DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks
This addresses the problem of adapting OPF solutions to dynamic power grids with plug-and-play distributed energy resources for power system operators, though it is incremental as it builds on existing DNN-based methods.
The paper tackles the lack of generalizability in machine learning models for solving AC optimal power flow (OPF) across varying power networks by proposing DeepOPF-U, a unified deep neural network that handles different and growing network topologies, achieving improved performance in simulations on IEEE test systems and a growing network.
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.