Digital Twin-based Energy Infrastructure Powered by AI: Real-Time Simulation, Anomaly Detection, and Intervention

Abstract

The increasing complexity and vulnerability of modern energy systems underscore the urgent need for intelligent, resilient, and sustainable infrastructure solutions. However, challenges such as cybersecurity risks, ethical governance concerns, and interoperability barriers hinder progress. This study investigates how the integration of digital twin (DT) and artificial intelligence (AI) technologies addresses these challenges and transforms energy infrastructure management. Using empirical datasets from the International Energy Agency (IEA) Digitalisation and Energy Database, the OpenEI Digital Twin Case Studies, the NREL Grid Modernization Consortium, and bibliometric records from the Dimensions AI Scholarly Platform, the study applies descriptive statistics, multivariate frequency analysis, event sequence analysis, and citation network mapping. Findings reveal that North America leads global DT-AI adoption at 65%, with performance optimization accounting for 103 documented use cases. Real-time AI-driven interventions demonstrated action windows of 2–7 minutes, achieving efficiency gains of 30–60%, maintenance cost reductions of up to 40%, and false alarm rate improvements by 50%. Scholarly analysis identified 1,280 relevant publications, exhibiting a 21.3% annual growth rate, with growing emphasis on explainable AI (XAI) and federated digital twin architectures. The study emphasizes the necessity of harmonized global standards for interoperability and ethical AI governance to ensure secure and scalable deployments. It advocates for increased investment in underrepresented regions and strengthened academic-industry collaborations. By addressing both technological capabilities and systemic challenges, this research offers actionable insights to advance the resilience, sustainability, and operational intelligence of future energy infrastructures.