Robust data-driven NLMPC for real-time microgrid management under uncertainties and false data injection attacks

Abstract

Abstract The integration of renewable energy sources in microgrids (MGs) enhances system efficiency but increases vulnerability to cyberattacks such as false data injection (FDI) attacks. This paper presents a robust data-driven nonlinear model predictive control (NLMPC) framework with the integration with Bayesian Neural Networks (BNNs). The BNN offers probabilistic state estimation, allowing uncertainty-aware prediction and early anomaly detection. By integration BNN within the NLMPC, the framework obtains combined detection, mitigation, and control of cyberattacks. Simulation results show that the proposed framework detects FDI attacks within 0.1 s, and stability is restored within 0.4 s. Frequency deviation is reduced by 99.7%, while active and reactive power fluctuation decreases by 85% and 87%, respectively. Moreover, battery storage operation remains within a safe limit. These results validate the effectiveness of the proposed framework for real-time cyber-resilient microgrids control under uncertainty and attack scenarios.