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Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF)problem received considerable attention recently. A key challenge of theseapproaches lies in ensuring the feasibility of the predicted solutions tophysical system constraints. Due to the inherent approximation errors, thesolutions predicted by DNNs may violate the operating constraints, e.g., thetransmission line capacities, limiting their applicability in practice. Toaddress this challenge, we develop DeepOPF+ as a DNN approach based on theso-called "preventive" framework. Specifically, we calibrate the generation andtransmission line limits used in the DNN training, thereby anticipatingapproximation errors and ensuring that the resulting predicted solutions remainfeasible. We theoretically characterize the calibration magnitude necessary forensuring universal feasibility. Our DeepOPF+ approach improves over existingDNN-based schemes in that it ensures feasibility and achieves a consistentspeed up performance in both light-load and heavy-load regimes. Detailedsimulation results on a range of test instances show that the proposed DeepOPF+generates 100% feasible solutions with minor optimality loss. Meanwhile, itachieves a computational speedup of two orders of magnitude compared tostate-of-the-art solvers.

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