Energy Efficient Optimization of Intelligent Microgrid Restoration Using Reservoir Computing and Blockchain-Driven Cyber Defense

Abstract

Ensuring cyber-physical resilience with the renewable integrated microgrids requires deploying intelligent, adaptive, and secure control coordination. This study proposes a new edge-intelligent microgrid restoration framework that uses reservoir computing for real-time fault recognition, along with a DAG-based blockchain to secure the cyber transactions in a lightweight and scalable manner. Based on temporal dynamic mechanisms, the RC model performs better than the conventional SNNs and yield 98.3% anomaly detection accuracy within 124 ms of latency, thus providing a proactive microgrid stabilization framework. At the same time, the DAG-based blockchain demonstrates that validation between peers happens quickly, with an average consensus delay of only 1.4 seconds, thereby providing real-time authentication with 99.5% success of node validation. During the grid disturbance, voltage deviation is limited to ±0.9%, with layered restoration control activated to predict and respond to faults in an energy-aware manner. Compared with the legacy edge-control mechanism, our framework reduces communication latency by 32% and improves energy resilience by 21.8%. Simulations conducted in MATLAB Simulink (for power event modeling) and IOTA-based Tangle Testnet (for blockchain trials) demonstrate superior scalability, cyber-fault isolation, and control adaptability. This research introduces a simulation-validated model that will advance the creation of trust-based, self-sovereign microgrids in power markets in the future, which are susceptible to cyber-attacks.