Graph Neural Network-Infused Digital Twin Platform with Transfer Learning and Quantum-Safe Protocols for Resilient Power System Control and Markets

This study introduces a pioneering digital twin platform that embeds graph neural networks (GNNs) to replicate power system topologies, where buses function as nodes and transmission lines serves as edges, facilitating precise real-time state estimation and predictive analytics. Transfer learning enables efficient adaptation of pre-trained GNN models from extensive synthetic datasets, such as modified IEEE benchmarks, to operational environments with scarce data, slashing training requirements by approximately 60% while upholding robustness across varied grid configurations from local distribution to national wholesale markets. Quantum-safe protocols, drawing on NIST-approved post-quantum algorithms like Kyber for key encapsulation and Dilithium for digital signatures, secure the continuous data synchronization between physical infrastructure and virtual models, defending against quantum-enabled threats such as harvest-now-decrypt-later scenarios without introducing latency penalties.The hybrid edge-cloud deployment supports advanced resilient control mechanisms, encompassing model predictive control for stabilizing grid operations amid faults and optimization routines for electricity market clearing that enhance social welfare under stochastic conditions involving renewables and demand response. Validation occurs through rigorous simulations on IEEE 118-bus, 300-bus, and large-scale 10,000-bus networks subjected to false data injection attacks, renewable intermittency, and topological shifts. Results reveal 25% reductions in fault recovery durations, 18% gains in locational marginal price precision, and near-perfect (99.9%) resilience to encrypted interceptions. Ablation analyses affirm the additive value of GNN topology awareness, which alone reduces state prediction errors by 32% compared to recurrent baselines, alongside transfer learning’s convergence acceleration and quantum cryptography’s seamless integration.This platform bridges critical gaps in cyber-physical security and operational agility, positioning it as a deployable solution for smart grid evolution, including distributed energy integration and peer-to-peer transactions. Prospects for enhancement involve hybrid quantum-classical computing to further optimize control horizons, ensuring long-term viability in decarbonized energy ecosystems.