Digital Twins in Advancing Net-Zero Wind Energy Systems: A Comprehensive Systematic Review

Digital Twin technology is becoming an innovative approach for managing renewable energy systems, particularly in wind-integrated smart grids. It is emerging as a key technology for optimising the performance, reliability, and adaptability of these complex infrastructures. The application of the digital twin in this domain has significantly advanced, enabling enhanced control, monitoring, and predictive capabilities. This systematic review analyses current research on digital twin implementation and effectiveness in wind energy systems, using PRISMA 2020 and PICOS frameworks to evaluate 26 peer-reviewed studies. Sourced from IEEE Xplore, Scopus, ScienceDirect, Web of Science, SpringerLink, Google Scholar, and ACM Digital Library, these studies included experimental simulations, validation models, and mixed-method approaches. Most focused on simulation-based testing, with limited real-world application. Using a credibility-weighted, semi-quantitative synthesis, performance outcomes were interpreted in relation to technology readiness and validation context. Wind farm and microgrid applications showed notable improvements in fault detection, load prediction, and cost efficiency. These technical improvements, including enhanced fault detection, load prediction, and cost efficiency, directly contribute to reduced Levelized Cost of Energy (LCOE) and enable higher renewable penetration, thereby displacing fossil fuel generation. However, challenges include system integration, SCADA compatibility, and missing cybersecurity compliance. Emerging trends like generative AI, agent-based automation, and hydrogen-based grid planning show potential. Digital twins demonstrate promise for renewable grids, but further research is needed to standardise interfaces, enhance long-term validation, and scale adaptive intelligence for complex, real-time energy systems. This review provides insights for advancing digital twin frameworks to accelerate net-zero energy transitions.