Dr. Mingjian Tuo has received his PhD degree in Electrical Engineering from the University of Houston, Houston, TX, USA in August 2023. Mingjian worked in the RPG lab as a PhD student from Aug. 2019 to Aug. 2023.

PhD Dissertation title: “Enhancing Frequency Stability of Low-Inertia Grids with Novel Security Constrained Unit Commitment Approaches”

First Job: Associate professor-equivalent tenured faculty, Department of Electrical and Information Engineering, Hubei University of Automotive Technology, Hubei, China.

Education

PhD, Electrical Engineering, University of Houston, TX, USA 2019-2023

Master of Science in Computer Application Technology, National Engineering Research Center for E-Learning, CCNU, Wuhan, China 2015-2018

Bachelor of Science in Electrical Engineering and Automation, Huazhong University of Science and Technology, Wuhan, China, 2011-2015

Publications at UH RPG Lab

1. Mingjian Tuo and Xingpeng Li, “Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Grids” in IEEE Transactions on Power Systems, vol. 38, no. 5, pp. 4134-4147, Sep. 2023.
2. Mingjian Tuo, Fan Jiang, Xingpeng Li, and Pascal Van Hentenryck, “Inertia-Constrained Generation Scheduling: Sample Selection, Learning-Embedded Optimization Modeling, and Computing Enhancement”, IEEE Transactions on Power Systems, online early access, Dec. 2025.
3. Mingjian Tuo and Xingpeng Li, “Machine Learning Assisted Inertia Estimation using Ambient Measurements”, IEEE Transactions on Industry Applications, vol. 59, no. 4, pp. 4893-4903, July/August 2023.
4. Mingjian Tuo, Xingpeng Li, and Pascal Van Hentenryck, “Machine Learning-assisted Dynamics-Constrained Day-Ahead Energy Scheduling”, Electric Power Systems Research, vol. 252, Jan. 2026.
5. Vasudharini Sridharan, Mingjian Tuo and Xingpeng Li, “Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model”, Energies, 15(20), 7606, Oct. 2022.
6. Mingjian Tuo and Xingpeng Li, “Long-term Recurrent Convolutional Networks-based Inertia Estimation using Ambient Measurements,” 2022 IEEE IAS Annual Meeting, Detroit, MI, USA, Oct. 2022.
7. Mingjian Tuo and Xingpeng Li, “Optimal Allocation of Virtual Inertia Devices for Enhancing Frequency Stability in Low-Inertia Power Systems”, 53rd North American Power Symposium (NAPS), Nov. 2021.
8. Mingjian Tuo and Xingpeng Li, “Dynamic Estimation of Power System Inertia Distribution Using Synchrophasor Measurements”, 2020 52nd North American Power Symposium (NAPS), Apr. 2021.
9. Mingjian Tuo, Arun Venkatesh Ramesh and Xingpeng Li, “Benefits and Cyber-Vulnerability of Demand Response System in Real-Time Grid Operations,” 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Oct. 2020.
10. Mingjian Tuo, Xingpeng Li, and Tianxia Zhao, “Graph Neural Network-based Power Flow Model”, 55th North American Power Symposium, Asheville, NC, USA, Oct. 2023.
11. Mingjian Tuo and Xingpeng Li, “Deep Learning based Security-Constrained Unit Commitment Considering Locational Frequency Stability in Low-Inertia Power Systems”, 54th North American Power Symposium, Salt Lake City, UT, USA, Oct. 2022.
12. Mingjian Tuo and Xingpeng Li, “Selectively Linearized Neural Network based RoCoF-Constrained Unit Commitment in Low-Inertia Power Systems”, 55th North American Power Symposium, Asheville, NC, USA, Oct. 2023.
13. Elias Raffoul, Mingjian Tuo, Cunzhi Zhao, Tianxia Zhao, Meng Ling, and Xingpeng Li, “Comparative Analysis of Machine Learning Models for Short-Term Distribution System Load Forecasting”, IEEE Electrical Power and Energy Conference (EPEC), Waterloo, ON, Canada, Oct. 2025.
14. Jonathan Yang, Mingjian Tuo, Jin Lu and Xingpeng Li, “Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting”, Texas Power and Energy Conference, College Station, TX, USA, Feb. 2024.

Classes:

• ECE 6379 Power System Operation and Modeling
• ECE 6327 Smart Grid Systems
• MATH 6366 Optimization Theory
• ECE 6343 Renewable Energy
• ECE 6397 Machine Learning and Computer Vision
• ECE 6397 State-Space Estimation with Physical Application

Research Interests:

Power systems operations and optimization, Power system stability and control, Deep learning for data analysis in power system, System dynamic modeling.