An approximation algorithm for future wind scenarios

Bita Analui, Anna Scaglione

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


In power system operations, the success of stochastic optimization methods in helping to cope with increasing penetration of wind and solar power, rests on the effective construction of scenario trees that efficiently and accurately approximate the true probability space of the renewable power stochastic processes. In this work, by analyzing the statistical properties of wind turbines' power outputs which are recorded in Washington state (WA) for years 2012-2014, we identify the existing gaps in traditional modeling approaches and propose a possible solutions. The key idea we propose is to view scenario tree generation as a compression scheme of the wind power trajectories, sidestepping completely the model selection approach. We argue that to retain key features of the high order statistics, one can directly quantize realizations over the optimization horizon. We propose two approaches: one based on directly using scenarios in the time domain and an alternative one based on performing the quantization in the finite subspace of Morlet-wavelets. We compare the accuracy of the scenario trees with the prevalent approach based on an ARMA model selection step and show that the direct construction outperforms this approach when the complexity of the scenario tree is fixed.

Original languageEnglish (US)
Title of host publication2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781467378024
StatePublished - Aug 24 2016
Event19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain
Duration: Jun 25 2016Jun 29 2016


Other19th IEEE Statistical Signal Processing Workshop, SSP 2016
CityPalma de Mallorca


  • compression
  • scenario tree generation
  • stochastic approximation
  • Wavelets
  • wind power scenarios

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications


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