DOI:10.35833/MPCE.2020.000495 |
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A Reinforcement-learning-based Bidding Strategy for Power Suppliers with Limited Information |
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Page view: 345
Net amount: 467 |
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Author:
Qiangang Jia1,Yiyan Li2,Zheng Yan1,Chengke Xu1,Sijie Chen1
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Author Affiliation:
1.Shang‐hai Jiao Tong University, Shanghai 200240, CHINA;2.the Department of Electrical and Computer Science, North Carolina State University, Raleigh 27695, USA
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Foundation: |
This work was supported by the National Natural Science Foundation of China (No. U1866206). |
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Abstract: |
The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors. Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy. However, this assumption may not be true in reality, particularly when a power market is newly launched. To help power suppliers bid with the limited information, a modified continuous action reinforcement learning automata algorithm is proposed. This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game. Simulation results verify the effectiveness of the proposed learning algorithm. |
Keywords: |
Power market ; bidding strategy ; limited information ; repeated game ; continuous action reinforcement learning automata |
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Received:July 20, 2020
Online Time:2022/07/15 |
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