Special Session III
AI based Optimization, Planning, and Fault Diagnosis of New Power Systems
With large-scale utilization and widespread integration of various renewable
energy (e.g., wind and solar energies) into modern power systems,
traditional power system that mainly consists of fossil energy has
experienced dramatical transformation. The proposed "New Power System"
concept aims to construct a clean, low carbon, safe, and efficient modern
energy system, which takes the renewable energy as the basis while other
fossil energy as the supplementary. Meanwhile, with the maturity of deep
learning, the improvement of computing power, and the wealth of big data
accumulated in the Internet era, artificial intelligence technology has
achieved a dramatical development and widespread applications in power
systems over the last decade. Due to the inherent strong randomness and
uncertainties characteristics of renewable energy, when the proportion of
renewable energy increases, frequent load transfer in a short time will be a
common scenario in the future. Therefore, it is difficult yet crucial to
undertake some advanced techniques in modern power system with large-scale
renewable energy integration, to ensure the optimal and reliable power
generation. Also, it is promising to combine digital technology and
artificial intelligence to realize efficient, accurate and timely
optimization and optimal planning of new power systems, which is beneficial
for the economic and efficient operation. Meanwhile, with the continuous
expansion of the power system scale and the increasing complexity of the
structure, a large number of alarm information flow into the dispatching
center in a short time, which far exceeds the processing capacity of the
operators, thus the power system fault diagnosis system is crucial for
decision-making reference.
The topics of interest include, but are not limited to:
• Planning of renewable energy system
•
Operation planning and control of energy
storage system
•
Transmission line and faults location
•
Life cycle perception and analysis
•
Fault diagnosis of electrical equipment
•
Big data and AI based optimization technology
•
Wind/Solar output power forecasting
•
Regulation technique for power system
intelligent power consumptio
Organizers
Yang Bo, Kunming University of Science and Technology, China
Bo Yang was born in China in 1988. He received his Ph.D. in Electrical Engineering from University of Liverpool, U.K. in 2015. He joined Kunming University of Science and Technology (KUST), China in 2016, and had held the positions of Lecturer in 2016-2017, Associate Professor (Early promoted) in 2017-2020, and Professor (Early promoted) since 2020, all at the Faculty of Electrical Power Engineering. His main research interests include advanced optimization and control of renewable energy systems, and AI applications in smart grid. He has published over 150 SCI/EI journal papers, including 2 hot papers and 10 ESI papers, as well as two monographies. He has won the 1st Yunnan provincial high-level youth talents special support plan in 2018 and played a role of editor/associate editor/editorial board member of five journals. He has been a committee member of the 18th Council of youth working committee of China electrotechnical society, standing directors of energy information society system technology committee and AI applications in dynamic power system technology committee in IEEE PES power system dynamics satellite committee (China). He has been the lead guest-editors of over 10 special issues in different journals and played as members of Technical Committee Member for over 10 international conferences.
Xiaoshun Zhang, Foshan Graduate School of Northeastern University, China
Xiaoshun Zhang obtained his Ph. D in Power System and Automation from South China University of Technology (SCUT), China in 2017. He is now an associate professor at Foshan Graduate School of Northeastern University, Foshan. Dr. Zhang’s research areas include power system operation & control, reinforcement learning & transfer learning, evolutionary computation, and distributed optimization. He has taken charge of 3 important research projects, including a project of National Natural Science Foundation of China, a project of National Natural Science Foundation of Guangdong Province, and a project of Research and Development Start-Up Foundation of Shantou University. Besides, he also has participated in more than 15 important research projects as the key researcher, such as the National Hi-Tech Research and Development Program of China, the National Key Basic Research Program of China, the National Natural Science Foundation of China, the Key Scientific and Technological Project of China Southern Power Grid, and so on. Dr. Zhang has published a book of Smart Generation Control, and published more than 100 peer-reviewed SCI/EI papers with 5 highly cited papers and more than 40 papers at the top journals.
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