Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Differentially private state estimation in distribution networks with smart meters
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-1835-2963
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-4876-0223
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9307-484X
2015 (English)In: Proceedings of the IEEE Conference on Decision and Control, IEEE conference proceedings, 2015, 4492-4498 p.Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

State estimation is routinely being performed in high-voltage power transmission grids in order to assist in operation and to detect faulty equipment. In low- and medium-voltage power distribution grids, on the other hand, few real-time measurements are traditionally available, and operation is often conducted based on predicted and historical data. Today, in many parts of the world, smart meters have been deployed at many customers, and their measurements could in principle be shared with the operators in real time to enable improved state estimation. However, customers may feel reluctance in doing so due to privacy concerns. We therefore propose state estimation schemes for a distribution grid model, which ensure differential privacy to the customers. In particular, the state estimation schemes optimize different performance criteria, and a trade-off between a lower bound on the estimation performance versus the customers' differential privacy is derived. The proposed framework is general enough to be applicable also to other distribution networks, such as water networks.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015. 4492-4498 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-188262DOI: 10.1109/CDC.2015.7402921ISI: 000381554504057Scopus ID: 2-s2.0-84962020083ISBN: 9781479978861 (print)OAI: oai:DiVA.org:kth-188262DiVA: diva2:937376
Conference
54th IEEE Conference on Decision and Control, CDC 2015, 15 December 2015 through 18 December 2015
Note

QC 20160615

Available from: 2016-06-15 Created: 2016-06-09 Last updated: 2017-03-20Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopushttp://cdc2015.ieeecss.org/

Search in DiVA

By author/editor
Sandberg, HenrikDan, GyörgyThobaben, Ragnar
By organisation
Automatic ControlCommunication TheoryACCESS Linnaeus Centre
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 49 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf