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Artificial Intelligence-Based Optimal Control and Resilient Operation in Cyber-Physical Power Systems
截止日期：9月15（如有延期请参见留言区）
By leveraging information and communication technologies, smart grids can implement two-way communication between various distributed energy sources to optimize the generation, distribution, and consumption of electricity. In recent years, as the rapid advance of communication networks and computing/control systems, especially in smart grids, such interdependency and coupling between physical and cyber spaces have attracted both academic and industrial attentions. Therefore, the concept of cyber-physical power system (CPPS) has been emerged for bridging the gap between physical and cyber layers, aiming to achieve a seamless collaboration in both worlds. While the CPPS framework offers enhanced flexibility, economy, and reliability, it also presents significant challenges in maintaining optimal control and resilient operation, particularly in systems with high penetration of renewable energy sources and inherent uncertainties and dynamic behaviors. In CPPS, the reliance on cyber networks for control and communication introduces risks such as time delays, cyberattacks, and data integrity issues, which can compromise control performance or even destabilize the system. To address these challenges, Artificial Intelligence (AI) has emerged as a powerful tool for enhancing the control and resilience of CPPS by learning from historical data and interacting with the dynamic system environment.
This special issue aims to provide a platform for academic and industry experts to share the latest advancements in AI-based optimal control techniques and resilient operation strategies for CPPS. We welcome papers that address a broad range of topics related to these areas.
1) Data-driven control strategies in cyber-physical power systems and microgrid systems
2) Machine learning for optimal control and operation in power systems and microgrids
3) AI-based cyber resilient control and operation solutions in CPPS
4) Data-driven fault detection and diagnosis in CPPS
5) Machine-learning approaches for real-time voltage and frequency control in smart grids
6) Interpretable machine learning for control and operation solutions in CPPS
7) Distributed and federated machine learning in CPPS
Dr. Yitong Shang
The Hong Kong University of Science and Technology, Hong Kong
ytshang@ust.hk
Dr. Yang Xia
Nanyang Technological University, Singapore
yang_xia@ntu.edu.sg
Dr. Alexis Pengfei Zhao
Chinese Academy of Sciences, Beijing, China
P.zhao0308@gmail.com
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