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Session 1: AI-Driven Optimization and Decision-Making for Power and Energy Systems in Industrial and Transportation Applications

“AI驱动的工业与交通电力能源系统优化与决策”

Session 1

AI-Driven Optimization and Decision-Making for Power and Energy Systems in Industrial and Transportation Applications “AI驱动的工业与交通电力能源系统优化与决策”

With the global transition toward low-carbon energy, the upgrading of industrial energy systems, and the increasing transformation of transportation energy systems, modern power and energy systems face growing challenges from high renewable penetration, multi-energy coupling, multiple uncertainties, and complex operational constraints. The low-carbon and intelligent transformation of energy and power systems in industrial production, ground transportation, marine vessels, and aviation creates increasing demands for efficient operation, optimal scheduling, and intelligent decision-making. Recent advances in machine learning, reinforcement learning, generative AI, large language models (LLMs), and AI agents provide new paradigms for forecasting, planning, scheduling, control, and decision-making. This Special Session focuses on integrating AI with mathematical optimization, physical models, and operational data, emphasizing AI-driven optimization and decision-making as well as domain adaptation, knowledge augmentation, and agent-based applications of LLMs in power and energy systems. The Special Session covers a broad range of topics, including energy management, energy storage optimization, multi-energy coordination, intelligent scheduling, condition monitoring, and safe and reliable operation of industrial and transportation energy systems. It aims to promote the development of smarter, more efficient, low-carbon, and reliable industrial and transportation power and energy systems.

随着全球能源低碳转型、工业能源系统升级及交通能源系统智能化进程加快,现代电力与能源系统面临新能源高比例接入、多能源耦合、多源不确定性及复杂运行约束等挑战。工业生产以及陆路交通、船舶、航空等领域能源动力系统的低碳化与智能化发展,对能源系统的高效运行、优化调度与智能决策提出了更高要求。近年来,机器学习、强化学习、生成式人工智能、大模型及智能体等技术快速发展,为复杂能源系统的预测、规划、调度、控制与决策提供了新范式。本专题聚焦人工智能与数学优化、物理模型及运行数据的深度融合,重点关注AI驱动的优化决策方法,以及大模型在电力能源垂直领域的适配、知识增强与智能体应用,涵盖工业及交通能源系统的能源管理、储能优化、多能源协同、智能调度、状态监测与安全运行等方向,推动工业与交通能源系统向智能、高效、低碳和可靠方向发展。  

Topics (Including but not limited to)
• AI驱动的电力与能源系统优化、调度与智能决策 / AI-driven optimization, scheduling, and intelligent decision-making
• 机器学习、深度学习与强化学习在电力能源系统中的应用 / Machine learning, deep learning, and reinforcement learning for energy systems
• 生成式人工智能、大模型、垂域适配与智能体应用 / Generative AI, large models, domain adaptation, and intelligent agents
• 数据驱动、模型驱动及物理信息融合的优化与控制 / Data-driven, model-based, and physics-informed optimization and control
• 新能源预测、优化调度与不确定性管理 / Renewable energy forecasting, scheduling, and uncertainty management
• 工业与综合能源系统智能优化及能源管理 / Intelligent optimization and energy management for industrial and integrated energy systems
• 储能系统智能调度、状态评估与优化控制 / Intelligent scheduling, state assessment, and optimal control of energy storage
• 陆路交通、电动汽车充放电及车网互动优化 / land transportation, EV charging/discharging, and vehicle-to-grid optimization
• 船舶与航空能源动力系统智能管理及优化控制 / Intelligent management and optimal control for marine and aviation energy and power systems
• 微电网、虚拟电厂、数字孪生、故障诊断与安全运行 / Microgrids, virtual power plants, digital twins, fault diagnosis, and safety and reliability operation

Chair: Assoc. Prof. Zhiyuan Wang, Zhengzhou University, China

Zhiyuan Wang, Ph.D., is an Associate Professor and Master’s Supervisor at Zhengzhou University. His research focuses on artificial intelligence and optimization-based decision-making for energy systems, including deep reinforcement learning, large language model-assisted decision-making, stochastic and robust optimization, data-physics collaborative modeling, and intelligent scheduling. He has participated in the development and engineering implementation of energy management and control systems for State Grid Corporation of China, China Southern Power Grid, China State Shipbuilding Corporation Systems Engineering Research Institute, Baosteel, Maanshan Iron & Steel, Ansteel, Zhanjiang Iron & Steel, and Nanjing Iron & Steel, and has accumulated extensive research and engineering experience in complex energy system modeling, optimal scheduling, and intelligent control. His research has been published in journals including IEEE Transactions on Industrial Electronics, IEEE/CAA Journal of Automatica Sinica, Information Sciences, and Scientia Sinica Technologica. He holds three authorized invention patents and currently serves as a Young Editorial Board Member of Industrial Engineering Journal.  


Co-chair: Dr. Yungui Huang, Energy Development Research Institute Co., Ltd., China Southern Power Grid, China

Yungui Huang, Ph.D., is a Strategic-Level Senior Technical Expert and Professor-level Senior Economist at Energy Development Research Institute Co., Ltd., China Southern Power Grid. His research primarily focuses on transportation, energy, and electric power. He has received the Second Prize for Outstanding Research Achievements from the Research Association for Party Building and Ideological and Political Work of Central State-Owned Enterprises, as well as the First and Second Prizes for Management Innovation from China Southern Power Grid. He has also been recognized as an Advanced Individual in Information Work by the State-owned Assets Supervision and Administration Commission of the State Council. In 2024, he authored the research report Accelerating Large-Scale Supply-Demand Interaction Between Electric Vehicles and the Power Grid to Foster New Quality Productive Forces in the Energy System at Minimum Social Cost, which received written instructions from a principal leader of the State Council. He also released the first Assessment Report on the Integrated Development of Transportation and Energy in the Five Southern Provinces and Regions.  

Co-chair: Dr. Xuerui Zhang, Zhengzhou University, China

Xuerui Zhang, Ph.D., is a Lecturer at the School of Computer and Artificial Intelligence, Zhengzhou University. She received her M.S. degree in Fundamental Mathematics and her Ph.D. degree in Control Theory and Control Engineering from Dalian University of Technology, and has an interdisciplinary research background in mathematics and control theory. Her research interests include deep reinforcement learning, optimization under uncertainty, and intelligent scheduling and optimization for industrial systems, with a particular focus on intelligent decision-making and optimization methods for complex real-world applications. Her research has appeared in IEEE/CAA Journal of Automatica Sinica, Control Theory & Applications, and the IEEE Congress on Evolutionary Computation (CEC), among other journals and conferences.  

Co-chair: Dr. Jiaming Dou, Institute of Electrical Engineering, Chinese Academy of Sciences, China

Dr. Dou is a Postdoctoral Fellow at the Institute of Electrical Engineering, Chinese Academy of Sciences. His research interests include AI security in power systems and the application of AI in optimal dispatch and decision-making. He was selected for the first batch of the Young Talent Support Project (Doctoral Student Program) of the China Association for Science and Technology, and was also selected for the Baidu Famou AI for Science Scientist Program. He has participated in more than 30 projects, including the National Key R&D Program, the National Natural Science Foundation of China, and State Grid science and technology projects. He has published or accepted 45 papers, including 17 as first author or corresponding author, and has 11 granted or filed patents. He is an Outstanding Reviewer for the Chinese journal Automation of Electric Power Systems and a Young Editorial Board Member of the newly established journal Deep Energy.  

Co-chair: Dr. Demin Xu, Institute of Electrical Engineering, Chinese Academy of Sciences, China

Dr. Demin Xu is an Assistant Researcher and Postdoctoral Fellow at the Institute of Electrical Engineering, Chinese Academy of Sciences. He received his Ph.D. in Control Theory and Control Engineering from Dalian University of Technology in 2025. His research interests include industrial energy-system modeling, safety and reliability maintenance, fault diagnosis, and safety reachability analysis. His work has been published in IEEE T-ASE, IEEE Transactions on Cybernetics, IEEE TIM, et al. He has served as a session co-chair for SEGRE2026(the Best Workshop) and ICPE2026, and as a reviewer for several IEEE journals, such as IEEE TSG, IEEE T-ASE, and IEEE TICPS, et al.  


Call for Papers Timeline / 征稿时间

  • Submission of Full Paper: November 30th, 2026
    投稿截止日: 2026年11月30日 

  • Notification Deadline: December 30th, 2026
    通知书发送: 2026年12月30日 

  • Registration Deadline: January 20th, 2027
    注册截止日期: 2027年1月20日