Energy Management Strategy for Smart Meter Privacy and Cost Saving

We design optimal privacy-enhancing and cost-efficient energy management strategies for consumers that are equipped with a rechargeable storage. The Kullback-Leibler divergence rate is used as privacy measure the expected cost-saving utility measure. corresponding strategy designed by optimizing weighted sum of both cost measures over finite time horizon, which achieved formulating our problem into belief-state Markov decision process problem. A computationally efficient approximated Q-learning method proposed generalization to high-dimensional problems an infinite horizon. At last, we explicitly characterize stationary policy achieves steady belief state greatly simplifies privacy-preserving strategy. performance practical approaches finally illustrated in numerical experiments.