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Dr. Chen Yongbao Nominated by Eni Award 2023

November 3, 2022

Recently, Chen Yongbao, a postgraduate from School of Energy and Power Engineering was nominated by Eni Award Committee and became the candidate for the award, 2023.
Eni Award’s story begins in 2007, when it became part of Eni's technological master plan, replacing the Eni-Italgas award for improving use of energy sources, promoting science and technology with environmental applications, and making the most of the new generations of researchers. It has ranked among 3 top awards (others are Fields Award, A.M Turing Award) in the field of natural science, A.M Turing Award. The members of the committee come from world-class universities such as MIT, Standford University, Cambridge University. Those who were nominated by the science committee can apply for the award.
The relative research achievement “Multi-objective residential load scheduling approach for demand response in smart grid” was published by Chen Yongbao in Sustainable Cities and Society (IF:10.696). Chen Zhe was the first author; USST was the first unit; He Ruikai, Tongji University, Liu Jingnan, USST, and associate professor Gao Ming and professor Zhang Li Xin were the co-authors.
This study proposes a demand response (DR) scheduling approach for residential buildings, aimed for four types of residential building loads: interruptible and deferrable loads, non-interruptible and deferrable loads, non-interruptible and non-deferrable loads, and air conditioning loads. This work was funded by the China Postdoctoral Science Foundation and National Natural Science Foundation of China.
Since Chen Yongbao joined in USST in 2020, he has been devoted to the research in energy saving of smart buildings, and carried out theoretical and applied research in emerging technological areas such as optimal control of building hvac system, grid interactive building and energy big data. He has published many high-level academic journals in Applied Energy(IF: 11.446), Energy(IF: 8.857), Sustainable Cities and Society(IF: 10.696), Scientific Data(IF: 8.501).


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