Privacy protection via joint real and reactive load shaping in smart grids. (December 2022)

• Record Type: Journal Article
• Authors: Kement, Cihan Emre; Ilić, Marija; Gultekin, Hakan; Cicek, Cihan Tugrul; Tavli, Bulent
• Is Part Of: Sustainable energy, grids and networks. Volume 32(2022)
• Publication Date: 2022-12
• Subjects: Demand shaping -- Load shaping -- Multi-objective optimization -- Privacy -- Reactive power -- Smart metering; Renewable energy sources -- Periodicals; Smart power grids -- Periodicals; Electric power systems -- Periodicals; 333.794
• DOI: 10.1016/j.segan.2022.100794
• Languages: English
• ISSNs: 2352-4677

Abstract

Abstract: Frequent metering of electricity consumption is crucial for demand-side management in smart grids. However, metered data can be processed fairly easily by employing well-established nonintrusive appliance load monitoring techniques to infer appliance usage, which reveals information about consumers' private lives. Existing load shaping techniques for privacy primarily focus only on altering metered real power, whereas smart meters collect reactive power consumption data as well for various purposes. This study addresses consumer privacy preservation via load shaping in a demand response scheme, considering both real and reactive power. We build a multi-objective optimization framework that enables us to characterize the interplay between privacy maximization, user cost minimization, and user discomfort minimization objectives. Our results reveal that minimizing information leakage due to a single component, e.g., real power, would suffer from overlooking information leakage due to the other component, e.g., reactive power, causing sub-optimal decisions. In fact, joint shaping of real and reactive power components results in the best possible privacy preservation performance, which leads to more than a twofold increase in privacy in terms of mutual information. Highlights: Impact of shaping real and reactive loads simultaneously for privacy is investigated. A multi-objective goal programming-based optimization framework is presented. Shaping a single load component is insufficient to attain the best possible privacy. Jointly shaping real and reactive power enhances consumer privacy by more than 50%. Joint load shaping for privacy increases cost and discomfort by, at most, 27%.