DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility

Tianyu Zhao, Xiang Pan, Minghua Chen, Andreas Venzke, Steven H. Low

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids
Number of pages6
PublisherIEEE
Publication date2020
ISBN (Print)9781728161273
DOIs
Publication statusPublished - 2020
Event2020 IEEE SmartGridComm: IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids -
Duration: 11 Nov 202013 Nov 2020

Conference

Conference2020 IEEE SmartGridComm
Period11/11/202013/11/2020

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