The digitization of management and operational processes is the key factor for a flexible, efficient, and resilient integrated energy system. Digitization requires standards that promote interoperability, both among different energy vectors and among different stakeholders. For these reasons, the report addresses issues related to the digitization of processes, with a focus on informative interoperability. Interoperability is facilitated by ontologies capable of describing the considered domain.

The report illustrates a concrete example of using the IEC CIM standard, updated to version 17. To enable the development of digital twins of the energy system, the BIM-CIM synergy is introduced.

Considering the use case of a transformer in a primary substation, the advantages of a collaboration between the civil design realm and the electrical planning&operation realm are demonstrated for managing the asset throughout its lifecycle.

In integrating different vectors, an ontology for the gas grid has been defined. With the digitization of the energy system, the ICT layer assumes a more significant role. It represents a critical point for the resilience of the system, both in terms of cybersecurity and the potential disruption of ICT services. For this reason, an ICT architecture typical of a DSO (Distribution System Operator) has been modeled, starting from the standard IEC CIM ontology.

Digitization plays a crucial role in improving the planning and management processes of individual companies or utilities. For this reason, a revision of multi-energy territory planning processes has been initiated, using innovative solutions capable of simplifying the management of data from heterogeneous sources and innovating the mapping of ground-based photovoltaic installations through satellite imagery. To enhance the capabilities of the MESP platform with algorithms for network planning, services have been added to perform scalability and replicability analyses of electrical networks.

Two different graph-based machine learning models have been developed for estimating voltages at nodes in an electrical network using data-driven techniques: Graph Convolutional Network and LightGBM. Within the context of grid maintenance processes, specific procedures have been studied concerning fault and maintenance processes, with a focus on assets in secondary substations.

The Report is available on the Italian site