Evaluating composite approaches to modelling high-dimensional stochastic variables in power systems

Mingyang Sun, Ioannis Konstantelos, Simon Tindemans, Goran Strbac

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

16 Citations (Scopus)

Abstract

The large-scale integration of intermittent energy sources, the introduction of shiftable load elements and the growing interconnection that characterizes electricity systems worldwide have led to a significant increase of operational uncertainty. The construction of suitable statistical models is a fundamental step towards building Monte Carlo analysis frameworks to be used for exploring the uncertainty state-space and supporting real-time decision-making. The main contribution of the present paper is the development of novel composite modelling approaches that employ dimensionality reduction, clustering and parametric modelling techniques with a particular focus on the use of pair copula construction schemes. Large power system datasets are modelled using different combinations of the aforementioned techniques, and detailed comparisons are drawn on the basis of Kolmogorov-Smirnov tests, multivariate two-sample energy tests and visual data comparisons. The proposed methods are shown to be superior to alternative high-dimensional modelling approaches.

Original languageEnglish
Title of host publication19th Power Systems Computation Conference, PSCC 2016
Place of PublicationPiscataway, NJ
PublisherIEEE
ISBN (Electronic)978-88-941051-2-4
DOIs
Publication statusPublished - 10 Aug 2016
Externally publishedYes
Event19th Power Systems Computation Conference, PSCC 2016 - Genova, Italy
Duration: 20 Jun 201624 Jun 2016

Conference

Conference19th Power Systems Computation Conference, PSCC 2016
CountryItaly
CityGenova
Period20/06/1624/06/16

Keywords

  • Clustering
  • copulas
  • dimensionality reduction
  • parametric statistics
  • stochastic dependence
  • uncertainty analysis

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