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Smart Meter Privacy Control Strategy Including Energy Storage Degradation
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0001-9672-2689
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-0036-9049
Power Systems Laboratory, ETH Zurich, Switzerland.ORCID iD: 0000-0002-3760-3505
Power Systems Laboratory, ETH Zurich, Switzerland.ORCID iD: 0000-0002-4312-616X
2019 (English)In: 2019 IEEE Milan PowerTech, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a degradation-aware privacy control strategy for smart meters by taking into account the capacity fade and energy loss of the battery, which has not been included previously. The energy management strategy is designed by minimizing the weighted sum of both privacy loss and total energy storage losses, where the weightage is set using a trade-off parameter. The privacy loss is measured in terms of Bayesian risk of an unauthorized hypothesis test. By making first-order Markov assumptions, the stochastic parameters of energy loss and capacity fade of the energy storage system are modelled using degradation maps. Using household power consumption data from the ECO dataset, the proposed control strategy is numerically evaluated for different trade-off parameters. Results show that, by including the degradation losses in the design of the privacy-enhancing control strategy, significant improvement in battery life can be achieved, in general, at the expense of some privacy loss.

Place, publisher, year, edition, pages
IEEE, 2019.
Keywords [en]
Smart meter privacy, energy storage system model, partially observable Markov decision process, Bayesian hypothesis testing, energy storage degradation, Privacy, Degradation, Batteries, Hidden Markov models, Energy loss, Bayes methods
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-259388DOI: 10.1109/PTC.2019.8810481Scopus ID: 2-s2.0-85072325395OAI: oai:DiVA.org:kth-259388DiVA, id: diva2:1351332
Conference
2019 IEEE Milan PowerTech
Note

QC 20191106

Available from: 2019-09-14 Created: 2019-09-14 Last updated: 2019-11-26Bibliographically approved

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