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- W4366999873 abstract "The paper proposes a novel End-to-End Learning and Repair (E2ELR) architecture for training optimization proxies for economic dispatch problems. E2ELR combines deep neural networks with closed-form, differentiable repair layers, thereby integrating learning and feasibility in an end-to-end fashion. E2ELR is also trained with self-supervised learning, removing the need for labeled data and the solving of numerous optimization problems offline. E2ELR is evaluated on industry-size power grids with tens of thousands of buses using an economic dispatch that co-optimizes energy and reserves. The results demonstrate that the self-supervised E2ELR achieves state-of-the-art performance, with optimality gaps that outperform other baselines by at least an order of magnitude." @default.
- W4366999873 created "2023-04-27" @default.
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- W4366999873 date "2023-04-23" @default.
- W4366999873 modified "2023-09-25" @default.
- W4366999873 title "End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch" @default.
- W4366999873 doi "https://doi.org/10.48550/arxiv.2304.11726" @default.
- W4366999873 hasPublicationYear "2023" @default.
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