Mbasso, Wulfran FendziHarrison, AmbeDagal, IdrissJangir, PradeepKhishe, MohammadKotb, HossamShaikh, Muhammad Suhail2026-01-312026-01-3120252211-467X2211-4688https://doi.org./10.1016/j.esr.2025.101814https://hdl.handle.net/20.500.12662/10647Digital 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.eninfo:eu-repo/semantics/openAccessCameroonDigital TwinRenewable energy systemsPredictive maintenanceReal-time monitoringCyber-physical systemsArtificial intelligenceReview Article10.1016/j.esr.2025.1018142-s2.0-105012295205Q161WOS:001545747300001Q1