Cunzhi Zhao

PhD Student
2020.01 - 2023.12
Email
czhao29obfuscate@cougarnet.uh.edu
Google Scholar
LinkedIn
PhD Defense

Cunzhi Zhao has received his PhD degree in Electrical Engineering from the University of Houston, Houston, TX, USA in December 2023. Cunzhi had worked in the RPG lab as a PhD student from Jan. 2020 to Dec. 2023.

PhD Dissertation title: “Optimal Energy Management for Battery Energy Storage System-Integrated Microgrids”

First Job: Assistant Professor (tenure-track), McNeese State University, Lake Charles, Louisiana, USA.

Education

PhD, Electrical Engineering, University of Houston, 2020 - 2023

M.S., Electrical Engineering, McNeese State University, 2018

B.S., Electrical Engineering, McNeese State University, 2015

Publications at UH RPG Lab

  1. Cunzhi Zhao and Xingpeng Li,“A Novel Real-Time Energy Management Strategy for Grid-Friendly Microgrid: Harnessing Internal Fluctuation Internally”, 52nd North American Power Symposium, (Virtually), Tempe, AZ, USA Apr. 2021.
  2. Cunzhi Zhao and Xingpeng Li,“A Novel Real-Time Energy Management Strategy for Grid-Supporting Microgrid: Enabling Flexible Trading Power”, IEEE PES General Meeting, (Virtually), Washington D.C., USA, Jul. 2021.
  3. Praveen Dhanasekar, Cunzhi Zhao and Xingpeng Li, “Quantitative Analysis of Demand Response Using Thermostatically Controlled Loads”, IEEE PES Innovative Smart Grid Technology, Washington D.C., USA, Feb. 2022.
  4. Cunzhi Zhao and Xingpeng Li, “An Alternative Method for Solving Security-Constraint Unit Commitment with Neural Network Based Battery Degradation Model”, 54th North American Power Symposium, Salt Lake City, UT, USA, Oct. 2022.
  5. Cunzhi Zhao and Xingpeng Li, “A 100% Renewable Energy System: Enabling Zero CO2 Emission Offshore Platforms”, 54th North American Power Symposium, Salt Lake City, UT, USA, Oct. 2022.
  6. Cunzhi Zhao, Jesus Silva-Rodriguez and Xingpeng Li, “Resilient Operational Planning for Microgrids Against Extreme Events”, Hawaii International Conference on System Sciences, Maui, Hawaii, USA, Jan. 2023.
  7. Cunzhi Zhao and Xingpeng Li, “Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation”, IEEE Transactions on Power Systems, early access, Jan. 2023.
  8. Ali Siddique, Cunzhi Zhao, and Xingpeng Li, “Microgrid Optimal Energy Scheduling with Risk Analysis”, Texas Power and Energy Conference, College Station, TX, USA, Feb. 2023.
  9. Cunzhi Zhao, Xingpeng Li, and Yan Yao, “Quality Analysis of Battery Degradation Models with Real Battery Aging Experiment Data”, Texas Power and Energy Conference, College Station, TX, USA, Feb. 2023.
  10. Cunzhi Zhao and Xingpeng Li, “Hierarchical Deep Learning Model for Degradation Prediction per Look-Ahead Scheduled Battery Usage Profile”, arXiv:2303.03386, Mar. 2023.
  11. Cunzhi Zhao and Xingpeng Li, “Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model” arXiv:2309.08853, Sep. 2023.
  12. Cunzhi Zhao and Xingpeng Li, “Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Scheduling” arXiv:2310.01758, Oct. 2023.

Research Interests

Microgrid Energy Management Strategy, Battery Degradation, Power System Optimization and Smart grids.

Papers (with link)

Microgrid Optimal Energy Scheduling Considering Neural Network based Battery Degradation

Linearization of ReLU Activation Function for Neural Network-Embedded Optimization:Optimal Day-Ahead Energy Scheduling

Computational Enhancement for Day-Ahead Energy Scheduling with Sparse Neural Network-based Battery Degradation Model

Hierarchical Deep Learning Model for Degradation Prediction per Look-Ahead Scheduled Battery Usage Profile

Quality Analysis of Battery Degradation Models with Real Battery Aging Experiment Data

Microgrid Optimal Energy Scheduling with Risk Analysis

Resilient Operational Planning for Microgrids Against Extreme Events

A 100% Renewable Energy System: Enabling Zero CO2 Emission Offshore Platforms

An Alternative Method for Solving Security-Constrained Unit Commitment with Neural Network Based Battery Degradation Model

Quantitative Analysis of Demand Response Using Thermostatically Controlled Loads

A Novel Real-Time Energy Management Strategy for Grid-Supporting Microgrid: Enabling Flexible Trading Power

A Novel Real-Time Energy Management Strategy for Grid-Friendly Microgrid: Harnessing Internal Fluctuation Internally

Resources (with link)

Microgrid Optimal Energy Scheduling with Battery Degradation Neural Network in Python

Dataset and Matlab Simulator for Battery Aging Tests

Learning-ready Dataset and Python Codes for Training a Battery Degradation Neural Network Model

Optimal Sizing of Offshore Hybrid Renewable Energy Systems in Python

HVAC-enabled Demand Response Quantification in Matlab