Day-ahead Strategic Bidding of Renewable Energy Considering Output Uncertainty Based on Deep Reinforcement Learning

被引：0
|
作者：
Ning, Longfei [1 ]
Liu, Feiyu [1 ]
Wang, Zhengfeng [2 ]
Feng, Kai [3 ]
Wang, Beibei [1 ]
机构：
[1] Southeast Univ, Sch Elect Engn, Nanjing, Peoples R China
[2] State Grid Corp China, State Grid Anhui Elect Power Co Ltd, Hefei, Peoples R China
[3] State Grid Corp China, China Elect Power Res Inst, Nanjing, Peoples R China
来源：
2024 6TH ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM, AEEES 2024 | 2024年
关键词：
conditional value-at-risk; electricity market; renewable energy bidding strategy; soft actor-critic algorithm;
D O I：
10.1109/AEEES61147.2024.10544921
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Uncertainty in the output of renewable energy sources can lead to fluctuations in the bidding revenue of their participation in the electricity market. To address this problem, a stochastic optimization model for strategic bidding of renewable energy unit considering output uncertainty is established. The conditional value-at-risk and Soft Actor-Critic algorithm are combined to form the CVaR-SAC strategic bidding algorithm that can control the risk of fluctuation in bidding revenue, and the effectiveness of the algorithm is verified through cases.
引用
收藏
页码：907 / 912
页数：6
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