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

Research output: NRELPresentation


The intermittent nature of renewable energy sources poses 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 more relevant to energy system planning as grids reach higher penetration levels of renewable energy. We present approaches for energy system planning based on scalable computational approaches which enable 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.
Original languageAmerican English
Number of pages13
StatePublished - 2020

Publication series

NamePresented at the 2020 INFORMS Annual Meeting, 7-13 November 2020

NREL Publication Number

  • NREL/PR-2C00-78098


  • electrical
  • grids
  • multi-stage stochastic optimization
  • scalable


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