Track 10

1. Organizers:

Chair: Wenyi Zhang (张文一), The Hong Kong Polytechnic University (香港理工大学)

Co-Chair: Xudong Li (李旭东), The Hong Kong Polytechnic University (香港理工大学)

Co-Chair: Mingfeng Ge (葛明峰), China University of Geosciences (中国地质大学)

2. Abstract:

English: With the rapid development of new type power systems, artificial intelligence (AI) and big data technologies are fundamentally reshaping the paradigms of perception, decision-making, and control in modern power grids. However, facing complex challenges such as the high penetration of renewable energy, the proliferation of distributed energy resources, deep cyber-physical integration, and the surging demand for computing power, traditional single-method optimization or control approaches are no longer sufficient to meet the requirements for highly resilient and autonomous grid operations. Focusing on AI and big data-empowered smart grids under the framework of "power-computing synergy," this forum fosters in-depth discussions on the synergistic design of distributed learning, power-computing coordination, online optimization, and resilient control. It aims to explore novel paradigms that deeply integrate multi-agent coordination, data-driven decision-making, and robust control mechanisms, thereby driving the coordinated development of theoretical innovation and engineering applications in smart grid operation and control.

中文: 随着新型电力系统建设的深入推进，人工智能与大数据技术正深刻重塑电网的感知、决策与控制范式。然而，面对高比例可再生能源接入、海量分布式资源涌现、信息物理深度融合以及算力需求激增等复杂挑战，传统的优化或控制方法已难以满足现代电网高弹性、高自治的运行需求。本论坛聚焦“电算协同”背景下AI与大数据赋能的智能电网系统，围绕分布式学习、电算协同交互、在线优化与弹性控制的协同设计展开深度交流，探索多智能体协调、数据驱动决策与鲁棒控制融合的新范式，推动智能电网调控运行领域的理论创新与工程应用协同发展。

3. Topics:

• Joint Dispatch and Optimization of Power and Computing Networks for Power-Computing Synergy / 面向“电算协同”的电力与算力网络联合调度与优化
• Data-driven power system state estimation and fault diagnosis / 数据驱动的电力系统状态感知与故障诊断
• Multi-agent reinforcement learning for grid dispatch and demand response / 多智能体强化学习在电网调度与需求响应中的应用
• Distributed optimization and coordinated control under big data / 大数据下的分布式优化与协调控制
• Game-theoretic methods and intelligent decision-making for electricity markets / 面向电力市场的博弈论方法与智能决策
• Resilient and robust control of power grids against cyber-physical attacks / 信息物理攻击下电网的弹性鲁棒控制
• Renewable energy power forecasting and uncertainty quantification / 新能源功率预测与不确定性量化
• Event-triggered and communication-constrained distributed control / 事件触发与通信受限下的分布式控制
• Application of large language models (LLMs) and foundation models in power systems / 大模型（LLM）与基础模型在电力系统中的应用探索
• Power-Computing Integration and Intelligent Control under Cloud-Edge-Terminal Collaborative Architectures / 云边端协同架构下的电力-算力融合与智能调控