Special Session IX
AI-Driven Operational Optimization and Control of Microgrids and Virtual Power Plants with High-Penetration Renewable Resources
With the global energy system transitioning toward low-carbon, intelligent, and sustainable solutions, high-renewable microgrids and virtual power plants (VPPs) have emerged as critical technologies for achieving efficient energy utilization and enhancing grid resilience. However, the integration of high proportions of renewable energy sources poses significant challenges, including increased system volatility, degraded power quality, difficulty in operation optimization, and reduced grid resilience. The rapid advancement of artificial intelligence (AI) provides innovative solutions and efficient tools to address these complex issues.
This Special Session focuses on AI-driven operational optimization and control technologies for microgrids and VPPs with high-penetration renewable resources. It aims to explore how advanced AI algorithms and tools can enhance the efficiency, stability, and resilience of these systems. The session will emphasize AI applications in renewable energy forecasting, system optimization, real-time control, fault diagnosis, and self-healing for microgrids and VPPs. Practical case studies and implementation experiences of AI technologies will also be shared.
The topics of interest include, but are not limited to:
• AI-based forecasting methods for renewable energy generations.
• Multi-objective optimal scheduling and real-time control of microgrids.
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Reinforcement learning-based adaptive control strategies for microgrids.
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AI-based aggregation and coordinated control of distributed energy resources.
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AI-driven decision-making and trading strategies for VPPs participating in electricity markets.
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Applications of AI in enhancing VPPs’ flexibility and grid resilience.
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AI-based configuration optimization and control of energy storage systems.
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AI-based optimization and control of hydrogen energy storage and fuel cell systems.
•
AI-based methods for collaborative optimization of hybrid energy storage systems.
Organizers
Wenliang Yin, Shandong University of Technology, China
Dr. Wenliang Yin is an Associate Professor at Shandong University of Technology. He holds a Ph.D. from North China Electric Power University, a joint Ph.D. program from the University of New South Wales, Australia, and a postdoctoral research position at the University of Sydney. His research focuses on renewable energy generation technology and equipment, wind power hydrogen production and storage, smart grids, and virtual power plants (VPPs).
Dr. Yin has led over 10 national and industry-sponsored projects, including grants from the National Natural Science Foundation of China, the Shandong Provincial Natural Science Foundation, and the State Grid Corporation of China. Currently, he serves as the Head of the Electrical Engineering Department at SDUT, Deputy Director of the Shandong Provincial Distributed Power Grid-Connection Demonstration Engineering Technology Research Center, Deputy Secretary-General of the Zibo Smart Grid Equipment Industrial Innovation Institute, and Leader of the Shandong Provincial Higher Education Young Innovation Team. He is also a Standing Council Member of the Shandong Energy Storage Association, a member of IEEE and CSEE. Additionally, he serves on the Young Scientists Committee of the Shandong Electronics Society and is a Young Editorial Board Member of Electric Engineering Technology and China Electric Power. Dr. Yin has published 47 academic papers, authored 10+ granted national invention patents, and contributed 30+ SCI/EI-indexed papers as first or corresponding author.
Lin Liu, University of Technology Sydney, Australia
Dr. Lin Liu obtained dual master's degrees
from both North China Electric Power University and South Ural State University
in 2024. In the same year, she earned his Ph.D. degree from the University of
Technology Sydney (UTS), under the supervision of internationally renowned
Professor Jianguo Zhu. Since 2024, she has been pursuing a second Ph.D. degree
at the Hong Kong Polytechnic University, supported by a President's Scholarship.
Her research focuses on the design and optimization of motor drive systems,
modeling of magnetic material properties, and the application of artificial
intelligence in power and energy systems.
Dr. Liu has authored or co-authored 39 academic papers, including 15 SCI papers
as first or corresponding author in top journals such as IEEE Trans. TIE and
TTE, and 4 leading Chinese EI papers in journals like《Power System
Technology》and《Journal of Mechanical Engineering》. She has participated in over
10 international conferences and delivered 3 invited reports. Dr. Liu has been
involved in research projects funded by the Australian Research Council (ARC),
the National Natural Science Foundation of China (NSFC), and key laboratory
programs. She served as a research assistant at UTS for 18 months. Currently, he
is a member of IEEE, a reviewer for authoritative journals in the electrical
field such as IEEE and IET, and a guest editor for Frontiers. She also holds the
position of Vice-Chair of the UTS IEEE Student Branch. She has been recognized
with the IEEE TIE Editorial Board's Outstanding Reviewer Award (Top 1%) and the
2022 UTS Outstanding Teaching Assistant Award (Top 1%).
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