The privacy analysis of battery control mechanisms in demand response: Revealing state approach and rate distortion bounds

Perfect knowledge of a user´s power consumption profile by a utility is a violation of privacy and can be detrimental to the successful implementation of demand response systems. It has been shown that an in-home energy storage system which provides a viable means to achieve the cost savings of instantaneous electricity pricing without inconvenience can also be used to maintain the privacy of a user´s power profile. The optimization of the tradeoff between privacy, as measured by Shannon entropy, and cost savings that can be provided by a finite capacity battery with zero tolerance for delay is known to be equivalent to a Partially Observable Markov Decision Process with non linear belief dependent rewards- solutions to such systems suffer from high computational complexity. In this paper, we propose a “revealing state” approach to enable computation of a class of battery control policies that aim to maximize the achievable privacy of in-home demands. In addition, a rate-distortion approach is presented to derive upper bounds on the privacy-cost savings tradeoff of battery control policies. These bounds are derived for a discrete model, where demand and price follow i.i.d uniform distributions. Numerical results show that the derived bounds are quite close to each other demonstrating the efficacy of the proposed class of strategies.