Large scale operational validation and upscaling of state-of-the-art (Generative) AI tools and models powering a next generation digital energy system
Forthcoming
Status as published by the data source.
Expected Outcome:
Project results are expected to contribute to all the following expected outcomes:
• Advanced, secure frugal (energy-, resource-, data- and cost-efficient, sustainable) AI tools for real-time grid operations (including protection), market operations (if relevant) and consumer empowerment in distributed energy systems.
• A demonstrated AI development environment integrated with the upgraded digital spine of the energy system and energy-saving consumer applications.
• Scaled-up capabilities for learning at edge nodes and federated systems using agent-based architectures.
• A governance framework and operating model for the Gen(AI)-powered digital spine. Scope:
Building up on previous actions in Horizon Europe (e.g. the digital spine of the energy system in cluster 5 as well as cloud and edge continuum and AI data processing activities in cluster 4) and Digital Europe (Common Reference Framework for energy consumer applications across the EU, Common Energy Data Space), projects are expected to:
• Validate (demo site and operational grid) and scale secure state-of-the-art specialised frugal (energy- and resource-efficient, data- and cost-efficient, locally deployed and sustainable) open-source (Generative-)AI tools and models for real-time grid (and market, if relevant) monitoring, balancing, forecasting and consumer empowerment in a distributed energy system.
• Demonstrate an AI development environment within the enhanced digital spine of the energy system, including its digital twin, which should incorporate the functionality of the Common Reference Framework for energy saving applications.
• Prototype and validate at scale machine learning at the edge node level and federated at swarm level in an agent-based architecture, leveraging new mechanisms for scaling down AI models as well as incremental and reinforcement learning.
• The project's deliverables should be compatible with the principles of the Common European Energy Data Space and consistent with the EU's Apply AI and Competitiveness Compass, ensuring that they promote AI adoption and competitiveness in line with EU policies, as well as the EU Digitalisation of the energy sector initiatives.
• Where relevant and possible, the selected projects are expected to make use of the European AI factories and AI gigafactories.
• The selected projects are expected, where available, to make use of and further develop commonly agreed European standards and to actively collaborate and contribute to the work of established and de facto European standards developing organisations and open-source communities and platforms. Selected projects are expected to contribute to the BRIDGE[1] initiative and actively participate in its activities.
Technology Readiness Level - Technology readiness level expected from completed projects
Activities are expected to achieve TRL 7-8 by the end of the project – see General Annex B. Activities may start at any TRL.
[1] https://bridge-smart-grid-storage-systems-digital-projects.ec.europa.eu/
- Status
- Forthcoming
- Deadline
- (time not stated)
- Opens
- Published
- Total budget
- €20,000,000
- Grant range
- €10,000,000 – €10,000,000
- Country
- European Union (EU-wide)
- Programme
- Horizon Europe (HORIZON)
- Official page
- Open at the source portal
Other calls under Horizon Europe (HORIZON)
- PV based electrification of the economy: Designing & optimising PV systems supporting industrial electrification and promoting participation in electricity markets (EUPI-PV Partnership)
- Advanced TSO control rooms to enhance grid observability, stability and resilience
- Advanced Distribution Management Systems (ADSM) for more efficient and flexible distribution grids
- Community of practice - Data-Driven Decision-Making in Energy
- Industrial processes and equipment for innovative, reliable and scalable tandem technologies (EUPI-PV Partnership)
- Integrated Approaches for Retrofitting Infrastructures with Innovative Energy Storage Technologies
- Demonstration of hydropower technologies for efficient and forward-looking refurbishment of existing hydropower plants
- Delivery of industrial CCUS clusters – Societal Readiness pilot
All calls under this programme
Similar opportunities
- Community of practice - Data-Driven Decision-Making in Energy
- Energy efficiency and sustainability of AI data processing in Data Centres (IA)
- Data sharing to support the training and development of AI foundation models in the energy sector
- International cooperation in AI (IA)
- AI supporting informed advice for farmers and foresters to improve competitiveness and sustainability
- Digital solutions for defining synergies in international renewable energy value chains
Where this came from
- Source document
- https://ec.europa.eu/info/funding-tenders/opportunities/data/topicDetails/horizon-cl5-2027-02-d3-24.json
- Document fingerprint
d9731d0b283822f9(SHA-256, first 16 hex characters)- Retrieved
- First recorded here
What has changed
- url first recorded as https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/horizon-cl5-2027-02-d3-24 on
- currency first recorded as EUR on
- amount_max first recorded as 10000000.00 on
- amount_min first recorded as 10000000.00 on
- budget_total first recorded as 20000000.00 on
- deadline first recorded as 2027-03-31 on
- opens_on first recorded as 2026-12-03 on
- published_on first recorded as 2025-12-12 on
- status_basis first recorded as source_status on
- status first recorded as forthcoming on
- title first recorded as Large scale operational validation and upscaling of state-of-the-art (Generative) AI tools and models powering a next generation digital energy system on
Data source
© European Union, 2026. Source: EU Funding & Tenders Portal. Reused under Commission Decision 2011/833/EU — CC BY 4.0.
Retrieved from the source on .
The source last updated this document on Thu, 25 Jun 2026 12:26:51 GMT.
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