  The electric grid is undergoing a major transition from fossil fuel-based
power generation to renewable energy sources, typically interfaced to the grid
via power electronics. The future power systems are thus expected to face
increased control complexity and challenges pertaining to frequency stability
due to lower levels of inertia and damping. As a result, the frequency control
and development of novel ancillary services is becoming imperative. This paper
proposes a data-driven control scheme, based on Reinforcement Learning (RL),
for grid-forming Voltage Source Converters (VSCs), with the goal of exploiting
their fast response capabilities to provide fast frequency control to the
system. A centralized RL-based controller collects generator frequencies and
adjusts the VSC power output, in response to a disturbance, to prevent
frequency threshold violations. The proposed control scheme is analyzed and its
performance evaluated through detailed time-domain simulations of the IEEE
14-bus test system.
