2015    Electrical and Computer Engineering，Georgia Institute of Technology    Ph.D.

2009    Power System and Automation，SJTU    M.S.

2006    Electrical Engineering and Automation，SJTU    B.S.

2022 - Pres.    Global Institute of Future Tech., SJTU    Tenure-Track Associate Professor

2020 - 2022    Pacific Northwest National Laboratory, U.S.A     Staff Electrical Engineer

2018 - 2020    Pacific Northwest National Laboratory, U.S.A    Senior Electrical Engineer

2015 - 2018    Pacific Northwest National Laboratory, U.S.A    Electrical Engineer

2009 - 2015    Georgia Institute of Technology, U.S.A    Research Assistant

• Ultra-real-time transient simulation of large-scale power grid based on high-performance parallel computing

• Intelligent control of power grid with high penetration rate of new energy and energy storage equipment based on deep reinforcement learning and federated learning

• Grid situational awareness and distributed control of new energy and energy storage equipment with high penetration rate based on digital twin technology

• Renke Huang, George. Cokkinides, and A.P. Meliopoulos, “Distribution System Distributed Quasi-Dynamic State Estimator”, IEEE Transactions on Smart Grid, Vol.7, no. 6 (2016): 2761-2770.

• Renke Huang, Ruisheng Diao, Yuanyuan. Li, et al., Calibrating parameters of power system stability models using advanced ensemble Kalman filter, IEEE Trans. on Power Systems, Vol. 33, no. 3 (2018): 2895-2905.

• Renke Huang, Yujiao Chen, Tianzhixi Yin, et al., Accelerated Derivative-free Deep Reinforcement Learning for Large-scale Grid Emergency Voltage Control, IEEE Trans. on Power Systems, Vol. 37, no. 1 (2022): 14-25.

• Renke Huang, Yujiao Chen, Tianzhixi Yin, et al., Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning, IEEE Trans. on Power Systems, accepted and early access, 2022.

• Renke Huang, Wei Gao, Rui Fan, Qiuhua Huang, A Guided Evolutionary Strategy Based Static Var Compensator Control Approach for Inter-area Oscillation Damping, IEEE Trans. on Industrial Informatics, Accepted in 2022 and Early Access.

• Qiuhua Huang, Renke Huang, Weituo Hao, et al., Adaptive power system emergency control using deep reinforcement learning, IEEE Trans. on Smart Grid, Vol. 11, no. 2 (2019): 1171-1182.

• Shaobu Wang, Renke Huang, Zhenyu Huang, et al., A Robust Dynamic State Estimation Approach Against Model Errors Caused by Load Changes, IEEE Trans. on Power Systems, Vol. 35, no. 6 (2020): 4518-4527

• Ramiji Hossin, Qiuhua Huang, and Renke Huang, “Graph Convolutional Network-Based Topology Embedded Deep Reinforcement Learning for Voltage Stability Control”, IEEE Trans. on Power Systems, Vol. 36, no. 5 (2021): 4848-4851.

• Shaobu Wang, Renke Huang, Ning Zhou, et al., Test for Non-synchronized Errors of State Estimation using Real Data, IEEE Trans. on Power Systems, accepted and early access, 2022.

• Rui Fan, Renke Huang, Shaobu Wang, Junbo Zhao, Wavelet and Deep-Learning- Based Approach for Generation System Problematic Parameters Identification and Calibration, IEEE Trans. on Power Systems, accepted and early access, 2022.

Senior member of the International Institute of Electrical and Electronics Engineers (IEEE).

Member of the U.S. Department of Energy's Science and Technology Program Review Committee

Reviewer of IEEE Transactions on Power Delivery, IEEE Transactions on Smart Grid, and IEEE Transactions on Power System

Member of the North American Western Large Grid Model Verification Committee

Member of the Synchrophasor Information Committee of the Great Power Grid of North America

Member of the Model Development Committee of the North American Electric Reliability Council

Fundamentals of electrical engineering