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.
@article{arxiv.2009.13088,
title = {Deep Reinforcement Learning for DER Cyber-Attack Mitigation},
author = {Ciaran Roberts and Sy-Toan Ngo and Alexandre Milesi and Sean Peisert and Daniel Arnold and Shammya Saha and Anna Scaglione and Nathan Johnson and Anton Kocheturov and Dmitriy Fradkin},
journal= {arXiv preprint arXiv:2009.13088},
year = {2020}
}