Advanced Optimization, AI Applications, and Market Mechanisms for Low-Carbon Integrated Energy Systems

The global transition towards carbon neutrality necessitates a profound transformation in energy systems. This transformation hinges on the deep integration of multi-energy carriers, the adoption of sophisticated intelligent technologies, and the design of efficient economic incentives. This session focuses on advanced modeling and optimization techniques for system design and dispatch, innovative applications of Artificial Intelligence and machine learning in forecasting, control, and anomaly detection, as well as the market architectures and pricing mechanisms of electricity and carbon markets that incentivize decarbonization and system flexibility. The goal is to bridge the critical gap between technological innovation and market-driven solutions in the pursuit of a sustainable, reliable, and economical energy future. Topics of interest include, but are not limited to:

1. Aggregation models and control strategies for distributed energy resources

2. Low-carbon multi-agent systems and decentralized coordination algorithms

3. Demand-side flexibility and market-based demand response

4. Advanced optimization techniques of low-carbon integrated energy systems

5. AI techniques for forecasting, state estimation, fault diagnosis and real-time control in increasingly complex and renewable-rich grids

6. Risk perception and fault location in power systems under multi-source data fusion

7. Algorithm design for transmission line operational status monitoring and detection

8. Synergistic trading mechanisms in electricity and carbon markets 

9. Carbon pricing, carbon markets, and their integration with energy markets

Chair:  

Ling Zheng, Changsha University of Science and Technology, China

Ling Zheng was born in Sichuan, China, in 1991. She obtained a Ph.D. degree from Hunan University in 2023. Her research interests include integrated energy system operation and optimization, energy economics and application of artificial intelligence technology in power systems.

Co-chairs：

Zhongnan Feng, Changsha University of Science & Technology, China

Zhongnan Feng was born in Hubei, China, in 1998. She obtained a Ph.D. degree. from Huazhong University. of Science & Technology in 2024. Her research interests include power system operation, energy economics, and microgrids.

Guannan Li, State Grid Sichuan Electric Power Research Institute, China

Guannan Li received his Ph.D. degree in Electrical and Electronic Engineering from Hong Kong Polytechnic University in 2025, and is currently a postdoctoral fellow at State Grid Sichuan electric power research institute. His research interests include transportation electrification, energy management, and reinforcement learning.

Su Shuangqing, Hunan City University, China

Shuangqing Su was born in Hunan, China, in 1995. She obtained a Master’s degree in Journalism and Communication from Hunan University in 2020. Her research interests includes fusing multi-source data for power outage detection and AI-driven social sensing for outage identification of power systems.