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Dr. Jin Zhao
Assistant Professor (Electronic & Elect. Engineering)
Assistant Professor (Electronic & Elect. Engineering)
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 was outstanding reviewers of IEEE trans. journals. She is an subject editor of IET GT&D, and she is the chair of IEEE Task Force AISR. Personal website for more information: https://sites.google.com/view/jin-zhao
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Chair of IEEE TF AISR (https://cmte.ieee.org/pes-rsei/)
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Steering Committee (PES rep) of IEEE DataPort (https://ieee-dataport.org/)
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Subject Editor of IET Generation, Transmission & Distribution
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Chinese
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English
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| IEEE Member, IEEE PES member. |
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| Alexander von Humboldt Fellow |
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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 - 11,
Notes: [https://doi.org/10.1016/j.isci],
Journal Article,
PUBLISHED
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Abhishek Duttagupta, Jin Zhao, Shanker Shreejith, Exploring Lightweight Federated Learning for Distributed Load Forecasting, IEEE SmartGridComm 2023 Conference, Glasgow, UK, 31/10/2023, 2023,
Conference Paper,
IN_PRESS
TARA - Full Text
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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, 2023,
Journal Article,
PUBLISHED
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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, 2022,
Journal Article,
PUBLISHED
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Power system resilience, high renewable energy integration, microgrids, low-carbon grids, deep learning & deep reinforcement learning.
