Track 21: Data-Driven Intelligent Operation and Control for Digitalized Power Systems 数据驱动的数字化电力系统智能运行与控制
Organizers
Professor, Hunan University
Professor, Tongji University
Associate Professor, Beihang University
Associate Professor, Beihang University
Abstract
In modern digitalized power systems, the availability of various advanced information and communication technologies has provided immense opportunities to enhance the intelligence level of system-wide operation and control by leveraging emerging data-driven analysis and decision-making methods, e.g., artificial intelligence and statistical learning. Consequently, this would largely help upgrade the power grids towards digitalized grids from a data-driven perspective. This track copes with recent advances and new trends in data-driven intelligent operation and control for modern power systems. It is planned to be organized in a mixed form, including not only professional talks given by invited researchers but also paper submissions and presentations by interested authors.
Topics
This track focuses on addressing the emerging topic of how to unlock the great potential of data-driven technologies to enhance the intelligence level of power system operation and control. Powerful data-driven solutions can be systematically developed by appropriately employing advanced artificial intelligence and statistical learning techniques to explore and mine valuable information hidden behind massive measurement data in practical power systems. For paper submissions, specific topics of interest include but are not limited to:
- Power system measurement data cleansing and data analytics
- Data-driven energy management and optimized operation
- Data-driven renewable generation/load forecasting
- Data-driven power system dynamics modeling and identification
- Data-driven power system dynamic stability/security assessment
- Power system fault/event detection based on artificial intelligence
- AI-empowered decision making of power systems with high renewables
- AI-empowered situational awareness and visualization
- AI-empowered risk hedging and stability control
- Reinforcement learning for power system decision making
Invited Speakers
Associate Professor, Beihang University