134 citations · 352 across the 27 of their papers we have counts for
34 papers
Learning to Solve the AC Optimal Power Flow via a Lagrangian Approach
Ling Zhang, Baosen Zhang
Using deep neural networks to predict the solutions of AC optimal power flow (ACOPF) problems has been an active direction of research. However, because the ACOPF is nonconvex, it…
Decentralized Safe Reinforcement Learning for Voltage Control
Wenqi Cui, Jiayi Li, Baosen Zhang
Inverter-based distributed energy resources provide the possibility for fast time-scale voltage control by quickly adjusting their reactive power. The power-electronic interfaces a…
An Iterative Approach to Improving Solution Quality for AC Optimal Power Flow Problems
Ling Zhang, Baosen Zhang
The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, whic…
Lyapunov-Regularized Reinforcement Learning for Power System Transient Stability
Wenqi Cui, Baosen Zhang
Transient stability of power systems is becoming increasingly important because of the growing integration of renewable resources. These resources lead to a reduction in mechanical…
An Iterative Approach to Finding Global Solutions of AC Optimal Power Flow Problems
Ling Zhang, Baosen Zhang
The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, whic…
Consensus-Based Set-Theoretic Control in Power Systems
Daniel Tabas, Baosen Zhang
Set-theoretic control is a useful technique for dealing with the uncertainty introduced into power systems by renewable energy resources. Although set operations are computationall…