Resilient Model Predictive Control of Distributed Systems Under Attack Using Local Attack Identification

TL;DR

This paper introduces a resilient distributed model predictive control framework that uses local attack identification to enhance robustness against malicious attacks and uncertainties in microgrid systems with renewable energy sources.

Contribution

It develops a novel attack identification method integrated with distributed MPC, tailored for systems with limited local information and security constraints.

Findings

Effective attack detection in microgrids demonstrated

Enhanced robustness against attacks shown through simulations

Framework adaptable to other distributed control systems

Abstract

With the growing share of renewable energy sources, the uncertainty in power supply is increasing. In addition to the inherent fluctuations in the renewables, this is due to the threat of deliberate malicious attacks, which may become more revalent with a growing number of distributed generation units. Also in other safety-critical technology sectors, control systems are becoming more and more decentralized, causing the targets for attackers and thus the risk of attacks to increase. It is thus essential that distributed controllers are robust toward these uncertainties and able to react quickly to disturbances of any kind. To this end, we present novel methods for model-based identification of attacks and combine them with distributed model predictive control to obtain a resilient framework for adaptively robust control. The methodology is specially designed for distributed setups with limited local information due to privacy and security reasons. To demonstrate the efficiency of the method, we introduce a mathematical model for physically coupled microgrids under the uncertain influence of renewable generation and adversarial attacks, and perform numerical experiments, applying the proposed method for microgrid control.