The digital transformation of the energy sector has intensified with the rising global demand for intelligent, data-driven, and computationally efficient techniques to bolster the reliability, safety, and adaptability of modern electricity infrastructure. This Special Issue (SI), entitled "Applications of Machine Learning and Artificial Intelligence in Modern Power and Energy Systems", presents a peer-reviewed collection of eleven high-quality papers encompassing the intersection of power engineering and advanced informatics, and covering a broad scope that ranges from physics-based simulations of high-voltage systems to the deployment of explainable AI (XAI) for grid stability and load forecasting. The core aim is to demonstrate machine learning and artificial intelligence applications in modern power and energy systems to bridge the gap between theoretical data science and practical electrical engineering. The primary motivation for this collection stems from the urgent need to ensure grid resilience in an increasingly volatile energy landscape driven by higher renewable penetration, distributed energy resources, and evolving cyber-physical threats. The SI is specifically addressed to professionals, such as power system operators, research scientists, and electrical engineers seeking to implement smart, adaptive solutions in real-world environments, providing them with the technical insights necessary to meet the economic and sustainability challenges of the evolving energy industry, and offering a comprehensive look at the state of the art in autonomous control and operational decision-making.
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