Kevan Li

Chenzhe (Kevan) Li

Research student
Master of Industrial Systems Engineering and Management, National University of Singapore, Bachelor of Industrial Engineering, Nanjing University of Aeronautics and Astronautics

Kevan is a PhD candidate at the ANU Centre for Energy Systems working on ....His supervisors are...

Research interests

 

Energy system modeling
Energy policy and economics
Hydrogen certification
Green hydrogen production

Awards and honours

 

1. ANU PhD Research Scholarship, July 2023
2. Innovation Award of of Nanjing University of Aeronautics and Astronautics, Dec 2021
3. Runner-up in the Undergraduate Academic Forum, Dec 2021
4. Second Prize of China College Student 'Internet Plus' Innovation and Entrepreneurship Competition in Jiangsu Province, Oct 2020

Publications

 

Chengzhe Li, Libo Zhang, Qunwei Wang, Dequn Zhou. Towards low-carbon steel: System dynamics simulation of policies impact on green hydrogen steelmaking in China and the European Union. Energy Policy, 2024,
188:114073. -IF:9
Link: https://doi.org/10.1016/j.enpol.2024.114073
- Construct a system dynamics model to simulate the development of green hydrogen steelmaking in China and EU under different carbon mitigation scenarios. 
- Evaluate the contribution of green hydrogen steel to the steel industry's carbon mitigation and provide the policy implications.

Chengzhe Li, Libo Zhang, Zihan Ou, Jiayu Ma. Using system dynamics to evaluate the impact of government
subsidy policies on the green hydrogen industry in China. Energy policy, 2022, 165:112981. -IF:9
Link: https://doi.org/10.1016/j.enpol.2022.112981
- Establish a system dynamics model to simulate the impact of the government subsidy policies on the
development of the green hydrogen industry in the next 30 years and give subsidy policies advice.
- Calculate the changes in the installed capacity, return on investment and carbon emission reduction benefits of green hydrogen under different subsidy scenarios.

Chengzhe Li, Libo Zhang, Zihan Ou, Qunwei Wang, Dequn Zhou. Robust model of electric vehicle charging station location considering renewable energy and storage equipment. Energy, 2022, 238: 121713. -IF:9
Link: https://doi.org/10.1016/j.energy.2021.121713
- Establish a robust optimization model for the location of electric vehicle charging stations with distributed
renewable energy and storage equipment. 
- Evaluate the impact of uncertainty of renewable energy output and charging demand on the grid.