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Title: Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints

Abstract

Here, we propose a novel global solution algorithm for the network-constrained unit commitment problem that incorporates a nonlinear alternating current (AC) model of the transmission network, which is a nonconvex mixed-integer nonlinear programming (MINLP) problem. Our algorithm is based on the multi-tree global optimization methodology, which iterates between a mixed-integer lower-bounding problem and a nonlinear upper-bounding problem. We exploit the mathematical structure of the unit commitment problem with AC power flow constraints (UC-AC) and leverage second-order cone relaxations, piecewise outer approximations, and optimization-based bounds tightening to guarantee a globally optimal solution at convergence. Numerical results on four benchmark problems illustrate the effectiveness of our algorithm, both in terms of convergence rate and solution quality.

Authors:
 [1];  [2];  [3];  [2];  [2]
  1. Purdue Univ., West Lafayette, IN (United States)
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  3. Clemson Univ., Clemson, SC (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Laboratories, Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1485467
Report Number(s):
[SAND-2018-11995J]
[Journal ID: ISSN 0885-8950; 668931]
Grant/Contract Number:  
[AC04-94AL85000]
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
[ Journal Volume: 34; Journal Issue: 2]; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; Optimal Power Flow; Unit Commitment; Optimization methods; Power system modeling

Citation Formats

Liu, Jianfeng, Laird, Carl Damon, Scott, Joseph K., Watson, Jean -Paul, and Castillo, Anya. Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints. United States: N. p., 2018. Web. doi:10.1109/TPWRS.2018.2876127.
Liu, Jianfeng, Laird, Carl Damon, Scott, Joseph K., Watson, Jean -Paul, & Castillo, Anya. Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints. United States. doi:10.1109/TPWRS.2018.2876127.
Liu, Jianfeng, Laird, Carl Damon, Scott, Joseph K., Watson, Jean -Paul, and Castillo, Anya. Wed . "Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints". United States. doi:10.1109/TPWRS.2018.2876127. https://www.osti.gov/servlets/purl/1485467.
@article{osti_1485467,
title = {Global Solution Strategies for the Network-Constrained Unit Commitment Problem with AC Transmission Constraints},
author = {Liu, Jianfeng and Laird, Carl Damon and Scott, Joseph K. and Watson, Jean -Paul and Castillo, Anya},
abstractNote = {Here, we propose a novel global solution algorithm for the network-constrained unit commitment problem that incorporates a nonlinear alternating current (AC) model of the transmission network, which is a nonconvex mixed-integer nonlinear programming (MINLP) problem. Our algorithm is based on the multi-tree global optimization methodology, which iterates between a mixed-integer lower-bounding problem and a nonlinear upper-bounding problem. We exploit the mathematical structure of the unit commitment problem with AC power flow constraints (UC-AC) and leverage second-order cone relaxations, piecewise outer approximations, and optimization-based bounds tightening to guarantee a globally optimal solution at convergence. Numerical results on four benchmark problems illustrate the effectiveness of our algorithm, both in terms of convergence rate and solution quality.},
doi = {10.1109/TPWRS.2018.2876127},
journal = {IEEE Transactions on Power Systems},
number = [2],
volume = [34],
place = {United States},
year = {2018},
month = {10}
}

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Cited by: 2 works
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Figures / Tables:

Fig. 1 Fig. 1: High-level description of the multi-tree approach for global solution of the UC-AC MINLP problem.

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Works referencing / citing this record:

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    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.