  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.
