Visible to the public Optimal Privacy-Enhancing And Cost-Efficient Energy Management Strategies For Smart Grid Consumers

TitleOptimal Privacy-Enhancing And Cost-Efficient Energy Management Strategies For Smart Grid Consumers
Publication TypeConference Paper
Year of Publication2018
AuthorsYou, Y., Li, Z., Oechtering, T. J.
Conference Name2018 IEEE Statistical Signal Processing Workshop (SSP)
ISBN Number978-1-5386-1571-3
Keywordsbelief state Markov decision process problem, Bellman dynamic programming, Conferences, consumer behavior, consumer privacy, cost-efficient energy management strategies, data privacy, decision theory, design problem, dynamic programming, energy cost, Energy management, energy management systems, energy storage, Human Behavior, kullback-leibler divergence, Kullback-Leibler divergence rate, Markov Decision Process, Markov decision process framework, Markov processes, Metrics, optimal energy management strategies, optimal privacy-enhancement, optimal solution, policy-based governance, privacy, privacy risk, privacy-cost trade-off, privacy-enhancement, pubcrawl, Resiliency, Signal processing, Smart Grid Consumeer Privacy, smart grid consumer privacy, smart grid consumers, Smart metering system, smart power grids, Standards, unauthorized testing

The design of optimal energy management strategies that trade-off consumers' privacy and expected energy cost by using an energy storage is studied. The Kullback-Leibler divergence rate is used to assess the privacy risk of the unauthorized testing on consumers' behavior. We further show how this design problem can be formulated as a belief state Markov decision process problem so that standard tools of the Markov decision process framework can be utilized, and the optimal solution can be obtained by using Bellman dynamic programming. Finally, we illustrate the privacy-enhancement and cost-saving by numerical examples.

Citation Keyyou_optimal_2018