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The optimal power flow (OPF) problem, as a critical component of power systemoperations, becomes increasingly difficult to solve due to the variability,intermittency, and unpredictability of renewable energy brought to the powersystem. Although traditional optimization techniques, such as stochastic androbust optimization approaches, could be leveraged to address the OPF problem,in the face of renewable energy uncertainty, i.e., the dynamic coefficients inthe optimization model, their effectiveness in dealing with large-scaleproblems remains limited. As a result, deep learning techniques, such as neuralnetworks, have recently been developed to improve computational efficiency insolving OPF problems with the utilization of data. However, the feasibility andoptimality of the solution may not be guaranteed, and the system dynamicscannot be properly addressed as well. In this paper, we propose an optimizationmodel-informed generative adversarial network (MI-GAN) framework to solve OPFunder uncertainty. The main contributions are summarized into three aspects:(1) to ensure feasibility and improve optimality of generated solutions, threeimportant layers are proposed: feasibility filter layer, comparison layer, andgradient-guided layer; (2) in the GAN-based framework, an efficientmodel-informed selector incorporating these three new layers is established;and (3) a new recursive iteration algorithm is also proposed to improvesolution optimality and handle the system dynamics. The numerical results onIEEE test systems show that the proposed method is very effective andpromising.

Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow