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

Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python