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Agent-Based Energy Sharing Mechanism Using Deep Deterministic Policy Gradient Algorithm

Author

Listed:
  • Yi Kuang

    (School of Electric Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Xiuli Wang

    (School of Electric Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Hongyang Zhao

    (School of Electric Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Yijun Huang

    (State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China)

  • Xianlong Chen

    (School of Electric Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

  • Xifan Wang

    (School of Electric Engineering, Xi’an Jiaotong University, Xi’an 710000, China)

Abstract

Balancing energy generation and consumption is essential for smoothing the power grids. The mismatch between energy supply and demand would not only increase the cost on both sides, but also has a great impact on the stability of the system. This paper proposes a novel energy sharing mechanism (ESM) to facilitate the consumption of local energy. With the help of the ESM, multiple prosumers have an opportunity to share surplus energy with neighboring prosumers. The problem is formulated as a leader–follower framework based on the Stackelberg game theory. To address the aforementioned problems, a deep deterministic policy gradient (DDPG) is applied to solve the Nash equilibrium (NE). The numerical results demonstrate that the proposed method is more stable than the conventional reinforcement learning (RL) algorithm. Moreover, the proposed method can converge to NE and find a relatively good energy sharing (ES) pricing strategy without knowing the specific system information. In short, it is notable that the proposed ESM can be seen as a win–win strategy for both prosumers and the power system.

Suggested Citation

  • Yi Kuang & Xiuli Wang & Hongyang Zhao & Yijun Huang & Xianlong Chen & Xifan Wang, 2020. "Agent-Based Energy Sharing Mechanism Using Deep Deterministic Policy Gradient Algorithm," Energies, MDPI, vol. 13(19), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5027-:d:418674
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    References listed on IDEAS

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