Interoperable digital twin ecosystems: enhancing risk management in distributed energy systems

Energy producers today operate in an environment where digitalisation has greatly increased both opportunity and complexity. Critical infrastructure, such as energy grids and industrial facilities, are now deeply interconnected, and their growing interdependencies create system-level vulnerabilities, meaning that disruption in one area can quickly cascade into others. Improving operational efficiency and resilience in this environment requires not just better data but the ability to share and act on insights across organisational and system boundaries. Digital Twin (DT) Ecosystems, which are networks of connected DTs that exchange data and insights in a coordinated and interoperable environment, provide a new way forward. By connecting digital representations of assets, operations, and processes, they enable real-time monitoring and predictive decision-making at scale. Yet, a major challenge remains: traditional risk management approaches operate in silos, making it difficult to understand cumulative risks or coordinate effective mitigation strategies across distributed operations. This paper presents a standards-based approach for enabling interoperable risk analytics within DT Ecosystems. The proposed risk event metamodel allows risk-related information to flow seamlessly between different systems and organisations. This enables operators to gain a holistic view of risks, improve situational awareness, and respond more effectively to dynamic conditions. A representative energy-sector use case is used to demonstrate how integrated risk information can support more resilient and efficient operations. By addressing key challenges in interoperability and collaboration, this work highlights a practical pathway for energy producers to strengthen reliability, reduce downtime, and enhance overall performance in an increasingly complex operating environment.