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Volume 9 Issue 1
Jan.  2022

IEEE/CAA Journal of Automatica Sinica

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Yanni Wan, Jiahu Qin, Xinghuo Yu, Tao Yang and Yu Kang, "Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 123-134, Jan. 2022. doi: 10.1109/JAS.2021.1004287
Citation: Yanni Wan, Jiahu Qin, Xinghuo Yu, Tao Yang and Yu Kang, "Price-Based Residential Demand Response Management in Smart Grids: A Reinforcement Learning-Based Approach," IEEE/CAA J. Autom. Sinica, vol. 9, no. 1, pp. 123-134, Jan. 2022. doi: 10.1109/JAS.2021.1004287

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

doi: 10.1109/JAS.2021.1004287
Funds:  This work was supported in part by the National Natural Science Foundation of China (61922076, 61725304, 61873252, 61991403, 61991400) and in part by the Australian Research Council Discovery Program (DP200101199)
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  • 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.

     

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  • 1 Since the energy demand and consumption of loads is affected by many factors, the state transition is rather difficult to obtain. Therefore, we next employ a model-free Q-learning method to solve the dynamic retail pricing problem.
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    Highlights

    • Study the price-based residential demand response management in smart grid considering PEV loads
    • Model the PB-RDRM from a social perspective, i.e., maximize the weighted sum of UC's profit and loads' cost
    • Propose a model-free reinforcement learning-based DR algorithm to address the uncertainties

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