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.

翻译：本文提出一种新颖的端到端学习与修复（E2ELR）架构，用于训练经济调度问题的优化代理。E2ELR将深度神经网络与封闭形式的可微修复层相结合，从而以端到端方式整合学习过程与可行性约束。该架构采用自监督学习进行训练，消除了对标注数据的需求以及离线求解大量优化问题的必要性。通过对包含数万个母线的工业规模电网进行能量与备用联合优化的经济调度评估，结果表明自监督E2ELR实现了最先进的性能，其最优性差距相比其他基线方法至少低一个数量级。