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Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids

Author

Listed:
  • Seongwoo Lee

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Joonho Seon

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Chanuk Kyeong

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Soohyun Kim

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Youngghyu Sun

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

  • Jinyoung Kim

    (Department of Electronic Convergence Engineering, University of Kwangwoon, Seoul 01897, Korea)

Abstract

Inefficiencies in energy trading systems of microgrids are mainly caused by uncertainty in non-stationary operating environments. The problem of uncertainty can be mitigated by analyzing patterns of primary operation parameters and their corresponding actions. In this paper, a novel energy trading system based on a double deep Q-networks (DDQN) algorithm and a double Kelly strategy is proposed for improving profits while reducing dependence on the main grid in the microgrid systems. The DDQN algorithm is proposed in order to select optimized action for improving energy transactions. Additionally, the double Kelly strategy is employed to control the microgrid’s energy trading quantity for producing long-term profits. From the simulation results, it is confirmed that the proposed strategies can achieve a significant improvement in the total profits and independence from the main grid via optimized energy transactions.

Suggested Citation

  • Seongwoo Lee & Joonho Seon & Chanuk Kyeong & Soohyun Kim & Youngghyu Sun & Jinyoung Kim, 2021. "Novel Energy Trading System Based on Deep-Reinforcement Learning in Microgrids," Energies, MDPI, vol. 14(17), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5515-:d:628715
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    2. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).

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