Document Type : Review Paper
Authors
1
Associate Professor, School of Energy Engineering and Sustainable Resources, Head of the Institute of Soft Technologies, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran
2
Master's Student in Energy Systems Engineering, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran
3
PhD Candidate in Energy Systems Engineering, School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Sciences and Technologies, University of Tehran, Tehran, Iran
Abstract
The energy industry is on the verge of a fundamental transformation, with power systems evolving into complex computational-physical ecosystems where digital markets, physical infrastructure, and cross-sector resources such as water are intricately intertwined. Managing this complexity requires moving from traditional tools to integrated and autonomous intelligence. This paper aims to provide a comprehensive analysis of the role of artificial intelligence as an enabling technology for the next generation of energy systems. In this review, key capabilities of artificial intelligence include fault prediction and detection, complex patterns, optimization of system performance, and support for decision-making in energy systems themselves. The analysis presented is based on four fundamental pillars of intelligence: (1) building energy markets through algorithmic trading and decentralized mechanisms, (2) enhancing grid resilience through rapid fault detection and recovery, (3) optimizing grid performance through power quality automation, and (4) enhancing system stability by optimizing the water-energy nexus. The innovation of this research lies in integrating these four domains and providing a multi-layered perspective that analyzes economic, technical, operational, and environmental aspects of enterprises. The findings show that artificial intelligence is shaping an integrated management paradigm that ranges from reinforcement algorithms and self-healing networks to robust optimization for renewable resources. The application of artificial intelligence improves power quality, extends asset lifetime, manages the water-energy nexus, and enhances grid resilience. However, challenges such as cybersecurity, reliability, and the simulation-reality gap remain.
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