2023 2nd International Conference on Smart Energy and Energy Internet of Things (SEEIoT 2023)

Keynote Speakers


Prof. Fushuan Wen, Zhejiang University,IEEE Fellow

Professor Fushuan Wen joined the faculty of Zhejiang University in 1991, and has been a full professor and the director of the Institute of Power Economics and Information since 1997, and the director of Zhejiang University-Insigma Joint Research Center for Smart Grids since 2010. He has also been a full professor in the Hainan Institute, Zhjiang University, Sanya, China, since 2022. 

He has been undertaking various teaching, research and visiting appointments in Singapore, Hong Kong, Australia, Brunei, Estonia, Denmark. He has published 200+ SCI-indexed papers, 710+ EI-indexed papers, and 780+ Scopus-indexed papers. His publications have been cited for 18300+ times. He has completed and is undertaking more than 200 grants and projects. He received many awards, including the most prestigious National Natural Science Award of China. He has been listed in "Most Cited Chinese Researchers" in eight consecutive years since 2015 by Elsevier.

He is the editor-in-chief of Energy Conversion and Economics (SPERI, IET, Wiley), the deputy editor-in-chief of Journal of Automation of Electric Power Systems, a subject editor in power system economics of IET Generation, Transmission and Distribution. 

He was elected to IEEE Fellow for contributions to fault diagnosis in power grids.

Speech Title: Energy Internet / Integrated Energy System: what should it look like?

Abstract: In this speech, the following topics will be covered:

1.Individual energy system vs energy internet / integrated energy system

2.Energy internet: how to define?

3.Energy internet: architecture and functions

4.Differences between smart grid and energy internet

5.Challenges and research topics in the development of energy internet / integrated energy system

6.Concluding remarks and prospects of future research


Prof. Dazhi Yang, Harbin Institute of Technology

Dazhi Yang is a professor with the Harbin Institute of Technology. He received the B.Eng., M.Sc., and Ph.D. degrees from the Department of Electrical Engineering, National University of Singapore, Singapore, in 2009, 2012, and 2015, respectively. In 2020, he received support from the National Talent Program, which is a high-prestige research award by the Ministry of Industry and Information echnology of China. In 2017, he became the youngest associate editor of the Solar Energy journal, and has been serving as one of four subject editors of that journal since 2019. He is an active participant of the International Energy Agency, Photovoltaic Power Systems Programme, Task 16. He has published more than 100 journal papers, with a total citation number of 5514 (Google Scholar), an H-index of 41, and an i-10 index of 91. Currently, he has most journal publications on solar forecasting in the world. In 2020 and 2021, he is listed as one of the world's top 2% scientists (for both single year and career) by Stanford University. In 2021 and 2022, he is listed as one of the world's top 100,000 scientists published by the Global Scholars Database.

Speech Title: Quantifying the Predictability of Solar Radiation

Abstract: Solar forecasting is a necessary part of building a new power system with a high percentage of new energy sources. Currently, the Two Rules have clear specifications for short- and ultra-short-term solar forecast submission and penalty mechanisms. However, since the accuracy of forecasts is closely related to geographic location, time, weather, and climate, the deduction and penalty mechanism based on forecast errors in the Two Rules is not reasonable. To explore the most reasonable method for assessing and scoring solar forecasts, this report discusses predictability and skill scores. In simple terms, predictability is the "difficulty factor" of a forecast, while skill score is the "completion level" of a forecast. The predictiveness and skill scores of solar radiation are mapped across the United States using the ensemble forecasts from the European Center and data from the U.S. National Solar Radiation Database. The results demonstrate the spatial and temporal distribution and properties of predictability.


Prof.  Zhenbin Zhang, Shandong University,IET Fellow

2011-2016: Ph.D. (Dr.-Ing.) in Electrical Engineering, “summa cum laude”, Technical University Munich, Munich, Germany.

2016-2017: Postdoc. in Electrical Engineering, “VDE-Award-2017” Recipient, Technical University Munich, Munich, Germany.

2017-present: Full Professor, Lab. (institute) Director, PI and Ambassador for International Collaboration, Shandong University, China

2018-2022: August-Wilhelm Scheer Guest Professor, Technical University of Munich, Germany

2023: Guest Professor/Senior Visiting Scientist, University of Cambridge, England

Prof. Zhang is IET Fellow, IET Chartered Engineer and IEEE Senior Member. He was selected as “National Distinguished Expert (1000 Talents Program) of China” and “Outstanding Young and Middle-aged Scholar of Shandong University”. In addition, as the leader of the scientific research team of “Smart Energy and Offshore Equipment”, he set up the Lab for More Power Electronic Energy Systems (MPEES). He focuses on intelligent energy and high-quality power conversion systems and their control techniques, specifically including interests of high-power low-cost offshore wind power, smart micro-energy system with energy storage, intelligent maintenance of power conversion system and predictive control.

Speech Title: Bias-Free Fast Start-Up Strategy and High-Performance Predictive Control Method for DC Energy Routers

Abstract: Energy routers based on dual active bridge (DAB) converters are key components of hybrid micro-energy systems. They facilitate efficient bidirectional energy flow, and flexible integration of renewable energy sources. Three technical shortcomings are associated with conventional control methods for the DAB converter, viz., poor startup performance, slow dynamic response, and poor parameter robustness. Therefore, our recent research proposes effective solutions to these challenges. Firstly, we introduce a simple and fast start-up strategy, along with DC bias suppression and controllable current stress. Secondly, to improve the dynamic performance and eliminate steady-state voltage error, a bias-free predictive control scheme is proposed. Finally, we propose a Kalman filter-based current-sensorless predictive control method, and a noise suppression algorithm based on variable step predictive control. All the proposed solutions, which are validated by simulations and experiments, also improve the robustness to parameter distortion and measurement noises.