  Power grids heavily rely on Automatic Generation Control (AGC) systems to
maintain grid stability by balancing generation and demand. However, the
increasing digitization and interconnection of power grid infrastructure expose
AGC systems to new vulnerabilities, particularly from cyberattacks such as
false data injection attacks (FDIAs). These attacks aim at manipulating sensor
measurements and control signals by injecting tampered data into the
communication mediums. As such, it is necessary to develop innovative
approaches that enhance the resilience of AGC systems. This paper addresses
this challenge by exploring the potential of deep reinforcement learning (DRL)
to enhancing the resilience of AGC systems against FDIAs. To this end, a
DRL-based controller is proposed that dynamically adjusts generator setpoints
in response to both load fluctuations and potential cyber threats. The
controller learns these optimal control policies by interacting with a
simulated power system environment that incorporates the AGC dynamics under
cyberattacks. The extensive experiments on test power systems subjected to
various FDIAs demonstrate the effectiveness of the presented approach in
mitigating the impact of cyberattacks.
