Stochastic Economic Dispatch of Wind Power under Uncertainty Using Clustering-Based Extreme Scenarios: Article No. 110158

Sujal Bhavsar, Ranga Pitchumani, Jon Maack, Ignas Satkauskas, Matthew Reynolds, Wesley Jones

Research output: Contribution to journalArticlepeer-review

2 Scopus Citations


Operation of power systems with high penetrations of renewable energy sources requires tools for robust decision making under uncertainty. Stochastic economic dispatch and stochastic unit commitment are effective techniques for planning and operation under uncertainty, whose effectiveness depends on the cardinality and quality of the scenario set. This article proposes a machine learning method using -means clustering for capturing relevant physical information from a large population of analog scenarios. Extreme scenario samples drawn from the clusters are used in a two-stage stochastic economic dispatch computation. The effectiveness of the proposed approach is assessed on a synthetic 200-bus system with a geographic footprint over Illinois, USA for four months from each season of WIND Toolkit data. The combination of -means clustering with importance sampling is shown to reduce the total operational cost by over 43% compared to sampling from populations based on heuristic clustering-based methods. Additionally, the variability in the mean cost is about 56% lower than the variability using Monte Carlo sampling. Moreover, the operational cost with the presented approach is shown to be close to the cost calculated based on a hindsight exact wind profile, signifying a highly accurate quantification of wind uncertainty by the presented -means clustering based sampling method.
Original languageAmerican English
Number of pages8
JournalElectric Power Systems Research
StatePublished - 2024

NREL Publication Number

  • NREL/JA-2C00-83940


  • extreme scenario generation
  • machine learning
  • stochastic economic dispatch
  • uncertainty quantification
  • wind power


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