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 prices of privacy loss 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.