Demand-side scheduling based on multi-agent deep actor-critic learning for smart grids

Joash Lee, Wenbo Wang, Dusit Niyato

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

Abstract

We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimize the overall cost under a real-time pricing scheme. While previous works have introduced centralized approaches in which the scheduling algorithm has full observability, we propose the formulation of a smart grid environment as a Markov game. Each household is a decentralized agent with partial observability, which allows scalability and privacy-preservation in a realistic setting. The grid operator produces a price signal that varies with the energy demand. We propose an extension to a multi-agent, deep actor-critic algorithm to address partial observability and the perceived non-stationarity of the environment from the agent's viewpoint. This algorithm learns a centralized critic that coordinates training of decentralized agents. Our approach thus uses centralized learning but decentralized execution. Simulation results show that our online deep reinforcement learning method can reduce both the peak-to-average ratio of total energy consumed and the cost of electricity for all households based purely on instantaneous observations and a price signal.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728161273
DOIs
StatePublished - 11 Nov 2020
Externally publishedYes
Event2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020 - Tempe, United States
Duration: 11 Nov 202013 Nov 2020

Publication series

Name2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020

Conference

Conference2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
Country/TerritoryUnited States
CityTempe
Period11/11/2013/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Deep learning
  • Multi-agent systems
  • Reinforcement learning
  • Smart grid
  • Task scheduling

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