Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach

doi:10.1109/JAS.2021.1004287

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	Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach
	Yanni Wan; Jiahu Qin; Xinghuo Yu; Tao Yang; Yu Kang
刊名	IEEE/CAA Journal of Automatica Sinica
	2022
卷号	9 期号:1 页码:123-134
关键词	Demand response management (DRM) Markovian decision process (MDP) Monte Carlo simulation reinforcement learning (RL) smart grid
ISSN号	2329-9266
DOI	10.1109/JAS.2021.1004287
英文摘要	This paper studies price-based residential demand response management (PB-RDRM) in smart grids, in which non-dispatchable and dispatchable loads (including general loads and plug-in electric vehicles (PEVs)) are both involved. The PB-RDRM is composed of a bi-level optimization problem, in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company (UC) by selecting optimal retail prices (RPs), while the lower-level demand response (DR) problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior. The challenges here are mainly two-fold: 1) the uncertainty of energy consumption and RPs; 2) the flexible PEVs’ temporally coupled constraints, which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM. To address these challenges, we first model the dynamic retail pricing problem as a Markovian decision process (MDP), and then employ a model-free reinforcement learning (RL) algorithm to learn the optimal dynamic RPs of UC according to the loads’ responses. Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches (i.e., distributed dual decomposition-based (DDB) method and distributed primal-dual interior (PDI)-based method), which require exact load and electricity price models. The comparison results show that, compared with the benchmark solutions, our proposed algorithm can not only adaptively decide the RPs through on-line learning processes, but also achieve larger social welfare within an unknown electricity market environment.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45979]
专题	自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica
推荐引用方式 GB/T 7714	Yanni Wan,Jiahu Qin,Xinghuo Yu,et al. Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(1):123-134.
APA	Yanni Wan,Jiahu Qin,Xinghuo Yu,Tao Yang,&Yu Kang.(2022).Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach.IEEE/CAA Journal of Automatica Sinica,9(1),123-134.
MLA	Yanni Wan,et al."Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach".IEEE/CAA Journal of Automatica Sinica 9.1(2022):123-134.