Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python
This work addresses power system control challenges for grid operators, but it is incremental as it applies an existing RL method to a specific domain.
The paper tackled voltage control of thermostatically controlled loads for power consumption regulation using reinforcement learning, achieving validation of the Q-learning algorithm for deterministic and stochastic initialization scenarios.
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.