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    Peer-to-Peer Energy Trading and Energy Conversion in Interconnected Multi-Energy Microgrids Using Multi-Agent Deep Reinforcement Learning

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    Shengrong_Bu_IEEE_TSG_paper.pdf
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    Author
    Chen, Tianyi
    Bu, Shengrong
    Liu, Xue
    Kang, Jikun
    Yu, F. Richard
    Han, Zhu
    Keyword
    Cogeneration
    Energy conversion
    Heat pumps
    Hydrogen
    Multi-agent deep reinforcement learning
    Multi-energy microgrids
    Natural gas
    P2P energy trading
    Peer-to-peer computing
    Reinforcement learning
    Resistance heating
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    URI
    http://hdl.handle.net/10464/15489
    Abstract
    A key aspect of multi-energy microgrids (MEMGs) is the capability to efficiently convert and store energy in order to reduce the costs and environmental impact. Peer-to-peer (P2P) energy trading is a novel paradigm for decentralized energy market designs. In this paper, we investigate the external P2P energy trading problem and internal energy conversion problem within interconnected residential, commercial and industrial MEMGs. These two problems are complex decision-making problems with enormous high-dimensional data and uncertainty, so a multi-agent deep reinforcement learning approach combining the multi-agent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm is proposed. The proposed approach can handle the high-dimensional continuous action space and aligns with the nature of P2P energy trading with multiple MEMGs. Simulation results based on three real-world MG datasets show that the proposed approach significantly reduces each MG's average hourly operation cost. The impact of carbon tax pricing is also considered.
    ae974a485f413a2113503eed53cd6c53
    10.1109/TSG.2021.3124465
    Scopus Count
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    Computer Science

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