标题： 学习追求交流最优潮流解并保证可行性 显示英文标题

标题： Learning to Pursue AC Optimal Power Flow Solutions with Feasibility Guarantees

摘要： 本文重点研究了配备可控分布式能源资源（DER）的配电馈线的交流最优潮流（OPF）问题。我们考虑了一种基于投影梯度流连续逼近的方法——称为安全梯度流——该方法融合了通过实时测量或潮流计算获得的电压和电流信息。这两种设置使在线和离线实现成为可能。安全梯度流涉及求解凸二次规划（QP）问题。为了提高计算效率，我们提出了一种新颖的框架，采用QP最优解映射的神经网络近似。由此产生的方法具有两个关键特征：(a) 即使在线实现或离线算法提前终止时，也能确保DER的设定点在实际可行范围内；(b) 确保收敛到交流OPF的一个严格局部优化器的邻域。所提出的方案在一个包含现实负荷和可再生能源发电的93节点配电系统上进行了测试。结果显示，在高可再生能源发电期间，我们的方法成功地将电压控制在限值内。 显示英文摘要

摘要： This paper focuses on an AC optimal power flow (OPF) problem for distribution feeders equipped with controllable distributed energy resources (DERs). We consider a solution method that is based on a continuous approximation of the projected gradient flow - referred to as the safe gradient flow - that incorporates voltage and current information obtained either through real-time measurements or power flow computations. These two setups enable both online and offline implementations. The safe gradient flow involves the solution of convex quadratic programs (QPs). To enhance computational efficiency, we propose a novel framework that employs a neural network approximation of the optimal solution map of the QP. The resulting method has two key features: (a) it ensures that the DERs' setpoints are practically feasible, even for an online implementation or when an offline algorithm has an early termination; (b) it ensures convergence to a neighborhood of a strict local optimizer of the AC OPF. The proposed method is tested on a 93-node distribution system with realistic loads and renewable generation. The test shows that our method successfully regulates voltages within limits during periods with high renewable generation.