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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.01200 |
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| _version_ | 1866910431931531264 |
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| author | Yang, Mei Liu, Gao Qiu andJunyong Liu, Kai |
| author_facet | Yang, Mei Liu, Gao Qiu andJunyong Liu, Kai |
| contents | This letter proposes a few-shot physics-guided spatial temporal graph convolutional network (FPG-STGCN) to fast solve unit commitment (UC). Firstly, STGCN is tailored to parameterize UC. Then, few-shot physics-guided learning scheme is proposed. It exploits few typical UC solutions yielded via commercial optimizer to escape from local minimum, and leverages the augmented Lagrangian method for constraint satisfaction. To further enable both feasibility and continuous relaxation for integers in learning process, straight-through estimator for Tanh-Sign composition is proposed to fully differentiate the mixed integer solution space. Case study on the IEEE benchmark justifies that, our method bests mainstream learning ways on UC feasibility, and surpasses traditional solver on efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01200 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network Yang, Mei Liu, Gao Qiu andJunyong Liu, Kai Systems and Control Machine Learning This letter proposes a few-shot physics-guided spatial temporal graph convolutional network (FPG-STGCN) to fast solve unit commitment (UC). Firstly, STGCN is tailored to parameterize UC. Then, few-shot physics-guided learning scheme is proposed. It exploits few typical UC solutions yielded via commercial optimizer to escape from local minimum, and leverages the augmented Lagrangian method for constraint satisfaction. To further enable both feasibility and continuous relaxation for integers in learning process, straight-through estimator for Tanh-Sign composition is proposed to fully differentiate the mixed integer solution space. Case study on the IEEE benchmark justifies that, our method bests mainstream learning ways on UC feasibility, and surpasses traditional solver on efficiency. |
| title | Learning-to-solve unit commitment based on few-shot physics-guided spatial-temporal graph convolution network |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2405.01200 |