Privacy-Preserving Distributed Optimal Power Flow With Partially Homomorphic Encryption

Distribution grid agents are obliged to exchange and disclose their states explicitly neighboring regions enable distributed optimal power flow dispatch. However, the contain sensitive information of individual agents, such as voltage current measurements. These measurements can be inferred by adversaries, other participating or eavesdroppers, leading privacy leakage problem. To address issue, we propose a privacy-preserving (OPF) algorithm based on partially homomorphic encryption (PHE). First all, exploit alternating direction method multipliers (ADMM) solve OPF in fashion. In this way, dual update ADMM encrypted PHE. We further relax augmented term primal with l 1 -norm regularization. addition, transform relaxed regularization semidefinite program (SDP), prove that transformation is exact. The SDP solved locally only sign messages from which preserves update. At last, strictly preservation guarantee proposed algorithm. Numerical case studies validate effectiveness exactness approach. particular, show cannot adversaries. Besides, obtains solutions very close global optimum, converges much faster compared competing alternatives.