Resilient Secondary Distributed Model Predictive Control for Autonomous Microgrid Against Cyber Threats

Abstract

This paper aims to present a novel framework for enhancing the cyber-resilience of microgrids (MGs) by integrating consensus-based distributed model predictive control (DMPC) with a residual-based Luenberger sliding mode observer (LSMO). The proposed framework uniquely combines the capability of DMPC for coordinated control among distributed generators (DG) with the robust anomaly detection mechanism of LSMO to ensure operational stability and security. This integration enables the system to detect and respond effectively to both stealthy and false data injection (FDI) attacks while minimizing computational complexity. Extensive simulations demonstrate the ability of the framework to mitigate the impact of cyber-attacks, ensuring voltage and frequency regulation under adversarial conditions. It has been demonstrated that the proposed framework significantly improves the detection accuracy of advanced cyber threats while maintaining system stability through efficient control coordination. In contrast to existing methods, the proposed framework maintains resilience and robust performance in the presence of cyber vulnerabilities. The efficacy of the proposed framework is validated through detailed simulation studies using the Matlab/Simulink software platform, achieving notable improvements in key performance parameters and demonstrating enhanced resilience against cyber-attacks to ensure reliable MG operations. This work contributes to advance resilient MG operations by offering an efficient solution for safeguarding critical energy infrastructure in dynamic and cyber-vulnerable environments.