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Volume 10 Issue 10
Oct.  2023

IEEE/CAA Journal of Automatica Sinica

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F. Y. Zhang, Q. Y. Yang, and  D. An,  “Privacy preserving demand side management method via multi-agent reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 1984–1999, Oct. 2023. doi: 10.1109/JAS.2023.123321
Citation: F. Y. Zhang, Q. Y. Yang, and  D. An,  “Privacy preserving demand side management method via multi-agent reinforcement learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 1984–1999, Oct. 2023. doi: 10.1109/JAS.2023.123321

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

doi: 10.1109/JAS.2023.123321
Funds:  This work was supported in part by the National Science Foundation of China (61973247, 61673315, 62173268), the Key Research and Development Program of Shaanxi (2022GY-033), the National Postdoctoral Innovative Talents Support Program of China (BX20200272), the Key Program of the National Natural Science Foundation of China (61833015), and the Fundamental Research Funds for the Central Universities (xzy022021050)
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  • The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information, seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning (RL) methods (i.e., deep Q learning (DQN), deep deterministic policy gradient (DDPG), QMIX and multi-agent deep deterministic policy gradient (MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

     

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  • 11 The data were provided by the State Grid Xi’an Electric Power Supply Company, Xi’an, Shaanxi, China.
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    Highlights

    • Novel demand side management (DSM) algorithm: a novel multi-agent reinforcement learning algorithm is proposed to produce the DSM control strategy for microgrids. Specifically, the proposed method adopts the idea of centralized training and decentralized execution paradigm to improve the stability of training experience in the multi-agent environment. Moreover, an attention mechanism is proposed to endow each microgrid the ability of adaptively determining which microgrid is more noteworthy for better cooperation
    • Explicit privacy protection method: an encryptor based on a neural network with fixed parameters is presented to transform the private information of microgrid (i.e., the demand response strategy and the energy consumption demand) from low-dimensional to high-dimensional in order to protect energy users' privacy. Furthermore, this paper theoretically prove that the proposed encryptor-based privacy preserving method will not affect both the convergence property and the training performance of the reinforcement learning algorithm
    • Complete experiments with real-world data: extensive simulations are performed to verify the effectiveness of the proposed method with the real-world energy consumption data of Xi'an, Shaanxi, China. Specifically, the proposed method is compared with state-of-the-art RL methods in terms of the bill payment, satisfaction and utility. The ablation studies are conducted to prove the proposed privacy protection method will not cause performance degeneration of reinforcement learning algorithm

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