Efficient asset management is crucial for utilities to prevent outages, extend equipment lifespan, reduce operational costs, and enhance the resilience of the electric power system. In managing network data for asset management, persistent issues include informational silos among different databases and during different asset life phases (design, planning, operation).

The transition to Artificial Intelligence (AI) exacerbates these limitations, delaying the creation of comprehensive data-driven models or digital twins of reality. An imminent challenge is overseeing the complete asset lifecycle, from initial design to decommissioning, while preventing information silos. A potential resolution involves creating “informative” digital twins that intelligently manage data from various databases using AI solutions.

This paper introduces a digital twin concept applied to asset management for the electricity grid. Emphasizing integration of information models, including Industry Foundation Classes (IFC) and IEC Common Information Model (CIM) 61968/61970, an informative digital twin based on knowledge graphs unlocks the full potential of AI for asset management, including the possible use of generative AI models.

While prioritizing IEC CIM is advantageous for integrated Building Information Modeling (BIM) and IEC CIM utilization in the electrical network, challenges persist in synchronizing knowledge graphs with IFC and IEC CIM databases. The paper addresses asset management within the Smart Grid Architecture Model (SGAM), utilizing IEC CIM standards. While IEC CIM facilitates data exchange within utilities, it lacks specific classes for planning and managing grid assets.

Instead, the BIM methodology and IFC standard are specific for asset management but are not specialized for the description of electrical grid components. To address this, the paper proposes combining IEC CIM with BIM and IFC, therefore creating an informative digital twin based on standard ontologies and knowledge graphs. As case study, IFC classes describing a High Voltage/Medium Voltage (HV/MV) power transformer in a primary substation have been mapped to correspondent IEC CIM classes.

This methodology ensures comprehensive modelling throughout the equipment lifecycle, resolves asset management challenges, and promotes interoperability among utility systems.