FRMNet: A Feasibility Restoration Mapping Deep Neural Network for AC Optimal Power Flow

Increasing renewable energy resources introduces uncertainties into utility grids, calling for more frequent use of alternative current optimal power flow (AC-OPF) than before. Conventional AC-OPF solvers are too slow for real-time applications; to speed up the computation, deep neural network (DNN) based solvers are introduced. However, their results typically cannot enforce the security constraints of the utility grid. To overcome this difficulty, FRMNet is proposed in this paper, which combines DNN with feasibility restoration mapping (FRM). The DNN receives the load as input and outputs a partial solution. The following FRM maps the infeasible solution to the feasibility region to satisfy the AC-OPF constraints. The proposed FRMNet has four advantages compared to conventional and DNN-based solvers. First, the feasibility of overall solutions is guaranteed via FRM. Second, FRMNet is a self-supervised training method so that it avoids the laborious preparation of the optimal solutions for a training dataset using conventional solvers. Third, FRM is differentiable so that AC-OPF information encoded in gradients is transferred to DNN, which leads to a DNN with high feasibility rate. Fourth, the overall computation time of FRMNet is fast due to its DNN's high feasibility rate. Comprehensive experiments are conducted on IEEE standard cases. Results show that FRMNet's outcomes satisfy all constraints at a significantly faster average speed than conventional solvers. When FRM participates in training, the performance of DNN is stably improved and achieves better results than other DNN-based models. Even DNN alone achieves a high feasibility rate, a negligible degree of constraint violations and a small optimality gap, compared to the state-of-the-art DNN based solvers.