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Q-learning Method for Managing Wind Farm Uncertainties through Energy Storage System Control
Song ZM(宋志美)1,2; Zang CZ(臧传治)2; Zhu, Lizhong1; Zeng P(曾鹏)2; Song ZM(宋志美)3,4; Zang CZ(臧传治)4; Zhu, Lizhong3; Zeng P(曾鹏)4
Department工业控制网络与系统研究室
Conference Name7th International Forum on Electrical Engineering and Automation, IFEEA 2020
Conference DateSeptember 25-27, 2020
Conference PlaceHefei, China
Source PublicationProceedings - 2020 7th International Forum on Electrical Engineering and Automation, IFEEA 2020
PublisherIEEE
Publication PlaceNew York
2020
Pages481-488
Indexed ByEI
EI Accession number20211210100112
Contribution Rank1
ISBN978-1-7281-9627-5
Keywordwind power generation (WPG) reinforcement learning (RL) Q-learning energy storage system (ESS)
AbstractIn 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.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/28627
Collection工业控制网络与系统研究室
Corresponding AuthorZang CZ(臧传治); Zang CZ(臧传治)
Affiliation1.School of automation and electrical engineering Shenyang Ligong University, Shenyang, China
2.State Key Laboratory of Robotics Shenyang, Institution of Automation Chinese Academy of Sciences, Shenyang 110016, China
3.School of automation and electrical engineering Shenyang Ligong University, Shenyang, China
4.State Key Laboratory of Robotics Shenyang, Institution of Automation Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
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]. New York:IEEE,2020:481-488.
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