GRAVITEQA leads the way for smart storage solutions and computational effectiveness towards a green energy transition.
GRAVITEQA is designed to revolutionize energy systems by leveraging gravitational storage, quantum computing, and AI technologies. Its objectives reflect the ambition to bridge scientific innovation with practical, impactful solutions, ensuring Europe leads in the clean energy transition.
Objective 2
Objective 3
Transform Decommissioned Coal Plants into Long-Duration Energy Storage Facilities
Objective 1
Enhance Reliability and Circularity of Sector-Coupled Electricity Grids
Leverage Quantum Computing and Quantum-Inspired Computing for Energy Optimization
Objective 5
Advance Trustworthy AI for Real-Time Grid Flexibility
Objective 4
Objective 6
Enhance Reliability and Circularity of Sector-Coupled Electricity Grids
Conduct Comprehensive Environmental Assessment of Project Technologies
Gravitational Storage, Quantum Computing, and AI for a Cleaner Energy Future: GRAVITEQA
GRAVITEQA focuses on integrating cutting-edge technologies like gravitational energy storage, quantum computing, and AI to revolutionize energy reliability and circularity. With a goal to enhance Europe’s clean energy transition, the project aims to tackle challenges in sector-coupled electricity grids and green ports by leveraging data-driven innovations and advanced computing methodologies. The project targets five cross-linked sectors: i) Demand-side orchestration, ii) RES planning, iii) Distribution grids management, iv) Green seaports and cold ironing, and v) Circular decommissioning of coal-fired plants and mines.
GRAVITEQA consists of 10 partners from 3 countries across Europe, combining expertise in energy technologies, quantum computing, artificial intelligence, and circular economy practices. These partners represent leading research institutions, innovative technology companies, and industry stakeholders committed to addressing the challenges of the clean energy transition.
GRAVITEQA VALIDATION CASES
The validation test cases for the technologies in the GRAVITEQA project include the following key activities:
Laboratory testing for Quantum-based technologies in energy system applications
To test quantum-based technologies in energy system applications involves validating quantum computing (QC) and quantum-inspired computing (QIC) against traditional methods, focusing on accuracy, processing speed, and scalability. The evaluation uses industry datasets to assess improvements in problem-solving efficiency and system optimization.
GRAVITEQA TECHNOLOGIES
GRAVITEQA’s key technologies and their progress from initial conceptualization to advanced stages (Technology Readiness Level – TRL 4) during the project’s lifetime.
Repurposes decommissioned coal power plants and mines to harness gravitational potential for energy storage.
Progress:
- Development of holistic methodology for the circular-by-design transformation of decommissioned conventional plants and abandoned mines
- Validation through simulations and lab experiments.
- TRL Progress: 2 → 4
Applies QC to solve Facility Location Allocation (FLA) and Load-Side Asset Management (LAM) problems.
Progress:
- Advanced QC algorithms for grid management validated in lab-controlled environments.
- Reduced computation time and enhanced solution scalability.
- TRL Progress: 2 → 4
Uses quantum-inspired techniques to address energy sector challenges before full-scale QC is viable.
Progress:
- Models developed for FLA and LAM using QUBO and Ising formulations.
- Demonstrated scalability and performance in simulations with real-world datasets.
- TRL Progress: 3 → 4
Enables end-to-end learning models to estimate grid flexibility potential and predicts energy consumption patterns in green ports with guarantee.
Progress:
- Developed AI-driven tools for non-convex optimization problems in distribution grids, to quantify flexibility potential in high DER (Distributed Energy Resource)-penetrated grids.
- Developed conformal prediction tool for energy consumption forecasting in green ports
- Reduced latency and improved solution accuracy for grid operators.
- Reduced uncertainty for optimal energy use and ship electrification at seaports
- TRL Progress: 2 → 4
Introduces edge intelligence to process data locally and enhance grid operation efficiency.
Progress:
- Implemented hardware acceleration techniques like FPGA to reduce computational overhead.
- Developed reference designs for real-time inference in smart grids.
- TRL Progress: 2 → 4
IN THE NEWS OF GRAVITEQA
Stay informed with the latest breakthroughs and updates
Date: June 26, 2025 Location: Frederic Joliot-Curie International House of Scientists, Varna, Bulgaria On June 26, 2025, ESO EAD, as part of the GRAVITEQA [...]
📅 Date: June 13, 2025 📍 Location: Kozani, Greece The University of Western Macedonia (UOWM) successfully hosted the 2nd Symposium of Research Projects, a [...]
Energy storage technologies are essential to the future of sustainable energy systems, enabling the integration of variable renewable sources (VRES) and enhancing grid reliability. [...]
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