Biography
Jin Zhao is an Assistant Professor at Trinity College Dublin. Her research interests include Resilient Energy System, Electricity, Operation of Highly Renewable Energy Integrated Systems, Microgrids and Machine Learning. She is the Alexander von Humboldt Fellow of Germany. She was a Research Scientist at The University of Tennessee (UTK). She received the B.E. and Ph.D. degrees from Shandong University, Jinan, China, all in the electrical engineering, in 2015 and 2020, respectively. She is a Subject Editor of IET Generation, Transmission & Distribution, and a regular reviewer for several IEEE and Nature Portfolio journals. She is the chair of IEEE Task Force AISR and PES representative of IEEE DataPort. She was an outstanding reviewer of several IEEE Trans. journals. Personal website for more information: https://sites.google.com/view/jin-zhao
Publications and Further Research Outputs
- Abhishek Duttagupta, Jin Zhao, Shanker Shreejith, Exploring Lightweight Federated Learning for Distributed Load Forecasting, IEEE SmartGridComm 2023 Conference, Glasgow, UK, 31/10/2023, 2023Conference Paper, 2023, TARA - Full Text
- J. Zhao, F. Li, H. Sun, Q. Zhang and H. Shuai, Self-Attention Generative Adversarial Network Enhanced Learning Method for Resilient Defense of Networked Microgrids Against Sequential Events, IEEE Transactions on Power Systems, 2023Journal Article, 2023
- J. Zhao, F. Li, S. Mukherjee and C. Sticht, Deep Reinforcement Learning-Based Model-Free On-Line Dynamic Multi-Microgrid Formation to Enhance Resilience, IEEE Transactions on Smart Grid, 2022Journal Article, 2022
- Qiwei Zhang, Fangxing Li, Xin Fang, Jin Zhao, Implications of Electricity and Gas Price Coupling in US New England Region, IScience, 27, (1), 2024, p1 - 11Journal Article, 2024
- X Hu, J Yang, Y Gao, M Zhu, Q Zhang, H Chen, J Zhao, Adaptive power flow analysis for power system operation based on graph deep learning, International Journal of Electrical Power & Energy Systems, 2024Journal Article, 2024
- J Zhao, F Li, Q Zhang, Impacts of renewable energy resources on the weather vulnerability of power systems, Nature Energy, 2024, p8Journal Article, 2024
Research Expertise
Power system resilience, high renewable energy integration, microgrids, low-carbon grids, deep learning & deep reinforcement learning.
Electrical engineering, Mathematical Sciences, Electrical engineering, electronic engineering, information engineering, Artificial intelligence and machine learning,
Recognition
- IEEE Member, IEEE PES member.
- Alexander von Humboldt Fellow
- Subject Editor of IET Generation, Transmission & Distribution
- Steering Committee (PES rep) of IEEE DataPort (https://ieee-dataport.org/)
- Chair of IEEE TF AISR (https://cmte.ieee.org/pes-rsei/)