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Exploiting soft constraints within decomposition and coordination methods for sub-hourly unit commitment

delete2022-07-01
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OA
AI
N
Niranjan Raghunathan *
M
Mikhail A. Bragin
B
Bing Yan
P
Peter B. Luh
K
K. Moslehi
X
Xiaoming Feng
Y
Yaowen Yu
C
Chien-Ning Yu
C
Chia‐Chun Tsai
DOI:10.1016/j.ijepes.2022.108023delete
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Abstract

Abstract

En 中文
Unit commitment (UC) is an important problem solved on a daily basis within a strict time limit. While hourly UC is currently used, they may not be flexible enough to accommodate the growing changes of demand and the increasing penetration of intermittent renewables. Sub-hourly UC is therefore recommended. This, however, will significantly increase problem complexity even under the deterministic setting because of the considerable increase of the number of intervals, leading to the drastic increase of the numbers of system coupling constraints and binary variables as compared to that of hourly UC. Consequently, existing methods may not be able to obtain good solutions within the time limit for large problems. In this paper, deterministic sub-hourly UC is considered with an innovative exploitation of soft constraints constraints that do not need to be strictly satisfied but their violations are penalized by predetermined coefficients. This, in conjunction with our recent Surrogate Absolute Value Lagrangian Relaxation approach where the relaxed problem is not required to be fully optimized, facilitates the formation and resolution of a new type of subproblems where soft system coupling constraints (e.g., reserve and transmission capacity constraints) are not relaxed. This then leads to a drastic reduction of the number of multipliers, decreased computational requirements, and improved solution quality. To further enhance the speed, a parallel version is developed. Testing results based on the Polish system demonstrate the effectiveness and robustness of both the sequential and parallel versions at finding high-quality solutions within the time limit.
Keywords:
Sub-hourly unit commitment
Soft constraints
Surrogate Lagrangian Relaxation (SLR)
Surrogate Absolute-Value Lagrangian Relaxa-tion (SAVLR)
Parallel algorithms
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I
International Journal of Electrical Power and Energy Systems
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abb
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hitachi limited
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Rochester Institute of Technology
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University of Connecticut
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