Work

Reinforcement Learning for Thermostatically Controlled Loads Control using Modelica and Python

Year: 2020

Type: conference-paper

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 Modelicabased 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.

Source: Linköping electronic conference proceedings

DOI: 10.3384/ecp202017431

Authors: Oleh Lukianykhin, Tetiana Bogodorova

Institutions: Ukrainian Catholic University, Rensselaer Polytechnic Institute

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W3021974136