Holistic mutual benefits aware P2P2G market among microgrids in a distribution network: A decentralized data-driven approach

Xiao Liu, Sinan Li, Cuo Zhang, Meng Liu and Jianguo Zhu

Applied Energy, 2025, vol. 387, issue C, No S0306261925002156

Abstract: In contrast to the traditional peer-to-grid (P2G) market, the emerging decentralized peer-to-peer-to-grid (P2P2G) trading can generate enormous potential to reduce the overall operational costs of microgrids (MGs) further. However, it is challenging to incorporate this decentralized market framework directly into the distribution network (DN) trading framework to account for mutual benefits holistically, impeding progress toward future smart electricity markets. This paper proposes an online non-iterative method based on data-driven multi-agent deep reinforcement learning. The decentralized P2P2G trading framework is formulated as partially observable Markov games (POMGs) to consider mutual benefits efficiently and make it compatible for DN operations. It is further integrated with a novel adaptive margin update (AMU) method to protect DN's topology information and return differential rewards to improve training efficiency and operation safety. Comprehensive numerical simulations on a modified IEEE test system demonstrate the superiority of the proposed method, outperforming other data-driven algorithms and a model-based optimization approach in smart electricity market applications.

Keywords: Peer to peer to grid trading,; Multi-agent deep reinforcement learning,; Distribution network,; Multiple microgrids (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125485

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