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Track 1

AI-Driven Intelligent Dispatch and Flexible Resource Optimization in Power Systems

1. Motivation As the penetration of renewable energy increases, the uncertainty and volatility of power systems have significantly risen. Flexible demand-side resources, including electric vehicles, distributed energy storage, smart buildings, and industrial adjustable loads, have become critical for maintaining grid stability and enhancing energy efficiency. This track focuses on AI-driven optimization strategies, exploring how intelligent scheduling, load aggregation, energy management, and Vehicle-to-Grid (V2G) technologies can facilitate efficient interaction between demand-side resources and the power grid.
This track aims to bring together the latest research advancements in the field, promoting the development of AI-based optimization algorithms, data-driven modeling, and decision-making mechanisms, thereby providing theoretical support and technical solutions for the future of smart grids and the energy internet.
2. Topics of Interest Including but not limited to the following directions:
 EV-Grid Interaction
 Charging Scheduling Optimization
 V2G and Energy Management
 EV User Behavior Modeling
 Charging Infrastructure Planning
 Hybrid Model-Data Driven Optimization for Power System Scheduling
 AI-Based Power System Operation Forecasting and Anomaly Detection
 Data-Driven Power Market Optimization and Demand-Side Management
 Computational Optimization Methods for Large-Scale Power Systems
3. Chair Information

Song Ke, Postdoctoral Fellow at the Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University. His research focuses on electric vehicle and grid interaction, as well as intelligent charging and discharging strategies.
Co-chair:

Siyuan Chen, Postdoctoral Fellow at the School of Electrical and Automation Engineering, Wuhan University. His research focuses on power system operation scheduling and the application of hybrid model-data driven technologies.

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