Deep Reinforcement Learning Based Residential Demand Side Management With Edge Computing

Tan Li, Yuanzhang Xiao, Linqi Song

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

13 Citations (Scopus)

Abstract

Residential demand side management (DSM) is a promising technique to improve the stability and reduce the cost of power systems. However, residential DSM is facing challenges under the ongoing paradigm shift of computation, such as edge computing. With the proliferation of smart appliances (e.g., appliances with computing and data analysis capabilities) and high-performance computing devices (e.g., graphics processing units) in the households, we expect surging residential energy consumption caused by computation. Therefore, it is important to schedule edge computing as well as traditional energy consumption in a smart way, especially when the demand for computation and thus for electricity occurs during the peak hours of electricity consumption.

In this paper, we investigate an integrated home energy management system (HEMS) who participates in a DSM program and is equipped with an edge computing server. The HEMS aims to maximize the home owner's expected total reward, defined as the reward from completing edge computing tasks minus the cost of electricity consumption, the cost of computation offloading to the cloud, and the penalty of violating the DSM requirements. The particular DSM program considered in this paper, which is a widely-adopted one, requires the household to reduce certain amount of energy consumption within a specified time window. In contrast to well-studied real-time pricing, such a DSM program results in a long-term temporal interdependency (i.e., of a few hours) and thus high-dimensional state space in our formulated Markov decision processes. To address this challenge, we use deep reinforcement learning, more specifically Deep Deterministic Policy Gradient, to solve the problem. Experiments show that our proposed scheme achieves significant performance gains over reasonable baselines.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
PublisherIEEE
ISBN (Electronic)9781538680995
ISBN (Print)9781538681008
DOIs
Publication statusPublished - Oct 2019
Event2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2019 - Beijing, China
Duration: 21 Oct 201923 Oct 2019

Publication series

NameIEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm

Conference

Conference2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2019
Country/TerritoryChina
CityBeijing
Period21/10/1923/10/19

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

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