Secure distributed solution for optimal energy consumption scheduling in smart grid Conference

Rahman, MA, Bai, L, Shehab, M et al. (2012). Secure distributed solution for optimal energy consumption scheduling in smart grid . 279-286. 10.1109/TrustCom.2012.252

cited authors

  • Rahman, MA; Bai, L; Shehab, M; Al-Shaer, E

authors

abstract

  • The demand-side energy management is crucial to optimize the energy usage with its production cost, so that the price paid by the users is minimized, while it also satisfies the demand. The recent proposed solutions leverage the two- way communication infrastructure provided by modern smart- meters. The demand management problem assumes that users can shift their energy usage from peak hours to off-peak hours with the goal of balancing the energy usage. The scheduling of the energy consumption is often formulated as a game- theoretic problem, where the players are the users and their strategies are the load schedules of their household appliances. The Nash equilibrium of the formulated game provides the global optimal performance (i.e., the minimum energy costs). To provide a distributed solution the users require to share their usage information with the other users to converge to the Nash equilibrium. Hence, this open sharing among users introduces potential privacy and security issues. In addition, the existing solutions assume that all the users are rational and truthful. In this paper, we first highlight the privacy and security issues involved in the distributed demand management protocols. Secondly, we propose an efficient clustering based multi-party computation (MPC) distributed protocol that enables users to share their usage schedules and at the same time preserve their privacy and confidentiality. To identify untruthful users, we propose a mechanism based on a third party verifier. Through simulation experiments we have demonstrated the scalability and efficiency of our proposed solution. © 2012 IEEE.

publication date

  • November 5, 2012

Digital Object Identifier (DOI)

start page

  • 279

end page

  • 286