Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866916162318630912 |
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| author | Pourahmadi, Farzaneh Kazempour, Jalal |
| author_facet | Pourahmadi, Farzaneh Kazempour, Jalal |
| contents | 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_08601 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier Pourahmadi, Farzaneh Kazempour, Jalal Optimization and Control Machine Learning Applications 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 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. |
| title | Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier |
| topic | Optimization and Control Machine Learning Applications |
| url | https://arxiv.org/abs/2310.08601 |