Key Technologies on Coordinated Operation and Control of Multiple Types of Resources for Active Power Grid Supporting

The growing integration of high shares of renewable energy and power electronics has led to the “dual-high” characteristics of modern power systems, while the increasing scale of distributed energy resources presents both opportunities and challenges for grid operation. Leveraging the active support capabilities of diverse distributed generation, load, and storage resources has become crucial for enhancing grid flexibility and stability. However, the variability, uncertainty, and multi‑temporal‑spatial coupling of these resources pose significant challenges to coordinated control and real‑time decision‑making. Traditional methods relying on deterministic forecasting and explicit physical models often struggle with issues of conservatism, accuracy, and adaptability in such complex environments. In recent years, data‑driven and learning‑based approaches have shown great potential to address these limitations and enable more intelligent, adaptive, and efficient coordination of multi‑type resources. This special session aims to explore advanced optimization, control, and learning‑based technologies that empower active grid support from distributed resources, with a focus on improving dynamic performance, resilience, and operational efficiency of future power systems.

1. Modeling and aggregation of multi‑type distributed energy resources for grid support

2. Aggregation modeling and virtual power plant control technologies for multiple types of distributed resources towards active grid support

3. Distributed cooperative optimization and autonomous operation strategies for source-load-storage resources in systems with high penetration of renewables

4. Plug-and-play and adaptive coordinated control methods for distributed resources based on multi-agent reinforcement learning

5. Optimal sizing and coordinated control technologies of energy storage systems for providing grid inertia and primary frequency response

6. Coordinated operation technologies of distributed energy storage clusters for enhancing grid voltage stability and power quality

7. Coordinated support architectures and strategies integrating energy storage and distributed resources for resilient recovery from extreme events

8.  Active support capability assessment and coordinated control strategy validation based on digital twin and real-time simulation

9.  Real-time coordinated regulation technologies for distributed resources considering cyber-physical security and communication latency

10. Market mechanisms and cooperative optimization methods to incentivize electric vehicles and flexible loads for active grid support

11. Coordinated operation technologies of mobile energy storage, distributed generation, and microgrids for enhancing distribution grid resilience and supply reliability

Chair:  

Jie Hu, Shenyang University of Technology, China

Jie Hu, a master's supervisor at Shenyang University of Technology, was selected for the first Liaoning Provincial Young Scientific and Technological Talent Support Project. He has presided over six horizontal/vertical projects, including the National Natural Science Foundation, Liaoning Provincial Science and Technology Department, and State Grid Liaoning Electric Power Research Institute Project. He has won several honors, including the second prize of the Institute of Electrical Engineering Science and Technology Progress Award (ranked fourth), the first prize of the demonstration technology achievement at the Comprehensive Smart Energy Conference, ESI's top 1% globally highly cited papers, and IEEE EI2 Best Conference Paper. He serves as the secretary-general of the 8th Energy Internet and Energy System Integration Conference and a member of the Intelligent Adaptive Collaborative Optimization Control Special Committee of the Chinese Association for Artificial Intelligence.

Co-chairs ：

Bingyu Wang, North China Electric Power University, China

Bingyu Wang receiced the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China, in 2021. She is currently an Lecturer with Northeastern University. She has authored or coauthored more than 20 papers, and authorized more than 10 invention patents. Her research interests include energy storage control and its application in power systems and cyber-physical energy system. She has an ongoing National Natural Science Foundation of China and 2 ongoing technical services projects. 

Yujia Huang, Shenyang University of Technology, China

Yujia Huang graduated from Northeastern University and was visiting PhD at Aalborg University in Denmark. She mainly conducts modeling and analysis, optimization and control research of distributed resources and integrated energy system. She was selected for the First Liaoning Province Young Science and Technology Talents Support Project, has published 20 papers in IEEE Transactions, Applied Energy, Proceedings of CSEE, Science China and so on, and 2 first-author ESI highly cited paper (Top 1%). Awarded by Outstanding PhD Thesis Award from IEEE Smart Grid Comm in 2025, CNKI Academic Excellence high PCSI paper, high cited paper, high download paper, 2025 IEEE EI2 and 2022 IEEE ICEI Best Conference Paper Award.

Qianyu Dong, Shenyang University of Technology, China

Qianyu Dong received the B.S. degree in Electrical Engineering from Shenyang University of Technology in 2017, and the M.S. degree in Power System Automation from Northeastern University in 2020. She received the Ph.D. degree from the Southern University of Science and Technology in 2025. Her research interests include resilience analysis of power distribution networks and artificial intelligence techniques for planning and operation of coupled power-transportation systems.