Scalable Transmission Expansion Under Uncertainty Using Three-Stage Stochastic Optimization

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2 Scopus Citations

Abstract

The intermittent nature of renewable energy poses new challenges for power grids due to its variable and uncertain 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.

Original languageAmerican English
Number of pages5
DOIs
StatePublished - Feb 2020
Event2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 - Washington, United States
Duration: 17 Feb 202020 Feb 2020

Conference

Conference2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
Country/TerritoryUnited States
CityWashington
Period17/02/2020/02/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

NREL Publication Number

  • NREL/CP-2C00-74749

Keywords

  • multi-stage stochastic optimization
  • transmission expansion

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