In recent years, power systems have experienced substantial growth marked by a high penetration of renewable energy sources, driven by the need to address the fossil fuel crisis and mitigate greenhouse gas emissions. While the integration of renewable sources brings significant environmental benefits by tackling pollution issues, it also introduces a set of challenges for the operation and control of modern power systems. These challenges encompass dynamic configuration, bi-directional power flow, resource uncertainty, etc.

Traditional optimization methods have encountered limitations in adapting to the complexities introduced by the increased penetration of renewable sources. However, the landscape is shifting with the emergence of artificial intelligence (AI) and machine learning technologies, offering promising solutions for the operation and control of modern power systems. These advanced technologies are proving to be indispensable tools in addressing the intricacies and dynamic nature of operating modern power systems, providing a versatile and adaptive approach to navigate the complexities arising from the integration of renewable energy sources.

This Research Topic aims to compile and establish a comprehensive guideline for the future trajectories of machine learning applications in modern power and energy systems, covering all related contributions in these domains. We welcome contributions from academia and industries to share their pioneering work. The scope of interest covers following topics, but is not limited to:

• Applications of artificial intelligence (AI) in the operation and control of power and energy systems.
• Addressing data/model drift for renewable energy forecasting.
• Applications of multi-agent deep reinforcement learning in power and energy systems.
• Decentralized and distributed operation and control of power and energy systems.
• Development of multi-energy systems integrating cooling, heating, and power.
• Energy management systems.
• Formal verification for machine learning applications in power systems
• Integration of renewable energy sources and mobile loads.
• Charging stations based on machine learning.
• Operation and control of power and energy systems.
• Peer-to-Peer energy trading in power systems.
• Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) algorithms and their applications in power and energy systems.
• Smart building energy management systems.

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