Privacy-Protecting Energy Management Unit through Model-Distribution Predictive Control

The roll-out of smart meters in electricity networks introduces risks for consumer privacy due to increased measurement frequency and granularity. Through various Non-Intrusive Load Monitoring techniques, consumer behavior may be inferred from their metering data. In this paper, we propose an energy management method that reduces energy cost and protects privacy through the minimization of information leakage. The method is based on a Model Predictive Controller that utilizes energy storage and local generation, and that predicts the effects of its actions on the statistics of the actual energy consumption of a consumer and that seen by the grid. Computationally, the method requires solving a Mixed-Integer Quadratic Program of manageable size whenever new meter readings are available. We simulate the controller on generated residential load profiles with different privacy costs in a two-tier time-of-use energy pricing environment. Results show that information leakage is effectively reduced at the expense of increased energy cost. The results also show that with the proposed controller the consumer load profile seen by the grid resembles a mixture between that obtained with Non-Intrusive Load Leveling and Lazy Stepping.Comment: Accepted for publication in IEEE Transactions on Smart Grid 2017, special issue on Distributed Control and Efficient Optimization Methods for Smart Gri

Privacy In The Smart Grid: An Information Flow Analysis

Project Final Report prepared for CIEE and California Energy Commissio

Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy

Smart meter privacy by suprression of low power frequency components

This paper focuses on the problems associated with privacy protection in smart grid. We will give an overview of a possible realization of a privacy-preserving approach that encompasses privacy-utility tradeoff into a single model. This approach proposes suppression of low power frequency components as a solution to reduce the amount of information leakage from smart meter readings. We will consider the applicability of the procedure to hide the appliance usage with respect to the type of home devices