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Personalized P2P energy trading system based on socio-demographic characteristic inference and AC network constraints

Author

Listed:
  • Zhao, Zehua
  • Luo, Fengji
  • He, Yu
  • Ranzi, Gianluca

Abstract

With increasingly prevalence of distributed renewable energy sources, Peer-to-Peer (P2P) energy trading has become an active research direction. This study explores the role of the participants’ Socio-Demographic Characteristics (SDCs) in the decision-making process P2P energy trading by proposing a personalized P2P energy trading system. The system periodically collects the participants’ bids and pair energy sellers and buyers to form transactions. An attention-based SDC inference system is developed, which identifies a participant’s SDCs from the on-site historical smart meter readings. Followed by this, the system analyzes the importance of energy buyers’ demands based on their SDCs, and an alternative current network constrained P2P energy market clearing model is formulated to maximize the participant population’s social warfare by considering their energy demand importance and economic benefits. Simulations based on real-world datasets are conducted to validate the proposed system.

Suggested Citation

  • Zhao, Zehua & Luo, Fengji & He, Yu & Ranzi, Gianluca, 2024. "Personalized P2P energy trading system based on socio-demographic characteristic inference and AC network constraints," Applied Energy, Elsevier, vol. 368(C).
    • Handle: RePEc:eee:appene:v:368:y:2024:i:c:s0306261924007165
    DOI: 10.1016/j.apenergy.2024.123333
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    References listed on IDEAS

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