Scalable Energy System Expansion Under Uncertainty Using Multi-Stage Stochastic Optimization

Research output: NRELPresentation


The intermittent nature of power from renewable energy sources poses new challenges for electrical grids. This is due to the variable and uncertain nature of the power output from these resources. These features of renewable generation are becoming more relevant to energy system planning as grids reach higher penetration levels of renewable energy. In this presentation we present approaches for energy system planning based on scalable computational approaches which enable the explicit consideration of operational uncertainties in the planning process. Using multi-stage stochastic programming and the progressive hedging algorithm, we compute energy system expansion decisions on modified versions of the RTS-GMLC test system augmented with large amounts of renewable generation.
Original languageAmerican English
Number of pages25
StatePublished - 2020

Publication series

NamePresented at the FERC Power Market Software Conference, 23-25 June 2020

NREL Publication Number

  • NREL/PR-2C00-77099


  • multi-stage optimization
  • stochastic optimization
  • transmission expansion


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