Using deep reinforcement learning in optimal energy management for residential house aggregators with uncertain user behaviors

Yujun Lin, Linfang Yan, Hongxun Hui, Yin Chen, Xia Chen, Jinyu Wen

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods.
Original languageEnglish
Title of host publication2024 IEEE 7th Student Conference on Electric Machines and Systems (SCEMS)
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)979-8-3503-6694-5
ISBN (Print)979-8-3503-6695-2
DOIs
Publication statusPublished - 25 Nov 2024

Publication series

Name2024 IEEE 7th Student Conference on Electric Machines and Systems (SCEMS)
PublisherIEEE
ISSN (Print)2771-7550
ISSN (Electronic)2771-7577

Keywords

  • Home energy management
  • electric vehicles (EVs)
  • deep reinforcement learning
  • soft actor-critic
  • dynamic user behaviors

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