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Hybrid AI-Control Framework for a next-generation grid-scale energy storage and system integration

Expected Outcome:

Project results are expected to contribute to all the following expected outcomes:

  • Advanced digital twin and AI technologies (i.e. hybrid) to support proactive grid storage management and an effective integration with energy and ancillary services markets, for systems with high levels of renewable energy.
  • Optimized energy storage operations resulting in reduced operational and ancillary service costs, extended asset lifespans and increased revenues from market participation while boosting renewable energy uptake for decarbonization.
  • Optimized energy dispatch and storage cycling, increasing grid resilience and cost efficiency, aiding in lowering energy costs for consumers.

Scope:

Projects are expected to develop and validate an integrated system that combines grid-scale energy storage with a hybrid AI control mechanism and optimisation within a digital twin framework. This system will enable proactive and adaptive management of storage and flexibility assets (e.g. large industrial plants with flexible electricity demand), ensure seamless integration with existing grid infrastructures (if applicable also with offshore systems) and electricity markets while enhancing system efficiency and resilience (also for rare/extreme events). The project will demonstrate real-time management for optimal energy dispatch, ancillary services and storage cycling, develop a scalable integration framework for seamless cross-border operability, and validate improvements in grid reliability through field demonstrations and simulations, providing a potential blueprint for EU-wide deployment. Proof of concept studies using PHIL technology applied to scaled systems are expected to be used for the proactive and adaptive management algorithms.

Selected projects are expected to contribute to the BRIDGE initiative [1] and actively participate in its activities.

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

Expected Outcome

Project results are expected to contribute to all the following expected outcomes:

  • Advanced digital twin and AI technologies (i.e. hybrid) to support proactive grid storage management and an effective integration with energy and ancillary services markets, for systems with high levels of renewable energy.
  • Optimized energy storage operations resulting in reduced operational and ancillary service costs, extended asset lifespans and increased revenues from market participation while boosting renewable energy uptake for decarbonization.
  • Optimized energy dispatch and storage cycling, increasing grid resilience and cost efficiency, aiding in lowering energy costs for consumers.

Scope

Projects are expected to develop and validate an integrated system that combines grid-scale energy storage with a hybrid AI control mechanism and optimisation within a digital twin framework. This system will enable proactive and adaptive management of storage and flexibility assets (e.g. large industrial plants with flexible electricity demand), ensure seamless integration with existing grid infrastructures (if applicable also with offshore systems) and electricity markets while enhancing system efficiency and resilience (also for rare/extreme events). The project will demonstrate real-time management for optimal energy dispatch, ancillary services and storage cycling, develop a scalable integration framework for seamless cross-border operability, and validate improvements in grid reliability through field demonstrations and simulations, providing a potential blueprint for EU-wide deployment. Proof of concept studies using PHIL technology applied to scaled systems are expected to be used for the proactive and adaptive management algorithms.

Selected projects are expected to contribute to the BRIDGE initiative [1] and actively participate in its activities.

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

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