6 papers
Safe Reinforcement Learning with Preference-based Constraint Inference
Chenglin Li, Grant Ruan, Hua Geng
Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to exp…
Decentralized Analysis Approach for Oscillation Damping in Grid-Forming and Grid-Following Heterogeneous Power Systems
Xiang Zhu, Xiuqiang He, Hongyang Qing +1
This letter proposes a decentralized local gain condition (LGC) to guarantee oscillation damping in inverter-based resource (IBR)-dominated power systems. The LGC constrains the dy…
Aggregating Inverter-Based Resources for Fast Frequency Response: A Nash Bargaining Game-Based Approach
Xiang Zhu, Hua Geng, Hongyang Qing +1
This paper proposes a multi-objective optimization (MOO) approach for grid-level frequency regulation by aggregating inverter-based resources (IBRs). Virtual power plants (VPPs), a…
Dynamic Virtual Power Plants with Robust Frequency Regulation Capability
Xiang Zhu, Hua Geng, Hongyang Qing +3
The rapid integration of inverter-based resources (IBRs) into power systems has identified frequency security challenges due to reduced inertia and increased load volatility. This…
Optimal Frequency Support from Virtual Power Plants: Minimal Reserve and Allocation
Xiang Zhu, Guangchun Ruan, Hua Geng
This paper proposes a novel reserve-minimizing and allocation strategy for virtual power plants (VPPs) to deliver optimal frequency support. The proposed strategy enables VPPs, act…
Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learning
Chenglin Li, Guangchun Ruan, Hua Geng
Safe reinforcement learning (RL) is a popular and versatile paradigm to learn reward-maximizing policies with safety guarantees. Previous works tend to express the safety constrain…