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dc.contributor.author
Zellner, M.
dc.contributor.author
De Rubira, T. Tinoco
dc.contributor.author
Hug, G.
dc.contributor.author
Zeilinger, Melanie N.
dc.contributor.editor
Dochain, Denis
dc.contributor.editor
Henrion, Didier
dc.contributor.editor
Peaucelle, Dimitri
dc.date.accessioned
2021-07-28T06:08:16Z
dc.date.available
2018-01-26T10:52:24Z
dc.date.available
2018-01-26T11:04:03Z
dc.date.available
2018-01-29T13:11:58Z
dc.date.available
2021-07-28T06:08:16Z
dc.date.issued
2017-07
dc.identifier.issn
2405-8963
dc.identifier.other
10.1016/j.ifacol.2017.08.1922
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/236168
dc.description.abstract
Smart meters enable a variety of new and useful applications for achieving a smart grid. Unfortunately, they also pose privacy risks by revealing sensitive information about consumers. In this work, techniques that exploit the benefits of smart meters and at the same time mitigate privacy risks are explored. More specifically, we consider the control of local energy storage devices of a group of consumers with the goal of minimizing energy cost and providing aggregate load smoothing to the grid. A differentially private distributed model predictive controller (MPC) is proposed by extending a framework recently proposed in the literature for this application. By using a distributed proximal gradient algorithm, the energy cost of consumers is minimized while keeping the load profile of each consumer private. Aggregate load smoothing is achieved by exchanging net consumption information between consumers. Since information about the consumer load profiles could be inferred from theses exchanges, the proposed controller is designed to ensure Differential Privacy (DP) and to protect this information. Numerical experiments are used to study a case with 30 consumers. The resulting trade-off between privacy and performance is studied as well as the effects of having a trustworthy or untrustworthy mediator handling the information exchanges between consumers.
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.subject
MPC
en_US
dc.subject
Differential privacy
en_US
dc.subject
Power systems
en_US
dc.subject
Smart grids
en_US
dc.subject
Energy Storage Operation
en_US
dc.title
Distributed Differentially Private Model Predictive Control for Energy Storage
en_US
dc.type
Conference Paper
dc.date.published
2017-10-18
ethz.book.title
20th IFAC World Congress. Proceedings
en_US
ethz.journal.title
IFAC-PapersOnLine
ethz.journal.volume
50
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
12464
en_US
ethz.pages.end
12470
en_US
ethz.event
20th IFAC World Congress. Proceedings
en_US
ethz.event.location
Toulouse, France
en_US
ethz.event.date
July 9-14, 2017
en_US
ethz.publication.place
Kidlington
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09563 - Zeilinger, Melanie / Zeilinger, Melanie
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02619 - Inst. Dynam. Syst. u. Regelungstechnik / Inst. Dynamic Systems and Control::09563 - Zeilinger, Melanie / Zeilinger, Melanie
en_US
ethz.date.deposited
2018-01-26T10:52:24Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2018-01-29T13:12:01Z
ethz.rosetta.lastUpdated
2022-03-29T10:45:05Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/235126
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/235119
ethz.COinS
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