Digital Twins for Clean Energy Systems: A State-of-the-Art Review of Applications, Integrated Technologies, and Key Challenges

Abstract

In the context of Industry 4.0, digital transformation is reshaping global energy systems. Among the key enabling technologies, Digital Twin (DT)—a dynamic, virtual replica of physical systems—has emerged as a critical tool for improving the performance, reliability, and safety of clean energy infrastructure. In line with the United Nations Sustainable Development Goals (SDGs)—particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities)—the integration of DTs presents unprecedented opportunities to enhance operational efficiency and support proactive decision making. This state-of-the-art review, focused on studies published in 2020–2025, summarizes applications of DTs across the energy value chain, encompassing a broad spectrum of sectors—including solar, wind, hydropower, hydrogen, geothermal, bioenergy, nuclear, and tidal energy—and their critical role in building-to-grid integration. It synthesizes foundational concepts, assesses the evolution of the DT from a predictive tool to a system-level risk-management platform, and provides a critical analysis of its impact. Furthermore, this review discusses the key challenges hindering widespread adoption, including the critical need for interoperability across systems, ensuring the cybersecurity of socio-technical infrastructure, and addressing the complexities of the human-in-the-loop problem. Key research gaps are identified to guide future innovation. Ultimately, this study underscores the transformative potential of DTs as essential tools for accelerating the digital transformation of the energy sector, offering a robust framework for both methodological development and practical deployment.

1. Introduction

Global warming and the resulting climate disruptions are among the most pressing challenges confronting humanity, significantly affecting ecosystems, intensifying extreme weather events, and destabilizing both natural and human systems worldwide [1]. Effectively addressing climate change demands transformative shifts across all sectors [2] and the incorporation of sustainable practices into economic, technological, and policy frameworks [3]. Clean energy sources and sustainable development initiatives have emerged as viable pathways for achieving decarbonization, resource efficiency, and enhanced long-term resilience [4]. Nonetheless, realizing these objectives requires addressing systemic challenges through advanced technological interventions aimed at optimizing energy systems and strengthening global sustainability efforts.

The global movement toward clean energy is driven primarily by the urgent need to curtail carbon emissions and mitigate climate risk [5]. Despite significant progress, the shift toward clean energy has introduced considerable challenges, such as resource intermittency, complexities related to grid integration, and rapidly evolving regulatory environments [6,7]. Expanding clean energy infrastructure involves managing extensive datasets from distributed generation sources while ensuring system reliability, efficiency, and economic viability—a task further complicated by the inherent variability of wind, solar, and other clean energy forms [8]. Moreover, the operational and maintenance requirements of clean energy infrastructure require advanced monitoring systems capable of real-time analysis, predictive diagnostics, and adaptive decision making [9,10,11].

In response to these challenges, digital transformation has become essential, integrating cutting-edge technologies, data analytics, cloud computing, and artificial intelligence (AI) to optimize energy systems, enhance operational resilience, and support evidence-based policymaking [12]. Central to this digital evolution is the concept of the Digital Twin (DT), a dynamic, virtual representation of physical assets, systems, or processes characterized by real-time synchronization and continuous feedback loops [13]. A DT can represent individual building components or entire energy grids and enables stakeholders to model [14], monitor, predict [15], and optimize real-world performance [16] with exceptional accuracy. The key characteristics of DTs in clean energy systems are illustrated in Figure 1.

Constructed from data obtained through embedded sensors, a DT provides continuous, iterative updates that reflect the actual operational status of its physical counterparts. For example, in wind energy systems, turbines equipped with numerous sensors capture data streams, such as power output, temperature, rotational speed, and structural stress, which are fed directly into their corresponding DTs [17]. This integration enables proactive maintenance, performance enhancement, and accelerated decision making [11].

DT architectures comprise multiple layers, each representing a distinct level of fidelity and analytical scope. At the most fundamental level, component twins model individual components, such as turbine blades, capturing localized parameters (e.g., vibration and material fatigue) to predict [18] and detect early signs of potential failure [19]. Asset twins integrate multiple component-level twins to form a cohesive virtual model of an entire device or machine; for example, for a wind turbine, data from blades [20], gearboxes [21,22], and generators are consolidated to evaluate overall performance and forecast potential failures. Furthermore, system twins encapsulate groups of interrelated assets, such as wind farms, solar arrays, or the integrated energy systems of a smart district [23].

By analyzing interactions among assets, system twins enable informed strategies for load balancing, efficiency improvements, and enhanced energy generation. Finally, at the highest level of abstraction, process twins integrate the performance of multiple systems, including energy production, storage, and grid distribution, to identify systemic inefficiencies and guide strategic decisions for broader energy ecosystems [23]. Complementary to the DT is the concept of a digital thread, a cohesive framework that connects and integrates data across various DT layers throughout the lifecycle of an asset or infrastructure. The digital thread extends from the initial conceptualization, design, and manufacturing stages to deployment, operation, maintenance, and eventual decommissioning. Although DTs provide real-time operational insights specific to assets or systems, the digital thread ensures a unified data flow and supports informed decision making across all life cycle stages [24]. Thus, this dual framework of DTs and the digital thread establishes a fully integrated information environment, significantly enhancing traceability, collaboration, and continuous improvement within complex, data-intensive domains, particularly in the rapidly evolving landscape of clean energy systems [25].

1.1. Emergence of Digital Twin Technology

The origins of the DT concept can be traced back to early space exploration efforts, particularly NASA’s use of mirrored systems during the Apollo missions in the late 1960s [26], as shown in Figure 2. Engineers have developed ground-based replicas to enable real-time troubleshooting as well as the simulation and optimization of spacecraft operations. The term “Digital Twin” was formally introduced by Michael Grieves in 2002 at the University of Michigan in the context of product life cycle management [27]. In this original conceptualization, a DT was defined as a virtual representation of a physical product capable of dynamic interactions and iterative improvements through continuous data exchange.

Over the next decade, NASA advanced this concept by integrating predictive analytics and real-time monitoring into DT models, specifically targeting mission-critical aerospace applications. Simultaneously, as the principles of Industry 4.0 gained traction in the 2010s, DT technologies rapidly permeated sectors such as manufacturing, healthcare, and urban infrastructure. This expansion was largely driven by advancements in computational modeling, big data analytics, machine learning (ML), and the Internet of Things (IoT). DT technology has evolved from an experimental approach to a foundational component of digital transformation strategies, significantly enhancing sustainability, operational performance, and risk mitigation across diverse industries, particularly within the rapidly evolving clean energy sector.

1.2. Integration of Digital Twin Technology with Clean Energy Systems

To address the growing need for efficient, nonpolluting energy solutions, the convergence of DT technology with clean energy initiatives is essential [28]. Although the terms “clean energy” and “renewable energy” are frequently used interchangeably, clarifying their distinctions enhances our understanding of the motivations behind promoting clean energy systems. Renewable energy encompasses sources that are naturally replenished over time, such as solar radiation, wind currents, hydropower, and biomass. However, certain forms of renewable energy, including biogas and biodiesel, still generate greenhouse gas emissions during combustion, thus contributing to atmospheric pollution despite their renewable nature [29]. By contrast, clean energy specifically refers to energy sources with very low or zero greenhouse gas emissions and minimal environmental impact [5]. Consequently, prioritizing inherently renewable sources, such as solar, wind, hydro, and geothermal energy, offers the most robust pathway for substantial carbon mitigation without inadvertently introducing new environmental pollutants [4].

Integrating DT technology into clean energy systems represents a crucial and strategically valuable response to the increasing complexities of renewable infrastructure, driven by rapid advancements in digital technologies. As countries intensify efforts to reduce greenhouse gas emissions, renewable energy sources, particularly solar and wind sources, face operational challenges, including resource intermittency [30], complex grid integration [31], and demanding maintenance schedules [32]. DTs address these challenges by enabling precise real-time monitoring, advanced predictive analytics, and informed decision-making capabilities. At the grid edge, this enables intelligent buildings to participate in demand response programs, transforming them from passive consumers into active “prosumers” (entities that both produce and consume energy through onsite generation and storage systems [33]), enhancing both local and systemic resilience. Operators utilizing DTs can effectively optimize asset performance, minimize downtime, and enhance grid stability using accurate, data-driven insights. Furthermore, as clean energy initiatives expand in scale, DT technology supports virtual testing and the iterative refinement of system designs before substantial physical investments are implemented. This strategy lowers costs and mitigates technological risks substantially. The integration of DT capabilities with clean energy objectives enhances operational resilience, accelerates technological innovation, and advances the sustainability goals essential for addressing climate change.

The global energy landscape continues to diversify and expand, creating an unprecedented need for real-time, data-driven intelligence to manage increasingly complex systems and regulatory frameworks [25]. DTs offer a comprehensive solution by creating dynamic virtual replicas of physical energy assets and entire systems, thereby enabling continuous monitoring, predictive modeling, and proactive optimization strategies [11]. By integrating live operational data with advanced analytical tools, DTs can effectively manage the inherent intermittency and variability associated with clean energy resources, thereby improving system reliability, reducing maintenance costs, and enhancing overall resilience [8]. The alignment of DT technology with the expanding deployment of clean energy is particularly significant as governments and industries intensify efforts to achieve ambitious decarbonization targets.

1.3. Objectives and Structure of the Review

The increasing urgency to establish a sustainable, net-zero energy future has intensified efforts to integrate advanced digital technologies with clean energy systems [11]. DT technology, in particular, offers significant potential for improving the efficiency, reliability, and scalability of renewable and clean energy infrastructure [8]. This review examines the state-of-the-art application landscape of DTs within the clean energy sector, critically assesses recent advancements, and identifies significant research gaps. It contributes to the broader discussion on digitalization in energy systems by providing insights aimed at accelerating the adoption of intelligent, data-driven solutions aligned with global sustainability targets, particularly the United Nations (UN) Sustainable Development Goals (SDGs) 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities).

The research framework guiding this review centers on three fundamental areas: the implementation of DT technology within the clean energy sector, key technical, economic, and regulatory challenges encountered during the adoption of DTs in clean energy systems, and enhancement of DT capabilities and effectiveness in clean energy applications via the integration of AI, ML, and deep learning (DL) methods.

Accordingly, the primary objectives of this study are threefold, each contributing to a holistic understanding of the role of DTs in the clean energy transition:

• Comprehensive cross-sector synthesis: Unlike previous reviews that typically focus on a single energy domain, this study analyzes state-of-the-art DT applications across the clean energy spectrum—ranging from utility-scale resources to decentralized Building-to-Grid (B2G) integration—identifying common methodological patterns and sector-specific innovations.
• Bridging clean energy supply and urban demand: It explicitly connects the “sustainable use of natural resources” with “sustainable urban planning” by critically examining B2G integration of clean energy systems. This highlights the DT’s evolving role from a passive monitoring tool to an active orchestration platform for smart districts and prosumers.
• Strategic roadmap for implementation: It identifies the critical technical, economic, and policy barriers hindering widespread adoption—particularly interoperability and human-in-the-loop challenges—and proposes strategic pathways for integrating AI-driven DTs into resilient clean energy systems.

To fulfill these objectives, the review is organized as follows: Section 2 details the systematic search strategy and selection criteria utilized for this study. Section 3 establishes foundational knowledge of DTs, detailing their historical evolution and technological foundations, before exploring their applications across the clean energy spectrum, including utility-scale assets and B2G integration. Section 4 discusses the prominent challenges and limitations associated with adopting DTs, including data interoperability, computational complexity, cybersecurity risks, and regulatory constraints. Finally, Section 5 concludes the review by outlining strategic recommendations and future research opportunities aimed at maximizing the role of DTs in promoting efficient, resilient, and sustainable clean energy transitions.

2. Methods

This section outlines the methodological framework applied in this state-of-the-art review. The material analyzed comprises a comprehensive corpus of peer-reviewed literature spanning the years 2020–2025. The following subsections detail the systematic search strategy utilized to identify these records (Section 2.1), the specific inclusion and exclusion criteria applied to select key studies (Section 2.1.1), and the thematic analysis methods used to synthesize the findings across diverse clean energy sectors.

2.1. Search Strategy and Database Selection

A state-of-the-art review was conducted to provide an in-depth understanding of the applications of DT technology in the field of clean energy. A comprehensive literature search was conducted across the following academic databases, selected based on the breadth of content, scholarly reliability, and recognized academic credibility: Scopus, Web of Science, IEEE Xplore, ScienceDirect, Google Scholar, Taylor & Francis Online, Wiley Online Library, the Directory of Open Access Journals, and arXiv (Cornell University).

Various combinations of targeted keywords were used for the search, including “digital twin,” “clean energy,” “green energy,” “renewable energy,” “sustainability,” “decarbonization,” “net-zero emissions,” “machine learning,” “artificial intelligence,” and “deep learning.” Boolean operators (AND and OR) were strategically employed to refine and narrow the search results. Additionally, database-specific query adjustments were implemented to optimize search precision and relevance. To ensure reproducibility, the primary search string was defined as follows: (“Digital Twin” OR “Digital-Twin”) AND (“Clean Energy” OR “Renewable Energy” OR “Solar” OR “Wind” OR “Hydro” OR “Hydrogen” OR “Geothermal” OR “Nuclear” OR “Bioenergy” OR “Tidal” OR “Building-to-Grid”).

2.1.1. Inclusion and Exclusion Criteria

Following the literature search, articles were systematically evaluated based on title and abstract screening. The inclusion criteria were as follows:

• Peer-reviewed journal articles, peer-reviewed conference papers, reviews, and book chapters published between 2020 and 2025.
• Studies explicitly investigating DT applications within the clean energy sectors.
• Articles examining the contribution of DT technologies to enhancing sustainability, efficiency, and operational optimization in clean energy systems.
• Research incorporating AI, DL, and ML methodologies in the context of DTs and clean energy systems.
• Publications written exclusively in English.

The exclusion criteria were as follows:

• Non-peer-reviewed publications, including editorials, conference papers, abstracts, notes, case reports, and short commentaries.
• Studies addressing DT applications outside the energy sector.
• Articles lacking a clear and direct connection to clean energy, renewable energy, sustainability, or DT technologies.
• Publications dated prior to 2020, considering the relatively recent advent and evolution of DT applications within the energy and sustainability contexts.

The initial search yielded 5912 records. After removing duplicates and performing the initial screening based on titles and abstracts, 480 records were retained. Subsequently, full-text evaluations of the selected publications were conducted to confirm their eligibility and relevance, resulting in a final set of 136 key publications. Data extraction was structured around predefined categories, including year of publication, study objectives, DT application domains, methodologies employed, key outcomes, limitations, and future research recommendations. A comprehensive thematic analysis was subsequently conducted to synthesize the findings, highlight critical trends, identify existing research gaps, and propose future research directions, thereby establishing a robust and coherent foundation for advancing the integration of DTs into clean energy systems.

2.1.2. Co-Occurrence Analysis of Keywords in Digital Twin Research for Clean Energy

To systematically clarify the current research landscape and thematic trends related to DT applications in clean energy systems, a co-occurrence analysis of keywords was performed. Using VOSviewer software version 1.6.20, a network was generated (Figure 3), illustrating prominent concepts, interrelationships, and emerging research clusters within the analyzed literature. The figure depicts connections among 90 keywords, selected from 1359 total keywords, meeting the minimum occurrence threshold of 4, based on 136 analyzed papers.

Figure 3 presents the keyword co-occurrence network mapping the conceptual landscape of DT research in clean energy systems. The largest node, DT, serves as the central anchor, confirming its role as the field’s unifying construct. Surrounding it, high-frequency terms coalesce into the following four well-defined thematic clusters:

• AI-Centric Optimization (green cluster): Keywords such as “machine learning,” “deep learning,” “reinforcement learning,” “forecasting,” and “optimization” co-occurred with technology-specific terms, including “wind turbine,” “photovoltaics,” and “power grids.” This cluster reflects an intense research focus on data-driven models that improve resource forecasting, fault detection, and operational scheduling for renewable assets.
• Smart-Grid Digitalization (red cluster): Nodes labeled “smart grid,” “clean energy,” “renewable energy sector,” “energy policy,” and “predictive maintenance” highlighted research integrating real-time analytics with grid-level decision support, emphasizing reliability and the policy-aligned deployment of clean technologies.
• Cyber–Physical Infrastructure & Security (blue cluster): The co-occurrence of “internet of things,” “cyber security,” “data analytics,” and “information management” indicated a parallel research stream focused on secure, cloud–edge architectures that enable continuous data exchange between physical assets and their DTs.
• Sustainability & Systems Integration (yellow/purple sub-clusters): Terms such as “energy efficiency,” “environmental impact,” “alternative energy,” and “digital storage” reflected holistic research integrating life cycle assessment, storage technologies, and multi-objective optimization with DT frameworks.

The network revealed that current research converges on (i) AI-enhanced modeling and control, (ii) smart-grid orchestration and policy alignment, (iii) secure IoT–cloud infrastructure, and (iv) system-wide sustainability assessment. These intersecting themes underscore a strategic research trajectory that employs DTs as cyber–physical intelligence layers to enable resilient, efficient, and low-carbon energy systems.

3. Evolution of Digital Twin Technology

Advances in information technology, including the rise of big data analytics, proliferation of IoT, and breakthroughs in AI and ML, have fundamentally reshaped the capabilities of DTs. These innovations have transformed static simulation models into dynamic, intelligent systems capable of real-time synchronization with physical assets and autonomous decision making [34]. Consequently, DT technology has rapidly extended beyond its aerospace and manufacturing origins, finding valuable applications across a broad spectrum of domains, such as healthcare, transportation, urban infrastructure, and, most notably, the clean energy sector.

In this expanding context, clean energy systems have emerged as particularly fertile ground for DT deployment. The inherent variability and distributed nature of renewable energy sources, such as wind, solar, and hydropower, demand adaptive, data-driven management strategies. DTs offer a powerful basis for real-time monitoring [35], predictive diagnostics [36], and operation optimization [37]. From wind turbine condition assessment and photovoltaic (PV) yield forecasting to the intelligent control of energy storage systems, DTs are now central to the development of resilient, efficient, and sustainable energy infrastructure. Their integration not only improves operational reliability and reduces maintenance costs but also supports strategic decision making aligned with long-term sustainability goals [38].

3.1. Key Differences Between Digital Twins and Conventional Models

Traditional simulation and modeling techniques, including computational fluid dynamics (CFD), finite element analysis, and static system simulations, have served as essential tools for validating system design, assessing performance, and optimizing processes. Despite their long-standing utility, these conventional modeling methods often exhibit significant limitations, particularly regarding their adaptability, responsiveness, and capacity for dynamic interaction with real-world systems. DT technology overcomes these constraints by incorporating continuous real-time data, interactive feedback mechanisms, and dynamic functionality [34].

One fundamental distinction is real-time data integration. Conventional models generally rely on historical or predefined datasets and are not equipped to incorporate evolving inputs from operational environments. By contrast, DTs maintain a continuously updated virtual representation synchronized with live sensor data from physical systems [39]. This synchronization enables accurate and immediate performance monitoring and supports real-time optimization, which is crucial for managing the fluctuating and often unpredictable conditions associated with renewable energy systems.

Another key differentiator is the concept of bidirectional communication, creating a true cyber–physical feedback system. Unlike the unidirectional information flow of conventional simulations, a DT operates on two distinct closed loops bidirectionally. The first is a recalibration loop, where real-time sensor data from the physical object flows continuously to the DT. This ensures that the digital representation is always synchronized and its parameters are updated to reflect the true state and health of its physical counterpart. The second is an optimal control loop, where calibrated models are used to simulate future scenarios and implement optimal control actions. These commands are then transmitted back to the physical system’s actuators, enabling adaptive and proactive operational control. This dual-loop architecture allows the DT to function as a dynamic, self-correcting system [13].

Finally, DTs demonstrate superior dynamic functionality and predictive capabilities compared with those of traditional models. Although conventional simulations provide static analyses under predetermined scenarios, DTs evolve dynamically using ML algorithms and AI-driven analytics [40]. This allows DTs to learn adaptively from ongoing operations, quickly detect anomalies [41], predict component failures [42], and facilitate scenario-based analyses under changing operational and environmental conditions. Such capabilities are particularly valuable for renewable energy applications, where external factors, such as weather variability, grid fluctuations, and demand dynamics, affect performance and reliability significantly. Collectively, these distinguishing features highlight the transformative potential of DT technology, positioning it as a robust and essential tool for enhancing efficiency, sustainability, and resilience in the increasingly complex landscape of clean energy systems. Transitions from conventional models to intelligent DTs are summarized in Figure 4.

3.2. Levels of Integration of Digital Twins in Clean Energy Systems

To comprehensively understand DT operations, the three fundamental levels of digital integration, Digital Model, Digital Shadow, and DT, must be distinguished, as summarized in Figure 5. This classification, established in the seminal work by Kritzinger et al. [43] and further elaborated in recent reviews [13], categorizes digital integration in renewable and clean energy systems into three distinct yet interrelated forms, each characterized by varying degrees of complexity, responsiveness, and interaction with physical systems. At the foundational level, the digital model represents a basic digital replication, defined primarily by static information or manually updated inputs without automated real-time data integration. Its applications are generally limited to initial feasibility assessments, offline simulations, and static performance optimization, typically at the component or simplified process levels [13].

The digital shadow introduces real-time automatic data flow but in a unidirectional manner—from physical assets to digital representations. This enables continuous monitoring, anomaly detection, condition assessment, and data visualization without actively influencing the physical system. For instance, a digital shadow might be employed in solar power installations to monitor real-time power output and detect performance issues; however, it does not send corrective commands directly or optimize asset performance interactively [13].

The highest integration level is represented by the true DT, distinguished by its bidirectional automatic communication channel, which enables ongoing, interactive exchanges between digital and physical environments. This advanced integration enables real-time control, predictive analytics, adaptive optimization, and dynamic decision making across multiple scales—from individual components to entire hybrid systems. Examples include wind turbine DTs, which monitor structural and operational data and actively adjust operational parameters to optimize performance and prevent potential failures [44].

The classification levels, ranging from component-level DTs focused on specific parts to hybrid-level twins that combine physics-based modeling and data-driven analytics, facilitate clearer implementation strategies and enhance the adaptive capabilities of clean energy systems. By understanding and carefully selecting the appropriate DT integration level, stakeholders can more effectively leverage DT technology to achieve operational excellence, enhanced sustainability, and greater resilience in clean energy infrastructure.

3.3. Relevance of Digital Twins in Clean Energy Systems

As the global energy landscape evolves toward greater sustainability and carbon neutrality, DT technology has emerged as a crucial tool for the management and optimization of clean energy systems [45]. The intrinsic variability of renewable energy sources, such as intermittency and seasonal variation in solar, wind, and hydropower generation, limits operational reliability and energy production efficiency. DTs offer a sophisticated approach to addressing these inherent challenges by providing real-time virtual representations of renewable energy assets and infrastructure, significantly enhancing the monitoring, optimization, and predictive capabilities of energy systems.

The primary importance of DTs in renewable energy lies in their ability to dynamically optimize power generation processes. By leveraging real-time data from environmental sensors and meteorological models, DTs enable the accurate forecasting of renewable resources, such as solar radiation, wind speeds, and water flow rates. By continuously updating these forecasts, DTs facilitate adaptive control strategies that maximize power output while minimizing operational losses. For example, DTs of PV installations can predict periods of cloud cover or reduced irradiance and dynamically adjust inverter settings or storage strategies to maintain optimal performance. Similarly, DT models of wind turbines can integrate wind speed forecasts, structural health data, and performance parameters to continuously recalibrate turbine operations, thereby optimizing power generation and extending asset longevity [46].

DTs play a crucial role in enhancing grid stability and facilitating effective energy storage management. The integration of renewable and clean energy introduces significant complexities in grid operations because of fluctuating generation profiles and the presence of distributed energy resources. DTs can comprehensively model grid interactions by incorporating real-time load dynamics, energy production data, and battery storage states. By enabling predictive analytics and adaptive energy dispatch strategies, DTs optimize state-of-charge levels in energy storage systems, ensure effective load balancing, and support demand response mechanisms. In decentralized microgrids, DTs enhance self-healing capabilities significantly by automatically detecting and responding to faults or disruptions, thus ensuring continuous energy supply and resilience in distributed energy environments [47].

Predictive maintenance is another crucial application area for DTs in clean energy systems [48]. Traditional maintenance practices rely heavily on periodic inspections and reactive interventions, often leading to unnecessary downtime and increased operational costs. Alternatively, DTs leverage real-time condition monitoring data, including vibrations, temperature anomalies, and structural stress indicators, alongside advanced ML algorithms to predict component failures and degradation patterns. This proactive maintenance strategy allows operators to perform timely interventions, thereby significantly reducing unplanned outages and maintenance costs. For instance, wind turbine DTs continuously assess vibration patterns, lubricant quality, and blade integrity, accurately forecasting component wear and potential failures weeks or even months in advance [49].

The integration of DTs with emerging technologies, such as AI, ML, and IoT, enhances the effectiveness and applicability of green energy infrastructure substantially [8]. IoT-enabled sensors enable comprehensive data acquisition across clean energy assets and transmit large volumes of operational data to DT platforms for advanced processing and analysis. ML and AI techniques facilitate data interpretation and support predictive diagnostics, anomaly detection, and autonomous optimization of system operations. This intelligent convergence enables the creation of sophisticated smart grids capable of automated control, efficient load balancing, and adaptive responses to fluctuations in energy supply and demand. Therefore, DTs promote the evolution of decentralized energy ecosystems, facilitating the transition of cities, industries, and communities toward renewable integration and long-term sustainability.

Additionally, DTs play a critical role in advancing sustainability performance metrics and supporting global decarbonization initiatives. These systems enable real-time monitoring and analysis of energy usage patterns, greenhouse gas emissions, and overall environmental impact, thus equipping decision-makers with accurate and timely information to support effective sustainability strategies. By quantifying carbon footprints and identifying inefficiencies in energy systems, DTs assist in formulating targeted actions for emissions reduction and resource optimization. By leveraging DT-driven analytics, energy stakeholders can align operations with net-zero targets, strengthen environmental compliance frameworks, and enhance transparency in reporting sustainability outcomes to regulatory bodies, investors, and the broader public. As the global energy sector advances toward decarbonization and digitalization, the adoption of DTs is expected to increase further, becoming an essential component of modern energy management strategies and a key pillar in achieving global climate objectives.

3.4. Applications of Digital Twins in Clean Energy Systems

As a core enabler of Industry 5.0’s vision for human-centric, sustainable, and resilient systems, DTs optimize the synergy between human expertise and machine intelligence, facilitating flexible, accurate, and environmentally conscious management of energy infrastructure [50]. The adoption of DT technology is reshaping the design, operation, and optimization of energy systems. By seamlessly integrating real-time sensor data with high-fidelity modeling and AI-driven analytics, DTs provide a robust framework for enhancing operational efficiency, predicting and mitigating faults, improving system resilience, and enabling accurate, adaptive energy forecasting.

With the rapid expansion of data availability and advancements in IoT infrastructure and computational technologies, the development of DTs is shifting toward data-driven architectures. Modern solar energy systems generate massive streams of operational and environmental data through distributed sensors, smart meters, and cloud-connected platforms. This influx of high-resolution, real-time data enables a shift in DTs from conventional model-based replicas into intelligent, adaptive systems that learn and optimize via continuous data integration. In this context, data-driven DTs leverage ML algorithms, statistical inference, and DL models to identify patterns, forecast energy yields, and adapt to dynamic operating conditions without requiring regular manual recalibration. These capabilities enhance the precision of fault detection, granularity of performance tracking, and responsiveness of control strategies substantially. Furthermore, as DT ecosystems become more interconnected and interface with edge computing devices and decentralized energy platforms, they facilitate real-time decision making and autonomous system adaptation, marking a paradigm shift in the management and optimization of solar energy assets within intelligent, future-ready grids. The following section presents an in-depth analysis of DT applications across key clean energy sectors, emphasizing the critical roles of these technologies in accelerating sustainable, intelligent energy transitions.

3.4.1. Solar Energy

Solar energy systems are broadly categorized into two primary types: PV and solar thermal systems. Although both harness solar radiation, they do so via fundamentally different mechanisms and serve distinct energy purposes. PV systems convert sunlight directly into electricity using semiconductor materials, whereas solar thermal systems use sunlight to produce heat, which can be applied either directly for thermal uses (e.g., water or space heating) or for electricity generation via concentrated solar power (CSP) technologies.

In PV systems, DTs provide significant value by enabling real-time performance monitoring, fault detection, system optimization [51], and predictive maintenance [52]. They achieve this by integrating environmental and operational data, such as irradiance, ambient temperature, and module efficiency, which allows for the simulation of energy output, early detection of anomalies [53], and predictive maintenance. DTs can dynamically model inverter behavior, assess degradation patterns in panels, and implement condition-based maintenance to minimize downtime and maximize energy output. Moreover, DTs support intelligent energy management in smart-grid contexts by simulating energy flow, coordinating with battery storage, and optimizing demand response. When augmented with AI, DTs further enhance planning accuracy, fault diagnostics, and forecasting capabilities, making them essential tools for operating and controlling PV-based energy systems.

DT applications in solar thermal systems, particularly in CSP setups, are equally impactful. In CSP plants, DTs simulate the entire thermal loop—from heliostat alignment and receiver performance to heat transfer, fluid dynamics, and thermal energy storage behavior. This enables real-time system optimization and accurate prediction of thermal output under varying solar conditions. For solar thermal heating systems, such as residential or industrial water heating or cooling systems [54], DTs are used to model collector efficiency, monitor heat transfer efficiency, and predict component wear. They also facilitate intelligent scheduling and integration with district heating networks to ensure energy-efficient operation. By enabling detailed monitoring and control across both PV and solar thermal domains, DTs play a transformative role in maximizing efficiency, reducing operational costs, and accelerating the deployment of smart, sustainable solar technologies.

Table 1. Recent research trends in DT-based approaches in PV and solar thermal energy systems.

Objective	Role of DT	Ref.
Predict energy production of PV panels	A DT framework for PV panels focused on energy production modeling and efficiency monitoring	[50]
Advance the condition monitoring and fault detection of urban distributed solar PV systems	Uses DTs as a tool for modeling, monitoring, and managing the five building-attached PV systems	[53]
Reduce carbon emissions and support zero-energy buildings	Provides real-time monitoring, analysis, and fault detection	[54]
Develop a cyber-resilient optimization framework	Misleads attackers, supports real-time cyber defense, and enhances grid security and operational resilience under cyber–physical threats	[55]
Enhance energy management	Simulates the effects of load profiles on microgrids, facilitating improved energy planning and decision making	[56]
Propose a distributed energy management strategy	Models microgrid components	[57]
Obtain the DT of a PV solar farm	Uses the obtained DT for anomaly detection	[58]
Optimize the scheduling of demand-responsive appliances	Uses DT structures for household users with various initial loads to simulate and design multiple scenarios	[59]
Load scheduling employing reinforcement learning for minimizing energy bills	Provides a mathematical framework for load scheduling in DT-based microgrids	[60]
Sustainable energy management for microgrids	Enables more accurate charging and discharging management of energy storage systems	[61]
Real-time fault detection for optimal scheduling	A DT model is developed to capture the complexities of renewable energy sources	[62]
Forecast PV power output	Uses DT to create a highly realistic simulation environment for accurate monitoring, optimal control, and decision support for power system operations	[63]
Analyze PV system failures	Constructs theoretical, feature, and visual twins based on the concept of DTs	[64]
Enhance PV system efficiency	Supports real-time monitoring, predictive maintenance, and operational optimization under varying environmental conditions	[15]
Optimize maintenance strategies under resource constraints	Supports real-time monitoring and decision making	[65]
Improve the prediction of bifacial PV system performance under varying conditions	Facilitates real-time power prediction with high accuracy and efficiency by simulating behavior under different shading scenarios, eliminating the need for direct irradiance measurements	[66]
Short-term PV power prediction	Accurately simulates PV system’s behavior under varying weather conditions, especially cloudy scenarios	[67]
Implement fault diagnosis	Generates typical data across operational states and builds a deep data model to learn the distribution characteristics of the mechanism model	[68]
Improve battery performance and prediction accuracy	Supports real-time monitoring, diagnostics, and error correction in domestic solar energy storage systems	[35]
Develop energy management system	Simulates distributed energy resources effectively within distribution grids	[69]
Detect, localize, and classify grid-connected PV array faults	Analyzes the current ratio of each PV array through detection and localization	[70]
Enhance interpretability	Eliminates the need for additional signals or sensors and estimates unknown parameters in the mechanism model using operational data	[71]
Facilitate fault detection	Facilitates optimal power tracking throughout the day and accurately replicates the behavior and attributes of a physical entity by integrating real data into the PV block	[72]
Optimize energy efficiency	Supports real-time adaptation to changing environmental conditions	[73]
Design solar thermal energy systems	Tracks and optimizes the flow of incoming solar power through a complex solar thermal storage system	[74]
Control and optimize start-up and shut-down processes	Performs real-time, dynamic simulation of temperature, enhancing control system reliability	[75]
Support control, optimization, and accurate dynamic simulation	Enables real-time decision making and system optimization under solar intermittency and part-load conditions	[76]
Identify cyberattacks involving false data injection	Simulates Fresnel plant operations and controller behavior	[77]
Predict solar flux density and enable nonintrusive receiver-efficiency assessment	Produces accurate flux maps, supporting semi-autonomous monitoring and control of CSP operations	[78]

provides a summary of recent DT applications in PV and solar energy systems.

An analysis of the literature revealed a stark imbalance in DT research for solar energy, with a predominant focus on PV systems over their solar thermal counterparts [50,63,67,74,75,78]. For PV systems, research is unequivocally focused on the management of intermittency; consequently, applications center on leveraging real-time data for high-fidelity forecasting and fault detection to enhance the predictability of grid-scale equipment. However, a critical distinction for building integration lies in the management of thermal dynamics [54,76]. This involves prioritized objectives: mitigating the heat-induced efficiency degradation inherent to PV operation while recovering the waste heat as a low-temperature resource. This synergy is fully realized in building-integrated PV (BIPV) and PV-thermal (PVT) systems, where control algorithms leveraging the DT must co-optimize electrical and thermal performance.

3.4.2. Wind Energy

The inherent complexity and variability of wind energy systems make them ideal candidates for DT implementation. DTs simulate wind turbine aerodynamics, mechanical loads, and electrical performance under various atmospheric conditions. By incorporating real-time Supervisory Control and Data Acquisition (SCADA) data, vibration analysis, and meteorological variables [79], DTs support advanced condition monitoring and predictive maintenance of components such as blades, gearboxes, and generators. In offshore wind farms, DTs are particularly valuable for remote performance assessments and optimization of maintenance scheduling [80]. Moreover, DTs enable advanced, farm-level control strategies, such as real-time wake steering and dynamic yaw adjustments, to maximize collective energy yield and reduce structural fatigue, thereby improving the overall operation and maintenance of wind farms [81]. DTs also support real-time operational control, resulting in improved operational efficiency and enhanced life cycle impact assessments via the integration of structural health data and long-term performance trends [82]. Recent applications of DTs in wind energy systems are summarized in

Table 2. Recent research trends in DT-based approaches for wind energy systems.

Objective	Role of DT	Ref.
Optimize performance	Estimates the real-time state of wind turbines	[83]
Monitor structural health	Reconstructs high-fidelity stress field in real time using sparse monitoring data	[84]
Optimize energy production	Accurately simulates and predicts wake effects within wind farms	[85]
Enhance wind turbine reliability	Monitors gearbox condition	[86]
Predict lifetime	Assesses production peculiarities and imperfections occurring during manufacturing	[87]
Perform intelligent operation and maintenance	Predicts faults in real time	[88]
Reliability-based maintenance optimization	Enhances risk-based structural integrity assessments and optimizing maintenance strategies.	[46]
Monitor remaining useful fatigue life	Performs online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example of fatigue mode	[89]
Detect surface damage	Detects and semantically segments wind turbine surface features in real time	[90]
Diagnose faults	Monitors and simulates actual operating conditions in real time	[91]
Design optimal wind turbine gearboxes	Models and simulates wind turbine gearboxes to improve their design, diagnosis, operation, and maintenance	[81]
Enhance offshore floating wind turbine performance	Controls the real-time structural state of composite wind turbine structures and forecasts the remaining useful life by tracking fatigue	[92]
Monitor structural health	Analyzes diverse damage scenarios and detects damage in structures	[93]
Estimate fatigue lifetime	Estimates structural states, aerodynamic estimators, and physics-based virtual sensing procedures	[94]
Monitor rotor blade aerodynamics	Performs real-time analytics and predictive modeling	[95]
Predict short-term wind power output	Reliably predicts wind power in real time	[96]
Diagnose faults	Determines the real-time operational status of distribution networks connected with distributed wind power	[97]
Improve the safety and lifespan of floating offshore wind turbines	Uses data-driven models to detect long-term drift and forecast axial tension in mooring lines, enabling proactive maintenance, stress reduction, and real-time safety warnings during operations	[98]
Predict spatiotemporal wind fields to improve energy yield prediction, monitoring, and structural load assessment	Reconstructs high-resolution wind flow fields in real time, enabling accurate mirroring and analysis of wind farm behavior	[99]

.

The dominant application of DT in wind energy, distinct from other renewables, is overwhelmingly focused on structural health monitoring and predictive maintenance to manage the immense mechanical stresses inherent to turbine operation [84,86,89,92,94]. This focus is intensified in the specialized application of DTs to building-integrated wind turbines (BIWTs), where the turbine and host building form a complex, co-dependent cyber–physical system. Here, the potential for symbiotic aerodynamic augmentation and sustainability branding is directly challenged by the limiting factors of severe structure-borne vibration and a prohibitively high levelized cost of electricity (LCOE), which have led to notable project failures. The role of the DT thus evolves from an energy forecaster into an integrated risk-management platform, enabling a hierarchical co-optimization; it must maximize energy yield [85,95,96,99] only within the strict, continuously monitored constraints, such as operational safety, structural integrity [92,98], and occupant acoustic comfort. This encapsulates the challenges associated with mechanical energy harvesting within a habitable environment.

3.4.3. Hydropower

In hydropower generation, DTs optimize the operation and maintenance of dams, turbines, and reservoir systems [100]. Virtual models of hydroelectric facilities integrate sensor data related to water inflow, head levels, and equipment vibrations to predict performance trends, monitor dam safety, and assess the impact of sedimentation. DT dashboards support decision making by visualizing real-time system status and enabling scenario testing for gate operation, flood control, and load dispatch optimization [101]. Additionally, DTs are implemented in urban water–energy nexus modeling, facilitating sustainable hydropower planning in coordination with water resource management and environmental flow requirements [102]. The integration of DTs enables the predictive maintenance of pump–turbine systems and enhances the structural monitoring of dam foundations and surrounding geotechnical conditions [100]. Recent applications of DTs in hydropower energy systems are summarized in

Table 3. Recent research trends in DT-based approaches for hydropower energy systems.

Objective	Role of DT	Ref.
Establish a predictive model	Enhances the robustness of hydro turbine failure prediction by simulating data for rare operating scenarios	[42]
Support maintenance decision making	Provides models for predictive maintenance	[103]
Detect and optimize faults in hydropower system operations	Enables real-time modeling, predictive analysis, and operational optimization, improving efficiency, fault detection accuracy, and system reliability	[101]
Manage system performance	Performs real-time monitoring and predictive analysis	[102]
Manage operations efficiently	Features modular components, including data acquisition, data processing, visualization, and integration layers, emphasizing scalability, adaptability, and secure interoperability	[104]
Design and develop a pump–turbine monitoring system	Visualizes and monitors actual pump–turbine operating conditions	[105]
Manage multipurpose hydropower	Optimizes a hybrid renewable water supply system	[106]
Detect defects	Facilitates the intelligent transformation of hydropower stations while reducing system maintenance costs	[107]
Develop an adaptive learning model	Dynamically models actual hydropower turbines	[108]
Increase the flexibility of hydropower	Ensures safe and effective transfer of the reinforcement learning algorithm’s strategy from a virtual test environment to the physical asset	[109]
Model Francis hydro turbines	Collects real-time operational data to train the nonlinear dynamics of the Francis hydro turbine	[110]

.

Shifting from the dynamic stresses of wind turbines, the application of DTs in hydropower addresses the challenges of managing massive, long-lifecycle civil infrastructure and complex water–energy systems. The literature shows a dual focus. At the component level, DTs enable the predictive maintenance of turbines and pumps [42,103] more critically, at the system level, they serve as holistic platforms [101,102,104] for monitoring dam safety and optimizing operations for multiple competing objectives, such as power dispatch, flood control, and environmental flows. The overarching goal is to enhance the inherent dispatchability and flexibility of hydropower plants [109], particularly their role in pumped-hydro storage in hybrid clean energy systems. By optimizing both macro- and micro-hydropower systems within the urban water–energy nexus, the DT provides the framework to schedule flexibility services—fast ramping, primary/secondary frequency regulation, and contingency reserves—to mitigate short-term variability and intermittency in wind and solar generation. This function directly mitigates curtailment and provides the essential grid stability needed to reliably integrate volatile renewables and distributed, building-level assets.

3.4.4. Hydrogen Energy

DT technologies are rapidly emerging as transformative tools in hydrogen value chains, from production and storage to utilization and safety [111]. For electrolyzer systems (proton exchange membrane (PEM) and alkaline), DTs simulate thermodynamic behavior, predict gas purity, and monitor degradation under dynamic loading conditions [112]. They facilitate techno-economic assessments for large-scale deployment by optimizing system configurations and energy efficiency [113]. In hydrogen fuel cell systems, DTs support predictive diagnostics, lifetime estimation, and real-time control strategies, particularly for transportation and microgrid applications. At hydrogen refueling stations, DTs enable plume dispersion forecasting, leak detection, and risk mitigation using DL-based simulations. Furthermore, DTs are essential for managing hydrogen integration into hybrid clean energy systems and pipeline networks, thereby enhancing both system safety and operational sustainability [114]. Recent applications of DTs in hydrogen energy systems are summarized in

Table 4. Recent research trends in DT-based approaches for hydrogen energy systems.

Objective	Role of DT	Ref.
Provide a comprehensive guide for developing a DT of a PEM electrolyzer for green hydrogen production	Enables real-time monitoring, fault detection, diagnosis, and predictive control to enhance efficiency and extend electrolyzer lifespan	[112]
Model and optimize energy conversion	Enables real-time simulation, predictive analysis, and power quality control to enhance power-to-hydrogen system efficiency and scalability	[113]
Develop a digital architecture for hydrogen production at a hydroelectric power station within a local smart grid using the Internet of Energy (IoE)	Simulates hydrogen integration into smart grids, supports architectural design, and enhances system efficiency through IoE and ML-based coordination	[114]
Develop a data-driven DT to model and control the dynamics of high-temperature PEM electrolyzer cells	Predicts power, hydrogen output, and temperature; enables advanced control to reduce overshoot and enhance system durability	[115]
Develop a DT of a megawatt-scale alkaline electrolyzer for safe analysis under complex operating conditions	Simulates key parameters (voltage, pressure, and H2 concentration), supports real-time control, and enables safe testing under wide fluctuations in power	[116]
Simulate and control a PEM water electrolyzer in a smart microgrid	Enables safe, controlled simulation of PEM water electrolyzer behavior, supports monitoring and control via a graphical user interface, and facilitates integration within PV-powered hydrogen microgrids	[117]
Analyze and compare PEM electrolyzer models	Supports smart microgrid integration by enabling flexible model handling, simulation, and visualization under various operating conditions	[118]
Validate safe and effective power electronic converter designs	Enables real-time simulations to test and verify converter behavior under operational conditions, reducing safety risks prior to deployment	[119]
Assess the feasibility and long-term performance of solar hydrogen-powered systems	Simulates 30 years of solar hydrail operation, enabling cost, emission, and sensitivity analyses to evaluate its viability as a sustainable alternative to diesel locomotives	[120]
Assess the performance of a hybrid edge–cloud electrolyzer management system	Enables real-time assessment, centralized monitoring, adaptive control, and coordination of hydrogen production to improve efficiency and enable long-term virtual plant integration	[121]

.

Moving from direct electricity generation to a clean chemical energy carrier, the application of DTs in the hydrogen sector addresses the challenges of managing a multi-stage value chain. The literature demonstrates a dual technical focus on process optimization to enhance electrolyzer efficiency [112,113,115,116] and critical safety management [119], using simulations to mitigate hazardous scenarios (e.g., gas dispersion). These technical models are frequently applied within broader techno-economic assessments aimed at overcoming the significant cost and infrastructure barriers to large-scale deployment [120]. Systemically, the hydrogen DT enables sector coupling, providing the models to manage the conversion of curtailed renewable electricity into a storable fuel [113,114,118]. This can provide long-term energy storage for grid resilience as well as a pathway to decarbonize heating in buildings by replacing fossil fuels in existing gas infrastructure.

3.4.5. Geothermal Energy

In geothermal applications, DTs enhance subsurface modeling, thermal extraction efficiency [122], and plant operation optimization [123]. They are used to construct high-fidelity reservoir models that integrate seismic, thermal, and flow data, enabling the accurate prediction of reservoir behavior. Real-time DT models monitor drilling operations, assess equipment conditions, and track performance metrics of heat exchangers and turbine systems. In enhanced geothermal systems, DTs play crucial roles in fracture network analysis, fluid circulation control, and reinjection planning [124]. Additionally, DTs assist in life cycle cost modeling and the optimization of system control logic, improve reliability, and support low-carbon baseload power generation. Recent applications of DTs in geothermal energy systems are summarized in

Table 5. Recent research trends in DT-based approaches for geothermal energy systems.

Objective	Role of DT	Ref.
Develop a high-fidelity DT for geothermal heat exchangers capable of extrapolating system behavior under non-routine conditions	Uses a meta-learning physics-informed neural network to predict performance without detailed heat transfer models, thereby enhancing control and safety in industrial applications	[40]
Elucidate operational mechanisms and optimize performance of geothermal–chemical heat pump systems with DT technology	Provides real-time simulation and data analysis for system optimization, component integration, and informed thermal fluid selection to improve efficiency and sustainability	[122]
Optimize geothermal power plant operations via digital simulations	Provides a flexible, data-driven environment for scenario testing and operational optimization using real-world data and reinforcement learning techniques	[123]
Develop a DT and augmented reality system for real-time monitoring and control of silica scaling in geothermal reinjection	Simulates geofluid behavior, enables Industrial Internet of Things-based real-time control, optimizes silica treatment conditions, and enhances operational efficiency through predictive and remote interventions	[124]
Optimize geothermal heating systems with heat energy storage to address supply–demand mismatches	Integrates life cycle data, enables accurate demand prediction, and performs multi-objective optimization of cost, energy use, and emissions, enhancing sustainability and operational efficiency	[125]
Improve efficiency and sustainability in shallow geothermal systems	Enables real-time monitoring, behavior simulation, trend and correlation analysis, and issue prediction to optimize performance and reduce maintenance costs	[126]
Evaluate and compare modeling approaches as a foundational step toward developing DTs for geothermal vaporizer systems	Supports the selection and integration of suitable models for simulating geothermal processes, enabling future DT development for improved operational flexibility and decision making	[127]
Design and implement a virtual real-time scale monitoring and pH control system for geothermal energy generation	Accurately mirrors the physical system to enable real-time monitoring and control of brine pH, minimizing silica scaling and improving geothermal plant efficiency	[128]
Design and implement a real-time DT for a geothermal doublet system to enable monitoring, anomaly detection, and operational optimization	Facilitates real-time monitoring of production, pipeline integrity (corrosion, erosion, and scaling), electric submersible pump performance, and enables prescriptive analytics for optimized geothermal plant operation	[129]
Optimize real-world geothermal plant operations	Enables accurate modeling of as-built geothermal systems, supports integration with AI and ML tools, and enhances operational efficiency through data-driven insights and system-wide analysis	[130]

.

DTs in the geothermal sector are primarily applied to manage the significant uncertainties and operational risks of complex subsurface systems [123,129,130]. The literature highlights their use in creating high-fidelity reservoir models and, critically, in mitigating unique geochemical challenges such as silica scaling and corrosion in both deep and shallow systems [124,128,129]. Systemically, the literature highlights the primary use of DTs to enhance the reliability of deep geothermal systems as a source of low-carbon baseload power, reinforcing grid stability [123,130]. However, from the perspective of urban energy integration, the application of DTs to shallow systems is particularly noteworthy [126]. It provides a direct pathway for autonomous control systems required to manage both real-time and long-term seasonal performance of ground-source heat exchangers, enabling a form of “self-driving” operation for a building’s thermal energy system [40,122,125,126].

3.4.6. Bioenergy

DT technology has emerged as a valuable enabler across diverse bioenergy applications, encompassing both biochemical and thermochemical conversion pathways. In anaerobic digestion systems (biogas plants), DTs facilitate real-time process monitoring [131], fault detection, and dynamic optimization by simulating microbial activity, substrate degradation kinetics, and gas yield [132]. For bioethanol production, DTs model fermentation kinetics, temperature regulation, and process efficiency to enable predictive maintenance and yield enhancement [131]. Moreover, DTs contribute to the optimization of biomass supply chains by modeling feedstock availability, transportation logistics, and storage dynamics, thus minimizing energy loss and cost inefficiencies. Algae-based bioenergy systems also benefit from DTs that manage photobioreactor conditions and simulate growth under varying environmental inputs. Moreover, DTs facilitate the integration of biomass into circular economy frameworks, supporting precision agriculture, residue valorization, and resource-efficient bioeconomy transitions. Via these processes, DTs can enhance both the technological and sustainable performances of bioenergy systems. Recent applications of DTs in bioenergy systems are summarized in

Table 6. Recent research trends in DT-based approaches for bioenergy systems.

Objective	Role of DT	Ref.
Develop an integrated DT framework for a biomass gasification power plant with CO2 capture to enhance operational efficiency and system insight	Functions as a comprehensive platform combining dynamic models, data-driven methods, and real-time data to enable bidirectional communication, simulation, and optimization of gasification-based power systems	[133]
Optimize supply chain logistics for biohydrogen production from agricultural residues to support the circular economy and decarbonization	Simulates and optimizes biohydrogen supply chains using greenfield analysis and Monte Carlo simulations to enhance efficiency and guide decision making	[134]
Optimize biorefinery operations for efficiency and sustainability while minimizing complexity in data acquisition and processing	Serves as a lean, data-driven modeling tool to monitor and optimize biorefinery processes in real time, enhancing operational efficiency with minimal data and sensor requirements	[135]
Design and analyze a circular biorefinery using microalgae biomass for sustainable methanol production	Models and simulates the biogas-to-biofuel process using integrated computational tools to evaluate carbon emissions, process yield, and operational feasibility	[136]
Improve accuracy and industrial applicability for co-digestion and biogas composition prediction	Enables real-time optimization of methane content and supports broader industrial deployment	[137]
Optimize biogas tri-reforming in a Pd–Ag membrane reactor for sustainable H2 production and greenhouse gas mitigation	Integrates CFD and ML models to simulate, predict, and optimize reactor performance, enhancing H2 selectivity, reducing CO2 emissions, and lowering computational costs	[138]
Optimize real-time biomass boiler performance	Provides real-time prediction and uncertainty-informed optimization of biomass boiler operations	[139]
Explore the integration of algal DTs into algal cultivation systems to enhance sustainability, efficiency, and smart infrastructure	Monitors and controls key parameters (nutrients, pH, dissolved oxygen, and CO2) in real time, enabling energy-efficient, cost-effective, and intelligent management of algal biomass production systems	[140]
Optimize biomass boiler operations under uncertainty using Bayesian decision theory	Integrates real-time data with science-based models to dynamically update operational setpoints, enabling AI-driven decision making that maximizes utility while accounting for system uncertainties	[141]
Optimize the residual biomass supply chain by integrating digital technologies to enhance efficiency and valorize waste	Supports supply chain modeling and scenario analyses to enable data-driven decision making and facilitate the transformation of residual biomass into value-added resources	[142]
Forecast biogas production in municipal co-digestion facilities using existing SCADA data to improve operational control	Establishes the foundation for a DT using ML models to predict biogas flow in real time from high-resolution SCADA data, enabling proactive process optimization	[143]
Enhance prediction and control of anaerobic digester performance for waste-to-energy optimization	Integrates physics-based ADM1 with neural networks in Pyomo to improve biogas production forecasting and conduct system sensitivity analyses for informed operational control	[144]
Validate and optimize H2S removal in a biogas scrubber using experimental data and process simulation	Simulates and analyzes scrubber performance in Aspen PLUS, enabling sensitivity analyses of key variables to optimize H2S absorption efficiency across varying operating conditions	[145]

.

In the bioenergy sector, the focus of research on DT applications has shifted from managing physical forces or geological uncertainty to optimizing complex biochemical and thermochemical conversion processes [133,138,144]. DTs are used for controlling the sensitive kinetics of systems (e.g., anaerobic digesters and biorefineries) and managing the intricate logistics of biomass supply chains to valorize waste streams [134,135,137,142]. Critically, this extends beyond process efficiency to sustainability governance, where a DT can be used to manage and verify factors such as feedstock sustainability and lifecycle greenhouse gas emissions, addressing the concern that not all bioenergy is inherently “clean” [133,134,136]. Systemically, the DT in this context acts as a key enabler for the circular economy by creating a direct link between urban waste streams and energy production [136,142,143]. This provides a pathway to generate dispatchable, low-carbon heat and power for buildings from local resources, where the DT enables the management of the inherent variability of biomass feedstocks and conversion kinetics to reliably close the loop between consumption and generation within a sustainable district.

3.4.7. Nuclear Energy

In the nuclear sector, DTs support a broad range of applications, including decision making, simulations, optimization, real-time operations, remote monitoring, and decommissioning planning [38,146]. They enable predictive maintenance and anomaly detection by leveraging continuous sensor feedback alongside thermal–hydraulic simulations of reactor components [147]. DTs are used to simulate core configurations, monitor neutron flux distributions, and predict the effects of aging on fuels and structural materials. Virtual control environments enable training simulations for operators, and DTs facilitate robotic automation, remote control, and inspection in high-radiation zones [148]. Moreover, DTs assist in cybersecurity risk assessments for autonomous systems and optimize economic performance using centralized off-site control strategies in microreactors. They also enhance safety protocols by enabling dynamic synchronization between DT models and physical systems, using adaptive optimization to reduce discrepancies and improve operational accuracy [149]. Furthermore, DTs support optimal sensor placement strategies by minimizing reconstruction errors under spatial and physical constraints, thereby improving the fidelity of flow field reconstruction and communication with experimental facilities [150]. Recent applications of DTs in nuclear energy systems are summarized in

Table 7. Recent research trends in DT-based approaches for nuclear energy systems.

Objective	Role of DT	Ref.
Optimize reactor operations to extend runtime and reduce operation and maintenance costs using AI- and ML-based multi-variable control strategies	Functions as a data-driven surrogate model within the Dynamic Optimization of Modular Operations framework, enabling uncertainty-aware optimization of reactor control schemes in real time	[37]
Implement autonomous anomaly detection to strengthen nuclear safeguards and operational safety	Provides real-time operational data to enable isolation forest-based anomaly detection, complementing physics-based models for enhanced monitoring in nuclear systems	[41]
Develop a robotic automation system for hazardous tasks in NPPs, focusing on nozzle dam replacement	Provides a high-fidelity simulation environment for training, testing, and validating autonomous robotic operations using deep reinforcement learning in dangerous and confined workspaces	[148]
Address synchronization challenges between DT models and real-world nuclear power systems to improve safety protocols	Functions as a dynamic model, optimized in real time using the TD3PSO algorithm, to reduce discrepancies and enhance operational accuracy with minimal human intervention	[149]
Minimize flow field reconstruction errors in nuclear systems through optimized sensor placement under spatial and noise constraints	Provides a framework for integrating optimized sensor data to enable accurate field reconstruction and enhance communication between experimental setups and virtual models	[150]
Develop a high-precision digital model for nuclear power plants (NPPs) by integrating physical laws into neural networks to enhance real-world applicability	Supports real-time monitoring and control by combining physics-informed and data-driven modeling, improving prediction accuracy and model generalization for NPP operations	[151]
Improve fatigue assessment accuracy for Class 1 components under Environmental Assisted Fatigue (EAF) by evaluating the effects of strain rate and load pairing on the Fen factor	Collects operational data and applies Influence Function analysis to automate stress evaluation and optimize operation parameters, thereby reducing EAF impact and improving assessment reliability	[152]
Develop a fast and accurate DL framework for predicting steady-state reactor power output during transients	Functions as a real-time predictive and monitoring system by integrating data-driven models with reactor operations to enhance safety, performance, and decision making	[153]
Enable real-time prediction of thermal–hydraulic parameters in nuclear systems using ML-driven virtual sensors	Enables real-time monitoring of system degradation and enhances system observability	[154]
Implement remote monitoring and anomaly detection in the AGN-201 research reactor	Enables real-time monitoring and detection of undeclared events using ML and reactor physics, supporting safeguards and oversight functions	[155]
Enhance multi-parameter, multi-step time-series prediction accuracy for NPPs using advanced DL	Integrates a pretrained transformer model with uncertainty quantification to enable reliable, adaptive forecasting for improved operational safety and efficiency	[156]
Develop a real-time surrogate modeling framework for cost-efficient, reliable operation of advanced reactors, such as fluoride-salt-cooled high-temperature reactors	Provides fast, accurate virtual representations by integrating physics-informed and data-driven models for autonomous control, degradation monitoring, and long-horizon operational optimization	[157]
Design a real-time 3D DT system to support the development and testing of robotic automation in NPPs	Simulates full-scope NPP operations and enables realistic, risk-free testing of intelligent robotic systems for inspection and maintenance tasks	[158]
Accelerate and improve the reliability of uncertainty quantification in nuclear fuel performance modeling to support DT development	Enables efficient, data-driven uncertainty quantification using nonintrusive polynomial chaos expansion, reducing computational burden while enhancing model accuracy and reliability	[159]
Enhance real-time parameter identification and state estimation in reactor operation DTs for optimized, near-carbon-free nuclear energy systems	Enables accurate, efficient online monitoring, anomaly diagnosis, and lifetime management of nuclear reactors	[160]
Evaluate DeepONet as a surrogate modeling method for real-time prediction in DT applications for nuclear energy systems	Enables fast, accurate, and generalizable real-time inference using DeepONet, reducing retraining needs and enhancing online predictive capability for complex reactor operations	[161]
Improve the accuracy and reliability of underwater inspection robot operations in nuclear reactor pools	Creates a real-time virtual replica of the robot for precise motion control and pose visualization, enabling safe and effective remote operation in high-radiation, unstructured underwater environments	[162]
Develop and evaluate an immersive DT interface for managing robot fleets in nuclear environments to improve safety and usability	Enhances situational awareness and remote management of cyber–physical robotic systems by integrating sensor data, mission parameters, and user interaction in a multimodal virtual environment	[163]
Enable accurate full-field reconstruction in NPP subsystems through optimized sensor placement under physical constraints	Establishes a two-way communication between physical systems and virtual models by integrating optimized sensor data, supporting subsystem-level twinning and aggregation into a comprehensive NPP DT	[164]

.

In the nuclear energy sector, the application of DTs is driven by the requirement to manage the extreme operational risks and safety requirements inherent to the technology [41,154,155]. Consequently, the literature is dominated by studies of their use in high-fidelity simulations of complex reactor physics [151,153,157,161], predictive monitoring for material degradation and fatigue [154,160], and as enabling platforms for robotic and remote operations in high-radiation environments [148,158,162,163]. Although DTs enhance the reliability of large-scale plants for grid stability [37,149,152], their most transformative role in urban energy systems lies in enabling the safe, autonomous, and remote operation of emerging microreactors. This provides a pathway for deploying long-term, carbon-free baseload power and heat directly at the district or campus scale, offering energy resilience for critical infrastructure.

3.4.8. Tidal and Ocean Energy

Studies identified in the literature search did not cover direct applications of DT to tidal and ocean energy systems. However, a logical extrapolation from adjacent domains, such as offshore wind energy, hydropower, and ocean engineering, reveals a clear research trajectory. Given the similar operational and environmental challenges, these logical extensions include tidal current forecasting, fatigue life assessment of components [165], hydrodynamic behavior analyses [166], optimization of turbine array configurations and energy yield, as well as integration into microgrids for autonomous operation and remote diagnostics. Beyond these component-level functions, the DT provides a system-of-systems tool to manage the impact of arrays on coastal infrastructure or to ensure the energy self-sufficiency of off-grid island communities. This extrapolation highlights promising research directions and underscores the need for future empirical validation and development specific to tidal energy.

3.4.9. Building-to-Grid Integration and Sustainable Districts

Although the preceding sections have centered on utility-scale clean energy generation, the efficacy of these systems is ultimately realized at the point of consumption within the built environment. This marks a paradigm shift from viewing buildings as passive energy sinks to recognizing them as active, responsive participants in the energy ecosystem. Central to this transformation are the concepts of B2G integration [167] and the emergence of buildings as “prosumers” [33]. The success of this integration hinges on cultivating energy flexibility, wherein a building can intelligently modulate its energy demand and generation patterns in real-time according to ambient conditions, occupants’ needs, and grid signals to decrease renewable curtailment and support decarbonization targets [168,169]. However, managing the complex interplay of onsite generation, energy storage, flexible loads, and stochastic occupant behavior exceeds the capabilities of conventional control systems [170]. Therefore, this subsection reviews the pivotal application of DT as the core enabling technology for modeling, monitoring, and optimizing bidirectional energy flows at the B2G interface [167], thereby laying the groundwork for resilient and sustainable buildings and districts.

At the individual building level, a DT functions as an intelligent energy management hub by integrating a heterogeneous stream of real-time data from building energy management systems (BEMS), IoT sensors (monitoring temperature, occupancy, and air quality), smart meters, and onsite clean energy systems. This rich data environment serves as the foundation for advanced control algorithms, most notably model predictive control (MPC) and reinforcement learning (RL) [171]. These strategies are widely recognized in the literature for their predictive and adaptive capabilities, making them better suited to the dynamic optimization challenges of B2G integration than conventional rule-based controls [172,173]. As summarized in

Table 8. Quantifiable performance improvements from DT-enabled clean energy integration in buildings.

Control Strategy	Application	Energy Savings	Peak Load Reduction	Other Key Metrics	Ref.
Improved Leader Particle Swarm Optimization (ILPSO) within DT-enabled BEMS	Smart home with PV, ESS, demand-responsive appliances, and grid interaction under real-time pricing	Not available (N/A)	Up to 38.5%	46.2% reduction in electricity cost (ECC)	[59]
DT with DL-driven MPC	Prosumer district storage optimization with solar PV and wind	15%	N/A	20% reduction in operational cost	[170]
General DT application (Review)	Overall building performance	30–40%	N/A	Facilitates ZEB integration	[176]
DT-enabled peer-to-peer (P2P) energy trading	Community-scale energy trading to optimize local solar PV consumption	Up to 54% (onsite RE consumption)	N/A	P2P trading increased PV’s contribution from 13% to 14%	[177]
Artificial neural networks and modified Grey Wolf optimizer within DT	Community microgrid of smart homes with PV panels and wind turbines, connected via a fog server for surplus power exchange under time-of-use pricing	Up to 55% (imported energy reduction)	N/A	Up to 67% reduction in electricity cost Facilitates peer-to-peer energy sharing	[178]
RL-based scheduling within DT	Smart home DR with solar PV, ESS, and appliance scheduling	Up to 35% reduction in grid imports against the baseline	N/A (load shifting achieved qualitatively)	71% reduction in electricity cost	[179]
DT and AI-based BEMS	Residential district with geothermal and PV	10%	N/A	Increased local energy self-sufficiency up to 70%	[180]
DT and AI-based Optimization	HVAC and lighting control with solar PV and wind turbine	29.7% (HVAC), 23.4% (Lighting), and 20.9–33.8% (Overall)	15–25%	50–70% improvement in energy self-sufficiency 40% reduction in unexpected equipment failures	[181]
DT and optimized threshold control	Distribution network flexibility in near real-time with PV, ESS, EV charging stations, and flexible loads (HVAC, commercial)	N/A	23.7% (import) and 27.4% (export)	3.57% increase in self-consumption rate and 33.01% reduction in reverse energy flows	[182]
DT and multi-agent RL scheduling	Smart home DR with solar PV and ESS	N/A	≈20–25% peak shaving (via load shifting and appliance scheduling)	Up to 72.3% reduction in electricity cost	[183]
DT and data–model fusion dispatch	Building cluster load control in DR with solar PV tracking	N/A	N/A	18.44% reduction in electricity cost; PV accommodation rate increased to >99%	[184]

, empirical and high-fidelity simulation studies have demonstrated significant, quantifiable improvements in building performance using DT-enabled predictive control. Beyond optimizing heating, ventilation, and air conditioning (HVAC) systems, the building-level DT oversees the scheduling of heat and electricity storage, lighting, and electric vehicle (EV) charging to maximize energy self-sufficiency and minimize reliance on the grid during high-cost periods. This smart control is also crucial for enabling active participation in demand response (DR) programs, where the DT can automatically precool or preheat spaces and curtail nonessential loads in response to utility signals [174], contributing to peak load reductions as high as approximately 90% via DT-enabled ESS and BEMS [175]. These operational optimization capabilities are foundational to achieving the performance targets of zero-energy buildings (ZEBs). Recent systematic reviews report improvements of up to 30–40% in building energy efficiency from DT-enabled predictive control (case-dependent), positioning DTs as key enablers of net-ZEBs via continuous, real-time performance optimization [176].

Scaling from a single building to a network of interconnected assets, the district-level DT operates as a “system-of-systems” to manage collective energy flows within a microgrid or smart local energy system [185]. This is often realized via a multi-agent architecture, where a central DT coordinates the actions of individual building agents to achieve district-wide objectives [184]. A primary application is the optimization of shared resources, such as community-scale battery storage or district heating and cooling networks. For instance, a DT of a district heating grid can optimize production planning, reduce network temperatures to enable the integration of low-grade renewable heat sources (e.g., geothermal or waste heat from power-to-X facilities), and simulate future network expansions [186]. Furthermore, the district DT can function as a virtual marketplace for peer-to-peer (P2P) energy trading within a transactive energy system, allowing buildings with surplus generation to sell power directly to neighbors [177], which can support local communities. These capabilities are fundamental to enhancing urban resilience and sustainability; by simulating responses to grid outages, a district DT can coordinate local generation and storage automatically to maintain critical services within an islanded microgrid. This contributes directly to SDG 11 by creating more resilient, efficient, and self-sufficient urban energy infrastructure, a linkage explicitly recognized in UN policy frameworks [187]. By facilitating higher penetration of local clean energy, these systems also advance the objectives of SDG 7.

Despite these significant advances, the widespread deployment of DTs at the building and district scale is hindered by several critical and interconnected challenges. First, the granular data required for effective control—including real-time occupancy patterns and user preferences—is highly sensitive, creating significant data privacy and security risks that demand robust cyber–physical security measures and a systematic ethical analysis that is currently lacking in many AI-driven control systems [188]. Second, the lack of interoperability between the fragmented ecosystem of proprietary BEMS, diverse IoT communication protocols, and siloed data models (e.g., BIM and GIS) remains the most significant technical barrier to creating a unified, scalable DT [189]. Third, unlike predictable industrial machinery, buildings are subject to the “human-in-the-loop” problem, where the stochastic and often irrational behavior of occupants is a dominant and highly uncertain determinant of energy consumption that is notoriously difficult to model [169]. Overcoming these barriers points toward a forward-looking research agenda. Future work must focus on cyber–physical–human systems or integrating sophisticated behavioral models into DTs to enable truly occupant-centric control [190]. At a larger scale, city-level DTs present a transformative opportunity for urban planning and policy simulation, providing virtual sandboxes to test the energy and social impacts of new infrastructure projects or regulations before physical implementation. Realizing this vision will require the development of scalable, secure cloud–edge architectures that can process vast amounts of data in real time while preserving privacy [191]. Ultimately, the building and district DT is positioned to become the central nervous system for future smart, sustainable cities—an indispensable tool for navigating the complex socio-technical transition to a decarbonized built environment.

4. Discussion

This section discusses the key findings of this review and contextualizes the role of DTs in optimizing clean energy systems—spanning utility-scale generation to building-level integration—from both technical and strategic perspectives. It further discusses the implications for industry and policy while identifying critical gaps in the literature and directions for future research. The growing integration of DT technologies into the clean energy sector signifies a paradigm shift in energy infrastructure development and management, positioning these tools as catalysts for achieving the global sustainability agenda.

4.1. Synthesis of Findings

The literature review clearly revealed that DTs are not simply advanced modeling tools but also comprehensive cyber–physical systems that enable intelligent, adaptive, and predictive management of energy infrastructure. Unlike traditional simulation models, which operate on static datasets and provide isolated insights, DTs evolve in synchrony with the corresponding physical systems, rendering them particularly effective for managing complex, high-variability energy environments. Figure 6 provides a visual summary of these applications.

Beyond the specific applications detailed in Figure 6, the findings reveal the evolution of the DT from a component-level predictive tool into a comprehensive, system-level platform for risk and resource management. This functional evolution is uniquely adapted to the core challenge of each energy source. For volatile renewable energy sources, such as solar and wind, the primary role of DTs is to support the management of intermittency via enhanced forecasting and structural reliability. For dispatchable, safety-critical sources, such as nuclear and geothermal, the focus shifts to enabling operational integrity and grid stability. For energy carriers, such as hydrogen and bioenergy, it facilitates process management across the entire value chain. This evolution reaches its most complex state at the building and district scale, where the functions transcend those of a simple asset model. Here, the DT contributes to socio-technical orchestration, enabling the co-optimization of energy flows for technical efficiency and against the dynamic constraints of grid stability, economic signals, and occupant needs.

A critical comparison of these applications reveals a distinct methodological divergence driven by sector-specific operational requirements. In sectors involving complex physical dynamics and strict safety margins, particularly hydrogen and nuclear, the literature shows a dominant reliance on physics-based models to ensure structural integrity and fault diagnosis. Conversely, in variable energy sources and B2G systems, where environmental inputs are stochastic and the primary goal is rapid optimization, the trend is heavily skewed toward data-driven and AI-centric models (e.g., ML, neural networks). However, the most significant emerging trend is the convergence toward hybrid DTs. By integrating physics-based boundary conditions with ML algorithms, these systems address the interpretability issues of pure black-box models while enabling the near real-time responsiveness required for modern grid integration.

These findings demonstrate that DTs improve system reliability and reduce operational uncertainties substantially, particularly in scenarios involving a fluctuating energy supply, diverse load demands, and distributed generation systems. Predictive capabilities embedded within DTs mitigate unplanned outages and asset degradation while enabling proactive energy management aligned with long-term decarbonization objectives. The development of hybrid DTs further enhances simulation accuracy, enabling real-time adaptation to nonlinear system behaviors and external stressors.

4.2. Implications for Industry and Policy

The widespread integration of DTs into the clean energy ecosystem signals a series of implications that extend beyond operational efficiency, fundamentally reshaping business models, technology development, and regulatory frameworks.

For industry stockholders, DTs are driving a fundamental shift toward data-centric operations, enabling a transition from a centralized utility model to a decentralized landscape of “prosumers” [192]. This creates new roles for energy service companies that manage fleets of intelligent buildings while presenting an opportunity for utilities to manage a more complex, distributed grid. These DT-enabled strategies can reduce unplanned downtime and extend equipment lifespans, leading to significant cost savings, reduced operational risk, and enhanced returns on investment [193]. In capital-intensive sectors, such as hydrogen production and offshore wind, where maintenance can be particularly expensive or hazardous, DTs offer safe and efficient autonomous tools for inspection and repair [46]. At a systemic level, this DT integration serves as the core system for new market models, such as P2P energy trading, while supporting the development of more resilient infrastructure, such as self-healing grids and automated clean energy plants [47].

For technology developers, the increasing adoption of DTs shifts the primary challenge from creating isolated, asset-level models to engineering interoperable, multi-scale platforms. A key hurdle is achieving seamless data fusion between heterogeneous systems, such as integrating the thermodynamic model of geothermal reservoirs with the real-time control of HVAC systems. Overcoming this requires the development of standardized data ontologies (e.g., semantic integration of IoT, BIM, and GIS), open-source platforms, and robust cloud–edge computing frameworks to foster interoperability and reduce implementation costs. Furthermore, the interoperation between the DTs of clean-energy systems and building clusters expands the attack surface and requires new cyber–physical security architectures to protect these integrated systems [36].

From a policy perspective, DTs offer significant value for energy governance and sustainability monitoring. They support compliance with environmental regulations by providing transparent, data-driven tracking of emissions, energy efficiency, and carbon offsets [194]. Furthermore, DTs enhance the feasibility of energy transition roadmaps by enabling the accurate modeling of complex energy scenarios and investment risks. However, their role transcends mere monitoring; as countries commit to net-zero targets, DTs serve as essential instruments for achieving the UN SDGs, demanding a shift from prescriptive rules to adaptive, data-driven regulatory frameworks. This is particularly critical for realizing the resilient urban infrastructure envisioned in SDG 11, for which the reliable and affordable clean energy mentioned in SDG 7 is a prerequisite. Although strategic government support via research and development funding and standardization initiatives is essential, a primary challenge is the creation of new policies to address the complex data privacy, ownership, and ethical issues that arise when co-optimizing utility data with sensitive occupant behavior.

4.3. Research Gaps and Future Directions

Despite the demonstrated potential of DT technology, several key challenges and research gaps hinder its widespread adoption and optimal utilization in clean energy systems. These gaps span the technical, economic, and organizational domains and require targeted investigation.

A significant and critical barrier is the lack of open DT ontologies, standardized data schemas, and modular, interoperable architectures that can support seamless integration across various assets, vendors, and control systems. Furthermore, a major gap identified in this review is “terminological ambiguity,” where the term DT is frequently applied to passive digital shadows or static digital models. Future research must rigorously adhere to the levels of integration defined in Section 3.2 (model, shadow, and twin) to ensure that control capabilities are accurately represented. Additionally, a standard model for DT architecture is lacking, and a consensus on the mechanism of construction is absent. The lack of a unified regulatory framework complicates collaboration, system interoperability, and the replication of best practices. Therefore, targeted research to establish common architectural models, protocols, and guidelines that can underpin a standardized, interoperable DT ecosystem across the clean energy domain is required.

Scalability and computational efficiency must be addressed. High-fidelity DTs of large-scale energy networks (e.g., regional grids or multi-source microgrids) require extensive data storage, real-time processing, and advanced model reduction techniques. Future research should explore edge computing, federated learning, and reduced-order modeling to ensure DTs remain responsive and cost-effective, even at scale.

Cybersecurity and data privacy have emerged as major concerns. The increased connectivity among physical assets, cloud platforms, and digital models exposes energy infrastructure to cyberattacks with particularly acute concerns around data privacy and the ethical handling of sensitive occupant data in residential and commercial buildings. Research must focus on building resilient DT ecosystems using secure communication protocols, encrypted data exchange, and AI-based intrusion detection systems.

The economic feasibility of DTs, particularly for small- and medium-sized enterprises, remains uncertain. Cost-effective DT deployment strategies, life cycle value modeling, and business case development must be explored to support broader adoption.

Socio-technical challenges and behavioral modeling are critical. For emerging applications such as BIWTs, research must address the co-optimization of energy yield against socio-technical constraints, such as structural vibration and occupant acoustic comfort. More broadly, a dominant source of uncertainty is the human-in-the-loop problem, requiring the integration of sophisticated behavioral models to create truly occupant-centric and reliable systems.

Integration with emerging technologies represents a key future frontier. Synergies with technologies, such as blockchain for decentralized energy markets or extended reality for immersive operator training, could revolutionize DT applications in clean energy but remain underexplored and require targeted pilot studies.

Continued research in these areas, together with pilot studies and cross-sector collaborations, will be critical to unlocking the full transformative power of DTs in global energy transitions. A visual summary of these multidimensional challenges, spanning technical, economic, and policy-related domains, is provided in Figure 7.

5. Conclusions

This review presents a comprehensive analysis of the evolving role of DT technology in renewable and clean energy systems. By examining the practical advancements across diverse energy sectors—including solar, wind, hydropower, hydrogen, geothermal, biomass, and nuclear energy—this study demonstrates that DTs have emerged as pivotal technological enablers for the UN SDGs, particularly SDGs 7 and 11. The capacity of DTs to generate real-time, data-driven, and predictive insights across the entire energy value chain represents a paradigm shift, offering a sophisticated solution for addressing variability and complex grid integration through dynamic simulations and continuous synchronization with physical systems.

Regarding contributions to practice and policy, this review highlights that DTs extend beyond technical optimization to serve as foundational tools for strategic planning. Via real-time emissions monitoring, life cycle impact analysis, and infrastructure modeling, these technologies support quantifiable assessments of sustainability performance, facilitate the identification of targeted investment opportunities, and inform data-driven regulatory frameworks. In the context of increasingly decentralized and interconnected energy systems, defined by hybrid generation portfolios, smart grids, and prosumer participation, DTs enable adaptive control strategies that support DR coordination, the implementation of virtual power plants, and the intelligent management of microgrid operations. They are thus not merely monitoring tools but the operational engines for data-driven, adaptive policy and new market models, such as P2P energy trading.

Despite these promising capabilities, significant challenges remain. Widespread adoption of this technology in the clean energy sector faces several challenges. These unresolved issues include ensuring the cybersecurity and ethical handling of sensitive occupant data and addressing the socio-technical complexities of the “human-in-the-loop” problem, alongside standardization, interoperability, and economic feasibility. Overcoming these limitations requires multidisciplinary research, cross-sectoral collaboration, and strategic policy support. Developing open access DT platforms, standardized ontologies, robust data governance frameworks, and scalable architectures is essential for unlocking the full potential of DTs in global energy transitions.

Future research directions should focus on enhancing the integration of DTs with emerging technologies, such as AI, ML, blockchain, and edge computing. These synergies can enhance the predictive and autonomous decision-making capabilities of DTs, enabling real-time optimization of complex, multi-source energy systems under uncertain and dynamic conditions. Additionally, novel applications in circular economy modeling, digital decarbonization twins, and climate-resilient infrastructure design have the potential to expand the scope of DTs beyond current boundaries. While this review has examined state-of-the-art applications, challenges, and pathways, a priority for future work is a systematic, comparative evaluation of DT software tools and implementation frameworks. Such an assessment would provide practitioners with actionable guidance and could accelerate adoption across the sector.

In conclusion, DT technology occupies a pivotal position at the convergence of digital transformation and clean energy innovation. As nations strive to achieve carbon neutrality, energy equity, and system resilience, DTs will function not only as operational tools but also as strategic assets that bridge the physical and digital domains of sustainable development. This analysis confirms that the future of the field lies in evolving from isolated asset monitors into interconnected system-of-systems platforms. The ongoing evolution and integration of DT technologies within the energy sector will be critical for realizing a truly integrated, intelligent, and net-zero energy ecosystem—from utility-scale assets to responsive buildings and sustainable cities—in the coming decades.