Transmission switching under wind uncertainty using linear decision rules

Yuqi Zhou, Hao Zhu, Grani A. Hanasusanto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Increasing penetration of wind and renewable generation poses significant challenges to the power system operations and reliability. This paper considers the real-time optimal transmission switching (OTS) problem for determining the generation dispatch and network topology that can account for uncertain energy resources. To efficiently solve the resultant two-stage stochastic program, we propose a tractable linear decision rule (LDR) based approximation solution that can eliminate the uncertain variables and lead to fixed number of constraints. The proposed LDR approach can guarantee feasibility, and significantly reduces the computational complexity of existing approaches that grows with the number of randomly generated samples of uncertainty. Numerical studies on IEEE test cases demonstrate the high approximation accuracy of the proposed LDR solution and its computational efficiency for real-time OTS implementations.

Original languageEnglish (US)
Title of host publication2020 IEEE Power and Energy Society General Meeting, PESGM 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728155081
DOIs
StatePublished - Aug 2 2020
Externally publishedYes
Event2020 IEEE Power and Energy Society General Meeting, PESGM 2020 - Montreal, Canada
Duration: Aug 2 2020Aug 6 2020

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2020-August
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2020 IEEE Power and Energy Society General Meeting, PESGM 2020
Country/TerritoryCanada
CityMontreal
Period8/2/208/6/20

Keywords

  • Linear decision rules
  • Optimal transmission switching
  • Sample average approximation
  • Two-stage stochastic program

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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