The increasing frequency of energy disruptions caused by extreme weather, cyber threats and grid instabilities requires advanced tools for predictive analysis and resilient decision-making. Digital Twins (DTs), as high-fidelity virtual replicas of energy assets and systems, offer a scientific and technical framework to simulate, monitor and optimize performance under diverse disruption scenarios.
A Digital Twin integrates real-time sensor data, historical records and physics-based models to create a dynamic representation of energy infrastructure. For instance, a DT of a microgrid or transmission network can model how cascading failures propagate, predict voltage instabilities and evaluate the response of distributed energy resources (DERs). This enables utilities and operators to test contingency plans virtually-such as demand response activation, distributed generation reconfiguration, or storage deployment-before implementing them in real-world operations.
In disruption scenarios, decision support derived from DTs is significantly enhanced through AI-driven analytics. Machine learning models embedded in DT platforms can forecast load variations, detect anomalies and quantify resilience metrics. By running “what-if” simulations, decision-makers can assess trade-offs between cost, recovery time and reliability, thus supporting evidence-based interventions. For example, simulating a cyber-attack on grid control nodes within a DT environment allows operators to identify vulnerabilities and optimize recovery strategies without risking physical assets.
The realistic advantage lies in the DT’s ability to unify multi-domain data-electrical, thermal, economic and environmental-into a single decision-support ecosystem. However, challenges such as interoperability, computational cost and cybersecurity of DT platforms must be addressed.
Way forward: The integration of Digital Twins with IoT, edge computing and blockchain for secure data exchange can revolutionize resilience planning. Collaboration between utilities, academia and regulators will be critical to establish open standards and scalable DT frameworks. As energy disruptions intensify, DT-based scenario testing will transition from research prototypes to indispensable tools for resilient, adaptive and sustainable energy systems.
Keywords: Digital Twin, Energy Resilience, Scenario Testing, Grid Disruptions, Decision Support, Distributed Energy Resources, Smart Energy Systems.