The traditional machine learning models to solve optimal power flow (OPF) are mostly trained for a given power network and lack generalizability to today's power networks with varying topologies and growing plug-and-play distributed energy resources (DERs). In this paper, we propose DeepOPF-U, which uses one unified deep neural network (DNN) to solve alternating-current (AC) OPF problems in different power networks, including a set of power networks that is successively expanding. Specifically, we design elastic input and output layers for the vectors of given loads and OPF solutions with varying lengths in different networks. The proposed method, using a single unified DNN, can deal with different and growing numbers of buses, lines, loads, and DERs. Simulations of IEEE 57/118/300-bus test systems and a network growing from 73 to 118 buses verify the improved performance of DeepOPF-U compared to existing DNN-based solution methods.

翻译：传统机器学习模型求解最优潮流（OPF）时大多针对给定电力网络训练，难以适应现代电网中拓扑结构变化以及即插即用分布式能源（DERs）日益增长的现状。本文提出DeepOPF-U，采用统一的深度神经网络（DNN）求解不同电力网络（包括逐步扩展的电网集合）中的交流最优潮流（AC OPF）问题。具体而言，我们针对不同网络中给定负荷向量及变长OPF解向量设计了弹性输入与输出层。该方法仅需单个统一DNN，即可处理不同规模和动态增加的节点、线路、负荷及DERs数量。在IEEE 57/118/300节点测试系统以及从73节点扩展至118节点的网络上的仿真结果表明，与现有基于DNN的求解方法相比，DeepOPF-U的性能更优。