Guest Editors
Prof. Dr. Bo Yang
Email: yangbo_ac@outlook.com
Affiliation: Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, 650500, China
Homepage:
Research Interests: AI-based optimization and control of renewable energy systems
Dr. Shuai Zhou
Email: zoey.zhou@aut.ac.nz
Affiliation: School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, 1010, New Zealand
Homepage:
Research Interests: power system control, integration of renewable energy (wind energy and PV systems), smart grids, artificial intelligence applied to power systems
Dr. Jianfeng Wen
Email: j.wen7@liverpool.ac.uk
Affiliation: Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, United Kingdom
Homepage:
Research Interests: power system planning and evolution analysis, power system dispatching and optimal control
Dr. Yiyan Sang
Email: ethanyys@foxmail.com
Affiliation: College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090, China
Homepage:
Research Interests: advanced technologies for modern power systems: renewable integration, HVDC stability, and intelligent sensing
Summary
The global pursuit of "dual carbon" goals and the rapid advancement of renewable energy technologies are accelerating the transition toward cleaner, more efficient, and more resilient energy systems. Meanwhile, breakthroughs in artificial intelligence (AI) have unlocked unprecedented opportunities to enhance the planning, operation, control, and market participation of modern energy systems. From renewable forecasting and uncertainty-aware dispatch to digital twins, autonomous control, and cross-domain coordination, AI is increasingly becoming a cornerstone technology for enabling reliable and economic energy transition.
Modern energy systems are evolving into highly coupled systems. On the supply side, high-penetration renewable generation introduces strong variability and uncertainty, posing challenges to system stability and operational flexibility. On the demand side, electrification and diversified loads (e.g., buildings, industry, data centers, and electric vehicles) reshape spatiotemporal consumption patterns and amplify the need for advanced demand response and flexibility management. At the same time, the increasing deployment of distributed energy resources, energy storage, microgrids, hydrogen and power-to-X technologies, and integrated energy systems requires coordinated multi-energy, multi-timescale optimization. These trends also create new challenges for situational awareness, reliability assessment, and cyber-physical security. In this context, leveraging AI to support decision-making, improve controllability, and strengthen robustness is of great significance for enhancing energy system security, stability, resilience, and economic efficiency.
This special issue aims to explore and study the application of AI in energy systems, focusing on collaborative optimization, scheduling, planning, control, and reliability enhancement across generation, networks, storage, loads, and markets. We invite researchers and experts worldwide to submit high-quality original research papers and commentary articles addressing challenges, opportunities, and future development trends in this field.
Potential topics aim at covering themes including, but not limited to:
1. AI for Planning and Operation of Low-Carbon Energy Systems;
2. AI for Renewable Forecasting and Uncertainty-Aware Dispatch;
3. AI-Enabled Optimization Operation of Integrated Energy Systems;
4. AI for Coordinated Control of distributed energy resources, Microgrids, and virtual power plants;
5. AI for Power System Security, Stability, and Resilience;
6. AI for Energy Markets, Demand Response, and Flexibility Management;
7. AI for Fault Diagnosis and Predictive Maintenance in Energy Systems;
8. AI for Electric Vehicles, Charging Infrastructure, and Grid Coordination.
Keywords
artificial intelligence, collaborative optimization, electric vehicles, grid scheduling, integrated energy systems, low carbon energy systems
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