Overview
Personal Profile
Mingzhi Zhang is a dedicated power system researcher specializing in electric vehicle (EV) grid integration, distribution system modeling and control, distributed energy resources, and flexibility/resilience of the power system. He has developed various smart charging management strategies for large-scale EV integration and excels at creating innovative tools that connect mobility and grid analysis. He has worked with several utility companies to tackle EV integration challenges and accelerate transportation electrification.
Research Interests
Electric vehicle grid integration
Distributed energy resources
Flexibility/resilience of the power system
Machine learning/reinforcement learning
Education/Academic Qualification
PhD, Electrical Engineering, North Carolina State University
Master, Electrical Engineering, North China Electric Power University
Bachelor, Electrical Engineering, North China Electric Power University
Master, Electrical Engineering, University of Wisconsin-Milwaukee
Fingerprint
- 1 Similar Profiles
Collaborations and Top Research Areas From the Past 5 Years
-
Analyzing SCM Grid Benefits from Electric Transportation
Bennett, J. & Zhang, M., 2025, 15 p.Research output: NLR › Presentation
-
Distribution Service Transformer Loading Analysis with EV Grid Integration
Zhang, M., Liu, Z., Panossian, N., Ucer, E., Pohl, E. & Kisacikoglu, M. J., 2025. 5 p.Research output: Contribution to conference › Paper
1 Scopus Citations -
Evaluating Load Coincidence and Distribution Grid Impacts of Residential EV Charging
Pohl, E., Ghosh, S., Paudyal, P., Zhang, M. & Kisacikoglu, M. J., 2025. 5 p.Research output: Contribution to conference › Paper
1 Scopus Citations -
Exploring the Use of Autonomous Unmanned Vehicles for Supporting Power Grid Operations
Zhou, Y., Feng, C., Zhang, M. & Yang, R., 2025. 5 p.Research output: Contribution to conference › Paper
-
High-Fidelity Analysis of EV Integration on Real Utility Feeders in Colorado
Ghosh, S., Jackson, D., Kisacikoglu, M. J., Liu, Z., Panossian, N., Paudyal, P., Pohl, E., Ucer, E. & Zhang, M., 2025, 37 p.Research output: NLR › Technical Report