This mini-review employs a systematic-narrative literature review methodology designed to provide both rigorous retrieval of relevant evidence and interpretive synthesis of interdisciplinary advances in AI-driven digital twins for renewable energy grids. The approach combines systematic search and selection procedures (to ensure reproducibility and coverage) with narrative synthesis (to integrate methodological, technological, and policy insights across engineering, computer science, and energy systems literature).

A structured search was performed across the following bibliographic databases and platforms: Scopus, Web of Science, IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, PubMed, and Google Scholar. Searches targeted literature published between 2010 and 2025 to capture foundational work and recent developments in AI, digital twins, and renewable energy applications. Search queries combined controlled vocabulary and free-text terms, using Boolean operators to improve precision and recall. Representative search strings included permutations of the following terms:

Search terms were adapted to the indexing and query syntax of each database; snowballing (forward and backward citation tracking) and hand-searching of key journals and conference proceedings (e.g., IEEE Transactions, Renewable Energy, Applied Energy, ACM/IEEE conference proceedings) supplemented database queries. Grey literature (technical reports, standards, and relevant white papers from utilities, grid operators, and international organisations) were sought selectively to inform practical deployment and policy discussions.

Studies were eligible for inclusion if they met all of the following criteria: (i) peer-reviewed articles, high-quality conference papers, or authoritative technical reports; (ii) focused on digital twin applications in power systems, renewable energy integration, microgrids, or DER orchestration where AI/ML methods are applied or discussed; (iii) presented empirical results, simulation experiments, methodological developments (e.g., hybrid modelling, PINNs), system architectures (cloud/edge implementations), or security/interoperability analyses relevant to real-time grid operation; and (iv) published in English between 2010 and 2025.

Exclusion criteria were: (i) articles solely concerned with digital twins outside the energy domain (unless offering transferable methods); (ii) opinion pieces lacking technical or methodological substance; (iii) non-English publications; (iv) undergraduate student theses and non-peer-reviewed blog posts except where they represented standards or vendor technical specifications cited for context; and (v) studies with insufficient methodological detail to assess performance claims (e.g., missing evaluation metrics or reproducible experimental setup).

Search results were de-duplicated and screened in two stages: title/abstract screening followed by full-text review. Two reviewers independently screened records and resolved disagreements by consensus; a third reviewer adjudicated unresolved conflicts. From each included study we extracted: bibliographic details, system domain (wind, solar, microgrid, hybrid), digital twin architecture (physical model, data pipelines, cloud/edge topology), AI methods used (ML, DL, RL, hybrid PINNs), datasets and experimental setup, evaluation metrics (forecast accuracy, time-to-detect faults, computational latency), demonstrated applications (forecasting, dispatch, predictive maintenance), scalability and deployment notes, and identified limitations (data quality, cybersecurity, interoperability). Extracted data were organised in evidence tables and synthesised narratively to identify common methods, performance benchmarks, gaps, and research opportunities. Where quantitative results were comparable (e.g., forecasting RMSE), they were tabulated to illustrate typical performance ranges.

Included studies were appraised for methodological rigour using a domain-adapted checklist assessing (i) clarity of objectives; (ii) adequacy of data description and preprocessing; (iii) completeness of methodological detail (model architectures, hyperparameters, training/validation procedure); (iv) appropriateness of evaluation metrics and baselines; and (v) disclosure of limitations and reproducibility artifacts (code, datasets). Risk of bias was noted where studies reported only favourable results without comparative baselines, used small or non-representative datasets, or lacked real-world validation. These assessments informed the weight accorded to individual findings during synthesis.

The review is limited by language (English-only) and by the practical difficulty of exhaustively capturing rapidly evolving pre-print and industrial literature; while major pre-prints and industry reports were considered when methodological detail was sufficient, some proprietary deployments and commercial platforms remain opaque. Publication bias toward positive results and heterogeneous evaluation metrics across studies constrained quantitative aggregation. These limitations are acknowledged in the interpretation of evidence and in recommended future research directions.

Digital twin technology is a virtual representation of a physical asset, system, or process that enables real-time monitoring, analysis, and decision-making (Fuller et al., 2020). In the context of energy systems, digital twins integrate data from sensors, communication networks, and computational models to provide a dynamic replica of grid operations. The core components typically include the physical system (e.g., solar panels, wind turbines, or grid infrastructure), a digital model that mirrors its behavior, and a communication layer that ensures continuous data exchange between the two (Imam, 2025). This integration allows operators to track asset performance and predict future states under varying conditions.

Modeling and simulation lie at the heart of digital twin development. Physics-based models, such as finite element or thermodynamic simulations, are widely used to capture the operational dynamics of energy components (Tao et al., 2024). These models can be combined with data-driven approaches, including machine learning algorithms, to enhance accuracy and adaptability in complex renewable energy environments. Hybrid models, which integrate physical laws with real-time sensor data, are particularly effective for predicting system failures, optimizing load management, and improving grid stability.

The deployment of digital twins has expanded rapidly within renewable energy grids. In wind energy, digital twins facilitate predictive maintenance by analyzing turbine blade stress and performance degradation (Liu et al., 2021). For solar power, they enable forecasting of photovoltaic output based on weather conditions and operational parameters (Chen et al., 2024). In microgrids and hybrid systems, digital twins optimize energy dispatch, balance supply-demand fluctuations, and integrate storage solutions to improve reliability (Lai et al., 2024). Collectively, these applications demonstrate how digital twins enhance resilience, reduce operational costs, and accelerate the transition to intelligent renewable energy systems. Table 1 and Figure 2 summarizes the major applications, mechanisms, and impacts.

Machine learning (ML) has increasingly become a fundamental component in the development of digital twins for energy systems, providing advanced capabilities for predictive analytics, real-time optimization, and decision support. ML algorithms leverage both historical datasets and real-time sensor data to uncover complex, non-linear relationships within energy system operations, enabling accurate forecasting of energy demand, renewable generation variability, and potential equipment failures (Sun et al., 2020; Tao et al. (2019)). Supervised learning techniques, including regression models, support vector machines (SVMs), and artificial neural networks (ANNs), have demonstrated high efficacy in load forecasting and performance prediction of energy assets. For example, ANNs can model highly non-linear behavior of wind turbines under variable wind conditions, while regression-based approaches can provide short-term and long-term electricity demand forecasts, critical for grid stability and energy market operations (Zhou et al., 2021). Similarly, SVMs are particularly effective in handling high-dimensional datasets for predicting anomalies in energy equipment performance, thus reducing the likelihood of unplanned outages (Di Persio et al., 2024).

Unsupervised learning methods, such as k-means clustering, hierarchical clustering, and principal component analysis (PCA), are widely employed for anomaly detection and fault diagnosis within digital twin frameworks (Ding Q. et al., 2025). These algorithms can identify deviations from normal operational patterns, classify unknown fault types, and optimize maintenance schedules without requiring labeled datasets, which is particularly advantageous for complex energy systems with limited failure records (Afridi et al., 2022). In addition, ensemble learning approaches such as random forests and gradient boosting have been utilized to improve predictive accuracy by combining multiple models, thereby reducing the risk of over-fitting and enhancing robustness under varying operational conditions. Recent advances in deep learning have further augmented the predictive capabilities of digital twins. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, can process temporal and spatial data from energy systems to detect subtle trends and patterns that traditional ML models may overlook. For instance, LSTMs are particularly effective in forecasting time-series energy consumption and generation profiles, accounting for seasonality, weather fluctuations, and system interdependencies (Waheed et al., 2024). In renewable grids, the most predictive inputs are typically SCADA/EMS/DER telemetry and grid measurements rather than generic time series alone: wind turbine pitch/rotor speed and gearbox temperature; PV inverter DC-link voltage, irradiance and module temperature; battery SoC/SoH and temperature; feeder voltage profiles; and where available, PMU synchrophasors (voltage/current phasors, frequency, RoCoF). Feature engineering that captures grid behavior net-load ramp rates, variability indices, curtailment flags, inverter operating modes (Volt/VAR, Volt/Watt), and event markers (switching/reclosing) often improves generalization because it encodes operational actions unique to renewable grids.

The integration of ML with digital twin technology enables the creation of hybrid predictive frameworks, where physics-based models are augmented with data-driven insights (Gebreab et al., 2024). This synergy allows for real-time monitoring of system health, predictive maintenance scheduling, and optimization of energy production and consumption. As a result, ML-enhanced digital twins not only reduce operational costs and system downtime but also improve reliability, resilience, and efficiency of renewable energy systems (Wang et al., 2022). From a comparative perspective, classical ML models (e.g., SVMs, Random Forests, Gradient Boosting) often outperform deep networks when datasets are relatively small, feature-engineered, and noise-limited because they impose stronger inductive biases and require fewer samples to generalize. In contrast, deep learning models (e.g., CNNs/LSTMs) typically outperform classical ML in high-dimensional sensor settings (high-frequency PMU synchrophasors and feeder-level SCADA/AMI streams, images/thermal maps) and when sufficient data are available, because they learn hierarchical representations and capture complex nonlinearities without extensive manual feature design. However, deep models introduce higher computational cost, risk of overfitting in data-scarce regimes, and reduced interpretability, making them less suitable for latency-constrained or safety-critical operations unless paired with explainability or hybrid physics constraints. Therefore, the choice of ML approach should be guided by data volume/quality, feature dimensionality, and deployment constraints (latency, compute budget, and interpretability requirements), not only predictive accuracy (Vanderhorst et al., 2024).

Deep learning (DL) has emerged as a transformative approach within digital twin frameworks, extending predictive and diagnostic capabilities beyond conventional machine learning by effectively capturing highly nonlinear relationships and temporal dependencies in renewable energy systems (Rahmani-Sane et al., 2025). Advanced neural network architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited to process the high-dimensional, time-series data generated by modern smart grids. CNNs excel in extracting spatial correlations from distributed sensor arrays, enabling the identification of localized anomalies in equipment such as photovoltaic arrays or wind turbine components, while RNNs, including long short-term memory (LSTM) networks, model temporal dependencies in energy production and load demand, improving forecasting accuracy under variable environmental and operational conditions (Chouksey, 2025). Beyond fault detection and forecasting, deep learning facilitates real-time system optimization. For instance, hybrid DL models combining CNNs and LSTMs can simultaneously capture spatial and temporal patterns, enabling predictive maintenance scheduling, energy loss minimization, and adaptive load balancing. These models are particularly advantageous in renewable energy grids where intermittent generation, such as solar irradiance or wind speed variability, introduces significant uncertainty and operational complexity (Ahmed et al., 2024).

Reinforcement learning (RL) complements DL by providing adaptive control capabilities for complex and dynamic energy systems. Within digital twin simulations, RL agents iteratively interact with the modeled environment, receiving feedback in the form of rewards or penalties, and progressively learning optimal control policies for energy dispatch, voltage regulation, and demand response management (Zhang et al., 2019). In renewable grids, RL/DRL policies must map to concrete actuation: inverter set-points (Volt/VAR, Volt/Watt), battery charge/discharge and reserve scheduling, dispatch of flexible loads, and microgrid islanding/reconnection logic. Unlike generic RL tasks, these actions are bounded by grid codes and network constraints; thus, DT-trained RL should include constraint handling (safe RL, penalties, or constrained optimization layers) to prevent voltage violations, frequency excursions, feeder overloads, and protection miscoordination. Hierarchical control is often required because fast inverter control (milliseconds–seconds) interacts with slower storage and demand-response decisions (minutes–hours). This trial-and-error learning process allows RL agents to handle uncertainties arising from fluctuating renewable outputs, varying load demands, and contingencies such as equipment failures or grid faults. Notably, deep reinforcement learning (DRL), which integrates deep neural networks with RL, further enhances decision-making by enabling high-dimensional state and action space management, allowing for sophisticated grid control strategies that traditional rule-based methods cannot achieve. While RL/DRL can outperform rule-based control in highly dynamic environments (high renewable penetration, frequent contingencies) because policies adapt through interaction, RL performance depends strongly on reward design, state observability, and simulation fidelity of the digital twin. Poorly specified rewards can yield unsafe or unstable behavior, and “reality gaps” between simulation and the physical grid can degrade performance after deployment. Moreover, RL commonly requires many training episodes and can be computationally intensive, which limits rapid deployment unless pre-training, transfer learning, or constrained/safe RL is used. In comparison, model predictive control (MPC) or optimization-based dispatch can provide stronger stability guarantees but may struggle with nonlinear uncertainty or scaling. Hence, RL is most beneficial when the DT environment is accurate enough for training, operational objectives change over time, and adaptive control is needed, whereas MPC/optimization may be preferred when formal constraints, explainability, and safety guarantees are paramount. (Massaoudi et al., 2023).

The combination of DL and RL within digital twin frameworks offers a robust paradigm for autonomous and intelligent energy system management. DL models provide precise forecasting and fault diagnosis, while RL agents leverage these insights to make adaptive, real-time control decisions. This integrated approach not only enhances operational efficiency and system reliability but also supports higher penetration of renewable energy sources, mitigating the risks of instability associated with variable generation and ensuring grid resilience in smart energy networks (Li et al., 2025).

The integration of machine learning (ML), deep learning (DL), and reinforcement learning (RL) within digital twins establishes a synergistic framework that significantly advances data-driven decision-making in renewable energy systems. Leveraging the complementary strengths of these computational paradigms allows digital twins to evolve from predictive models into intelligent platforms capable of real-time monitoring, control, and optimization (Tao et al., 2019; Khan et al., 2022). ML algorithms form the foundational predictive layer, enabling accurate forecasting of load demand, renewable generation variability, and equipment degradation. DL architectures, including CNNs and LSTMs, extend predictive capabilities, handling complex, high-dimensional sensor datasets and capturing both spatial and temporal patterns across distributed energy resources. RL introduces an adaptive control layer, enabling the digital twin to autonomously learn optimal operational strategies through interactions with simulated environments, enhancing decision-making under uncertain and dynamic conditions (Zhang et al., 2019).

Data fusion techniques play a pivotal role in this convergence, integrating heterogeneous datasets from weather predictions, market signals, smart meters, and IoT-enabled sensors. This integration generates a comprehensive view of the grid’s operational state, allowing the digital twin to anticipate fluctuations in generation and demand, detect emerging faults, and recommend corrective actions before system performance is compromised (Khan et al., 2022). Fused data also supports multi-objective optimization, with decision-making algorithms minimizing operational costs, enhancing reliability, and reducing environmental impact simultaneously. Intelligent orchestration enables digital twins to perform real-time power flow optimization, dynamic load balancing, and predictive maintenance scheduling. RL-based control policies adjust dispatch strategies to maximize renewable utilization while maintaining grid stability, and ML-driven anomaly detection triggers maintenance interventions to prevent equipment failure. DL-based forecasting of renewable output enhances coordination of distributed energy resources, mitigating intermittency challenges that often hinder large-scale integration (Li et al., 2022). The convergence of these technologies enhances grid resilience, reduces operational inefficiencies, and supports the transition toward highly flexible and sustainable energy systems. This integrated approach forms the foundation for next-generation smart grids capable of self-optimization, accommodating increasing penetration of renewables while ensuring economic and environmental sustainability. As digital twins continue to evolve, the synergy of ML, DL, and RL remains essential for achieving autonomous, predictive, and adaptive energy management at scale. Critically, “better forecasting accuracy” does not always translate into “better grid outcomes.” For example, marginal improvements in RMSE may provide limited operational benefit if dispatch decisions are constrained by network limits, ramping constraints, or market rules. Conversely, slightly less accurate forecasts paired with uncertainty quantification (prediction intervals) can outperform point forecasts in operational value by enabling risk-aware reserve sizing and robust dispatch. Similarly, multi-objective optimization introduces explicit trade-offs between cost, reliability, emissions/curtailment, and asset degradation; selecting a solution requires stakeholders to prioritize objectives (e.g., minimizing curtailment may increase cycling of batteries and accelerate degradation). Therefore, comparative evaluation should include not only predictive metrics (RMSE/MAE) but also decision metrics (curtailment reduction, stability violations, outage risk, and asset lifetime impacts), ideally assessed within the DT loop.

Load balancing constitutes a cornerstone for the reliable operation of modern renewable energy grids, particularly given the inherent intermittency and variability of solar and wind generation. Digital twins, integrated with artificial intelligence (AI) techniques, facilitate dynamic load forecasting and adaptive demand response strategies that synchronize energy consumption with available generation capacity (He et al., 2021). These systems exploit historical consumption patterns, weather forecasts, and real-time sensor data to create predictive models capable of anticipating fluctuations in both supply and demand. Optimized allocation of distributed energy resources (DERs), including photovoltaic systems, wind turbines, and energy storage units, is achieved through simulations within the digital twin environment. Such predictive orchestration ensures system equilibrium, prevents overloads, and maintains voltage and frequency stability across the grid. Incorporating AI-driven load balancing enables near-real-time adjustments to energy dispatch, allowing renewable generation to be fully utilized while reducing reliance on conventional, fossil-fuel-based backup sources (Zhou et al., 2021). In load balancing, supervised learning (e.g., LSTM-based load forecasting) is strong for anticipatory scheduling because it leverages historical temporal patterns; however, it can underperform during regime shifts (new consumption behaviors, extreme events) unless retrained and monitored for drift. RL-based demand response and dispatch can outperform static heuristics by adapting control actions to real-time conditions, but it requires carefully designed reward functions to avoid undesirable outcomes (e.g., excessive load shifting that violates comfort or industrial constraints). Hybrid approaches, forecasting and constrained optimization/MPC often provide the best trade-off in practice: forecasts provide look-ahead, while optimization enforces grid constraints and safety margins, improving reliability compared with purely data-driven control.

Demand response mechanisms represent an essential component of this framework, allowing flexible consumers such as industrial facilities or smart homes to adjust consumption in response to grid conditions. Digital twin simulations evaluate the impact of these interventions on overall system performance, optimizing participation schedules to flatten peak demand periods and enhance grid flexibility. Machine learning models can predict consumer behavior and energy usage trends, while reinforcement learning algorithms determine optimal control policies that maximize renewable utilization and minimize operational costs (Li et al., 2022). Furthermore, digital twins enable scenario analysis for extreme or unanticipated conditions, such as sudden drops in wind generation or spikes in demand. These simulations support proactive strategies, including energy storage dispatch, load shedding, or temporary curtailment of non-critical loads, ensuring continuous grid stability. The integration of AI-driven load balancing with digital twin technology, therefore, not only enhances operational efficiency but also supports large-scale integration of renewables, facilitates consumer engagement, and strengthens overall system resilience. In practice, operators often prefer “forecast + constrained optimization/MPC” because it provides look-ahead while enforcing feeder limits and voltage constraints an important requirement in weak grids with high PV penetration and reverse power flows.

The intermittency of renewable energy sources such as solar photovoltaics and wind turbines remains a significant challenge to grid stability. AI-enhanced digital twins mitigate this challenge through advanced forecasting, adaptive dispatch strategies, and energy storage optimization (Morstyn et al., 2019). Neural networks and reinforcement learning algorithms embedded within digital twin models improve short-term and long-term predictions of renewable generation, enabling grid operators to proactively manage variability. Hybrid optimization techniques also allow digital twins to coordinate renewable sources with battery systems, thus reducing curtailment rates and improving grid reliability under high renewable penetration scenarios (Xu et al., 2023).

Operational reliability of renewable energy grids depends on early detection of equipment faults and efficient maintenance scheduling. AI-driven digital twins provide advanced fault detection capabilities by continuously monitoring system states, identifying anomalies, and simulating potential failure scenarios in real time (Rasheed et al., 2025). Machine learning classifiers, trained on labeled fault datasets, enable rapid diagnosis of common issues in turbines, inverters, and storage systems. Predictive maintenance strategies supported by digital twin analytics extend equipment lifetimes, minimize downtime, and reduce maintenance costs. Such approaches also enhance safety and resilience by preventing cascading failures within interconnected grid components. For fault detection, supervised classifiers generally outperform unsupervised anomaly detection when labeled fault data are representative because they learn fault-specific signatures and yield clearer diagnostic categories. However, labeled fault data are often scarce and biased toward common failures, making supervised models brittle to novel or evolving faults. In such cases, unsupervised and self-supervised methods (e.g., autoencoder reconstruction error, clustering of embeddings) can outperform supervised models for early warning by detecting “unknown unknowns,” although they may produce more false alarms and require human-in-the-loop validation. A pragmatic DT strategy is staged detection: unsupervised anomaly screening for early warning, followed by supervised diagnosis where labels exist, and physics-informed/hybrid checks to reduce false positives and improve trust. Fault modes in renewable grids are often inverter- and power-electronics-driven (DC-link capacitor aging, IGBT thermal stress, controller instability, harmonics/THD, islanding), which differ from conventional synchronous-generation faults. DT-enabled PdM should therefore combine electrical indicators (harmonics, voltage flicker, reactive power oscillations) with thermal/operational signals from inverters and storage, since purely mechanical or purely electrical monitoring can miss cross-domain precursors in inverter-dominated systems.

Reliable data streams are essential for training artificial intelligence models and sustaining accurate digital twin representations of renewable energy systems. In many regions, particularly within developing energy infrastructures, access to high-resolution, real-time datasets remains limited (Ding S. L. et al., 2025). Missing, noisy, or inconsistent data undermine the performance of forecasting algorithms, compromise anomaly detection accuracy, and reduce the fidelity of simulations. The heterogeneity of data sources, including weather sensors, smart meters, and distributed energy resources, further complicates integration due to variations in granularity, formats, and communication protocols (Khan et al., 2022). Addressing data scarcity and ensuring robust data preprocessing pipelines are therefore critical for enabling effective AI-driven optimization (Table 2).

Digital twins in renewable energy grids demand high computational capacity to process vast volumes of sensor data, run complex simulations, and execute optimization models in real time. While cloud computing and edge computing architectures provide scalable solutions, challenges arise in balancing latency, cost, and energy efficiency (Fuller et al., 2020). As the number of interconnected renewable energy assets grows, so does the complexity of maintaining synchronized digital twin environments. High-fidelity models require significant memory and processing power, raising barriers to widespread adoption in resource-constrained contexts. Research into lightweight modeling approaches, distributed AI, and federated learning frameworks is ongoing, but large-scale deployments remain computationally intensive (Liu et al., 2023).

Integration of AI-enhanced digital twins within renewable energy grids introduces new cybersecurity risks due to the reliance on interconnected IoT devices, communication networks, and cloud-based infrastructures. Vulnerabilities such as data breaches, model poisoning, and denial-of-service attacks can compromise both the physical grid and its digital counterpart (Aheleroff et al., 2021). Furthermore, interoperability challenges emerge when combining diverse hardware and software systems across different vendors and energy markets. Ensuring secure, standardized protocols for data exchange and system integration is essential to prevent fragmentation and reduce risks associated with cyber-physical attacks. Trustworthy AI governance frameworks, coupled with robust encryption and authentication mechanisms, are necessary to safeguard the reliability of AI-driven digital twins in critical energy infrastructures. Overall, the dominant trade-off across AI-driven DT approaches is between adaptability and assurance: deep models and RL offer higher flexibility under uncertainty, but increase compute burden and reduce interpretability, whereas physics-based and optimization-driven methods provide stronger constraints and transparency but may underperform when dynamics are poorly modeled or highly stochastic. Hybrid DTs aim to balance these trade-offs by combining physical constraints with data-driven learning.

Advancements in AI are expanding digital-twin capabilities specifically for renewable energy grid optimization. In this context, generative AI is most relevant when it directly improves DT operation and decision-making under grid constraints. Key grid-facing opportunities include: (i) synthetic data generation to augment scarce fault and extreme-event records (e.g., rare inverter instabilities, feeder overvoltage episodes, black-start/islanding events); (ii) scenario generation for stress-testing renewable ramps, congestion, and contingency events inside the DT; (iii) surrogate modeling to accelerate AC power-flow and time-domain simulations for near-real-time decision support; and (iv) operator-facing “DT copilots” that translate DT outputs into recommended actions (e.g., inverter set-point adjustments, storage dispatch, curtailment choices) while preserving auditable constraint logic.

To avoid “hallucinated control,” generative components should be deployed as decision-support layers (scenario generation, summarization, or surrogate acceleration) that remain bounded by physical models, grid codes, and safety constraints.

The integration of physics-based models with AI-driven methodologies has led to the development of hybrid digital twins. These models leverage the interpretability and reliability of physics-based simulations alongside the adaptability and learning capabilities of AI algorithms. Such hybrid approaches are particularly beneficial in scenarios where data is sparse or incomplete, as they combine the strengths of both paradigms to provide more accurate and robust predictions. For example, the use of hybrid physics-informed neural networks (PINNs) has shown promise in applications like structural health monitoring and health management, where understanding the underlying physical processes is crucial for accurate modeling and prognosis (Wu et al., 2024).

The widespread adoption of AI-driven digital twins necessitates the development of supportive policy frameworks that address data privacy, standardization, and ethical considerations (Evangeline, 2025). Governments and regulatory bodies must collaborate with industry stakeholders to establish guidelines that facilitate the responsible deployment of these technologies. From an industrial perspective, the integration of digital twins can lead to significant improvements in operational efficiency, predictive maintenance, and resource optimization. However, challenges such as high implementation costs, data integration complexities, and the need for skilled personnel must be addressed to ensure successful adoption (Owen et al., 2010). In terms of sustainability and resilience for renewable grids, AI-driven DTs are most impactful when applied to grid-operator objectives such as curtailment reduction, congestion management, hosting-capacity improvement, outage prevention/restoration, and lifecycle-aware asset management for DER fleets (inverters, storage, transformers, feeders). Practical adoption therefore depends on (i) interoperability and standardized data exchange between EMS/SCADA/DERMS platforms and DT toolchains; (ii) clear governance for model risk (validation, monitoring, change control) in safety-critical settings; and (iii) regulatory alignment for how DT-informed actions are audited (e.g., why an inverter-control change or curtailment decision was made). Accordingly, the sustainability case should be evaluated using grid-relevant metrics (reduced losses and curtailment, avoided diesel backup operation, improved restoration time, reduced asset replacement via predictive maintenance) alongside the energy/carbon footprint of DT analytics (“Green AI”) to ensure net benefit (Table 3).

Sustainability-aligned AI-driven digital twins should incorporate technical methodologies that explicitly connect real-time optimization with environmental and lifecycle outcomes. One direction is energy-aware intelligent cyber-physical deployment, where DT analytics are partitioned across edge-cloud layers to reduce latency and communication overhead while also constraining compute energy use; sustainability attributes (e.g., energy consumption of the digital pipeline) become first-class design requirements rather than afterthoughts (He and Bai 2021).

A second direction is DT-enabled lifecycle sustainability, extending DT objectives beyond operational KPIs (frequency/voltage, losses, cost) to include asset lifetime extension, reduced replacement waste, and decision-making that accounts for long-term degradation under different dispatch/control policies. Lifecycle-oriented DT practices have been formalized in sustainability-focused DT frameworks in adjacent domains and can be adapted to renewable-grid assets and DER fleets (Cicceri et al., 2023). Finally, the “Green AI” agenda motivates reporting and optimizing the computational footprint (training/inference energy, hardware intensity) of AI models embedded in DTs, especially for always-on forecasting and control. For renewable grids, this enables a clearer sustainability accounting: net benefits should consider both (i) avoided emissions via improved renewable utilization and reduced downtime, and (ii) the energy overhead of DT + AI computation.