184 citations · 971 across the 87 of their papers we have counts for
91 papers
Structured Neural-PI Control with End-to-End Stability and Output Tracking Guarantees
Wenqi Cui, Yan Jiang, Baosen Zhang +1
We study the optimal control of multiple-input and multiple-output dynamical systems via the design of neural network-based controllers with stability and output tracking guarantee…
Leveraging Predictions in Power System Frequency Control: an Adaptive Approach
Wenqi Cui, Guanya Shi, Yuanyuan Shi +1
Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controller…
Equilibria of Fully Decentralized Learning in Networked Systems
Yan Jiang, Wenqi Cui, Baosen Zhang +1
Existing settings of decentralized learning either require players to have full information or the system to have certain special structure that may be hard to check and hinder the…
An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guarantees
Ling Zhang, Daniel Tabas, Baosen Zhang
In this paper, we consider the scenario-based two-stage stochastic DC optimal power flow (OPF) problem for optimal and reliable dispatch when the load is facing uncertainty. Althou…
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks
Jochen Stiasny, Baosen Zhang, Spyros Chatzivasileiadis
The dynamic behaviour of a power system can be described by a system of differential-algebraic equations. Time-domain simulations are used to simulate the evolution of these dynami…
Efficient Reinforcement Learning Through Trajectory Generation
Wenqi Cui, Linbin Huang, Weiwei Yang +1
A key barrier to using reinforcement learning (RL) in many real-world applications is the requirement of a large number of system interactions to learn a good control policy. Off-p…
Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning
Daniel Tabas, Ahmed S. Zamzam, Baosen Zhang
Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swa…
Optimal Inverter-Based Resources Placement in Low-Inertia Power Systems
Atinuke Ademola-Idowu, Baosen Zhang
An increase in the integration of Inverter Based Resources (IBRs) to the electric grid, will lead to a corresponding decrease in the amount of connected synchronous generators, res…
Cyber-Physical Attack Leveraging Subsynchronous Resonance
Bosong Li, Baosen Zhang, Daniel S. Kirschen
This paper discusses how a cyber attack could take advantage of torsional resonances in the shaft of turbo-generators to inflict severe physical damage to a power system. If attack…
Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees
Wenqi Cui, Yan Jiang, Baosen Zhang +1
We study the control of networked systems with the goal of optimizing both transient and steady-state performances while providing stability guarantees. Linear proportional-integra…
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach
Yan Jiang, Wenqi Cui, Baosen Zhang +1
Frequency control plays a pivotal role in reliable power system operations. It is conventionally performed in a hierarchical way that first rapidly stabilizes the frequency deviati…
Safe and Efficient Model Predictive Control Using Neural Networks: An Interior Point Approach
Daniel Tabas, Baosen Zhang
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but suffers from the computational burden of repeatedly solving an optimization pro…
Equilibrium-Independent Stability Analysis for Distribution Systems with Lossy Transmission Lines
Wenqi Cui, Baosen Zhang
Power distribution systems are becoming much more active with increased penetration of distributed energy resources. Because of the intermittent nature of these resources, the stab…
Data-Driven Modeling of Aggregate Flexibility under Uncertain and Non-Convex Load Models
Sina Taheri, Vassilis Kekatos, Harsha Veeramachaneni +1
Bundling a large number of distributed energy resources through a load aggregator has been advocated as an effective means to integrate such resources into whole-sale energy market…
State-of-Charge Aware EV Charging
Yize Chen, Baosen Zhang
Recent proliferation in electric vehicles (EVs) are posing profound impacts over the operation of electrical grids. In particular, due to the physical constraints on charging stati…
A Frequency Domain Approach to Predict Power System Transients
Wenqi Cui, Weiwei Yang, Baosen Zhang
The dynamics of power grids are governed by a large number of nonlinear differential and algebraic equations (DAEs). To safely operate the system, operators need to check that the…
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…
Computationally Efficient Safe Reinforcement Learning for Power Systems
Daniel Tabas, Baosen Zhang
We propose a computationally efficient approach to safe reinforcement learning (RL) for frequency regulation in power systems with high levels of variable renewable energy resource…
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…
A Convex Neural Network Solver for DCOPF with Generalization Guarantees
Ling Zhang, Yize Chen, Baosen Zhang
The DC optimal power flow (DCOPF) problem is a fundamental problem in power systems operations and planning. With high penetration of uncertain renewable resources in power systems…