The DiTri Project aims to develop and validate a Digital Twin (DT) for hybrid AC/DC low-voltage distribution networks in order to improve control, flexibility, and resilience.

DT thus serves as an enabling technology for the optimal operation of hybrid networks, and, as of now, there is no DT implementation for this type of network. The DT proposed in this project is based on a distributed framework of innovative sensors and low-cost actuators to support the management of the AC distribution network and the DC portions integrated within it. The project aims to develop a DT to facilitate the operation and increase the operational efficiency of a low-voltage AC/DC hybrid distribution network. The DT will implement Artificial Intelligence (AI) techniques and develop predictive models for generation and load. Key features will include: real-time monitoring of the hybrid grid’s status using innovative, low-cost IoT sensors; prediction of generation and load profiles using machine learning and deep learning algorithms, combining historical and real-time data; analysis of the hybrid grid’s behavior to improve its operation (through balancing generation and demand, and reducing backflows and losses); dynamic regulation of systems (generation/storage/consumption) to optimize resource utilization by minimizing curtailment and increasing flexibility.

The expected results include a genuine scientific and technological improvement over the state of the art (since no hybrid network demonstration technology has yet been developed), validated through the pilot plant, with potential for industrial application in the distribution market. The project aims to contribute significantly to the expected impacts related to grid digitization, particularly in alignment with Mission Innovation’s Green Powered Future Mission. These results can be summarized as follows:

• A fully functional Digital Twin platform (with integrated modules for data acquisition, condition estimation, forecasting, flexibility management, and APIs) featuring a configurable user interface.
• Low-cost sensors (iSensor): field-validated prototype, technical specifications, and documentation for certification and market release.
• Cybersecurity/deception modules: prototypes and a library of decoys integrated into the platform.
• Pilot integration: release of the containerized platform, integration with the ASM Terni hybrid network, and operational testing (use cases). TRL 7 validation.
• Deliverables related to replicability and exploitation (guidelines, patentability analysis, business plan).

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