210 citations · 276 across the 16 of their papers we have counts for
16 papers
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
Ramij R. Hossain, Tianzhixi Yin, Yan Du +5
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Rece…
Safe Reinforcement Learning for Grid Voltage Control
Thanh Long Vu, Sayak Mukherjee, Renke Huang +1
Under voltage load shedding has been considered as a standard approach to recover the voltage stability of the electric power grid under emergency conditions, yet this scheme usual…
Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery
Yan Du, Qiuhua Huang, Renke Huang +4
In this work we propose a novel data-driven, real-time power system voltage control method based on the physics-informed guided meta evolutionary strategy (ES). The main objective…
Barrier Function-based Safe Reinforcement Learning for Emergency Control of Power Systems
Thanh Long Vu, Sayak Mukherjee, Renke Huang +1
Under voltage load shedding has been considered as a standard and effective measure to recover the voltage stability of the electric power grid under emergency and severe condition…
Quantifying Bounds of Model Gap for Synchronous Generators
Peng Wang, Shaobu Wang, Renke Huang +1
In practice, uncertainties in parameters and model structures always cause a gap between a model and the corresponding physical entity. Hence, to evaluate the performance of a mode…
Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
Sayak Mukherjee, Renke Huang, Qiuhua Huang +2
This paper presents a novel hierarchical deep reinforcement learning (DRL) based design for the voltage control of power grids. DRL agents are trained for fast, and adaptive select…
Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus,…
Safe Reinforcement Learning for Emergency LoadShedding of Power Systems
Thanh Long Vu, Sayak Mukherjee, Tim Yin +3
The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid's security and resilience. In particular, the increased uncertai…
Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control
Renke Huang, Yujiao Chen, Tianzhixi Yin +6
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability. With increased uncertainties and rapidly changing operati…
Optimal Future Sub-Transmission Volt-Var Planning Tool to Enable High PV Penetration
Quan Nguyen, Xinda Ke, Nader Samaan +9
This paper proposes a reactive power planning tool for sub-transmission systems to mitigate voltage violations and fluctuations caused by high photovoltaic (PV) penetration and int…
Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
Tong Ma, Renke Huang, David Barajas-Solano +2
We propose a new forecasting method for predicting load demand and generation scheduling. Accurate week-long forecasting of load demand and optimal power generation is critical for…
The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms
Sogol Babaeinejadsarookolaee, Adam Birchfield, Richard D. Christie +20
In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using t…
Parameters Calibration for Power Grid Stability Models using Deep Learning Methods
Renke Huang, Rui Fan, Tianzhixi Yin +2
This paper presents a novel parameter calibration approach for power system stability models using automatic data generation and advanced deep learning technology. A PMU-measuremen…
Adaptive Power System Emergency Control using Deep Reinforcement Learning
Qiuhua Huang, Renke Huang, Weituo Hao +3
Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based…
A Comparative Study of Interface Techniques for Transmission and Distribution Dynamic Co-Simulation
Qiuhua Huang, Renke Huang, Rui Fan +5
Transmission and distribution dynamic co-simulation is a practical and effective approach to leverage existing simulation tools for transmission and distribution systems to simulat…
A Reference Implementation of WECC Composite Load Model in Matlab and GridPACK
Qiuhua Huang, Renke Huang, Bruce J. Palmer +5
The composite load model (CLM) proposed by the Western Electricity Coordinating Council (WECC) is gaining increasing traction in industry, particularly in North America. At the sam…