Clustering time series over electrical networks

Daniil Vankov, Ivan Zorin, David Pozo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

The growing number of renewable energy sources in electrical networks introduces new uncertainties in the electrical network nodes. Reducing the size of electrical networks helps to understand their structure better as well as to plan capacity updates more effectively. The ways of reducing representation of an electrical networks is not a trivial task. In this paper, we consider different methods of clustering of nodal time series data renewable power networks. We propose a clustering method for spatial and temporal data size reduction with local renewable energy as a main driver. The proposed methods are applied to an illustrative 9-bus, 118-bus case studies, and the RE-Europe dataset network.

Original languageEnglish
Title of host publicationSEST 2020 - 3rd International Conference on Smart Energy Systems and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147017
DOIs
Publication statusPublished - Sep 2020
Event3rd International Conference on Smart Energy Systems and Technologies, SEST 2020 - Virtual, Istanbul, Turkey
Duration: 7 Sep 20209 Sep 2020

Publication series

NameSEST 2020 - 3rd International Conference on Smart Energy Systems and Technologies

Conference

Conference3rd International Conference on Smart Energy Systems and Technologies, SEST 2020
Country/TerritoryTurkey
CityVirtual, Istanbul
Period7/09/209/09/20

Keywords

  • DC-OPF
  • Renewable energy
  • Time-series clustering
  • Uncertainty

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