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The existence of multiple load-solution mappings of non-convex AC-OPFproblems poses a fundamental challenge to deep neural network (DNN) schemes. Asthe training dataset may contain a mixture of data points corresponding todifferent load-solution mappings, the DNN can fail to learn a legitimatemapping and generate inferior solutions. We propose DeepOPF-AL as anaugmented-learning approach to tackle this issue. The idea is to train a DNN tolearn a unique mapping from an augmented input, i.e., (load, initial point), tothe solution generated by an iterative OPF solver with the load and initialpoint as intake. We then apply the learned augmented mapping to solve AC-OPFproblems much faster than conventional solvers. Simulation results over IEEEtest cases show that DeepOPF-AL achieves noticeably better optimality andsimilar feasibility and speedup performance, as compared to a recent DNNscheme, with the same DNN size yet elevated training complexity.

DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings