GRAVITational StoragE, Quantum computing, and AI for enhanced Circularity and Reliability in Clean transition-affected sector-coupled electricity grids

The clean energy transition requires at least a 55% reduction in GHG emissions (from 1990 levels) by 2030, according to the Fit for 55 package.

Thus, electricity grids will be called upon to operate in an overall context of 50% electricity production from RES of any scale by 2030.

Thus, several challenges will become even more apparent in the following years: (i) reliability issues in electricity grids due to rapid decarbonization, (ii) lack of circularity in conventional power plants decommissioning process, (iii) operational issues in sector-coupled systems under mass electrification scenarios, (iv) significant computation and data processing efforts are required to manage the future grid.

GRAVITEQA highlights the synergetic benefits of gravitational storage, Quantum Computing (QC) and Quantum Inspired Computing (QIC), and data-driven, trustworthy AI-based analytics services.

GRAVITEQA develops and validates 9 components/methodologies up to a TRL 4: (i) QC and QIC for the Facility Location Allocation and Load-side assets management problems, (ii) a generic and holistic methodology to find the optimal energy storage technology or mix of them, to transform a coal power plant and mine into a long duration energy storage plant, (iii) repurposing of available assets case study capable of providing long-term storage and enhancing recyclability of a under-decommissioning thermal power plant and an abandoned coal plant, (iv) conformal prediction for robust energy demand of cold ironing, (v) optimal charging of cold ironing and EVs respecting grid constraints for reliable green port operation, (vi) seaport electrification strategy for seaports: analysis and scenarios planning, (vii) fast nodal flexibility region estimation algorithm for 3-phase grids unlocking the flexibility services procurement from distributed RES, (viii) end-to-end trustworthy learning for non-convex optimization problems, and (ix) a reference design for edge inference in smart grid applications.