116 citations · 283 across the 10 of their papers we have counts for
4 papers · 1 filter
DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
Tianyu Zhao, Xiang Pan, Minghua Chen +2
Deep Neural Networks (DNNs) approaches for the Optimal Power Flow (OPF) problem received considerable attention recently. A key challenge of these approaches lies in ensuring the f…
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
Andreas Venzke, Guannan Qu, Steven Low +1
This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems a…
Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow
Ilgiz Murzakhanov, Andreas Venzke, George S. Misyris +1
This paper introduces a framework to capture previously intractable optimization constraints and transform them to a mixed-integer linear program, through the use of neural network…
Second-Order Cone Relaxations of the Optimal Power Flow for Active Distribution Grids
Lucien Bobo, Andreas Venzke, Spyros Chatzivasileiadis
Convex relaxations of the AC Optimal Power Flow (OPF) problem are essential not only for identifying the globally optimal solution but also for enabling the use of OPF formulations…