Federated deep MPC-enabled digital twin and multiagent learning framework for secure and scalable smart nano grid energy management

Abstract

This study introduces a novel Federated Secure Dynamic Optimization Framework (FSDOF) to improve smart nano grid systems’ energy management, fault tolerance, and cybersecurity. The framework suggested combines Digital Twin (DT) technology and Multiagent Reinforcement Learning (MARL) to assist in real-time decision-making and decentralized control. In essence, FSDOF integrates three major components: Federated Deep Model Predictive Control (FD-MPC) to schedule energy optimally, SecureGraph-FedNet (SG-FedNet) to communicate through Graph Neural Networks and Autoencoders securely, and Dynamic Stochastic Neuro-Evolution Optimizer (DSNEO) to adaptively handle and optimize under uncertainty. The system demonstrated 98.52 % energy efficiency, DC voltage stabilization in 0.5 seconds, and Bit Error Rate (BER) of 0.012, which is better than the traditional DRL methods. SG-FedNet guarantees a security confidence of 0.99 in federated learning with differential privacy. Scalability and resilience of the system were confirmed by large-scale simulations on more than 100 nodes with less than 4 % performance degradation. These findings make FSDOF a scalable and strong solution to next-generation smart energy networks.