Q-learning Method for Managing Wind Farm Uncertainties through Energy Storage System Control
文献类型：会议论文
| 作者 | Song ZM(宋志美)1,2; Zang CZ(臧传治)1 ; Zhu, Lizhong2; Zeng P(曾鹏)1
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| 出版日期 | 2020 |
| 会议日期 | September 25-27, 2020 |
| 会议地点 | Hefei, China |
| 关键词 | wind power generation (WPG) reinforcement learning (RL) Q-learning energy storage system (ESS) |
| 页码 | 481-488 |
| 英文摘要 | In this paper, We are committed to improving the revenue of wind farm when wind farm and energy storage system (ESS) cooperate and interact with the main grid. The main challenge is the uncertainty of wind power generation (WPG). Based on WPG forecasting, the reinforcement learning (RL) method is used to overcome the impact of WPG uncertainty. The (RL) method used is classic Q-learning. Compared with other (RL) methods, Q-learning is widely applied and easy to converge. Especially, (RL) methods can realize online decision-making, and the decision-making will tend to be optimal. The simulation results show that the method in this paper can effectively reduce the uncertainty of WPG and increase the revenue of wind farms. |
| 产权排序 | 1 |
| 会议录 | Proceedings - 2020 7th International Forum on Electrical Engineering and Automation, IFEEA 2020
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-9627-5 |
| 源URL | [http://ir.sia.cn/handle/173321/28627]  |
| 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 | Zang CZ(臧传治) |
| 作者单位 | 1.State Key Laboratory of Robotics Shenyang, Institution of Automation Chinese Academy of Sciences, Shenyang 110016, China 2.School of automation and electrical engineering Shenyang Ligong University, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Song ZM,Zang CZ,Zhu, Lizhong,et al. Q-learning Method for Managing Wind Farm Uncertainties through Energy Storage System Control[C]. 见:. Hefei, China. September 25-27, 2020. |
入库方式： OAI收割
来源：沈阳自动化研究所
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