Artificial Intelligence in Power Systems Operation and Control

Dear Colleagues,

Currently, power system operation and control are challenged by the higher penetration of power-electronic-interfaced energy sources at both the transmission and distribution sides. Furthermore, the complexity and uncertainty of supply and demand, in conjunction with the forthcoming electrification of the transportation and heating sectors, necessitates the active participation and coordination of distribution system operators with transmission system operators to maximize flexibility. However, as the different time scales of operation and control become less distinct and start to overlap, traditional methods struggle to maintain the same level of performance.

The realization of smart grids permits the adaptation of artificial intelligence (AI), which as in many other scientific fields, appear as an ideal candidate for modernizing monitoring, optimization and control, due to their ability for modelling complex relationships, detecting anomalies, and revealing the fundamental structure of a given system, to name a few things. As the number of controllable elements (with different characteristics) increases in smart grids, fast and accurate predictions/estimations can contribute to improving the situational awareness, exploitation, operation and protection of the system. However, recent advances have also demonstrated the ability of AI, and specifically neural networks, to integrate the physical model of a system to exploit knowledge already acquired. This opens a new avenue of possibilities for AI, such as the embedment of the knowledge synthesized by neural networks into power-system optimization processes.

This Special Issue is dedicated to promoting high-quality research addressing the aforementioned concerns by means of AI. Specifically, the topics that this Special Issue intends to cover, but is not limited to, are:

• Hybrid AI-physics models.
• AI applications in power-system optimization.
• AI-driven smart grids.
• Renewables and demand integration through AI-based methodologies.
• AI-based solutions for system protection.
• Interpretable AI models.
• Novel AI-based monitoring/situational awareness algorithms.
• Risk and uncertainty modelling with AI.

Prof. Dr. Pedro Rodriguez
Prof. Dr. Tomislav Dragicevic
Guest Editors

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