Scalable Stochastic Transmission Expansion: A Use Case for ExaSGD

Jonathan Maack, Devon Sigler, Ignas Satkauskas, Matthew Reynolds, Wes Jones, Shrirang Abhyankar, Slaven Peles, Chris Oehmen

Research output: NRELPoster


The intermittent nature of renewable energy poses new challenges for power grids due to its variable and un- certain power output. These features of renewable generation are becoming more relevant to transmission planning as grids reach higher penetration levels of renewable energy. In this paper we present an approach for transmission planning based on scalable computational approaches which enable the explicit consideration of operational uncertainties in the planning process. Using three-stage stochastic programming and the progressive hedging algorithm, we compute transmission expansion decisions on a modified RTS-GMLC test system. We augment the grid with large amounts of wind generation and consider many operational scenarios subject to wind uncertainty. This is an example of a possible use of the ExaSGD security constrained AC optimal power flow solver.
Original languageAmerican English
StatePublished - 2020

Publication series

NamePresented at the Exascale Computing Project Annual Meeting, 3-7 February 2020, Houston, Texas

NREL Publication Number

  • NREL/PO-2C00-75840


  • Exascale Computing Project
  • multistage stochastic optimization
  • progressive hedging
  • transmission expansion


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