Modern wind-turbine technology has advanced well beyond the classic doubly-fed induction generator (DFIG) approach, which was predominant in earlier onshore installations [36]. The latest generation of high-capacity turbines uses permanent-magnet synchronous generators (PMSGs) and, increasingly, superconducting direct-drive (SCDD) machines. These devices obviate the need for mechanical gearboxes and offer much higher torque density with lower maintenance [37]. Superconducting wind-generator prototypes (such as the 10 MW class HTS-PMSG developed under EU ECOSwing) have achieved up to 45% mass reduction and 2%–3% increase in conversion efficiency relative to conventional copper-wound machines [38]. The removal of slip rings and rotor excitation losses gives improved part-load efficiency and increased reliability, which are important factors for remote or offshore hybrid applications where maintenance access is expensive [39]. Incorporation of these advanced generators with modular multilevel converters (MMCs) affords the high DC-link voltages used for multi-megawatt operation and permits low-harmonic, fault-tolerant performance [40–45]. The distributed sub-module structure of the MMC enables redundancy and softens THD to less than 2% [46]. Fig. 1 presents an original layered architecture synthesizing grid-interactive wind generation, hydrogen conversion, and AI-based orchestration, reflecting recent advances reported in hybrid energy systems research.

A general layered structure for a next-generation hybrid-wind energy system with interconnections and associated control solution in Fig. 1. Such a system would consist of (i) a high-efficiency turbine/generator interface, (ii) a multi-staged power-electronic conversion chain, (iii) hydrogen production and storage modules, and (iv) grid integration using advanced AC/DC links. Energy-vector flexibility is a key characteristic of the proposed architecture, enabling electrical, chemical, and thermal interaction by means of a common control infrastructure.

At the electrical system layer, three main access architectures have been proposed for hybrid wind.

Coordination Hub (CH): The CH is the EMI acting as the coordination hub to connect electric generation, storage, and hydrogen production module. DC-coupled configurations utilize EMI, integrating bi-directional converters that operate in both buck and boost modes across the spectrum. This setup controls the DC-bus voltage and facilitates bi-directional power flow between storage and the grid [51]. The advanced solid-state transformer (SST) can further address these additional functionalities in addition to providing galvanic isolation and performing voltage transformation associated with dynamic reactive-power support in an electronic converter. SSTs use several low-voltage converter cells connected in series to process kV-level outputs and deliver ms-order response, which is critical for hybrid grids with intermittent wind input. At the supervisor level, the hierarchical control algorithm dispatches the electrolyzer loading, hydrogen compression, and battery-charging request schedule. These controllers are model-predictive control (MPC) 8 or reinforcement-learning (RL)-based, to reduce the DC-voltage oscillation and preserve the system stability while disturbed. With the help of real-time wind prediction and hydrogen consumption signal, EMI adjusts power dispatch at different times for electrical export and production, and obtains the optimal usage of existing wind power [52]. For grid-support tasks, the EMI is based on an artificial inertia control loop that emulates the inertial behaviour of synchronous machines. This feature also improves frequency stability in response to sudden wind ramps or load fluctuations, which is especially important for high-renewable interconnection cases [53]. The component rating values for a megawatt-scale hybrid configuration, commonly found in state-of-the-art demonstrations, are shown in Table 2, which provides representative values. An ±800 VDC-linked 5 MW PMSG turbine interfaced with a 500 kW PEM electrolyzer and 1 MWh battery module illustrates a balanced configuration. System-level efficiencies over 92% have been achieved when MMC converters and SiC devices are used.

The component ratings summarized here represent typical configurations reported across pilot and demonstration studies rather than standardized utility-scale installations. Efficiency values correspond to nominal operating conditions and may vary with loading profile, thermal management strategy, and control implementation. These figures are provided to illustrate representative system configurations, not guaranteed field performance. Taken together, the energy-management layer transforms a hybrid wind-energy plant from a fixed generation facility into a dynamic energy hub capable of switching vectors instantly between electricity, hydrogen, and storage. This architecture, together with an AI-enabled predictive controller (described in Section 4), enables autonomous decision-making along with efficient and robust fault-tolerant operation, as necessary for future smart grids.

A comparative examination of the reviewed studies indicates that no single electrical architecture is universally optimal for hybrid wind–hydrogen systems; rather, architectural suitability is strongly conditioned by deployment scale, grid strength, and protection maturity. AC-coupled configurations remain dominant in utility-scale onshore installations due to their compatibility with established grid codes and protection practices, despite their lower end-to-end efficiency arising from multiple conversion stages. On the other hand, DC-coupled and hybrid AC/DC architectures are shown experimentally to have higher conversion efficiencies than TG-AC versions (closer to the ideal 1) because of fewer power stages and integration with DC loads like discrete electrolyzers and batteries. Nonetheless, pilot applications show that the greater need for fast DC fault detection, coordinated protection, and advanced control schemes partly limits these advantages. Reported efficiency and harmonic distortion deviations in these studies are mainly due to the choice of converter topology (two-level or MMC), switching frequency, filter design, and grid SCR. Under controlled conditions, MMC-based structures with high effective switching frequencies and distributed submodules typically deliver lower levels of THD than other topologies—though this is not always the case in weak-grid conditions, nor is it supported by testing partial prototypes. Significantly, the low-THD and high-efficiency levels reported in many studies are based on simulations or laboratory experiments under ideal grid impedance and disturbance scenarios. The evidence available ranges from simulation-based studies for early architectural comparisons, limited laboratory-scale prototypes to partially validate converter-level performance, and a few scale pilot or utility systems that have demonstrated long-term operational capabilities. As a result, one should interpret the performance envelopes in the literature as indicative rather than directly comparable, and thus must ensure that architectural decisions are made based on grid conditions, protection preparedness, and system-level objectives—rather than just nominal efficiency numbers. The electrical configuration of a hybrid wind power system substantially controls the quality of the power, its dynamic response, and controllability, thereby influencing the possible operating domain for subsequent processes that convert hydrogen. Converter topology, harmonic performance, and DC-link stability are the parameters that determine the permissible ramp rates and current profiles delivered to the electrolyzers; they also affect efficiency and degradation response. Thus, the AC-, DC-, or hybrid AC/DC selections cannot be considered independently of the hydrogen system’s needs. This interconnection inspires a detailed examination of hydrogen integration pathways and their functional implications, as explained in the next section [52,53].

The wind-to-hydrogen (W2H) conversion is one of the most promising sector-coupling technologies in a decarbonized energy system. It means excess renewable electricity can be converted into storable, transportable fuel that might otherwise be curtailed during periods of low demand or oversupply [54]. The basic mechanism is based on electrolyzing (splitting) water to form hydrogen and oxygen using wind-born electric power as depicted schematically in Fig. 3. Electrolysis has been primarily developed over the last decade, and three prominent electrolyser families have been distinguished in present research activity and end-user installation: alkaline electrolysis (AEL), proton-exchange membrane electrolysis cell (PEMEL), and solid-oxide electrolysis cell (SOECs). AEL cells, which are usually based on KOH as electrolyte, have suffered from low dynamic response along with less current density (0.2–0.4 A cm⁻²), although it has been durable and cheap [55]. PEM systems, however, utilize a solid polymer electrolyte (typically Nafion^®), which permits both a small size design, operation under high pressure (up to 30 bar), and a transient response time of less than one second. This helps them be more compatible with the less-than-constant wind generation. SOECs operate at high temperatures (600°C–850°C) with ceramic electrolytes, such as yttria-stabilized zirconia (YSZ), achieving very high electrical-to-hydrogen efficiencies (up to 90%). However, they are not yet technologically mature due to issues with material lifetime and thermocycling [56].

Efficiency values are reported on a lower heating value (LHV) basis, unless otherwise stated. Reported ranges primarily correspond to stack-level efficiencies under nominal operating conditions and do not include balance-of-plant losses unless explicitly indicated. Operating temperatures and pressures reflect typical values reported in the cited studies and may vary with system design and control strategy. In hybrid wind systems, the type of electrolyzer has a significant impact on control strategy and overall round-trip efficiency. Table 3 presents the total number of operating conditions, including system efficiency (65%–82%), pressure, temperature, and degradation rate. Despite their high capital cost, PEM electrolyzers are gaining ground in dynamic wind applications thanks to their fast response and small footprint. Fig. 3 depicts an original DC-link integration scheme for electrolyzer coupling, consistent with control strategies discussed in recent wind-to-hydrogen studies. Reported efficiency values are compiled from studies employing different electrolyzer technologies and operating assumptions and are therefore presented as indicative performance envelopes. Unless otherwise specified, efficiencies are expressed on a lower heating value (LHV) basis and predominantly correspond to stack-level performance under nominal temperature and pressure conditions. System-level efficiencies, including balance-of-plant losses, compression, and power-electronic interfacing, are typically lower and not uniformly reported across sources. Variations further arise from current density, dynamic loading profiles, and control strategy, and direct comparison across studies should therefore be interpreted with caution.

The integration of the electrolyzer sub-system in the hybrid DC link is important. When wind-farm power fluctuates, the electrolyzer’s load should track this power availability in the millisecond range to avoid disturbance of the DC-bus voltage. The power-electronic interface is commonly used to control this coupling, and the way it is managed is shown in Fig. 3: wind-produced DC is supplied into the electrolyzer by a bidirectional converter, which properly controls its voltage as well as current for operation. The power-quality filters and protective relays are oriented to shunt the stack during an overcurrent or line fault [57].

The electrical interconnection between the wind generator, DC link, and electrolyzer plant is what determines the operation and lifetime of the complete hybrid plant. To ensure steady operation against fast variation in wind profiles, DC-coupled electrolyzers utilize model-predictive control (MPC), which continuously predicts the voltage and current trajectories to reduce ripple and enhance their efficiency [58]. MPC strategies operate on short time scales (≈50–200 ms), modulating the duty ratio of DC/DC converters and controlling hydrogen production based on the instantaneous wind harvest surplus. Reactive power handling is also a crucial challenge when electrolyzer units are connected to grid-connected converters. Grid-forming converters combined with electrolyzer transformers maintain voltage and frequency, as well as the active and reactive power. The converters described in Fig. 3 mimic virtual synchronous machine operation, and thus can enhance fault-ride-through capability (FRTC) as well as indigenous stability of the local grid. Advanced real-time power-sharing algorithms dynamically distribute output between hydrogen production and electrical export by using electrolyser operation to favor curtailment phases. This is done with synchronized messaging between the emergency traffic management system (EMS) and the shared control infrastructure (as will be detailed in Section 2.3). Additionally, fault-tolerant topologies with redundant converter legs maintain untarnished hydrogen production at partial hardware degradation [59].

From the control point of view, the hydrogen-production system works in three modes:

Stable wind facing the RC mode was changed to a CC mode for constant hydrogen production.

Constant-power (CP) mode reacts by altering current as a function of the wind velocity variations.

Droop regulated mode and power will be equally divided between electrolyzer and any auxiliary loads present.

The supervisory EMS controls activation of these modes by sensing real-time data from wind-speed sensor, DC-bus voltage meter and stack-temperature monitor.

When produced, hydrogen can then be stored and utilized by different pathways. Among the 3 major storage modes are compressed gas HYD, liquefied hydrogen (LH₂) and liquid organic hydrogen carriers LOHCs. It has time, space and safety benefits:

Short and medium-term CM liquid gas value performance is best with technically mature compressed (200–700 bar) storage. The Type-IV Composite pressure vessels are of a high safety factor and modular scalability.

Hydrogen storage is performed in liquid form at −253°C with higher volumetric energy density but resulting in significant boil-off losses (~0.2%–0.4% day⁻¹) [60–62].

Liquid organic hydrogen carriers (LOHCs) based on the chemically binding of liquid compounds, such as methyl-cyclohexane and dibenzyltoluene for ambient condition storage. Conversion yields are low (~70%), but LOHCs facilitate logistics and distribution. As shown in Fig. 4, the power-to-hydrogen-to-power pathway reflects a formulated paradigm consistent with novel hydrogen-based storage concepts.

The stored hydrogen is, in turn, inverted back to electricity using fuel cell stacks, providing a steady power output during low-wind or grid-island situations. This is defined as Power-to-Hydrogen-to-Power (P2H2P) and shown in Fig. 4. Fuel cells are high-efficiency devices (40%–60%) that can be started quickly and help support wind fluctuations, primarily through proton-exchange-membrane (PEMFC) and solid oxide fuel cells (SOFC). Hybrid plants are also investigating combined heat and power (CHP) architectures, in which waste heat from the fuel-cell stack is utilized for district heating or desalination. This raises the total energy use factor (EUF) above 85%. As shown in Fig. 4, the DC-bus facilitates the bidirectional interaction between the grid and the hydrogen tank via the fuel-cell stack. The EMS decides to either feed the hydrogen locally for CHP operation or transport it through pipelines for industrial use. Adding sectors such as mobility and heavy transport further enhances economic viability. N24 Some existing European demonstration projects (e.g., H2Future, Refhyne) already combine wind-to-hydrogen systems with fuel-cell vehicles in a manner that allows for real-time application of demand response using hydrogen refueling infrastructure. The combination of renewable generation, sectoral coupling, and intelligent energy management sets hydrogen as the pivotal component of the upcoming integrated energy landscape [63–65].

The literature reviewed shows that the selection of electrolyzer in hybrid wind systems is a multi-dimensional trade-off, with efficiency, dynamic response, degradation behavior and technological maturity. The reported electrical-to-hydrogen efficiencies to date are often mentioned as being achieved at stack-level performance during steady-state conditions and therefore, plant-level efficiencies which take into account balance-of-plant components (e.g., compression, cooling and power electronics) will be lower. At P2H2P configurations, further conversion losses when hydrogen is stored and reconverted via the fuel cell reduce overall round-trip efficiency even more. Due to rapid periodicity, PEM electrolyzers are often considered for simulated and pilot stage HW applications. In comparison, alkaline systems possess higher technological maturity but slower dynamic response. Of the many electrolyzers available, SOECs have demonstrated the highest theoretical efficiencies for hydrogen production, but their high-temperature operation leads to stability and cycling issues in many laboratory-scale demonstrations. Published dynamic response indices and degradation rates should be regarded as highly context specific. Multiple factors that affect the electrolyzer current ramping capabilities include electrochemical behavior, power electronic interfaces, current density limits and supervisory control strategy. Also, degradation depends on cycling frequency, load variation and current density during operation rather than just electrolyzer technology. Because of these dependencies, system-level efficiency and lifetime performance for such devices must be determined in real-world operating conditions rather than using nominal efficiency values. The introduction of hydrogen technologies brings slow storage dynamics, process troubles and very large safety-related problems which are absent in the ordinary electrical energy storage. The variability of wind generation, electrolyzer ramp constraints and degradation also makes it necessary to coordinate predictively, adaptively and across time horizons. These coupled dynamics are not efficiently dealt with using traditional rule-based control methods. consequently, state-of-the-art AI-enabled forecasting, optimization and digital twin frameworks have proven to be instrumental in orchestrating hybrid wind–hydrogen systems under uncertainty (the next chapter).

Through the application of AI methods in hybrid wind-hydrogen systems, it has enabled complete assistance to the processes involved in forecasting both wind and hydrogen accordingly by bringing substantial changes in their planning activities accordingly. Time-series models such as ARIMA or persistence model (if relying on historical data) and numerical weather prediction (NWP) based approaches often do not represent the nonlinear dependence structures and multivariate interactions in wind energy systems adequately [66]. AI-based models, deep learning (DL) techniques that learn spatiotemporal dependencies from large meteorological and turbine databases, overcome these shortcomings. Hybrid CNN–LSTM networks are one of the most advanced methods for wind-speed prediction or forecasting among others. Here, CNN layers are used to capture spatial patterns from wind field data of multiple sensors, and LSTM layers are utilized to analyze sequential dependency and temporal evolution [67]. These hybrid networks are successful in providing mean absolute percentage errors (MAPE) lower than 5%, surpassing standalone LSTM or CNN architectures by a margin of 20%–30% in rugged areas. Fig. 5 outlines an AI-orchestrated supervisory hierarchy developed based on forecasting and control paradigms reported in recent AI-driven energy management literature.

As shown in Fig. 5, the AI-managed hierarchical supervisory control system has three levels of functionality:

Prediction Layer: Uses CNN–LSTM structures for the prediction of high-resolution (and short-term) wind and load [68].

Optimization Layer: It is responsible for deciding on time and power forecast uncertainty.

Actuation Layer: Implements the commands at the turbine, electrolyzer, and converter via closed-loop control.

This architectural structure allows real-time-like synchronization of the wind power production, hydrogen generation, and grid interaction. In combination with dispatch based on reinforcement learning (Section 4.2), this capability may be used separately to allow for autonomous adaptation to evolving weather and market conditions, effectively turning the system into a self-optimising cyber-physical energy system [69]. In addition, probabilistic prediction models are increasingly being used to quantifying uncertainty using Bayesian deep learning and ensemble learning. These approaches, outputting prediction intervals rather than point forecasts, allow risk-sensitive decisions to be made in scheduling power and dispatching hydrogen. The approach is extended to multi-horizon forecasting of 5-min, hourly and daily combined forecasts enabling rolling optimization balancing fast and slow energy dynamics across changing conditions [70]. In hybrid wind–hydrogen systems, a CNN–LSTM architecture can be adapted to collectively predict local short-term wind power availability and electrical load. The convolutional layers learn spatial features from the meteorological inputs (e.g., wind speed fields, directional gradients), whereas, LSTM layers model dependencies over time in past wind and demand trends. Instead of a single-point forecast, the model provides an uncertainty band (e.g., prediction intervals) during the scheduling horizon. These limits are then applied onsite using an energy management system (EMS) as boundaries for both electrolyzer dispatch and grid exchange decisions in performance of a risk-to-reward analysis to reduce the likelihood of constraint violation given forecast uncertainty.

Game-Theoretic and Multi-Agent Scheduling for Coupled Electricity–Hydrogen Dispatch

Along with reinforcement learning–based scheduling methodologies, game-theoretic and multi-agent frameworks have gained increasing interest for coordinating coupled electricity–hydrogen systems under decentralized and competitive operating environments. These formulations treat system elements (in this case wind generators, but also electrolyzers, battery storage, hydrogen tanks, grid interfaces etc.) as rational agents who cooperatively (and while obeying physical and market “rules”) seek to optimize their own or collective objectives. Using game-theoretic scheduling rather than pure data-driven RL has many advantages. Introducing utility functions and equilibrium concepts (e.g., Nash or Stackelberg equilibria), the proposed methods allow to achieve better interpretability, analytical tractability and converging properties under particular assumptions. This is especially critical in the context of hybrid wind–hydrogen systems, where electrolyzer dispatch, grid interactions, and storage use need to satisfy safety-of-operation, equity-in-use, and market participation constraints. Furthermore, multi-agent settings of the games naturally aggregate distributed decision makers and reduce dependence on centralized controllers and long history training data. However, game-theoretic techniques have limitations when dealing with highly uncertain renewable environments. Computation is also exact for the equilibrium, in which case simplified system models are considered, a known payoff structure for the modification mechanism is used, and uncertainty is somewhat limited, which may result in limited adaptability to rapidly changing winds. In contrast, RL-based scheduling offers greater flexibility and adaptability in a non-stationary environment but also increases data dependency, reduces interpretability, and poses potential safety hazards during exploration. As an example, we refer to recent game-theoretic energy scheduling for community multi-energy systems using quantum swarm intelligence, demonstrating how the equilibrium coordination strategy can be extended to include electrical, hydrogen, and mobility-based demand. When applied to wind–hydrogen hybrids, such frameworks can facilitate coordinated electrolyzer ramping, storage allocation, and grid exchange while still operating without any diluted control logic. These approaches therefore complement reinforcement learning and digital twin methods, suggesting that hybrid scheduling strategies combining game-theoretic structure with learning-based adaptation represent a promising direction for scalable and reliable wind–hydrogen system operation [68–70].

Reinforcement learning (RL) extends the functionality of classic control by allowing decisions to be based on learned experience. In hybrid wind-hydrogen systems, RL agents learn to optimally distribute power between the turbine, electrolyzer, battery storage, and grid interface dynamically to maximize cumulative rewards according to various performance metrics, including energy efficiency, hydrogen yield, and component lifetimes [71]. Every RL agent interacts with the environment by receiving states (e.g., wind speed, DC-link voltage, hydrogen tank pressure), taking actions (e.g., power dispatch commands), and observing rewards (e.g., system efficiency, penalized degradation cost). The objective is to find an optimal policy π(s)* that maximizes the expected sum of rewards over time. An advanced version including DDPG and PPO enables the continuous-action control that can be applied to hybrid power systems [72]. These algorithms are applied in the multi-agent framework, where several agents (i.e., wind turbines, electrolyzers, and storage units) represent various subsystems. These agents learn to optimize the system level rather than just isolated components through shared state representations and cooperative learning [73].

Main results Recent research has revealed:

Speed-up of convergence: Multi-agent DDPG architectures converge 25% faster than independent single-agent systems due to the joint experience buffers.

Improved robust performance: It is proven that the RL-based controllers keep the system asymptotically stable with wind speed forecast error of ±20& in wind velocity.

Deterioration-sensitive dispatch: Objective functions with deterioration cost terms decrease long-term maintenance costs by 12%–18%.

Additionally, RL agents can also interact with Digital Twins (DTs) (Section 4.3) based on real-time simulation and training capabilities. In these settings, the DT environment offers false feedback in training the controller and thus significantly mitigates hazardous physical experiments. This integration of RL with digital-twin simulation allows for safe policy learning, and is crucial for high-capital infrastructures in energy [74–76]. Through DDPG, we can guarantee the coordination of real-time power allocation according to wind variability in either electrolyzer or battery storage. This state vector contains short-term wind power forecasts, DC-link voltage, hydrogen tank pressure and battery SOC. The action space contains continuous power split commands that dictate the allocation of available wind power between the electrolyzer, battery, and grid connection. The reward function is designed to maximize hydrogen production while balancing penalties for electrolyzer over-ramping, battery over-cycling, and constraint violations. Such formulations enable smooth power transitions and degradation-aware operation, although reported performance gains are typically demonstrated in simulation or digital twin environments under predefined assumptions.

For the coexisting multiple faults, such as the induction motor bearings and IM fault, traditional diagnosis methods cannot make a correct diagnosis for both of them at the same time. The system is the next iteration of the digital-twin technology (DTT), which makes seamless connections between physical and virtual realms by sharing data in real time [77]. At hybrid wind-hydrogen plants, DTs generate high-fidelity real-time copies from turbines, converters, and electrolysers, among other entities, including storage facilities, facilitating predictive diagnostics, virtual testing, and life cycle optimization. The digital-twin-enabled fault diagnosis framework shown in Fig. 6 is conceptualized using principles established in predictive maintenance and cyber-physical system research.

A traditional digital-twin architecture can be seen in Fig. 6, which consists of three closely-related layers:

Physical Layer: It is the wind-energy and hydrogen-production assets themselves that are embedded with devices monitoring, among other things, electrical, thermal, and mechanical properties.

Virtual Layer: Consists of physics-based models and data-driven ML surrogates emulating real-time operational dynamics.

Communication Layer: Enables bidirectional communication between physical and virtual goods through IoT gateways and edge computing platforms.

The twin constantly measures the real and simulated indicators of performance, spotting discrepancies that could be a sign of wear or imminent breakdown. Indeed, under ultimate conditions, CNN-autoencoder models based on transfer learning can anticipate converter IGBT degradation 20% before traditional threshold-based approaches [78].

Table 4 highlights some of the essential AI algorithms used in digital twin applications for fault prediction and optimization, summarizing these as a quick reference guide. It incorporates multiple models—reinforcement-learning twins for adaptive control, GNNs for subsystem interdependencies and autoencoders for anomaly detection. Training time and computational resources are key factors for the edge deployment. Additionally, Table 5 provides frequently used open-source datasets and evaluation metrics for comparing AI models inspired by hydrogen and wind energy systems [79], some notable repositories include the IEA Annex 58 wind Bench project and the National Renewable Energy Laboratory Wind Turbine Database (WT-DB). Performance is usually measured against RMSE, MAE and R2.

Conversely, DTs and AI cooperate to build a self-learning monitoring system in which the models improve over time as new data becomes available. This capability allows to automatically discover failures, schedule maintenance in advance, and optimize long-term performance. All of these are critical for grid-integrated hydrogen production, which must be highly dependable [80].

The AI-enabled control and digital twin-based approaches have great potential performance in hybrid wind–hydrogen systems, whereas their practical implementation calls for substantial methodology development efforts as well as deployment processes. The RL formulations in the literature typically define multi-objective rewards that comprise energy use, hydrogen generation and component status, while also including penalties for hitting constraints (e.g., DC-link voltage excursion, excessive ramp-rate or state-of-charge restrictions). Such formulations are critically dependent on the reward scale (which is treated as a hyperparameter in practice) and do not necessarily generalize to other operating regimes. Data laboriousness is a key issue that hinders application in practical use cases. Most AI models rely on high-fidelity SCADA data, meteorological inputs, electrolyzer telemetry and ground-truth degraded or faulty labels which are widely available in simulation or digital-twin setups but limited during early field deployments. As a result, the reported performance gains are mostly based on offline training on synthetic or historical datasets and to a lesser degree on edge online inference. Computational cost too restricts practical use in real time. Deep reinforcement learning models are often trained offline on GPU-accelerated systems, but online execution must be simplified according to latency and reliability constraints. Furthermore, convergent behavior seen during simulation does not imply robustness to real-world perturbations and introduces an apparent gap between the effectiveness of simulations and reality; the so-called simulate-then-execute gap. As a result, most real-world implementations use hard safety constraints such as DC-link voltage, electrolyzer current density, battery state of charge and grid-support obligations triggers that are implemented by hierarchical supervisory layers based on rules or MPC–RL hybrid schemes. To contextualize the evidentiary basis of reported performance metrics and AI-enabled control claims, representative studies are classified in Table 6 by deployment scale, methodological approach, and approximate technology readiness level.

This classification is based on the most common fold arrangements observed in literature reviewed and is not exhaustive. Reported measures are application-specific and may depend on modeling assumptions, data availability, operational profile, and depth of validation. The technology readiness levels (TRLs) are approximate estimates based on conventional TRL definitions. Hence, AI-based control and optimization interworking procedures can lead to better forecasting accuracy and system efficiency; nevertheless, their decisions should still be at par with grid operational constraints and protection. Thus, the emergent (EM) schemes produced by AI models should be feasible through power electronic interfaces; also, in compliance to grid codes, and maintained stable operation and interaction from electrical and compressed hydrogen storage systems. This calls for a wider exploration of grid-forming/grid-following control designs, multi-layered storage coordination and market participation schemes that will collectively dictate the real-world feasibility of AI-orchestrated hybrid wind–hydrogen systems.

A significant advancement in renewable energy architectures, hybrid wind-hydrogen systems provide the grid with bidirectional generating units. These universal systems offer dispatchability, stability, and resilience at various load levels and can function as grid-forming or grid-following. Converters use VSM control mechanisms to simulate the behavior of synchronous generators when they are in grid-forming mode [81]. By regulating the converter’s active and reactive power injections, these control strategies simulate synthetic inertia and damping to improve frequency stability during sudden disturbances like load shedding or changes in renewable output [82]. Fig. 7 illustrates a hierarchical grid-interactive control structure inspired by virtual synchronous machine and grid-forming converter studies.

The grid-integrated hybrid wind-hydrogen system’s hierarchical control structure, which includes real-time coordination between turbine converters, electrolyzers, storage interfaces, and the primary power grid, is shown in Fig. 7. Three overlapping loops make up the architecture:

Direct Control (Inertia-Emulation Layer): By simulating the inertia and damping characteristic of synchronous generators, VSM algorithms are implemented in this layer to remove frequency and voltage deviations.

Secondary Control (Voltage Restoration Layer): Maintains the voltage at its nominal level by adjusting reactive power and converter set-points.

Level 3 Control (Grid-Coordination Layer): Deals with power delivery, grid coupling, and the synchronization of foreign microgrids or distribution systems.

Each of the layers is connected to an AI-supported supervisory control that accounts for intelligent logistics between the wind-based generation and hydrogen conversion. For example, for grid contingencies (two-way), the supervisory system may be able to divert excess generation to hydrogen production using water electrolysis and restore balance with the assistance of a storage in a battery or capacitor [83]. Additionally, adaptive droop control and PLL-less synchronization approaches become new candidates for classical frequency controllers. These assist to make the hybrid that is half power plant, half windfarm stays in sync with weak grids without causing harmonic distortions or phase instabilities. Accordingly, hybrid converters can operate as virtual inertia sources and increase system resilience during transients [84].

The storage of energy is a critical requirement for the successful operation of hybrid wind–hydrogen systems. Short-term storage, such as lithium-ion batteries or supercapacitors, helps to deal with fast transients and power smoothing. In contrast, long-term storage can be achieved by hydrogen for long-term energy shifting and seasonal load matching. The dynamic interplay of the storage devices during intermittent fault scenarios is illustrated in Fig. 8. When the input voltage sags or an output short-circuit happens, such coordinated control of BESS and the hydrogen electrolyzer module quickly restores stabilization of the DC-link. This hybrid control scheme can reduce the VDR up to 45%, thus reducing the oscillating tendency and avoiding converter saturation [85]. The AI-supervised hierarchical storage coordination depicted in Fig. 8 reflects recent advances in hybrid storage and EMS optimization strategies.

Table 7 summarizes the key performance criteria between major storage technologies. In this regard, lithium-ion batteries (LIBs) with a high energy density of 30–40 Wh/kg and round-trip efficiency >90% are best-suited for high-frequency applications, including ramp-rate smoothing. Even faster response (<1 s) at the expense of low energy capacity is achieved with supercapacitors. On the contrary, hydrogen storage (energy densities of ca 33 kWh/kg) has no parallel in seasonal buffering capacity, albeit with a notably lower round-trip efficiency (35%–50%), considering conversion losses [86].

The storage tiers can, for instance, be coupled via DC-coupled hybrid energy storage systems (HESS) controlled by a bi-directional DC/DC converter controlling charge and discharge operations. AI-based controllers are used to adjust thrust power flow, and they do so in a dynamic manner using predicted demand, SOC, and degradation cost, thus avoiding severe wear on individual units [87]. In energy management applications, model predictive control (MPC) is employed to regulate storage cascades; while BESS smoothens the fast fluctuations, hydrogen deals with long-term mismatches. Moreover, the EMS is capable to find real-time optimal scheduling of storage usage according to various factors, including weather data, market prices and hydrogen demand, thus improving power quality and system inertia which positively affects fault recovery capability by FRT and frequency stability recovery after faults. If you know the system and need predictive maintenance in control center, regulation can also be achieved by linking to SCADA systems at the control center, extending lifespan. The energy density and efficiency for battery and hydrogen-based storage technologies are summarized across literature with various system boundaries, operating conditions, and time horizons. Note that reported values may correspond to component-level, subsystem-level, or system-level evaluations based on the study. Results depend on storage setup, auxiliary energy need, cycling depth, ambient conditions and measuring approach.

As sophisticated hybrid systems become available, the interaction shifts from market and grid support to intelligent microgrid trading. Blockchain and artificial intelligence (AI) are combining to create self-governing energy trading networks that facilitate safe, transparent P2P (microgrid-to-microgrid) transactions. In this context, machine learning models trained on pricing, weather, and consumption data are used by AI-supported trading platforms to forecast energy supply and demand [88]. When specific criteria are met, smart contracts integrated into blockchain networks automatically carry out and verify energy transactions (e.g., available hydrogen levels; grid capacity). The administrative burden and settlement delays associated with traditional market transactions are decreased by this decentralization. Dynamic pricing based on time-of-use (ToU) tariff models and marginal generation cost is the key to increased profitability and the goal of equitable energy distribution. Electrolysis for hydrogen storage can be expanded when wind power is abundant, and excess hydrogen can be exported to adjacent industrial locations or stored for re-electrolysis at times of high demand. However, the load curve flattens as a result of the discharge of BESS modules and stored hydrogen to meet local grid demand when renewable generation declines [89]. By actively participating in ancillary services like frequency regulation and reactive power control, artificial intelligence-based market analysis assesses capacity factor utilization, carbon intensity, and revenue optimization at the macro level. Furthermore, you would have auditable and traceable records of hydrogen production, storage, and use with DLTs in IoT-based asset monitoring. You could also confirm compliance with future green hydrogen certification frameworks. Together, grid-interactive operation, multi-layered storage, and AI-mediated market participation will turn hybrid wind-hydrogen plants from passive power producers into active prosumers capable of trading on digital markets and balancing energy production and consumption on their own [90].

The reviewed studies collectively indicate that grid-interactive hybrid wind–hydrogen systems can provide enhanced frequency support, voltage regulation, and dispatchability; however, these capabilities are accompanied by increasing architectural and operational complexity. Grid-forming converter control offers promising potential in weak grids and low system inertia, since it mimics synchronous machine operation; however, it places high requirements on protection coordination, fault ride-through capability, and compatibility with the previous grid. On the other hand, grid-following methods are more mature and have received regulatory acceptance, but offer little autonomous stability support. Storage coordination provides an additional example of a system-level compromise. Although BESS are well-suited to fast transient response and power smoothing, hydrogen storage offers long-duration and seasonal energy shifting, with the trade-off of lower round-trip efficiency and slower response times. A hybrid storage system can avoid such problems, but requires SCADA and a reliable communication network. At large-scale roll-out for deployment: grid functions to support the system (e.g., basic storage and short-term storage) are in commercial development, while higher-integrated value-chain features such as AI multi-market interactions, autonomous operations using grid-forming techniques, or hydrogen-based balancing are mostly tested at pilot/demonstration stages. This maturity mismatch highlights the need to align control sophistication with regulatory preparedness and system reliability requirements [86–90].

The designs of the converters have also gotten more complicated to deal with the problems of power density, reliability, and efficiency that modern hybrid wind-to-hydrogen systems need to solve. Wide band gap (WBG) semiconductors, such as silicon carbide (SiC) and gallium nitride (GaN), are at the center of this change [91]. These semiconductors offer a lot of performance benefits over traditional silicon-based devices [92]. This is because WBG converters can operate up to 50 kHz and higher switching frequency compared with the Si-based converter, which leads to a smaller converter size, less filter size, lower switching loss, and better thermal performance. These are desirable properties for offshore wind-to-hydrogen schemes, which require modularity, weight reduction, and thermal stability [93]. SiC-based DC/DC bidirectional converters and matrix inverters can provide faster transient response and improved efficiency over variable wind and electrolyzer loading conditions in those systems [94]. There are advanced control algorithms, in addition, such as nonlinear predictive control (NPC) and model-based adaptive modulation, that can improve the dynamic response of the system. Also, these converters include active thermal balancing and real-time fault-tolerant gating, such that stable operation whilst the power is being quickly transitioned to the load (or removed from the rail) is provided. These converter-level innovations across the hybrid DC link are summarized graphically in Fig. 9.

Due to the two-way power flow and the high bulblike fault traveling speed, it is essential to focus on protection coordination in hybrid renewable–hydrogen systems. As traditional protection concepts are not applicable in DC fault isolation, utilization of advanced solid-state circuit breakers (SSCB) and hybrid DC breakers (HDCB), which can achieve less than [94,95]. To best show this point, Fig. 9 highlights an original DC protection coordination scheme having a close match to cutting-edge solid-state breaker and rapid fault-isolation studies.

The protection system consists of solid-state DC breakers, fault-current limiters (FCLs), and smart relay coordination as depicted in the flowchart in Fig. 9. All the above components work in a layered architecture which can fulfill the or the purpose of fault isolation and site protection. The fast-switching semiconductor devices (e.g., SiC MOSFETs) implemented in SSCBs can facilitate de-arc (i.e., zero or no arc) [96–99]. Furthermore, the introduction of cooperative relay networks enhances fault detection capabilities for converters, electrolyzers, and grid interfaces. In addition, supervisory controllers have protection sequencing logic programmed in them that dictates shutting down the converters first before isolating the hydrogen stacks to avoid the loss of electrolyzers. The parameters of interest are transient DC link voltage levels, fault current rates of rise and energy dissipation curves at protections. Quick fault elimination and post-event power restoration to meet safety (IEC 62909 or IEEE 1547.9) [100–104] permitted.

Reliability in hybrid systems is also more than just component lifespans, but also condition monitoring and prognostics-based maintenance. This is applied in the present day, in such a way that sensor fusion of thermal, vibration, and acoustic signals is used to achieve early detection of degradation in power-electronic modules (IGBTs, SiC MOSFETs) [105]. Reliability modeling using data in our approach involved Bayesian prognostics and machine learning-based remaining useful life (RUL) estimation. The techniques employ fault history and operational stress measures to predict wear-out trends of components, and to optimise maintenance intervals [106].

Table 8 lists the prevalent failure types associated with converter applications, including:

IGBT solder fatigue and bond-wire pull-up as dedicated failure modes.

Dielectric insulation breakdown.

DC busbar corrosion.

It is a sensor drift in the current or voltage transducer.

Most of the detection algorithms, such as wavelet decomposition, Kalman filtering, and long short-term memory (LSTM) models, can contribute to real-time fault classification and prognostic scoring [107–109].

Using DT platforms to design these health-monitoring systems enables predictive maintenance by continuously comparing operational data with virtual copies of the assets. It increases the mean time to repair (MTTR) by almost 1 month while maintaining the system’s availability and durability of the electrolyzer. Operators can monitor fault probabilities and automatically adjust risk-based maintenance schedules based on machine-learning reliability models integrated into dynamic asset health dashboards—a step toward resilient, self-diagnosing renewable-hydrogen grids [110–120].

With growing reliance on AI-based forecasting and optimization for control in hybrid wind–hydrogen systems, cybersecurity and data integrity become determining factors of system reliability and safety as well. Compared to traditional rule-based control systems, AI-powered architectures rely heavily on continuous data flows from sensors and communication networks, and SCADA systems that control them essentially broaden the system’s potential attack surface. A realistic threat model for AI-operated hybrid plants considers sensor spoofing and data falsification incidents in which the learning algorithms’ input space is distorted by maliciously injected wind, load, or electrolyzer measurements. Compromise of SCADA communication pathways can cause late, unsent, or modified control signals, leading to hazardous electrolyzer ramping, battery overdischarge, and grid code breaches. Adversarial data inputs, from both malicious and sensor-fault origins, can compromise the performance of machine learning models trained under nominal conditions, especially for reinforcement learning agents operating near operational edges. A few mitigation strategies have been proposed in the literature to enhance robustness. In such cases, techniques for outlier detection and anomaly identification (e.g., statistical change detection and model-based residual analysis) can be used to exclude corrupted or implausible measurements before feeding them into AI pipelines. Safe state estimation approaches, which integrate physical system models with redundant sensing and confidence weighting, serve as another layer of defense, enhancing the safety of data-driven predictions by reconciling them with physics-based constraints. Such methodologies are critical when dealing with hybrid systems, as the electrical, hydrogen, and storage systems have their own time constants. Safety- and fallback mechanisms are still required from an operational safety perspective. In practice, hierarchical control architectures are increasingly adopted, with AI decision layers controlled by rule-based or model-predictive safety controllers. If such uncertainty metrics exceed safety limits for control (e.g., data loss, model divergence, or detection of cyber anomalies), a safe controller is activated, which enforces hard saturation limits on the DC-link voltage, electrolyzer ramp rates, and storage state of charge. This multi-level approach allows for AI-driven optimization improvements to be made while maintaining system resilience. On balance, cybersecurity considerations support a particular framing of AI controllers, namely the need to treat them as augmentative rather than autonomous decision-makers in grid-critical hybrid wind–hydrogen plants. Data integrity, robustness and safe fallback operation must therefore be addressed as a prerequisite for scaling AI-based control from simulation and pilot studies to reliable commercial deployment [120].

The next generation will have superconducting and magnetless generators that lose the least amount of energy and have the highest power density [121]. HTS generators could be very important for future megawatt-scale wind turbines, especially for offshore uses where nacelles need to be small and efficient [122–125]. Replacing copper windings with HTS tapes at 77–20 K can cut losses in the copper by up to 60%, which means more torque density and less mass.

Also, generator technologies like magnetless reluctance generators (axial-flux and hybrid excitation synchronous machines) don’t need rare-earth materials [126]. This greatly lowers the environmental impact and supply-chain risk over the life of the product. Using cryo-free cooling systems and fault current limiter superconductors makes operation more reliable and fault tolerance better [127]. Thermo-magnetic coupling models, quench detection, and superconducting power take-off (PTO) systems that connect directly to DC-link converters to reduce energy loss are the main focus of current work [128]. These advances together make a) higher power density (>8 MW per device), b) further lowered maintenance frequency, and c) improved offshore robustness achievable, thus being key to the next-generation renewable wind-hydrogen economy [129–131].

For autonomous hybrid energy systems, the SSTs are considered an enabling technology. SSTs combine high-frequency isolation, bidirectional AC/DC conversion, and modular multilevel structures to deliver flexible power flow control and voltage regulation between hybrid grid networks [132–137]. Unlike conventional transformers, SSTs allow for active control of the energy flow in real time with power semiconductor devices (SiC/GaN) and integrated controllers that provide fault ride-through, load sharing, and harmonic filtering. SSTs allow direct interconnection between variable speed generator, electrolyzer, and DC micro-grid without extra conversion stages in the wind-hydrogen plants, and thus improve system efficiency. In addition, their bidirectional characteristic facilitates the easy energy transfer among renewable generation sources, hydrogen storage units, and smart loads, hence providing islanding and grid resynchronization functions [138–141]. Featuring embedded self-diagnostic capabilities and plug-and-play modularity, SSTs are anticipated to serve as a cornerstone of intelligent hybrid grids in compliance with the IEEE 2030.10 standard for the next-gen DC distribution. By 2030, SST-based infrastructures are foreseen to decrease the converter losses by ~25% and reliability indices (SAIDI, SAIFI) through adaptive fault-routing [142].

6G networks are merging with quantum-inspired optimization technology, changing distributed energy management [143]. Telecom edge controllers enabled by 6G, delivering latencies under 1 ms, will synchronise dynamic assets—turbines, electrolysers, batteries, and power converters—between distributed geographic islands in hybrid systems [144–148]. Then, we develop coordinated decision-making in a URLLC model based on the resulting LLSC approach by utilizing real-time streams. In this scenario, edge intelligence redistributes the computational tasks among micro data nodes installed in local substations and quantum-inspired algorithms to calibrate control profiles such as reactive power dispatch, hydrogen produced rate, and battery charge cycle. Methods such as quantum annealing and tensor network optimization outshine classical solvers when coping with high-dimensional energy scheduling problems, which allows for real-time power routing under uncertainty [149–153]. Furthermore, 6G-included DTs simulate the dynamic states of hybrid systems for enabling self-learning from feedback loops. If complemented with an AI-enabled cyber-physical resilience infrastructure, such systems would be capable of anticipating and mitigating both cyber intrusions and fault spreading as well as communication delays. By 2040, wind-hydrogen grids that are autonomous and self-healing, using 6G-edge-quantum frameworks, shall become a reality, with online distributed learning agents maximizing sync cost minimization of reaction to faults and operational efficiency [154].

Techno-economic models and policy frameworks, however, would need to continue to develop in sync with rapid technological progress in order to facilitate the mass adoption of these new technologies. Standardisation continues to be an ongoing concern—particularly with respect to data interoperability, cybersecurity, and hydrogen safety standards. The lack of harmonised lifecycle emissions accounting, hydrogen certification schemes, and carbon price frameworks is a challenge for investors and operators looking to profit in the long term [155]. The long-term technology roadmap shown in Fig. 10 synthesizes future development trajectories discussed across multiple techno-economic and policy studies.

The Technology Roadmap (2025–2050) of milestones in generator development, AI optimization, and energy market integration is shown in Fig. 10. The roadmap suggests that solid-state transformer penetration maturity will be achieved during the mid-2030s to mid-2040s, where quantum-assisted dispatch optimization strategies will emerge in pilot form. Beyond 2045, policy attention is likely to turn to the recyclability and carbon-neutral life-cycle of hybrid-energy components through a circular-economy approach [156].

Emerging strategic research gaps are presented in Table 9 by four domains:

AI Interpretability: Explainable energy dispatch systems and transparent decision-making in neural control layers.

Cyber-Resilience: Antispoof resilient architectures for cyberphysical coordinated attacks and sensor spoofing.

Cost Modeling: Dynamic levelised cost analysis for hydrogen and storage systems in an uncertain carbon price.

Design: Standardisation of renewable hydrogen standards and multi-jurisdictional energy trading rules.

Such a confluence of technology and governance will drive the speed of wind-hydrogen transition, creating digitally orchestrated power ecosystems free of emissions [156–160]. Table 6 illustrates the heterogeneous maturity of hybrid wind–hydrogen subsystems, with electrical and grid-support functions approaching commercial readiness, while AI-driven real-time control remains largely at demonstration-level TRLs.

To systematically contextualize the maturity of the technologies reviewed, this section considers the maturity of the technologies according to a technology readiness level (TRL) lens used to distinguish those that have been validated in simulation vs. components demonstrated at laboratory or pilot scale or subsystems moving into commercial deployment. A TRL framework must be sufficient to provide practical insights into the performance metrics of research prototypes that are likely not to be implemented on a grid-scale level. Power electronic converters are the most advanced subsystem in hybrid wind–hydrogen operating principle. Offshore wind and high-voltage direct current (HVDC) applications have achieved TRL 7–9 for modular multilevel converters (MMCs) and wide-bandgap (WBG) semiconductor technoeconomic interfaces, with extensive field deployment and grid-code certification. Yet, the existing converters developed for DC-coupled hybrid systems including integrated hydrogen loads are less mature in addressing issues related to converter protection, fault isolation, and coordinated control, specifically in multi-terminal and weak-grid environments. Electrolyzer technologies vary in their readiness by chemistry and operating regime. Alkaline electrolyzers (AEL) are commercially available and generally considered TRL of 8–9, though they are hardly suited for harsh dynamic operation. PEM electrolyzers. Although frequently included in pilot-scale projects for renewable integration, proton exchange membrane (PEM) technology typically ranges from TRL 6 to 8, indicating good performance with variable input power but some remaining challenges, including theoretical cost and long-term degradation concerns. In contrast, solid oxide electrolysis cell (SOEC) technology remains primarily at TRL 3–5, with the majority of publications and reports limited to laboratory scale due to durability, thermal cycling sensitivity/balance-of-plant complexity. Grid-forming control methods for converter-dominated wind generation systems have advanced rapidly but are at varying levels of maturity. Although grid-following controllers have been fully commercialized (TRL 8–9), grid-forming controllers with synthetic inertia, direct voltage regulation, and black-start functionality are still generally demonstrated at TRL 5–7. Their extensive integration is limited due to outstanding concerns regarding protection coordination, compatibility with existing grid assets, and synchronization with evolving grid codes [152–160].

AI and DT/Metasystem orchestration frameworks currently have the least maturity overall. Machine learning–based forecasting models in operational use for wind power have progressed to TRL 5–7; forecast-enhanced optimal control via AI and reinforcement learning–based energy management systems remain at TRL 3–5, since most documented benefits are found only in simulation or digital twin environments. Several challenges remain, including the availability of training data, the computational complexity and accuracy of simulation-based learning models, the interpretability of prediction outputs, and methodologies for safety-certified learning dynamics to maintain grid-critical operations. Lastly, cybersecurity/data integrity measures constitute a standing-readiness gap across all subsystems. Traditional industrial cybersecurity measures are commercially available (TRL 7–9); however, their integration with AI-managed hybrid energy systems is still in the development phase (TRL 4–6). Threats of sensor spoofing, adversarial data manipulation, and compromised supervisory control are rarely explicitly considered in current pilot programs, suggesting an opportunity to develop resilient system architectures that integrate secure communications, anomaly detection, and fail-safe control layers [160].

In summary, the TRL-based comparison indicates that electrical infrastructure is clearly ahead of hydrogen integration and AI orchestration, suggesting that near-term deployment plans should be centered on staged integration. To overcome these maturity gaps, coordinated progression in system validation, regulatory coordination, and cyber security assurance must be achieved to provide aa viable pathway from demonstration to large-scale commercial deployment.