Solving Non-linear Optimization Problem in Engineering by Model-Informed Generative Adversarial Network (MI-GAN)
Solving Non-linear Optimization Problem in Engineering by Model-Informed Generative Adversarial Network (MI-GAN)
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DOI：
10.1109/icdmw58026.2022.00035
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Yuxuan Li;Chaoyue Zhao;Chenang Liu
中科院分区：
文献类型：
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作者：
Yuxuan Li;Chaoyue Zhao;Chenang Liu
优化模型已广泛应用于许多工程系统中，以解决系统运行和管理的相关问题。例如，在电力系统中，最优潮流（OPF）问题是电力系统运行的关键组成部分，可以使用优化模型来制定。具体来说，交流 OPF (AC-OPF) 问题具有挑战性，因为一些约束是非线性和非凸的。而且，由于电力系统可能具有较高的可变性，优化模型的系数可能会发生变化，增加了OPF问题的求解难度。尽管已经研究了传统的优化工具和深度学习方法，但解决方案的可行性和优化性可能仍然不能令人满意。因此，本文基于最近开发的模型通知生成对抗网络（MI-GAN）框架，提出了一种用于解决不确定性下的非线性AC-OPF问题的定制版本。这项工作的贡献可以概括为两个主要方面：（1）为了确保生成的解决方案的可行性并提高其最优性，考虑和设计了两个重要的层，即可行性过滤层和最优性过滤层； (2) 通过合并这两个新层来通知生成器，设计了一个高效的模型通知选择器并将其集成到 GAN 架构中。 IEEE 测试系统上的实验证明了所提出的方法解决非线性 AC-OPF 问题的有效性和潜力。
Optimization models have been widely used in many engineering systems to solve the problems related to system operation and management. For instance, in power systems, the optimal power flow (OPF) problem, which is a critical component of power system operations, can be formulated using optimization models. Specifically, the alternating current OPF (AC-OPF) problems are challenging since some of the constraints are non-linear and non-convex. Moreover, due to the high variability that the power system may have, the coefficients of the optimization model may change, increasing the difficulty of solving the OPF problem. Although the conventional optimization tools and deep learning approaches have been investigated, the feasibility and optimality of the solutions may still be unsatisfactory. Hence, in this paper, based on the recently developed model-informed generative adversarial network (MI-GAN) framework, a tailored version for solving the non-linear AC-OPF problem under uncertainties is proposed. The contributions of this work can be summarized into two main aspects: (1) To ensure the feasibility and improve the optimality of the generated solutions, two important layers, namely, the feasibility filter layer and optimality-filter layer, are considered and designed; and (2) An efficient model-informed selector is designed and integrated to the GAN architecture, by incorporating these two new layers to inform the generator. Experiments on the IEEE test systems demonstrate the efficacy and potential of the proposed method for solving non-linear AC-OPF problems.