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Online Learning of Stable Integer Variables in Unit Commitment Using Internal Information
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DOI：10.1109/TPWRS.2023.3258699.png)
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摘要
En 中文
In a set of similar unit commitment (UC) problems, a variable is called a stable integer variable (SIV) if its optimal value is 0 or 1 on a regular basis, which reflects an inherent pattern in UC solutions. The computational complexity can be significantly reduced by fixing SIVs with a minor chance of accuracy loss. To identify SIVs, this letter proposes a method to collect internal solution information from the initial branch-and-bound process of artificial mixed-integer programming problems generated from the given problem. A distinct advantage of this method is that because no prior offline training is needed, learning is instance-specific and has no dependency on the training set. With identified SIVs, machine learning-based acceleration is achieved. Based on 50 test cases from publicly available and practical data, the proposed method improves the percentage of solved problems within the clearing time window from 68% to 96% compared with commercial solvers, counting the total time of data collection, prediction, and optimization.
Keyword:
Data collection
Training
Data models
Programming
Optimization
Linear programming
Flowcharts
Data-driven
online learning
mixed-integer programming (MIP)
unit commitment (UC)
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
机构
引用论文
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MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming libraryMIPLIB 2017: 第6个混合整数编程库的数据驱动编译
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