable, secure and competitive energy supply. In line with the scope of cluster 5, this includes activities in the areas of renewable energy; energy system, grids and storage; as well as Carbon Capture, Utilisation and Storage (CCUS).

This Destination contributes directly to the Strategic Plan’s Key Strategic Orientations ‘Green transition’, ‘Digital transition’ and ‘A more resilient, competitive, inclusive and democratic Europe’.

In line with the Strategic Plan, the overall expected impact of this Destination is to contribute to the ‘Ensuring more sustainable, secure and competitive energy supply through solutions for smart energy systems based on renewable energy solutions’.

This destination contributes to the activities of the Strategic Energy Technology Plan (SET Plan) and its implementation working groups.

The main impacts to be generated by topics under this Destination are:

Renewable energy

1. Energy producers have access to efficient and competitive European renewable energy and renewable fuel technologies with a solid knowledge base and are able to deploy them to enhance the EU’s energy security and reach its climate neutrality objectives, in a sustainable way in environmental (e.g., biodiversity, multiple uses of land and water, natural resources, pollution) and socioeconomic terms, and in line with the Sustainable Development Goals.
2. Technology providers have access to European, competitive, resilient, reliable, sustainable, and affordable value chains of renewable energy and renewable fuel technologies including emerging ones, and with strong export potential to supply both the EU internal and global markets. They benefit also from circular renewable energy technologies that are safe and sustainable by design with reduced and diversified external dependence on critical raw materials^[1].
3. Economic sectors benefit from better integration of renewable energy and renewable fuel-based solutions that are, among others, competitive, cost-effective, efficient, flexible, reliable, and sustainable. Such integration is facilitated through digitalisation and integration of artificial intelligence of renewable energy technologies that provide network stability and reliability.
4. European industries benefit from a reinforced export potential of renewable energy and renewable fuel technologies, also through international partnerships, and become more competitive in innovative renewable energy technologies in Europe and globally.
5. European researchers benefit from a stronger community and from a reinforced scientific basis on renewable energy and renewable fuel technologies including emerging ones, also through international collaborations.
6. European citizens have access to an energy market that is fair and equitable, more resilient, uses all different types of local renewable energy resources, and is less dependent on fossil fuels imports. Citizens experience less fuel and energy poverty, and also benefit from new employment and upskilling opportunities. Local communities benefit from a more decentralized, affordable, and secure energy system and from multiple uses of land and water.

Energy systems, grids and storage

1. R&I actions will support the just digital and green transformation of the energy system through advanced solutions for accelerating the energy systems integration and decarbonisation. The developed clean, sustainable solutions will contribute to making the energy system work better for actors and supply more reliable, resilient and secure energy – even under increasingly more frequent extreme climate events.
2. The solutions developed will contribute to increase flexibility and grid hosting capacity for renewables through optimizing cross sector integration and grid scale storage as well as cover off-grid situations. They will improve the preparedness of the electricity system to support the EU's binding target for 2030 of minimum of 42.5% renewables in the gross final energy consumption (with the aspiration to reach 45%), and full decarbonisation by 2050. They will enable further electrification of demand and will enhance the competitiveness of the European value chain, reduce pressure on resources (also by making technologies ‘circular by design’) and decrease dependencies. Such solutions would also enable a better EU resilience to climate risks.
3. The solutions will improve consumer awareness and engagement in the energy transition, via innovative offers and services (e.g. demand response, energy communities) and will target different types of consumers, including “hard to reach” population groups (such as energy poor or low-income households). This will result in increased trust in, and uptake of the new products and services entering the energy system.

Carbon capture, use and storage (CCUS) and carbon dioxide removal (CDR)

1. Accelerated deployment of carbon capture, use and storage (CCUS) as a CO2 emission mitigation option in electricity generation and/or in industry applications, as well as carbon dioxide removal for negative emissions.

Legal entities established in China are not eligible to participate in both Research and Innovation Actions (RIAs) and Innovation Actions (IAs) falling under this destination. For additional information please see “Restrictions on the participation of legal entities established in China” found in General Annex B of the General Annexes.

[1] For an example of a methodology for the assessment of sustainability, circularity and contribution to EU resilience and technological autonomy of clean energy technology in the R&I pipeline, please see Study on circular approaches for a sustainable and affordable clean energy transition

Expected Outcome:

Projects are expected to contribute to all the following expected outcomes:

• Effective and innovative methods for gathering, sharing and using large data sets in energy applications for the purpose of training AI models while ensuring privacy and security.
• Advanced and - wherever possible - open-source AI foundation models to support the digitalisation of the energy system, through improved grid observability, forecasting of supply and demand, advanced storage and renewables integration, demand side flexibility and energy efficiency.
• Enhanced cooperation, knowledge sharing and interoperability among energy system actors for secure and seamless data exchange.
• Advanced methodologies for AI model development ensuring that the results are FAIR (Findable, Accessible, Interoperable, Reusable) beyond the project ending, open source and accommodating for technology evolution.

Scope:

The quality of AI models depends strongly on the amount, quality, and representativeness of the data used for their training. Projects are expected to focus on developing, training and testing AI foundation models, using extensive data sets to accelerate the energy transition in key focus areas of the energy sector. Projects are expected to:

• Building on the results of previously funded projects on the Common European energy data space^[1] as well as the ongoing work within the Data 4 Energy expert group, projects are expected to demonstrate innovative methods and data governance strategies for sharing data among various energy actors. Projects are expected to demonstrate that they have access to large relevant datasets at the time of submitting the proposal.
• Projects are expected to develop innovative strategies for sharing data in an effective, secure and transparent way to support the development of AI foundation models. An indicative non exhaustive list of strategies that can be explored is the use of data space connectors or federated training of models to allow the integration of confidential data; the creation of synthetic data as an intermediate step to fill data gaps or improve the quality of the data could also be explored. Projects could also explore other innovative data sharing strategies, such as an ‘AI gym’. The data governance strategies that will be developed in the project should be designed in a way to be scalable and operational even after the end of the project.
• Projects are expected to develop foundation models tailored for the energy sector, addressing at least one -or more- of the following use cases: planning and operation of electricity grids (including static power flow modelling and dynamic EMT modelling), forecasting, congestion management, anomaly detection, fault diagnosis, predictive maintenance, flexibility management, demand-side energy efficiency, smart and bidirectional charging of EVs or other use cases that contribute to the objective of digitalisation of the energy system.
• Projects are expected to perform in-depth testing and validation of developed foundation models in lab demonstrators and real-life pilots, building on the results and using the facilities of previously funded projects on “AI testing and experimentation facilities (TEFs)”^[2]. Regulatory sandboxes could also be considered for real-life pilot implementations.
• Projects are expected to bring together a wide group of stakeholders including data owners (for example energy utilities, grid operators, asset owners), application developers (for example startups, SMEs, hyperscalers) and model deployers (for example industry, grid operators, equipment manufacturers) providing a space of cooperation and collaborative data exchange.
• To scale up the training and development of the models, projects can benefit from the computing capacity of the AI factories that were recently announced by the European Commission^[3], in particular the three AI factories that aim to develop AI applications for the energy sector.
• AI models are expected to respect ethical, safety and security principles; furthermore, transparency, explainability, and accountability should be embedded by design in the model development. Open-source development practices should be pursued wherever feasible. Efforts should be made to avoid biases, ensuring that the data produced is representative of diverse populations.

Selected projects are expected to contribute to the BRIDGE initiative^[4] and actively participate in its activities.

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[1] Establish the grounds for a common European energy data space

[2] AI Testing and Experimentation Facility (TEF) for the energy sector – bringing technology to the market

[3] AI Factories

[4] https://bridge-smart-grid-storage-systems-digital-projects.ec.europa.eu/