@misc{f3d04b75564d4aeb8beb3e40b1a3a8c0,
title = "Scalable Stochastic Transmission Expansion: A Use Case for ExaSGD",
abstract = "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.",
keywords = "Exascale Computing Project, multistage stochastic optimization, progressive hedging, transmission expansion",
author = "Jonathan Maack and Devon Sigler and Ignas Satkauskas and Matthew Reynolds and Wes Jones and Shrirang Abhyankar and Slaven Peles and Chris Oehmen",
year = "2020",
language = "American English",
series = "Presented at the Exascale Computing Project Annual Meeting, 3-7 February 2020, Houston, Texas",
type = "Other",
}
