Deep Neural Networks approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the feasibility of the predicted solutions to physical system constraints. Due to the inherent approximation errors, the solutions predicted by Deep Neural Networks (DNNs) may violate the operating constraints, e.g., the transmission line capacities, limiting their applicability in practice. To address this challenge, we develop DeepOPF+ as a DNN approach based on the so-called "preventive" framework. Specifically, we calibrate the generation and transmission line limits used in the DNN training, thereby anticipating approximation errors and ensuring that the resulting predicted solutions remain feasible. We theoretically characterize the calibration magnitude necessary for ensuring universal feasibility. Our DeepOPF+ approach improves over existing DNN-based schemes in that it ensures feasibility and achieves a consistent speed up performance in both light-load and heavy-load regimes. Detailed simulation results on a range of test instances show that the proposed DeepOPF+ generates 100% feasible solutions with minor optimality loss. Meanwhile, it achieves a computational speedup of two orders of magnitude compared to state-of-the-art solvers.
|Title of host publication||Proceedings of 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids|
|Number of pages||6|
|Publication status||Published - 2020|
|Event||2020 IEEE SmartGridComm: IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids - |
Duration: 11 Nov 2020 → 13 Nov 2020
|Conference||2020 IEEE SmartGridComm|
|Period||11/11/2020 → 13/11/2020|