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Privacy-Preserving Consensus-Based Energy Management in Smart Grids

机译：智能电网中基于隐私保护的共识能源管理

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摘要

Privacy has become a big concern for consumers in electricity consumption activities, as privacy disclosure may cause losses to individuals. Since the information exchange and update in distributed energy management (DEM) of smart grids leaves eavesdroppers an opportunity to obtain the private information, it is worth studying privacy disclosure of DEM and design effective privacy-preserving schemes. In this paper, we investigate the privacy concern of a consensus-based DEM algorithm, where both generation units and responsive consumers cooperatively maximize the social welfare. First, we reveal that the private information of consumers including the electricity consumption and the sensitivity to the electricity price can be disclosed under traditional consensus-based DEM. Then, we propose a secret-function-based privacy-preserving algorithm to preserve the private information, where each node adds zero-sum and exponentially decaying noises to the original data for communications. It is assumed that local secret function can only be known by neighboring nodes. To relax this assumption, we propose a privacy-preserving algorithm, where each node utilizes real information for the state update and broadcasts the one with noise. We show that both of two proposed algorithms can preserve the privacy and the privacy degree is analyzed through (ε,δ) -data-privacy. At the same time, the convergence and optimality of final solution are maintained. Extensive simulations demonstrate the effectiveness of proposed algorithms.
机译：隐私已成为消费者在电力消费活动中的一大关注点，因为隐私披露可能会给个人造成损失。由于智能电网的分布式能源管理（DEM）中的信息交换和更新使窃听者有机会获取私有信息，因此有必要研究DEM的隐私公开并设计有效的隐私保护方案。在本文中，我们研究了基于共识的DEM算法的隐私问题，在该算法中，生成单元和响应型消费者都协同地最大化了社会福利。首先，我们揭示了可以在传统的基于共识的DEM下公开消费者的私人信息，包括用电量和对电价的敏感性。然后，我们提出了一种基于秘密函数的隐私保护算法来保留私有信息，其中每个节点将零和噪声和指数衰减的噪声添加到原始数据中进行通信。假定本地机密功能只能由相邻节点知道。为了放松这一假设，我们提出了一种隐私保护算法，其中每个节点利用真实信息进行状态更新，并用噪声广播该信息。我们表明，两种提出的算法都可以保留隐私，并且通过（ε，δ）-data-privacy分析了隐私程度。同时，保持了最终解的收敛性和最优性。大量的仿真证明了所提出算法的有效性。

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