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Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier
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DOI：10.1109/TPWRS.2024.3400405.png)
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The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. This paper provides a pragmatic solution, showing how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to warm start their solver and speed up their computation significantly. For the prediction, we train linear and kernelized support vector machine classifiers, providing an out-of-sample performance guarantee if properly regularized, converting to distributionally robust classifiers. For the unit commitment problem, we solve a mixed-integer second-order cone problem. Our results based on the IEEE 6- and 118-bus test systems show that the kernelized support vector machine (SVM) with proper regularization outperforms other classifiers, reducing the computational time by a factor of 1.7. In addition, if there is a tight computational limit, while the unit commitment problem without warm start is far away from the optimal solution, its warmly-started version can be solved to (near) optimality within the time limit.
Keyword:
support vector machine
Gaussian kernel function
Unit commitment
conic programming
conic programming
warm start
warm start
conic programming
warm start
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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