State-of-the-Art Techniques for Large-Scale Stochastic Unit Commitment

Bernard Knueven, Jean-Paul Watson, James Ostrowski, David Woodruff

Research output: NRELPresentation


Recent advances in deterministic unit commitment, both formulaic and algorithmic, along with modern algorithmic approaches for stochastic programming, have enabled the solution of stochastic unit commitment problems with hundreds of scenarios on large-scale transmission networks. In this presentation, we will give an overview of these methods, including lazy transmission constraint generation, lower-bounding techniques, and heuristics, all of which can be executed in concert with customized decomposition approaches for optimization under uncertainty. We demonstrate the effectiveness of these techniques on the TAMU Texas7K synthetic transmission network, leveraging realistic high-resolution forecasts based on NREL renewable resource availability data. The software leveraged for these demonstrations is available via the open-source software packages EGRET (for electrical grid optimization) and mpi-sppy (for optimization under uncertainty).
Original languageAmerican English
Number of pages21
StatePublished - 2021

Publication series

NamePresented at the FERC 2021 Software Conference, 23 June 2021

NREL Publication Number

  • NREL/PR-2C00-80357


  • decomposition
  • stochastic optimization
  • unit commitment


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