Energy efficient cyber-physical control of renewable microgrids using edge-AI enabled IoT and secure blockchain coordination

As microgrid complexity increases, cyber-physical coordination must be secure and efficient. This research designs an edge-AI blockchain framework for the cyber-physical management of renewable-integrated microgrids. The edge devices call SNNs for low-power real-time learning and fault detection functions; Hyperledger Fabric performs the functions of energy transaction validation and energy access control. The microgrid edge node leverages the SNN to predict faults and perform switching optimization lately on noise and latency. The blockchain guarantees trusted peer-to-peer communication and secure provenance of data. The system boasts Cyber Fault Detection Accuracy (CFDA) of 97.6%, Consensus Delay < 2.3 s and Voltage Deviation < ± 1.1%. The edge-AI + blockchain system reduces communication overhead and enhances energy authentication efficiency by 28% over centralized control. The architecture also allows adaptive restoration mechanisms upon disturbance and integrates hierarchical control across layered microgrid clusters. Edge AI speeds up anomaly detection with minimal computation costs while maintaining grid observability. The unified system is aimed at providing greater cyber resilience and real-time suitability for adverse operational conditions. Simulations performed on MATLAB Simscape and Hyperledger test networks verify that such an arrangement improves system stability, recovers from faults, and extends its controlability to a great extent, thus standing in line to the developing standards and polices for a decentralized microgrid setup.