论文标题
通过拉格朗日双重性，无监督的深度学习AC最佳功率流
Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality
论文作者
论文摘要
非凸AC最佳功率流（AC-OPF）是电力系统分析中的基本优化问题。常规求解器的计算复杂性通常很高，不适合实时操作中的大规模网络。因此，基于深度学习的方法已引起密集的关注，以离线进行耗时的培训过程。监督学习方法可能会产生可行的AC-OPF解决方案，并具有较小的最佳差距。但是，他们通常需要传统的求解器来生成培训数据集。本文提出了一个基于AC-OPF的基于端到端的无监督学习框架。我们开发了一个深神网络，以输出一组决策变量，而通过求解AC功率流程方程来恢复其余变量。采用快速解耦的功率流求解器，以进一步减少计算时间。此外，我们建议使用改进的增强拉格朗日功能作为培训损失。根据约束违规程度对乘数进行动态调整。广泛的数值测试结果证实了我们提出的方法比某些现有方法的优势。
Non-convex AC optimal power flow (AC-OPF) is a fundamental optimization problem in power system analysis. The computational complexity of conventional solvers is typically high and not suitable for large-scale networks in real-time operation. Hence, deep learning based approaches have gained intensive attention to conduct the time-consuming training process offline. Supervised learning methods may yield a feasible AC-OPF solution with a small optimality gap. However, they often need conventional solvers to generate the training dataset. This paper proposes an end-to-end unsupervised learning based framework for AC-OPF. We develop a deep neural network to output a partial set of decision variables while the remaining variables are recovered by solving AC power flow equations. The fast decoupled power flow solver is adopted to further reduce the computational time. In addition, we propose using a modified augmented Lagrangian function as the training loss. The multipliers are adjusted dynamically based on the degree of constraint violation. Extensive numerical test results corroborate the advantages of our proposed approach over some existing methods.
