As Europe’s energy system faces new challenges and accelerates green and digital transitions, generative Artificial Intelligence (GenAI) is emerging as a transformative enabler for critical infrastructure—and is already unlocking “quick wins” across Europe’s energy landscape, optimizing operational efficiency, workforce resilience, planning of large-scale infrastructures, and system flexibility.
GenAI is taking the central stage in Europe’s endeavor to place strategic sectors as AI front-runners, prioritizing the energy system. Time is ticking, and some dossiers are piling up: an unprecedented power outage, the integration of variable renewable energy, the increase in electrification, the aging infrastructure, and the growing cyber risks.
In response, the EU advanced with initiatives such as the AI Innovation Package, the Digitalization of Energy Action Plan, the Common European Energy Data Spaces and the forthcoming Apply AI Strategy.
This approach aims to establish GenAI as a building block of the EU’s AI and digital industrial policy, while offering a broader perspective on gaps and possibilities for scaling GenAI responsibly across the EU’s energy sector, supporting everything from real-time grid operations to infrastructure planning and workforce transformation.
From the lab to the grid, Europe’s GenAI initiatives are already demonstrating how AI can optimize grids, reduce CO₂ emissions, and empower energy professionals. Some of these advances, already beyond theoretical concepts, are being showcased through events like the “GenerativeAI4EU: Accelerating Quick Wins for Smart Grids” webinar, hosted by the European Commission in collaboration with the Adra Association and key industrial and research actors.
Look out in the blackout
Weeks after this workshop, a power outage left more than 50 million people in Spain and Portugal without electricity: in a matter of seconds, more than half of the electricity-generation capacity was lost—Spain’s power grid lost the equivalent of 60% of its national demand.
Following the blackout, questions about the grid’s resilience and how to deal with the fluctuation attached to the penetration of renewables with existing systems arose. AI has often been highlighted as a promising solution to address these phenomena, ensuring a truly positive impact on decision-making.
Ricardo Bessa, INESC TEC researcher, has been stressing the importance of developing models that combine real data and physical knowledge—like physics-informed models—to improve consumption and production forecasts, support real-time operational decisions and increase the resilience of the energy system.
“GenAI has proven benefits and can play a crucial role in accurately forecasting energy demand, helping to balance electricity supply and demand. Fundamental models trained on large data sets enable state estimation, forecasting and contingency analyses, improving the management of power grids,” he said.
An encompassing strategy
As the U.S. and China race for AI supremacy, the EU pushes for AI adoption and carves out its competitive edge with strategic regulation, green tech leadership, and targeted funding. The Commission is supporting the dissemination and uptake of AI through its Competitiveness Compass, a strategic framework aiming to make Europe a global hub for innovation, clean technologies, and climate neutrality.
Ensuring energy efficiency, privacy, and scalability is a priority and there are practical applications of generative AI already delivering results across Europe’s energy systems. As grid congestion, decentralized renewables, and rising electrification challenge operators, GenAI is emerging as a vital enabler for demand forecasting, dynamic grid management, and operational resilience.
The European Commission (EC) has been highlighting how GenAI is moving from concept to deployment. In the Netherlands, grid operators like Tennet and Alliander are benefiting from GenAI’s contributions, optimizing real-world operations and paving the way for AI solutions to have a crucial role in accelerating the adoption of “renewable energy sources like solar and wind, managing demand-side dynamics with advanced forecasting and optimization while targeting high use of existing grid capacity,” as highlighted by the EC.
The role of foundation models in state estimation and contingency analysis, the promise of interoperability in IoT and assets onboarding and edge computing, and AI to unlock demand-side flexibility are some of the advances highlighted in the webinar.
Natural language tools for energy planning (Artelys), automated IoT integration (Siemens), and real-time congestion management using reinforcement learning (enliteAI) are some examples how Europe’s strategy is delivering immediate value for Europe’s energy sector.
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Europe backs generative AI to drive clean energy transformation (2025, May 13)
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