DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow
  in Multiple Networks
Heng Liang Changhong Zhao
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.