Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866910396900704256 |
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| author | Authier, Jules Haider, Rabab Annaswamy, Anuradha Dorfler, Florian |
| author_facet | Authier, Jules Haider, Rabab Annaswamy, Anuradha Dorfler, Florian |
| contents | To maintain a reliable grid we need fast decision-making algorithms for complex problems like Dynamic Reconfiguration (DyR). DyR optimizes distribution grid switch settings in real-time to minimize grid losses and dispatches resources to supply loads with available generation. DyR is a mixed-integer problem and can be computationally intractable to solve for large grids and at fast timescales. We propose GraPhyR, a Physics-Informed Graph Neural Network (GNNs) framework tailored for DyR. We incorporate essential operational and connectivity constraints directly within the GNN framework and train it end-to-end. Our results show that GraPhyR is able to learn to optimize the DyR task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00728 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems Authier, Jules Haider, Rabab Annaswamy, Anuradha Dorfler, Florian Machine Learning Systems and Control Optimization and Control To maintain a reliable grid we need fast decision-making algorithms for complex problems like Dynamic Reconfiguration (DyR). DyR optimizes distribution grid switch settings in real-time to minimize grid losses and dispatches resources to supply loads with available generation. DyR is a mixed-integer problem and can be computationally intractable to solve for large grids and at fast timescales. We propose GraPhyR, a Physics-Informed Graph Neural Network (GNNs) framework tailored for DyR. We incorporate essential operational and connectivity constraints directly within the GNN framework and train it end-to-end. Our results show that GraPhyR is able to learn to optimize the DyR task. |
| title | Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems |
| topic | Machine Learning Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2310.00728 |