  The aim of the project is to investigate and assess opportunities for
applying reinforcement learning (RL) for power system control. As a proof of
concept (PoC), voltage control of thermostatically controlled loads (TCLs) for
power consumption regulation was developed using Modelica-based pipeline. The
Q-learning RL algorithm has been validated for deterministic and stochastic
initialization of TCLs. The latter modelling is closer to real grid behaviour,
which challenges the control development, considering the stochastic nature of
load switching. In addition, the paper shows the influence of Q-learning
parameters, including discretization of state-action space, on the controller
performance.
