Call for Tracks

ICPEA 2026 | Hefei, China

Track 6: AI-Driven Intelligent Development of Power Systems

专题 6: 人工智能驱动的电力系统智能化发展

Track Chair(s) & Co-Chair(s) / 专题主席与共同主席

Jiaqi Ruan (阮嘉祺), Associate Research Fellow, Sichuan University, 副研究员, 四川大学 |
Chenxi Zhang (张晨曦), Associate Research Fellow, Fuzhou University, 副研究员, 福州大学 |
Shuyi Wang (王抒一), Lecturer, Shanghai University of Electric Power, 讲师, 上海电力大学 |

Abstract / 论坛简介

English: With the large-scale integration of new energy sources, the continuous enhancement of power electronics levels, and the increasing complexity of power system operation, artificial intelligence is becoming an important technological force to promote the intelligent development of power systems. This forum focuses on the deep integration of artificial intelligence and power systems, centering around cutting-edge directions such as intelligent perception and prediction, optimal operation and scheduling, autonomous decision-making and control, large models and agents, trustworthy artificial intelligence, and safety defense. It aims to discuss key scientific issues, core methods, and engineering applications of next-generation artificial intelligence technologies empowering the operation, control, and decision-making of new power systems.

中文: 随着新能源大规模接入、电力电子化程度持续提升以及电力系统运行复杂性的不断增加，人工智能正在成为推动电力系统智能化发展的重要技术力量。本论坛聚焦人工智能与电力系统深度融合，围绕智能感知与预测、优化运行与调度、自主决策与控制、大模型与智能体、可信人工智能及安全防御等前沿方向展开交流，探讨新一代人工智能技术赋能新型电力系统运行、控制与决策的关键科学问题、核心方法与工程应用。

Topics / 主题征稿范围

  • Artificial Intelligence-Driven Perception, Analysis, and Prediction in Power Systems
    人工智能驱动的电力系统感知、分析与预测
  • Intelligent Prediction and Uncertainty Modeling for Renewable Energy and Load
    新能源与负荷智能预测及不确定性建模
  • Data-Driven Optimal Operation and Intelligent Dispatch of Power Systems
    数据驱动的电力系统优化运行与智能调度
  • Applications of Reinforcement Learning and Autonomous Decision-Making in Power Systems
    强化学习与自主决策在电力系统中的应用
  • Large Language Model and Agent-Driven Power System Intelligence
    大语言模型与智能体驱动的电力系统智能化
  • Physics-Informed Fusion and Mechanism-Data Collaborative Modeling
    物理信息融合与机理—数据协同建模
  • Trustworthy Artificial Intelligence and Explainable Artificial Intelligence in Power Systems
    电力系统可信人工智能与可解释人工智能
  • Robustness, Security, and Adversarial Defense of Artificial Intelligence Models
    人工智能模型的鲁棒性、安全性与对抗防御
  • Digital Twins and Intelligent Operation and Control of Power Systems
    数字孪生与电力系统智能运行控制
  • Planning, Operation, and Control of New Power Systems Empowered by Artificial Intelligence
    人工智能赋能的新型电力系统规划、运行与控制