Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning
文献类型：期刊论文
| 作者 | Feiye Zhang; Qingyu Yang; Dou An |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2023 |
| 卷号 | 10期号:10页码:1984-1999 |
| 关键词 | Centralized training and decentralized execution demand side management multi-agent reinforcement learning privacy preserving |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2023.123321 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52397]  |
| 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 | Feiye Zhang,Qingyu Yang,Dou An. Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(10):1984-1999. |
| APA | Feiye Zhang,Qingyu Yang,&Dou An.(2023).Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning.IEEE/CAA Journal of Automatica Sinica,10(10),1984-1999. |
| MLA | Feiye Zhang,et al."Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning".IEEE/CAA Journal of Automatica Sinica 10.10(2023):1984-1999. |
入库方式： OAI收割
来源：自动化研究所
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