Yuheng Zhang

Room 2044, Xinao Building

Peking University, 5 Yiheyuan Road

Beijing 100871, P.R. China

I am a Ph.D candidate in the Department of Industrial Engineering and Management at Peking University, advised by Prof. Guannan He. I received my B.S. degree in Department of Energy and Resources Engineering at Peking University in 2022. In the past years’ study, I had a deep understanding of environmental and energy systems in a high level and felt the importance of such integrated systems in the carbon neutrality context and sustainable development.

Research Interests

My research interests include low-carbon energy systems, optimization, machine learning, and climate change. Currently I’m researching on novel energy systems in the carbon neutrality context. Recent research concerns zero-emission energy systems for hard-to-abate sectors including maritime shipping and heavy-duty truck sectors in the transportation industry. If you feel interested in my research, please feel free to contact me via emial.

Hobbies

My hobbies include reading books and watching classic movies. Recently I’m attracted to dream pops. Regarding sports, billiards and tennis are my favorites.

Selected Publications

2022

A model-adaptive clustering-based time aggregation method for low-carbon energy system optimization

Yuheng Zhang, Vivian Cheng, Dharik S Mallapragada, and 2 more authors

IEEE Transactions on Sustainable Energy, 2022

Abs Code

Intermittent renewable energy resources like wind and solar introduce uncertainty across multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find the least-cost solution that manages the multi-timescale variability using an optimal portfolio of flexible resources. However, input data that capture such multi-timescale uncertainty are characterized with a long time horizon and high resolution, which brings great difficulty to solving the optimization model. Here we propose a model-adaptive time aggregation method based on clustering to alleviate the computational complexity, in which the energy system is solved over selected representative time periods instead of the full time horizon. The proposed clustering method is adaptive to various energy system optimization models or settings, because it extracts features from the optimization models to inform the clustering process. Results show that the proposed adaptive method can significantly lower the error in approximating the solution of the optimization model with the full time horizon, compared to traditional time aggregation methods.