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Privacy Preserving Distributed Energy Trading

EasyChair Preprint no. 3514

11 pagesDate: May 30, 2020


The smart grid incentivizes distributed entities with local generation (e.g., smart homes, and microgrids) to establish multi-agent systems for enhanced reliability and energy consumption efficiency. Distributed energy trading has emerged as one of the most important multi-agent applications on the power grid by enabling entities to sell their excessive local energy to each other or back to the grid. However, multi-agent energy trading requests all the agents to disclose their sensitive data (e.g., each agent's fine-grained local generation and demand load). In this paper, to the best of our knowledge, we propose the first privacy preserving distributed energy trading framework, Private Energy Market (PEM), in which all the agents privately compute an optimal price for their trading (ensured by a Nash Equilibrium), and allocate pairwise energy trading amounts without disclosing sensitive data (via novel cryptographic protocols). Specifically, we model the trading problem as a non-cooperative Stackelberg game for all the agents (i.e., buyers and sellers) to determine the optimal price, and then derive the pairwise trading amounts. Our PEM framework can privately perform all the computations among all the agents without a trusted third party. We prove the privacy, individual rationality, and incentive compatibility for the PEM framework. Finally, we conduct experiments on real datasets to validate the effectiveness and efficiency of the PEM.

Keyphrases: incentive compatibility, multi-agent, Privacy, Secure Computation, Smart Grid, Stackelberg game

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shangyu Xie and Han Wang and Yuan Hong and My Thai},
  title = {Privacy Preserving Distributed Energy Trading},
  howpublished = {EasyChair Preprint no. 3514},

  year = {EasyChair, 2020}}
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