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Start date: 2020Subject: search.filters.subject.Privacy

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    Item type:Publication,
    Bilateral privacy-preserving energy sharing in network-constrained models via homomorphic encryption-based distributed optimization
    (2026-05-01)
    Wang, Hongli
    ;
    Yang, Jun
    ;
    Li, Gaojunjie
    ;
    Wu, Fuzhang
    ;
    Xie, Lilong
    The proliferation of distributed energy enables prosumers to participate in local trading, yet privacy concerns and network constraints hinder market implementation. This paper proposes a bilateral privacy-preserving energy sharing mechanism that secures sensitive data while enforcing grid operational limits. We develop a prosumer decision model incorporating utility functions and transfer distance factors. To resolve privacy-coordination conflicts, a distributed optimization framework compatible with nonlinear models is designed by integrating gradient descent and dual ascent. This framework guarantees convergence under problem convexity and Lagrangian gradient existence. Furthermore, a homomorphic encryption scheme is integrated to enable dual-blind computations, which prevents plaintext disclosure to either prosumers or the operator while maintaining network safety. Theoretical analysis confirms the scheme's correctness and computational tractability. Numerical simulations on IEEE 14-bus and 33-bus systems validate the mechanism regarding convergence, bilateral privacy protection, and social welfare enhancement. Finally, the algorithm's scalability is demonstrated through penalty-based acceleration, which meets the practical deployment requirements of modern power system.