一种隐私保护的最优潮流分布式控制
A Privacy-Preserving Distributed Control of Optimal Power Flow
摘要：
本文将分布式最优潮流问题转化为一个最大化不可微凹函数的最优化问题。通过现有的分布式算法解决这样的问题可能会导致数据隐私问题，因为在算法内交换的解决方案信息可以被对手用来推断数据。为了保护数据隐私，在本文中，我们提出了一个差分私人投影次梯度（DP-PS）算法，其中包括一个解决方案加密步骤。我们证明了由DP-PS生成的序列在期望、概率和概率1下收敛。此外，我们表明，收敛速率的预期是由用户选择的DP-PS的目标隐私级别的影响。我们进行了数值实验，证明了DP-PS的收敛性和数据隐私保护。
英文摘要：
We consider a distributed optimal power flow formulated as an optimization problem that maximizes a nondifferentiable concave function. Solving such a problem by the existing distributed algorithms can lead to data privacy issues because the solution information exchanged within the algorithms can be utilized by an adversary to infer the data. To preserve data privacy, in this paper we propose a differentially private projected subgradient (DP-PS) algorithm that includes a solution encryption step. We show that a sequence generated by DP-PS converges in expectation, in probability, and with probability 1. Moreover, we show that the rate of convergence in expectation is affected by a target privacy level of DP-PS chosen by the user. We conduct numerical experiments that demonstrate the convergence and data privacy preservation of DP-PS.
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