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

翻译：最优潮流（OPF）问题是电力系统运行的关键组成部分，由于可再生能源给电力系统带来的波动性、间歇性和不可预测性，其求解日益困难。虽然传统优化技术（如随机优化和鲁棒优化方法）可用于解决OPF问题，但面对可再生能源的不确定性（即优化模型中的动态系数），这些方法在处理大规模问题时效果有限。因此，近年来深度学习技术（如神经网络）被开发出来，利用数据提高解决OPF问题的计算效率。然而，这些方法可能无法保证解的可行性和最优性，也无法妥善处理系统动态特性。本文提出了一种基于优化模型信息的生成对抗网络（MI-GAN）框架，用于求解不确定性条件下的OPF问题。主要贡献概括为三个方面：（1）为确保解的可行性并提升最优性，提出了三个重要层：可行性过滤层、比较层和梯度引导层；（2）在基于GAN的框架中，建立了一个融合这三个新层的高效模型信息选择器；（3）提出了一种新的递归迭代算法，用于改进解的最优性并处理系统动态特性。在IEEE测试系统上的数值结果表明，所提方法非常有效且前景广阔。