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A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids

Author

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  • Ning Wang

    (School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)

  • Weisheng Xu

    (School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)

  • Weihui Shao

    (Education Technology and Computing Center, Tongji University, Shanghai 200092, China)

  • Zhiyu Xu

    (School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)

Abstract

Decision-making of microgrids in the condition of a dynamic uncertain bidding environment has always been a significant subject of interest in the context of energy markets. The emerging application of reinforcement learning algorithms in energy markets provides solutions to this problem. In this paper, we investigate the potential of applying a Q-learning algorithm into a continuous double auction mechanism. By choosing a global supply and demand relationship as states and considering both bidding price and quantity as actions, a new Q-learning architecture is proposed to better reflect personalized bidding preferences and response to real-time market conditions. The application of battery energy storage system performs an alternative form of demand response by exerting potential capacity. A Q-cube framework is designed to describe the Q-value distribution iteration. Results from a case study on 14 microgrids in Guizhou Province, China indicate that the proposed Q-cube framework is capable of making rational bidding decisions and raising the microgrids’ profits.

Suggested Citation

  • Ning Wang & Weisheng Xu & Weihui Shao & Zhiyu Xu, 2019. "A Q-Cube Framework of Reinforcement Learning Algorithm for Continuous Double Auction among Microgrids," Energies, MDPI, vol. 12(15), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2891-:d:252180
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    References listed on IDEAS

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    Cited by:

    1. Harri Aaltonen & Seppo Sierla & Rakshith Subramanya & Valeriy Vyatkin, 2021. "A Simulation Environment for Training a Reinforcement Learning Agent Trading a Battery Storage," Energies, MDPI, vol. 14(17), pages 1-20, September.
    2. Wang, Longze & Liu, Jinxin & Yuan, Rongfang & Wu, Jing & Zhang, Delong & Zhang, Yan & Li, Meicheng, 2020. "Adaptive bidding strategy for real-time energy management in multi-energy market enhanced by blockchain," Applied Energy, Elsevier, vol. 279(C).

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