Scalable Transmission Expansion Under Uncertainty Using Three-Stage Stochastic Optimization

Devon Sigler; Jonathan Maack; Ignas Satkauskas; Matthew Reynolds; Wesley Jones

doi:10.1109/ISGT45199.2020.9087776

Scalable Transmission Expansion Under Uncertainty Using Three-Stage Stochastic Optimization

Devon Sigler, Jonathan Maack, Ignas Satkauskas, Matthew Reynolds, Wesley Jones

Computational Science

Research output: Contribution to conference › Paper › peer-review

3 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 language	American English
Number of pages	5
DOIs	https://doi.org/10.1109/ISGT45199.2020.9087776 https://doi.org/10.1109/ISGT45199.2020.9087776
State	Published - Feb 2020
Event	2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 - Washington, United States Duration: 17 Feb 2020 → 20 Feb 2020

Conference

Conference	2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020
Country/Territory	United States
City	Washington
Period	17/02/20 → 20/02/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

NREL Publication Number

NREL/CP-2C00-74749

Keywords

multi-stage stochastic optimization
transmission expansion

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Sigler, D., Maack, J., Satkauskas, I., Reynolds, M., & Jones, W. (2020). Scalable Transmission Expansion Under Uncertainty Using Three-Stage Stochastic Optimization. Paper presented at 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020, Washington, United States. https://doi.org/10.1109/ISGT45199.2020.9087776, https://doi.org/10.1109/ISGT45199.2020.9087776

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Sigler, D , Maack, J , Satkauskas, I , Reynolds, M & Jones, W 2020, 'Scalable Transmission Expansion Under Uncertainty Using Three-Stage Stochastic Optimization', Paper presented at 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020, Washington, United States, 17/02/20 - 20/02/20. https://doi.org/10.1109/ISGT45199.2020.9087776, https://doi.org/10.1109/ISGT45199.2020.9087776

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AU - Sigler, Devon

AU - Maack, Jonathan

AU - Satkauskas, Ignas

AU - Reynolds, Matthew

AU - Jones, Wesley

PY - 2020/2

Y1 - 2020/2

N2 - 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.

AB - 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.

KW - multi-stage stochastic optimization

KW - transmission expansion

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Y2 - 17 February 2020 through 20 February 2020

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Scalable Transmission Expansion Under Uncertainty Using Three-Stage Stochastic Optimization

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

UN SDGs

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