Privacy-preserving energy theft detection in smart grids

In the U.S., energy theft causes six billion dollar losses to utility companies (UCs) every year. With the smart grid being proposed to modernize current power grids, energy theft may become an even more serious problem since the “smart meters” used in smart grids are vulnerable to more types of attacks compared to traditional mechanical meters. Therefore, it is important to develop efficient and reliable methods to identify illegal users who are committing energy theft. Although some schemes have been proposed for the UCs to detect energy theft in power grids, they all require the users to send their private information, e.g., load files or meter readings at certain times, to the UCs which invades users' privacy and raises serious concerns about privacy, safety, etc. As far as we know, we are the first to investigate the energy theft detection problem considering users' privacy issues. In this paper, we propose to solve in a distributed fashion a linear system of equations (LSE) for the users' “honesty coefficients”, which indicate the users are honest when equal to 1 and are fraudulent when larger than 1. In particular, we develop two distributed privacy-preserving energy theft detection algorithms based on LU decomposition, called LUD and LUPD, respectively, which can identify fraudulent users without invading any user's privacy. Compared to LUD, LUPD requires higher execution time but is stable even in large-size systems. Moreover, the LUD and LUPD algorithms are proposed in the case that users commit energy theft at a constant rate, i.e., with constant honesty coefficients. We also propose adaptive LUD/LUPD algorithms to account for the scenarios where the users have variable honesty coefficients. Extensive simulations are carried out and the results show that the proposed algorithms can efficiently and successfully identify the fraudulent users in the system.