Digital twins in renewable energy systems: A comprehensive review of concepts, applications, and future directions

Digital Twin (DT) technologies are rapidly transforming the design, operation, and lifecycle management of renewable energy systems. This systematic review investigates DT applications across four major renewable energy domains—solar, wind, hydro, and hybrid systems—encompassing all lifecycle phases from initial design and simulation to maintenance and end-of-life (EoL) optimization. Using a PRISMA-guided methodology and keyword-driven thematic classification, the study analyzes over 150 peer-reviewed and industry-sourced publications from 2014 to 2024. A novel taxonomy is introduced to categorize DT applications by energy type and lifecycle phase, offering a structured and comprehensive perspective on current practices and research directions. The review synthesizes enabling technologies within a layered DT architecture, highlighting the roles of Artificial Intelligence (AI), Internet of Things (IoT), cloud/edge computing, and big data in realizing scalable, intelligent, and autonomous systems. Real-world deployments—such as GE's Digital Wind Farm and Huawei's DT-enhanced solar inverters—demonstrate tangible benefits, including up to a 25 % reduction in downtime and 10–20 % improvements in energy yield. Key challenges are critically examined, including model fidelity, data heterogeneity, standardization, and cybersecurity. In response, the study outlines a forward-looking agenda aligned with global sustainability frameworks such as the UN Sustainable Development Goals (SDGs) and the EU Green Deal. By integrating fragmented literature into a coherent, application-driven framework, this work advances academic understanding, supports industrial innovation, and informs policy development for the next generation of intelligent renewable energy systems. • Offers a disciplined taxonomy of Digital Twin (DT) applications spanning solar, wind, hydro, and hybrid renewable energy systems. • Using a PRISMA-based approach with explicit inclusion criteria and classification rationale, reviews spanning 150 papers (2014–2024). • Stacks enabling technologies including big data, IoT, cloud/edge computing, and artificial intelligence into a layered DT architecture. • Offers real-world industrial and academic case studies with measurable advantages in system optimization, energy output, and maintenance savings. • Points out key issues including cybersecurity, model integrity, and standardizing and provides a forward-looking road map fit for world energy policies.