Intelligent Energy Virtualization for Sustainability: Meta-Analysis of AI-Based Digital Twins and Federated Learning in Zero-Carbon Grid Optimization

Abstract

The accelerating shift toward decentralized, zero-carbon energy grids presents major operational challenges due to renewable energy's intermittency, demand variability, and increasing cyber-physical threats, thereby reducing the effectiveness of traditional centralized grid management. This systematic review and meta-analysis investigated how AI-driven digital twins and federated learning (AI-DT-FL) work together as a dual solution to enable high-fidelity virtual grid modeling with privacy-preserving distributed intelligence. A preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based multibase search from 2020 to 2025 identified 50 eligible studies, and pooled estimates were calculated using a random-effects model. The meta-analysis showed notable performance improvements, with a pooled effect size of -0.5339, and the greatest gains were seen in microgrids and European deployments. The discussion suggests that this technological synergy improves prediction accuracy, energy efficiency, and cybersecurity resilience; however, available evidence remains limited due to the dominance of simulation-based studies and inconsistent benchmarks. Overall, integrated AI-DT-FL architectures show significant potential for a secure, zero-carbon energy transition, supported by thorough sensitivity analyses.