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Optimal Privacy-Enhancing and Cost-Efficient Energy Management Strategies for Smart Grid Consumers
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).ORCID iD: 0000-0002-0036-9049
2018 (English)In: 2018 IEEE Statistical Signal Processing Workshop, SSP 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 144-148Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 144-148
Keywords [en]
Kullback-Leibler divergence, Markov decision process, privacy-cost trade-off, Smart metering system, Consumer behavior, Costs, Decision making, Dynamic programming, Economic and social effects, Electric power transmission networks, Energy management, Markov processes, Risk assessment, Signal processing, Cost trade-off, Design problems, Energy management strategies, Expected energy, Kullback Leibler divergence, Markov Decision Processes, Optimal solutions, Smart metering systems, Smart power grids
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-236742DOI: 10.1109/SSP.2018.8450736Scopus ID: 2-s2.0-85053832174ISBN: 9781538615706 (print)OAI: oai:DiVA.org:kth-236742DiVA, id: diva2:1257596
Conference
20th IEEE Statistical Signal Processing Workshop, SSP 2018, 10 June 2018 through 13 June 2018
Note

Conference code: 139091; Export Date: 22 October 2018; Conference Paper; Funding details: VR, Vetenskapsrådet; Funding details: 2015-06815, CHIST-ERA; Funding text: The work has been supported by the Swedish Research Council (VR) within the CHIST-ERA project COPES under Grant 2015-06815.. QC 20181022

Available from: 2018-10-22 Created: 2018-10-22 Last updated: 2018-10-22Bibliographically approved

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Oechtering, Tobias J.

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