Atmospheric circulation archetypes as clustering criteria for wind power inputs into probabilistic power flow analysis

Amaris Dalton, Bernard Bekker, Matti Juhani Koivisto

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

The variability of the wind resource is the primary challenge associated with the introduction of large scale wind power onto electricity networks. In addressing uncertainties associated with wind power, probabilistic power flow analysis (PPF) is often used in resolving current or future system states. These simulations are informed by input scenarios - i.e. time series that represent conditions being simulated. Thereby defining appropriate input scenarios is a critically important part of the process. This study proposes a novel methodology for clustering wind power time series based on the dominant concurrent atmospheric circulation patterns. These patterns are classified into generalized architypes using Self Organizing Maps. Thereby the probabilistic properties and data dependency structures of wind farms are approximated at the hand of the causative weather phenomena. It was found that this methodology resulted in significant variations in the probabilistic properties of wind power time series and the correlations between wind generators. It is anticipated that this methodology could be effectively applied in defining the input characteristics into operational scenarios within a PPF analysis, and inform correlations between wind farms in spatiotemporal probabilistic forecasts.
Original languageEnglish
Title of host publicationProceedings of 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)
Number of pages6
PublisherIEEE
Publication date2020
ISBN (Electronic)978-1-7281-2822-1
DOIs
Publication statusPublished - 2020
Event2020 International Conference on Probabilistic Methods Applied to Power Systems - Liege, Belgium
Duration: 18 Aug 202021 Aug 2020

Conference

Conference2020 International Conference on Probabilistic Methods Applied to Power Systems
CountryBelgium
CityLiege
Period18/08/202021/08/2020
Series2020 International Conference on Probabilistic Methods Applied To Power Systems (pmaps)
ISSN2642-6757

Keywords

  • Self organising maps
  • Probabilistic power flow
  • Wind power
  • Stochastic modeling

Cite this

Dalton, A., Bekker, B., & Koivisto, M. J. (2020). Atmospheric circulation archetypes as clustering criteria for wind power inputs into probabilistic power flow analysis. In Proceedings of 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) IEEE. 2020 International Conference on Probabilistic Methods Applied To Power Systems (pmaps) https://doi.org/10.1109/PMAPS47429.2020.9183659