UDP: Usage-Based Dynamic Pricing With Privacy Preservation for Smart Grid

Abstract

Abstract-Smart sensing and wireless communication technologies enable the electric power grid system to deliver electricity more efficiently through the dynamic analysis of the electricity demand and supply. The current solution is to extend the traditional static electricity pricing strategy to a time-based one where peak-time prices are defined to influence electricity usage behavior of customers. However, the time-based pricing strategy is not truly dynamic and the electricity resource cannot be optimally utilized in real time. In this paper, we propose a usage-based dynamic pricing (UDP) scheme for smart grid in a community environment, which enables the electricity price to correspond to the electricity usage in real time. In the UDP scheme, to simplify price management and reduce communication overhead, we introduce distributed community gateways as proxies of the utility company to timely respond to the price enquiries from the community customers. We consider both community-wide electricity usage and individual electricity usage as factors into price management: a customer gets higher electricity unit price if its own electricity usage becomes larger under certain conditions of the community-wide collective electricity usage. Additionally, we protect the privacy of the customers by restricting the disclosure of the individual electricity usage to the community gateways. Lastly, we provide privacy and performance analysis to demonstrate that the UDP scheme supports real-time dynamic pricing in an efficient and privacy-preserving manner.

Evolutionary game theoretic demand-side management and control for a class of networked smart grid

“…Optimization of the network smart grid system implies that, in the transient process to reach the equilibrium, some indexes can be optimized. The grid provider is capable of influencing decisions of users in the network by presenting dynamic pricing strategies (Liang et al, 2013;Jiang et al, 2014). It is possible that the smart grid provider and some of the users cooperate to affect decisions of other users, such that the common benefit can be improved.…”

EPPDR: An Efficient Privacy-Preserving Demand Response Scheme with Adaptive Key Evolution in Smart Grid

“…For example, when λ d = 5 and λ x = 10, as shown in Fig. 7a, the number of information leakage is in the interval [5,15]. However, when λ d = 5 and λ x = 20, as shown in Fig.…”

“…However, when λ d = 5 and λ x = 20, as shown in Fig. 7c, the number of information leakage is in the interval [5,25]. Note that λ x represents the intensity of X i , where X i is the time interval of Round i .…”

“…As smart grid is closely related to people's daily lives, to resolve the security and privacy concerns in smart grid is crucial [4], [5]. Note that in the smart grid networks, adversaries might eavesdrop the communication between users and control center and identify the users' electricity demand.…”

High-Dimensional Crowdsourced Data Distribution Estimation with Local Privacy

“…Especially in IoT applications, the ubiquitous but resource-constrained sensors require extremely high efficiency and low overhead. For example, privacypreserving real-time pricing mechanisms require not only effective privacy guarantees for individuals' electricity usage but also fast response to the dynamical changes of demands and supply in the smart grid [24]. Thus, it is important to provide an efficient privacy-preserving method to publish crowdsourced high-dimensional data.…”