Privacy-Preserving Consensus-Based Energy Management in Smart Grids

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 $(\epsilon, \delta)$-data-privacy. At the same time, the convergence and optimality of final solution are maintained. Extensive simulations demonstrate the effectiveness of proposed algorithms.