DeepOPF-FT : One Deep Neural Network for Multiple AC-OPF Problems with Flexible Topology

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)964-967
Journal / PublicationIEEE Transactions on Power Systems
Issue number1
Online published7 Nov 2022
Publication statusPublished - Jan 2023


We propose DeepOPF-FT as an embedded training approach to design one deep neural network (DNN) for solving multiple AC-OPF problems with flexible topology and line admittances, addressing a critical limitation of learning-based OPF schemes. The idea is to embed the discrete topology representation into the continuous admittance space and train a DNN to learn the mapping from (load, admittance) to the corresponding OPF solution. We then employ the trained DNN to solve AC-OPF problems over any power network with the same bus, generation, and line capacity configurations but different topology and/or line admittances. Simulation results over IEEE 9-/57- bus and a synthetic 2000-bus test cases demonstrate the effectiveness of our design and highlight the training efficiency improvement of DeepOPF-FT over training one DNN for every combination of power network topology and line admittances.

Research Area(s)

  • Admittance, deep neural network, Network topology, Optimal power flow, Optimized production technology, Switches, Topology, Training, Voltage