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

页码：907 / 912

页数：6

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