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A Preference-Driven UC Optimization Paradigm

delete2025-11-10
delete0
PRE
AI
C
Cong Zeng
J
Jizhong Zhu
A
Alberto Borghetti
Y
Yixi Chen
DOI：10.1109/TPWRS.2025.3631305delete
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Abstract

Abstract

En 中文
Binary variables in unit commitment (UC) problems invalidate gradient-based directional information, often causing computational bottlenecks. Existing binary algorithms ignore a tendency of these variables towards 0 or 1, which affects efficiency. To improve performance, this letter formalizes this tendency as preference and leverages it to guide the optimization process. A solution-set-based global optimization algorithm is introduced to handle to non-convexity arising from complex operational constraints. The results show that the guided algorithm has improved efficiency and robust global convergence ability.
Keywords:
Unit commitment (UC)
preference
binary optimization
knowledge-embedding

Journal

IEEE Transactions on Power Systems cover
IEEE Transactions on Power Systems
IF:
7.2
Papers:
1.1W
Citations:
5.0W

Organization

U
university of bologna
Scholars:
5.6K
Papers: 2.4K
Citations: 0
S
south china university of technology
Scholars:
6.7W
Papers: 5.1W
Citations: 85