116 citations · 283 across the 10 of their papers we have counts for
13 papers
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment
Rahul Nellikkath, Andreas Venzke, Mohammad Kazem Bakhshizadeh +2
A significant increase in renewable energy production is necessary to achieve the UN's net-zero emission targets for 2050. Using power-electronic controllers, such as Phase Locked…
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…
Physics-Informed Neural Networks for Power Systems
George S. Misyris, Andreas Venzke, Spyros Chatzivasileiadis
This paper introduces for the first time, to our knowledge, a framework for physics-informed neural networks in power system applications. Exploiting the underlying physical laws g…
Efficient Creation of Datasets for Data-Driven Power System Applications
Andreas Venzke, Daniel K. Molzahn, Spyros Chatzivasileiadis
Advances in data-driven methods have sparked renewed interest for applications in power systems. Creating datasets for successful application of these methods has proven to be very…
Verification of Neural Network Behaviour: Formal Guarantees for Power System Applications
Andreas Venzke, Spyros Chatzivasileiadis
This paper presents for the first time, to our knowledge, a framework for verifying neural network behavior in power system applications. Up to this moment, neural networks have be…
Inexact Convex Relaxations for AC Optimal Power Flow: Towards AC Feasibility
Andreas Venzke, Spyros Chatzivasileiadis, Daniel K. Molzahn
Convex relaxations of AC optimal power flow (AC-OPF) problems have attracted significant interest as in several instances they provably yield the global optimum to the original non…
Chance-Constrained AC Optimal Power Flow Integrating HVDC Lines and Controllability
Andreas Venzke, Lejla Halilbasic, Adelie Barre +2
The integration of large-scale renewable generation has major implications on the operation of power systems, two of which we address in this work. First, system operators have to…
Efficient Database Generation for Data-driven Security Assessment of Power Systems
Florian Thams, Andreas Venzke, Robert Eriksson +1
Power system security assessment methods require large datasets of operating points to train or test their performance. As historical data often contain limited number of abnormal…
Convex Relaxations of Probabilistic AC Optimal Power Flow for Interconnected AC and HVDC Grids
Andreas Venzke, Spyros Chatzivasileiadis
High Voltage Direct Current (HVDC) systems interconnect AC grids to increase reliability, connect offshore wind generation, and enable coupling of electricity markets. Considering…
Convex Relaxations of Chance Constrained AC Optimal Power Flow
Andreas Venzke, Lejla Halilbasic, Uros Markovic +2
High penetration of renewable energy sources and the increasing share of stochastic loads require the explicit representation of uncertainty in tools such as the optimal power flow…