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

DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks