Special Session Introduction

In modern industrial power grids, the availability of massive measured information has provided immense opportunities to enhance the intelligence level of system-wide situational awareness and decision making by leveraging emerging data analytics and machine learning technologies. Consequently, this would largely help upgrade modern power grids towards smart grids from a digitalized data-driven perspective. This special session presents recent advances and new trends in data-driven situational awareness and decision making for industrial smart grids. It is planned to be organized in a mixed form, including not only professional talks given by invited researchers but also paper submissions and presentations by interested authors.

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Technical Outline of the Session:

This special session focuses on addressing the emerging topic of how to unlock the great potential of data analytics and machine learning to enhance industrial smart grid operation. Powerful data-driven situational awareness and decision-making solutions can be systematically developed by appropriately employing advanced machine learning techniques to explore and mine valuable information hidden behind massive measurement data in practical grids. For paper submissions, specific topics of interest include but are not limited to:

• Data cleansing and analytics in smart grids
• Data-driven energy management and optimized operation
• Data-driven renewable generation/load forecasting
• Data-driven power system dynamics modeling and identification
• Data-driven power system dynamic stability/security assessment
• Power system fault/event detection based on data analytics
• Data-driven situational awareness and visualization
• Data-driven risk hedging and stability control
• Reinforcement learning for power system decision making

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Special Session Organizers

Lipeng Zhu, Associate Professor, Hunan University

Lipeng Zhu received the B.S., M.S., and Ph.D. degrees from Huazhong University of Science and Technology, Wuhan, China, in 2012, from Wuhan University, Wuhan, China, in 2015, and from Tsinghua University, Beijing, China, in 2018, respectively, all in electrical engineering. He worked as a Post-doctoral Fellow/Senior Research Assistant at The University of Hong Kong from 2018 to 2021. He is currently an Associate Professor at Hunan University. His research interests mainly include data-driven power system stability and control, synchrophasor measurement technologies, and energy informatics in smart grids. In recent five years, Dr. Zhu has authored/co-authored more than 30 journal/conference papers in the field of power and energy, with 17 ones published in IEEE flagship journals.

Xinran Zhang, Assistant Professor, Beihang University

Xinran Zhang received the B.S. degree and Ph.D. degrees in electrical engineering and automation from Tsinghua University, Beijing, China, in 2011 and 2016, respectively, both with honors. From 2016 to 2020, he was a Postdoctoral Fellow with the Department of Electrical and Electronic Engineering, The University of Hong Kong. Since 2021, he has been an Assistant Professor with the School of Automation Science and Electrical Engineering, Beihang University, Beijing, China. He is an IEEE Senior Member and a review editor of Frontiers in Energy Research. His research interests include power system stability and control, wide-area damping control system, load modeling, and the application of artificial intelligence in power systems.

Le Zheng, Lecturer, North China Electric Power University

Le Zheng received the B.S. and Ph.D. degrees from Tsinghua University, Beijing, China, in 2011 and 2017, respectively, all in electrical engineering. He worked as a Post-doctoral Fellow at Stanford University from 2017 to 2019. He is currently a Lecturer at North China Electric Power University. His research interests mainly include power system stability and control, and big data techniques in power systems. Dr. Zheng has published more than 20 journal papers and presented at more than 5 international conferences.