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A fast ES-based method for solving SCUC problem
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AI
DOI：10.1016/j.epsr.2023.109978.png)
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Abstract
En 中文
Security-Constrained Unit Commitment (SCUC) is the foundation for the safe and stable operation of the power system. However, existing methods for solving SCUC problems are either computationally expensive or inaccurate. An Encoding Strategy SCUC (ES-SCUC) approach that consists of a classifier and a linear system solution is proposed in this paper. First, the classifier predicts the ES, including tight constraints and on/off values, and then the ES is substituted into the Linear system to get an approximate solution. Experimental results show that the ES-SCUC solves for case300 SCUC problems within milliseconds. Compared to the state-of-the-art solver, it achieves speedups of two orders of magnitude with high accuracy.
Keywords:
Encoding strategy (ES)
Security-constrained unit commitment (SCUC)
Machine learning (ML)
Milliseconds
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