Scalable Transmission Expansion Under Uncertainty Using Three-stage Stochastic Optimization

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 p...

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Vydané v:Innovative Smart Grid Technologies s. 1 - 5
Hlavní autori: Sigler, Devon, Maack, Jonathan, Satkauskas, Ignas, Reynolds, Matthew, Jones, Wesley
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.02.2020
Predmet:
Costs
Load modeling
Planning
Programming
Propagation losses
Renewable energy sources
Smart grids
Stochastic processes
Transmission line measurements
Uncertainty
ISSN:2472-8152
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Popis
Shrnutí: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.
ISSN:2472-8152
DOI:10.1109/ISGT45199.2020.9087776