Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python

• URL: http://arxiv.org/abs/2005.04444v1
• Date: Sat, 9 May 2020 13:35:49 GMT
• Title: Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python
• Authors: Oleh Lukianykhin, Tetiana Bogodorova
• Abstract summary: The project aims 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 was developed using Modelica-based pipeline. The paper shows the influence of Q-learning parameters, including discretization of state-action space, on the controller performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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