Energy Efficient Federated Graph Reinforcement Learning and Digital Twin Assisted Demand Response for Blockchain-Enabled Self-Healing Smart Grids

Abstract Smart grids today are highly decentralized and data-driven, with the growing incorporation of renewable energy resources, electric vehicles, distributed energy systems and intelligent edge devices. Energy efficiency, operational resilience, privacy protection and secure real-time coordination under renewable intermittency and demand uncertainty, however, is a challenging task when large-scale users participate. This paper presents a novel federated graph reinforcement learning-based digital twin predictive intelligence, blockchain trust management, and adaptive self-healing control integrated framework called Energy Efficient Federated Digital Twin Framework (EEFDTF) for Blockchain-Enabled Self-Healing Smart Grids to overcome these challenges. The proposed framework introduces distributed energy management and demand flexibility coordination without revealing the raw data of the consumers, improving privacy and scalability. A digital twin layer continuously replicates the operating conditions of the grid, and can foresee future operating conditions to guide proactive operational decisions. Moreover, a permissioned blockchain infrastructure offers trust less transaction validation, enforcement of trust and automatic enforcement of energy management policies by energy-aware smart contracts. An adaptive, self-recovery mechanism is embedded to identify abnormal operation conditions, risk during operation, and to take autonomous recovery action to ensure the stability and continuity of the grid’s operation. The framework is validated by co-simulation involving MATLAB/Simulink–Python and under renewable variability, load uncertainty, communication delays and cyber-physical disturbances, while under high-participation operating conditions. The experimental results show the energy utilization efficiency is 98.7%, the coordination accuracy of demand flexibility is 97.8%, the peak load reduction is 38.9%, the improvement of renewable energy utilization is 35.6%, the success rate of self-healing is 96.4%, the accuracy of blockchain validation is 99.1%, and the average response time is 21ms. The results validate the effectiveness of the proposed framework as a scalable, secure, resilient and energy efficient solution for the next-generation autonomous smart grid operation.