On the Privacy-Cost Tradeoff of Battery Control Mechanisms in Demand Response: Selective Information Protection

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 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. In this paper, we assume a user is only interested in hiding some basic information such as his presence/absence, or a particular usage pattern and study the model with multiple levels of battery state, demand and price, and 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. Numerical results based on real electricity and pricing data show that our proposed strategy performs close to the upper bound of the optimal tradeoff between privacy preserving and cost saving.