1. Physics-Informed Graph Neural Network for Dynamic Reconfiguration of Power Systems
- Author
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Authier, Jules, Haider, Rabab, Annaswamy, Anuradha, and Dorfler, Florian
- Subjects
Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Systems and Control ,Mathematics - Optimization and Control ,Statistics - Machine Learning - Abstract
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., Comment: 8 pages, 5 figures, 2 tables. To appear at PSCC 2024
- Published
- 2023