Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python
Ukrainian Catholic University（乌克兰天主教大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
