Deep Reinforcement Learning for DER Cyber-Attack Mitigation

TL;DR

This paper explores using deep reinforcement learning to develop control strategies for distributed energy resources (DER) to actively mitigate cyber-attacks in smart grid systems, enhancing grid resilience.

Contribution

It introduces a novel application of deep reinforcement learning for real-time control of DER units to counteract cyber-physical attacks in electrical distribution networks.

Findings

Deep reinforcement learning effectively learns mitigation strategies.

Control algorithms improve system resilience against cyber-attacks.

Demonstrated potential for real-time DER control in attack scenarios.

Abstract

The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption, to an active network with hundreds of distributed controllers dynamically modulating their operating setpoints as a function of system conditions. This transition is being achieved through standardization of functionality through grid codes and/or international standards. DER, however, are unique in that they are typically neither owned nor operated by distribution utilities and, therefore, represent a new emerging attack vector for cyber-physical attacks. Within this work we consider deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.