no code implementations • 4 Mar 2024 • Patrick Salter, Qiuhua Huang, Paulo Cesar Tabares-Velasco
Residential buildings account for a significant portion (35\%) of the total electricity consumption in the U. S. as of 2022.
no code implementations • 21 Nov 2023 • Sayak Mukherjee, Ramij R. Hossain, Sheik M. Mohiuddin, YuAn Liu, Wei Du, Veronica Adetola, Rohit A. Jinsiwale, Qiuhua Huang, Tianzhixi Yin, Ankit Singhal
Improving system-level resiliency of networked microgrids is an important aspect with increased population of inverter-based resources (IBRs).
no code implementations • 8 Oct 2023 • Qiuhua Huang, Renke Huang, Tianzhixi Yin, Sohom Datta, Xueqing Sun, Jason Hou, Jie Tan, Wenhao Yu, YuAn Liu, Xinya Li, Bruce Palmer, Ang Li, Xinda Ke, Marianna Vaiman, Song Wang, Yousu Chen
Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56000 scenarios.
no code implementations • 17 Dec 2022 • Sayak Mukherjee, Ramij R. Hossain, YuAn Liu, Wei Du, Veronica Adetola, Sheik M. Mohiuddin, Qiuhua Huang, Tianzhixi Yin, Ankit Singhal
This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids.
no code implementations • 6 Dec 2022 • Ramij R. Hossain, Tianzhixi Yin, Yan Du, Renke Huang, Jie Tan, Wenhao Yu, YuAn Liu, Qiuhua Huang
We propose a novel model-based-DRL framework where a deep neural network (DNN)-based dynamic surrogate model, instead of a real-world power-grid or physics-based simulation, is utilized with the policy learning framework, making the process faster and sample efficient.
no code implementations • 2 Dec 2021 • Thanh Long Vu, Sayak Mukherjee, Renke Huang, Qiuhua Huang
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 usually trips a massive amount of load inefficiently.
no code implementations • 29 Nov 2021 • Yan Du, Qiuhua Huang, Renke Huang, Tianzhixi Yin, Jie Tan, Wenhao Yu, Xinya Li
Reinforcement learning methods have been developed for the same or similar challenging control problems, but they suffer from training inefficiency and lack of robustness for "corner or unseen" scenarios.
no code implementations • 21 Oct 2021 • Antoine Marot, Benjamin Donnot, Karim Chaouache, Adrian Kelly, Qiuhua Huang, Ramij-Raja Hossain, Jochen L. Cremer
We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence.
no code implementations • 29 Sep 2021 • Hao-Lun Hsu, Qiuhua Huang, Sehoon Ha
One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases.
no code implementations • 29 Jan 2021 • Sayak Mukherjee, Renke Huang, Qiuhua Huang, Thanh Long Vu, Tianzhixi Yin
We exploit the area-wise division structure of the power system to propose a hierarchical DRL design that can be scaled to the larger grid models.
no code implementations • 13 Jan 2021 • Renke Huang, Yujiao Chen, Tianzhixi Yin, Qiuhua Huang, Jie Tan, Wenhao Yu, Xinya Li, Ang Li, Yan Du
In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm.
no code implementations • 22 Jun 2020 • Renke Huang, Yujiao Chen, Tianzhixi Yin, Xinya Li, Ang Li, Jie Tan, Wenhao Yu, YuAn Liu, Qiuhua Huang
Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability.
1 code implementation • 9 Mar 2019 • Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, Zhenyu Huang
Power system emergency control is generally regarded as the last safety net for grid security and resiliency.