27 citations · 204 across the 36 of their papers we have counts for
36 papers
Online learning for robust voltage control under uncertain grid topology
Christopher Yeh, Jing Yu, Yuanyuan Shi +1
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is challenging…
Predicting Strategic Energy Storage Behaviors
Yuexin Bian, Ningkun Zheng, Yang Zheng +2
Energy storage are strategic participants in electricity markets to arbitrage price differences. Future power system operators must understand and predict strategic storage arbitra…
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…
Optimal Vehicle Charging in Bilevel Power-Traffic Networks via Charging Demand Function
Yufan Zhang, Sujit Dey, Yuanyuan Shi
Electric vehicle (EV) charging couples the operation of power and traffic networks. Specifically, the power network determines the charging price at various locations, while EVs on…
Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow
Jie Feng, Wenqi Cui, Jorge Cortés +1
Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders the…
Machine Learning Accelerated PDE Backstepping Observers
Yuanyuan Shi, Zongyi Li, Huan Yu +3
State estimation is important for a variety of tasks, from forecasting to substituting for unmeasured states in feedback controllers. Performing real-time state estimation for PDEs…
BEAR: Physics-Principled Building Environment for Control and Reinforcement Learning
Chi Zhang, Yuanyuan Shi, Yize Chen
Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lac…
Energy Storage Price Arbitrage via Opportunity Value Function Prediction
Ningkun Zheng, Xiaoxiang Liu, Bolun Xu +1
This paper proposes a novel energy storage price arbitrage algorithm combining supervised learning with dynamic programming. The proposed approach uses a neural network to directly…
Carbon-Aware EV Charging
Kai-Wen Cheng, Yuexin Bian, Yuanyuan Shi +1
This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective…
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage Control
Jie Feng, Yuanyuan Shi, Guannan Qu +3
Deep reinforcement learning has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power sys…
Robust Online Voltage Control with an Unknown Grid Topology
Christopher Yeh, Jing Yu, Yuanyuan Shi +1
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability. However, accurate topology identification is a challengin…
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
Sahin Lale, Yuanyuan Shi, Guannan Qu +3
Learning a dynamical system requires stabilizing the unknown dynamics to avoid state blow-ups. However, current reinforcement learning (RL) methods lack stabilization guarantees, w…
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 and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles
Songyang Han, He Wang, Sanbao Su +2
With the development of sensing and communication technologies in networked cyber-physical systems (CPSs), multi-agent reinforcement learning (MARL)-based methodologies are integra…
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
Kevin Huang, Sahin Lale, Ugo Rosolia +2
Current state-of-the-art model-based reinforcement learning algorithms use trajectory sampling methods, such as the Cross-Entropy Method (CEM), for planning in continuous control s…
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Yize Chen, Yuanyuan Shi, Daniel Arnold +1
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids. Faced with…
Polymatrix Competitive Gradient Descent
Jeffrey Ma, Alistair Letcher, Florian Schäfer +2
Many economic games and machine learning approaches can be cast as competitive optimization problems where multiple agents are minimizing their respective objective function, which…
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Yujia Huang, Huan Zhang, Yuanyuan Shi +2
Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by…
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training
Alexander Pan, Yongkyun Lee, Huan Zhang +2
Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges. Data-driven decision-makin…
Understanding the Safety Requirements for Learning-based Power Systems Operations
Yize Chen, Daniel Arnold, Yuanyuan Shi +1
Recent advancements in machine learning and reinforcement learning have brought increased attention to their applicability in a range of decision-making tasks in the operations of…
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Yuanyuan Shi, Guannan Qu, Steven Low +2
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world powe…
End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning
Yuanyuan Shi, Bolun Xu
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net dem…
Stable Online Control of Linear Time-Varying Systems
Guannan Qu, Yuanyuan Shi, Sahin Lale +2
Linear time-varying (LTV) systems are widely used for modeling real-world dynamical systems due to their generality and simplicity. Providing stability guarantees for LTV systems i…