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With the growing adoption of renewable energy sources such as solar, wind, and biomass, households and businesses with surplus electricity can engage in peer-to-peer (P2P) energy trading via the power grid. Existing P2P energy trading models often leverage blockchain technology to establish a secure trading environment and employ auction theory from microeconomics to determine pricing and incentivize participation. However, due to blockchain’s transparency, these models lack adequate protection for users’ location privacy. Moreover, energy, unlike conventional auctioned goods, incurs transmission losses—an aspect often overlooked in current research. To address these challenges, this study employs homomorphic encryption to safeguard users’ location privacy and utilizes data partitioning techniques to enhance computational efficiency, achieving a 1.5 \(\times \) speed improvement over conventional methods. Furthermore, by optimizing the trading sequence using a greedy algorithm, the proposed approach reduces energy transmission losses by approximately 30% across four different power network configurations in P2P energy trading scenarios.

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Homomorphic Encryption-Driven Low-Loss P2P Energy Privacy-Preserving Auction

  • 会议论文

  • 发表日期：

    2026年

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  • Enze Kang,
  • Yunhua He,
  • Keshav Sood,
  • Chao Wang,
  • Xu Wang,
  • Ke Xiao

摘要

With the growing adoption of renewable energy sources such as solar, wind, and biomass, households and businesses with surplus electricity can engage in peer-to-peer (P2P) energy trading via the power grid. Existing P2P energy trading models often leverage blockchain technology to establish a secure trading environment and employ auction theory from microeconomics to determine pricing and incentivize participation. However, due to blockchain’s transparency, these models lack adequate protection for users’ location privacy. Moreover, energy, unlike conventional auctioned goods, incurs transmission losses—an aspect often overlooked in current research. To address these challenges, this study employs homomorphic encryption to safeguard users’ location privacy and utilizes data partitioning techniques to enhance computational efficiency, achieving a 1.5 \(\times \) speed improvement over conventional methods. Furthermore, by optimizing the trading sequence using a greedy algorithm, the proposed approach reduces energy transmission losses by approximately 30% across four different power network configurations in P2P energy trading scenarios.