arrow
Return

Network reduction in the Transmission-Constrained Unit Commitment problem

delete2012-11-01
delete17
PRE
AI
J
James Ostrowski *
Jianhui Wang cover
Jianhui Wang (Jianhui Wang)
DOI:10.1016/j.cie.2012.02.013delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
This paper examines a preprocessing technique for a Mixed Integer Linear Programming (MILP) formulation of the Transmission-Constrained Unit Commitment problem (TC-UC). Incorporating transmission constraints into the Unit Commitment problem can significantly increase the size and difficulty of the problem. By examining the structure of the transmission network, variables that have no impact on the quality of the overall solution can be identified and removed. This preprocessing can reduce the time needed to solve the linear programming relaxation of the MILP, and as a result, the MILP itself. Illinois's transmission network was used to test the benefit of the proposed technique. Preprocessing was able to remove 30% of the buses in the transmission network. This reduction led to a significant decrease in the time needed to solve a 24 h TC-UC problem. An added benefit of the preprocessing is that symmetry can be introduced into the problem. Identifying this symmetry and exploiting it can improve overall solution times even further. (C) 2012 Published by Elsevier Ltd.
Keywords:
Unit Commitment
Transmission network
Mixed Integer Linear Programming
Network reduction

Journal

Computers and Industrial Engineering cover
Computers and Industrial Engineering
IF:
6.5
Papers:
1.0W
Citations:
3.8W

Organization

U
University of Tennessee Knoxville
Scholars:
1.1W
Papers: 9.4K
Citations: 17
University of Tennessee System cover
University of Tennessee System
Scholars:
2.9W
Papers: 2.6W
Citations: 115
Cited Papers

Cited Papers

Fast Identification of Inactive Security Constraints in SCUC Problems
err2010-11-01
err169
PREAI
errZhai, Qiaozhu; Guan, Xiaohong; Cheng, Jinghui; Wu, Hongyu
errShare
errSave
Terrestrial, benthic, and pelagic resource use in lakes: results from a three-isotope Bayesian mixing model
err2011-05-01
err0
PREAI
errChristopher T. Solomon; Stephen R. Carpenter; Murray K. Clayton; Jonathan J. Cole; James J. Coloso; Michael L. Pace; M. Jake Vander Zanden; Brian C. Weidel
errShare
errSave
errShare
errSave
errShare
errSave
researcher View more