End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch
This addresses the challenge of efficient economic dispatch in power grids for energy operators, but it is incremental as it builds on existing optimization proxy methods with a novel integration approach.
The paper tackled the problem of training optimization proxies for large-scale economic dispatch by proposing an End-to-End Learning and Repair (E2ELR) architecture, which achieved state-of-the-art performance with optimality gaps outperforming other baselines by at least an order of magnitude on industry-size power grids.
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.