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…
A Graph Neural Network with Auxiliary Task Learning for Missing PMU Data Reconstruction
Bo Li, Zijun Chen, Haiwang Zhong +2
In wide-area measurement systems (WAMS), phasor measurement unit (PMU) measurement is prone to data missingness due to hardware failures, communication delays, and cyber-attacks. E…
External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting
Haoran Li, Muhao Guo, Marija Ilic +2
Accurate residential load forecasting is critical for power system reliability with rising renewable integration and demand-side flexibility. However, most statistical and machine…
ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics
Haoran Li, Muhao Guo, Yang Weng +2
Non-stationary power system dynamics, influenced by renewable energy variability, evolving demand patterns, and climate change, are becoming increasingly complex. Accurately captur…
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…