Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage-Distribution Networks
文献类型：期刊论文
| 作者 | Zhong, Lihua1; Ye, Tong2,3; Yang, Yuyao1; Pan, Feng1; Feng, Lei1; Qi, Shuzhe1; Huang, Yuping2,3 |
| 刊名 | PROCESSES
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 12期号:9页码:17 |
| 关键词 | shared energy storage ladder-type carbon price low-carbon optimal scheduling deep reinforcement learning deep deterministic policy gradient algorithm |
| DOI | 10.3390/pr12091791 |
| 通讯作者 | Yang, Yuyao(yangyuyao@gd.csg.cn) |
| 英文摘要 | As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems. |
| WOS关键词 | DEMAND |
| 资助项目 | National Key R&D Program of China ; Science and Technology Project of China Southern Power Grid[GDKJXM20230256] ; Energy Bureau of Guangdong Development and Reform Commission[1689833186433] ; Guangdong Basic and Applied Basic Research Foundation[2023A1515012372] ; [2022YFF0606600] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001323804100001 |
| 出版者 | MDPI |
| 资助机构 | National Key R&D Program of China ; Science and Technology Project of China Southern Power Grid ; Energy Bureau of Guangdong Development and Reform Commission ; Guangdong Basic and Applied Basic Research Foundation |
| 源URL | [http://ir.giec.ac.cn/handle/344007/43106]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Yang, Yuyao |
| 作者单位 | 1.Guangdong Power Grid Co Ltd, Metrol Ctr, Qingyuan 511545, Peoples R China 2.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China 3.Univ Sci & Technol China, Sch Energy Sci & Engn, Hefei 230026, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhong, Lihua,Ye, Tong,Yang, Yuyao,et al. Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage-Distribution Networks[J]. PROCESSES,2024,12(9):17. |
| APA | Zhong, Lihua.,Ye, Tong.,Yang, Yuyao.,Pan, Feng.,Feng, Lei.,...&Huang, Yuping.(2024).Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage-Distribution Networks.PROCESSES,12(9),17. |
| MLA | Zhong, Lihua,et al."Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage-Distribution Networks".PROCESSES 12.9(2024):17. |
入库方式： OAI收割
来源：广州能源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。