Digital Twin–enabled predictive maintenance in wind, solar PV, hydropower, and battery energy storage systems: a review and maturity framework

Abstract

The rapid digital transformation of energy systems is reshaping how renewable energy assets are monitored, maintained, and optimized across their operational lifecycles. Among emerging digital intelligence paradigms, Digital Twin (DT) technologies have gained prominence as dynamic cyber–physical systems that integrate real-time data, physics-based modeling, and artificial intelligence to enable predictive maintenance and reliability enhancement. This review synthesizes DT technologies applied to predictive maintenance in wind turbines, solar photovoltaic plants, hydropower facilities, and battery energy storage systems. Its novelty lies not only in framing DTs as decision-centric digital intelligence systems that connect sensing, analytics, and maintenance actions to sustainability-oriented outcomes, but also in proposing a Digital Twin Predictive Maintenance Maturity Framework. The framework structures the progression of DT-enabled maintenance from monitoring-oriented implementations toward diagnostic, prognostic, prescriptive, and increasingly autonomous maintenance ecosystems, while integrating architectural, analytical, operational, and deployment-related dimensions for comparative assessment and future research prioritization. The paper further examines enabling technologies, domain-specific implementations, key benefits, and major limitations, and identifies future directions related to explainable and trustworthy intelligence, federated DT ecosystems, autonomous maintenance, and standardized evaluation. Overall, the review positions DT-enabled predictive maintenance as a foundational enabler of intelligent, resilient, and sustainable renewable energy systems. © 2026 The Authors