A Fast and Scalable Transmission Switching Algorithm for Boosting Resilience of Electric Grids Impacted by Extreme Weather Events

Tanveer Hussain; Siddharth Suryanarayanan; Timothy M. Hansen; S. M.Shafiul Alam

doi:10.1109/ACCESS.2022.3179470

A Fast and Scalable Transmission Switching Algorithm for Boosting Resilience of Electric Grids Impacted by Extreme Weather Events

Tanveer Hussain
, Siddharth Suryanarayanan
, Timothy M. Hansen
, S. M.Shafiul Alam

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

A fast and scalable heuristic transmission switching (TS) algorithm for boosting resilience by reducing the load shed in electricity networks impacted by extreme weather events (EWEs) is presented. Finding the best transmission line to switch within a suitable time is the main challenge for wider applicability of TS. Here, we propose an algorithm that: i) finds the optimal TS candidate faster than well-known algorithms; ii) is compatible with existing optimal power flow formulations; and iii) scales for larger, realistic systems. Proof-of-concept results on the IEEE 39-bus, IEEE 118-bus, and the large-scale Polish 2383-bus systems validate our claims. Further, a case study of TS for improving grid resilience in a real-life hurricane event (Harvey, 2017) using a synthetic version of Texas' ERCOT electric grid is presented for practical application. Finally, a path forward to utilize our proposed TS algorithms for evolving EWEs is discussed.

Original language	English
Pages (from-to)	57893-57901
Number of pages	9
Journal	IEEE Access
Volume	10
Early online date	Jun 1 2022
DOIs	https://doi.org/10.1109/ACCESS.2022.3179470
State	Published - 2022

Keywords

Contingency analysis
extreme weather events
high-performance computing
load shed reduction
post contingency violations
resilience
transmission switching

INL Publication Number

INL/JOU-21-62879
114835

Access to Document

10.1109/ACCESS.2022.3179470

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@article{f0f513844aae4575a5bfcd2a353ceb87,

title = "A Fast and Scalable Transmission Switching Algorithm for Boosting Resilience of Electric Grids Impacted by Extreme Weather Events",

abstract = "A fast and scalable heuristic transmission switching (TS) algorithm for boosting resilience by reducing the load shed in electricity networks impacted by extreme weather events (EWEs) is presented. Finding the best transmission line to switch within a suitable time is the main challenge for wider applicability of TS. Here, we propose an algorithm that: i) finds the optimal TS candidate faster than well-known algorithms; ii) is compatible with existing optimal power flow formulations; and iii) scales for larger, realistic systems. Proof-of-concept results on the IEEE 39-bus, IEEE 118-bus, and the large-scale Polish 2383-bus systems validate our claims. Further, a case study of TS for improving grid resilience in a real-life hurricane event (Harvey, 2017) using a synthetic version of Texas' ERCOT electric grid is presented for practical application. Finally, a path forward to utilize our proposed TS algorithms for evolving EWEs is discussed.",

keywords = "Contingency analysis, extreme weather events, high-performance computing, load shed reduction, post contingency violations, resilience, transmission switching",

author = "Tanveer Hussain and Siddharth Suryanarayanan and Hansen, \{Timothy M.\} and Alam, \{S. M.Shafiul\}",

note = "Funding Information: This work was supported in part by INL Laboratory Directed Research and Development Program under DOE Idaho Operations Office under Contract DE-AC07-05ID14517 to Colorado State University and under Contract 5301566 and Contract 501523 to SDSU, in part by NSF under Grant 1903726, and in part by the Roaring Thunder Cluster at SDSU through NSF under Grant CNS-1726946. Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2022",

doi = "10.1109/ACCESS.2022.3179470",

language = "English",

volume = "10",

pages = "57893--57901",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "IEEE",

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T1 - A Fast and Scalable Transmission Switching Algorithm for Boosting Resilience of Electric Grids Impacted by Extreme Weather Events

AU - Hussain, Tanveer

AU - Suryanarayanan, Siddharth

AU - Hansen, Timothy M.

AU - Alam, S. M.Shafiul

N1 - Funding Information: This work was supported in part by INL Laboratory Directed Research and Development Program under DOE Idaho Operations Office under Contract DE-AC07-05ID14517 to Colorado State University and under Contract 5301566 and Contract 501523 to SDSU, in part by NSF under Grant 1903726, and in part by the Roaring Thunder Cluster at SDSU through NSF under Grant CNS-1726946. Publisher Copyright: © 2013 IEEE.

PY - 2022

Y1 - 2022

N2 - A fast and scalable heuristic transmission switching (TS) algorithm for boosting resilience by reducing the load shed in electricity networks impacted by extreme weather events (EWEs) is presented. Finding the best transmission line to switch within a suitable time is the main challenge for wider applicability of TS. Here, we propose an algorithm that: i) finds the optimal TS candidate faster than well-known algorithms; ii) is compatible with existing optimal power flow formulations; and iii) scales for larger, realistic systems. Proof-of-concept results on the IEEE 39-bus, IEEE 118-bus, and the large-scale Polish 2383-bus systems validate our claims. Further, a case study of TS for improving grid resilience in a real-life hurricane event (Harvey, 2017) using a synthetic version of Texas' ERCOT electric grid is presented for practical application. Finally, a path forward to utilize our proposed TS algorithms for evolving EWEs is discussed.

AB - A fast and scalable heuristic transmission switching (TS) algorithm for boosting resilience by reducing the load shed in electricity networks impacted by extreme weather events (EWEs) is presented. Finding the best transmission line to switch within a suitable time is the main challenge for wider applicability of TS. Here, we propose an algorithm that: i) finds the optimal TS candidate faster than well-known algorithms; ii) is compatible with existing optimal power flow formulations; and iii) scales for larger, realistic systems. Proof-of-concept results on the IEEE 39-bus, IEEE 118-bus, and the large-scale Polish 2383-bus systems validate our claims. Further, a case study of TS for improving grid resilience in a real-life hurricane event (Harvey, 2017) using a synthetic version of Texas' ERCOT electric grid is presented for practical application. Finally, a path forward to utilize our proposed TS algorithms for evolving EWEs is discussed.

KW - Contingency analysis

KW - extreme weather events

KW - high-performance computing

KW - load shed reduction

KW - post contingency violations

KW - resilience

KW - transmission switching

UR - https://www.scopus.com/pages/publications/85131752691

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DO - 10.1109/ACCESS.2022.3179470

M3 - Article

AN - SCOPUS:85131752691

SN - 2169-3536

VL - 10

SP - 57893

EP - 57901

JO - IEEE Access

JF - IEEE Access

ER -

A Fast and Scalable Transmission Switching Algorithm for Boosting Resilience of Electric Grids Impacted by Extreme Weather Events

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