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Title: Demand-side scheduling based on multi-agent deep actor-critic learning for smart grids
Authors: Lee, Joash
Wang, Wenbo
Niyato, Dusit
Keywords: Engineering::Computer science and engineering
Engineering::Mechanical engineering
Issue Date: 2020
Source: Lee, J., Wang, W., & Niyato, D. (2020). Demand-side scheduling based on multi-agent deep actor-critic learning for smart grids. Proceedings of the IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm 2020). doi:10.1109/SmartGridComm47815.2020.9302935
Project: National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, NRF2017EWT-EP003-041 
Singapore NRF2015-NRF-ISF001-2277 
Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007 
A*STARNTU- SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing RGANS1906 
Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (WASP/NTU) under grant M4082187 (4080) 
Singapore Ministry of Education (MOE) Tier 1 (RG16/20) 
NTU-WeBank JRI (NWJ-2020-004) 
Alibaba Group through Alibaba Innovative Research (AIR) Program 
Alibaba-NTU Singapore Joint Research Institute (JRI) 
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 multiagent, 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.
DOI: 10.1109/SmartGridComm47815.2020.9302935
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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