Review Article: Quantum-AI Convergence for Smart Grid Cybersecurity: A Systematic Review of Emerging Technologies, Threats, and Resilient Architectures
Abstract
Smart grids have revolutionized the way electricity is generated, transmitted, and consumed by utilizing various technologies, including artificial intelligence, IoT devices, advanced communication networks, and distributed energy resources, which have enhanced the efficiency, reliability, and sustainability of modern power systems. This interconnected ecosystem, however, has multiplied the potential attack surface, and quantum computing poses a new risk to the traditional means of securing encoded data, requiring quantum-resilient security solutions. Many reviews focus on a specific aspect of AI, PQC, quantum computing, or smart grid cybersecurity. Following the 2024 PRISMA extension to the PRISMA 2020 methodology, this review systematically analyzed peer reviewed studies published between 2024 to 2026, retrieved from IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, ACM Digital Library, Wiley Online Library, MDPI, and Google Scholar. A thematic analysis of 24 selected studies showed that the major research areas were Artificial Intelligence and Machine Learning (58.3%), Quantum Computing and Quantum Security (50.0%) and Smart Grid Cybersecurity (41.7%). The literature reviewed was evenly distributed between PQC (25.0%) and IoT/CPS security (25.0%), while the other topics, namely Quantum Key Distribution, Digital Twins, Blockchain, and Quantum Reinforcement Learning, were comparatively less explored with 8.3% in each study. The review brings together advances in intrusion detection, anomaly detection, predictive threat intelligence, quantum machine learning, post-quantum cryptography, quantum key distribution, explainable artificial intelligence and digital twins system. Additionally, it reveals new threats such as harvest-now-decrypt-later attacks, incorrect data injection, ransomware, distributed denial-of-service attacks, advanced metering infrastructure, substations, microgrids, and distributed energy resource vulnerabilities. Last, this review identifies key research gaps on interoperability, scalability, computational complexity, privacy, and real-world deployment, as well as introduces a conceptual framework of Quantum-AI convergence for resilient smart grid architectures, and presents a future research roadmap to direct researchers, industry practitioners, energy utility operators, and policymakers in securing and shaping energy infrastructures.