Cloud Computing Based Demand Response Management using Deep Reinforcement Learning
文献类型：期刊论文
| 作者 | Song CH(宋纯贺)1,2 ; Han GJ(韩光洁)3; Zeng P(曾鹏)1,2
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| 刊名 | IEEE Transactions on Cloud Computing
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| 出版日期 | 2022 |
| 卷号 | 10期号:1页码:72-81 |
| ISSN号 | 2168-7161 |
| 关键词 | Demand response cloud computing load aggregator water heater deep reinforcement learning |
| 产权排序 | 1 |
| 英文摘要 | Demand response is an effective way for ensuring safety and stabilization of power grid by maintaining the balance between the supply and the demand of power grid, and this paper focuses on using electric water heaters for demand response. In addition to considering comfort and price factors as did in previous works, this paper considers the overshoot temperature and its influence on demand response. First, a theoretical model of the heating and cooling processes of the electric water heater is established; second, the demand response process using electric water heaters is analyzed, including the influences of the physical parameters and the settings of electric water heaters on the demand response process; third, a model is established considering the demand response requirement, the comfort of owners of electric water heaters, and the electricity price, simultaneously; fourth, an optimization method based on deep reinforcement learning is proposed for demand response using electric water heaters. Meanwhile, the influence of parameters on the results of demand response is discussed in details. Experimental results show the effectiveness of the proposed method |
| WOS关键词 | ELECTRIC WATER-HEATERS |
| 资助项目 | National Key Research and Development Program[2017YFE0125300] ; Jiangsu Key Research and Development Program[BE2019648] ; National Natural Science Foundation of China-Guangdong Joint Fund[U1801264] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000766635400007 |
| 资助机构 | National Key Research and Development Program, No.2017YFE0125300 ; Jiangsu Key Research and Development Program, No.BE2019648 ; National Natural Science Foundation of China-Guangdong Joint Fund (U1801264) |
| 源URL | [http://ir.sia.cn/handle/173321/29948]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Han GJ(韩光洁); Zeng P(曾鹏) |
| 作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China 2.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Department of IOT Engineering, Hohai university, Changzhou, China |
| 推荐引用方式 GB/T 7714 | Song CH,Han GJ,Zeng P. Cloud Computing Based Demand Response Management using Deep Reinforcement Learning[J]. IEEE Transactions on Cloud Computing,2022,10(1):72-81. |
| APA | Song CH,Han GJ,&Zeng P.(2022).Cloud Computing Based Demand Response Management using Deep Reinforcement Learning.IEEE Transactions on Cloud Computing,10(1),72-81. |
| MLA | Song CH,et al."Cloud Computing Based Demand Response Management using Deep Reinforcement Learning".IEEE Transactions on Cloud Computing 10.1(2022):72-81. |
入库方式： OAI收割
来源：沈阳自动化研究所
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