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 • 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 • 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.