Work performed so far has focused on the technical and scientific development, integration, and preliminary validation of an integrated digital solution for renewable and multi‑carrier energy systems. Activities in RP1 covered multi‑energy system modelling and data handling, renewable generation and load forecasting, asset health modelling and fault detection, hierarchical and distributed optimisation and control, definition of a scalable system architecture, and the preparation of realistic validation environments and use‑case datasets. These activities establish the scientific and technological foundations required for system‑level validation in the next project phases.
A central achievement is the development of modular, real‑time capable digital twins for electricity networks and thermal networks (district heating/cooling), as well as key renewable and sector‑coupling assets. Component‑level models for power systems (e.g. network elements and loads) and thermal systems (e.g. piping networks, pumps, storage and conversion units) were implemented with interoperability and control‑oriented design in mind, enabling consistent simulation, analysis and integration across energy vectors. Plug‑and‑play concepts were implemented via standardised interfaces to support flexible configuration and future scaling of multi‑carrier system representations.
In parallel, a unified forecasting framework for renewable generation was designed and validated, including short‑term forecasting models for wind and photovoltaic generation, intended for operational integration with the digital twin and control layers. These forecasting capabilities support predictive operation by reducing uncertainty in renewable production and enabling anticipatory decision‑making for system optimisation and control.
A further core technical achievement is the development of health and fault‑aware methods. A health model library was established for multiple asset classes relevant to the project (e.g. storage and conversion assets), together with fault detection approaches combining model‑based and data‑driven techniques. This work provides the basis for predictive maintenance, early anomaly identification, and health‑aware operation, and it supports the extension of asset‑level health models toward integrated system‑level digital twin functionality.
On the control and optimisation side, key elements of the parameter and state estimation toolbox and hierarchical/distributed model predictive control approaches were developed and tested in simulation. The project advanced reconfigurable and self‑healing control concepts to enable continued operation under faults, asset disconnection, or topology changes, supporting resilience and adaptability in decentralised multi‑energy systems. Cybersecurity considerations were analysed and incorporated through security‑aware monitoring and detection concepts aligned with the needs of digitalised energy infrastructures.
A scalable and interoperable system integration architecture was defined and implemented in initial form, enabling the Energy Management System layer to interface with the advanced digital components (digital twins, forecasting, etc). Integration and validation procedures were prepared, including system‑level testing approaches intended to ensure consistent interoperability and readiness for pilot deployment.
Finally, the project defined and structured representative real‑world use cases and validation frameworks, including energy community and sector‑coupled electricity/thermal scenarios across different contexts.
