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.
Cite
@article{arxiv.2304.11726,
title = {End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch},
author = {Wenbo Chen and Mathieu Tanneau and Pascal Van Hentenryck},
journal= {arXiv preprint arXiv:2304.11726},
year = {2023}
}