57 citations · 127 across the 16 of their papers we have counts for
17 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…
Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang +3
Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address…
A Projection-Based Approach for Distributed Energy Resources Aggregation
Yiran Wang, Haiwang Zhong, Guangchun Ruan
Aggregating distributed energy resources (DERs) is of great significance to improve the overall operational efficiency of smart grid. The aggregation model needs to consider variou…
Life cycle economic viability analysis of battery storage in electricity market
Yinguo Yang, Yiling Ye, Zhuoxiao Cheng +4
Battery storage is essential to enhance the flexibility and reliability of electric power systems by providing auxiliary services and load shifting. Storage owners typically gains…
Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach
Xinyue Wang, Haiwang Zhong, Guanglun Zhang +3
With the increasing proportion of renewable energy in the generation side, it becomes more difficult to accurately predict the power generation and adapt to the large deviations be…
Low-Carbon Economic Dispatch of Bulk Power Systems Using Nash Bargaining Game
Xuyang Li, Guangchun Ruan, Haiwang Zhong
Decarbonization of power systems plays a crucial role in achieving carbon neutral goals across the globe, but there exists a sharp contradiction between the emission reduction and…
Transmission Congestion Management with Generalized Generation Shift Distribution Factors
Shutong Pu, Guangchun Ruan, Xinfei Yan +1
A major concern in modern power systems is that the popularity and fluctuating characteristics of renewable energy may cause more and more transmission congestion events. Tradition…
Evaluation of Look-ahead Economic Dispatch Using Reinforcement Learning
Zekuan Yu, Guangchun Ruan, Xinyue Wang +3
Modern power systems are experiencing a variety of challenges driven by renewable energy, which calls for developing novel dispatch methods such as reinforcement learning (RL). Eva…
Short-Term Electricity Price Forecasting based on Graph Convolution Network and Attention Mechanism
Yuyun Yang, Zhenfei Tan, Haitao Yang +2
In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential tr…
Quantitative Assessment of U.S. Bulk Power Systems and Market Operations during COVID-19
Guangchun Ruan, Jiahan Wu, Haiwang Zhong +2
Starting in early 2020, the novel coronavirus disease (COVID-19) severely affected the U.S., causing substantial changes in the operations of bulk power systems and electricity mar…
Review of Learning-Assisted Power System Optimization
Guangchun Ruan, Haiwang Zhong, Guanglun Zhang +3
With dramatic breakthroughs in recent years, machine learning is showing great potential to upgrade the toolbox for power system optimization. Understanding the strength and limita…
A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector
Guangchun Ruan, Dongqi Wu, Xiangtian Zheng +5
The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late March. As the U.S. begins to…