Next-Generation Wind Hybrid Energy Systems: Grid-Interactive, Hydrogen-Enabled, and AI-Orchestrated Pathways for Sustainable Electrification

1  Introduction

The energy sector is undergoing a significant global shift due to the pressing need to address the effects of climate change, technological advancements, and decarbonization requirements [1]. Wind power has emerged as a crucial component of renewable energy generation in the last ten years [2]. The diversification and decarbonization of the world’s electricity supply from renewable sources are greatly aided by wind power. By 2024, wind power will generate about 20% of renewable electricity, with a total installed capacity of over 850 GW worldwide [3]. However, as the proportion of renewable energy increases, power systems will face more intermittency, curtailment, and grid stability problems that could jeopardize both operational security and economic viability [4–6]. However, as a mature technology, its operating period is less variable, its instantaneous resource and transmission capacity conditions are limited, and it is unable to meet dispatch requirements; it is also unable to support voltage, leading to significant wind curtailment [7]. Grid-interactive hybrids that combine wind energy with storage and hydrogen production (using AI-driven models) have been developed to address these limitations. In addition, such integration can provide secondary services such as frequency regulation, voltage control, and peak-load shaving [8–11]—the process of hybridization. A flexible energy system intelligently combines various energy vectors, including thermal, electrochemical, and electromagnetic energies, as the foundation for resilience and interoperability. In this regard, the most promising configurations are hybrid systems based on wind and hydrogen [12]. Water electrolysers can receive excess electricity from wind turbines through power-to-X conversion pathways, which transform the hydrogen produced into an energy carrier for storage and transportation [13–15]. It increases wind energy’s temporal and spatial flexibility, facilitating trans spatial energy transfer and seasonal storage for use in transportation, industry, and homes [16]. To facilitate deep decarbonization across numerous industries and account for variations in wind generation, hydrogen serves as a bridge and buffer. It can readily convert hydrogen back into electricity using fuel cells [17–21], a process similar to synthetic fuel manufacturing. It can be directly used for industrial purposes, further contributing to the large-scale integration of the value chain and grid system and to the establishment of a comprehensive ecological self-built coverage model. Due to advancements in digital twins (DT) and AI/ML, the operational framework for a hybrid energy system is evolving alongside the hydrogen wave [22]. It has never been possible to forecast wind availability so accurately; AI-based prediction models are already forecasting wind availability, load demand, and hydrogen production potential in real time, though they still require further research and development. The control strategy using reinforcement learning optimizes generation and power allocation in real time among wind, electrolyzer, and energy storage devices to balance efficiency, cost, and component degradation trade-offs [23]. Moreover, digital twin solutions have enabled the virtual replication of physical entities, such as turbines, conversion systems, and electrolysers. These CP environments enable predictive maintenance, fault detection, and scenario testing under different operating conditions [24–28]. In combination, AI and DT technologies are moving towards autonomic, self-organizing energy systems in which human intervention is kept to a minimum; their performance continuously learns to achieve maximum reliability.

Although numerous advances have been achieved over this time in individual areas such as wind energy conversion, hydrogen production, and power electronic control, there has not yet emerged, at the system level, an understanding of convergence. The majority of current reviews study only the mechanical design of turbines, or the electrochemistry of electrolyzers, or AI methodologies separately. Nevertheless, a unified vision for next-generation hybrid wind systems incorporating these components within electrical engineering frameworks has yet to be successfully established [29]. This gap is therefore addressed in this review through a systematic mapping of the interdisciplinary progress that enables grid-interactive, hydrogen-enabled, and AI-orchestrated hybrid wind systems. It highlights the electric design developments, including converter topologies and grid integration, hydrogen-enabled pathways for power-to-fuel conversion and storage, and AI-based orchestration approaches for predictive control and optimization [30–35]. The synthesis presented here relies on information from over 160 peer-reviewed articles published from 2013 to 2026, covering Applied Energy, Energy Conversion and Management, Renewable & Sustainable Energy Reviews, and IEEE Transactions on Industrial Informatics. Through a critical analysis of these contributions, this study identifies the technological trajectory that leads from conventional generation systems to sustainable, multi-vector electrification.

In contrast to existing review work that focuses primarily on these technologies independently, this review adopts a more system-centric approach. It elucidates inter-domain coupling mechanisms that dictate the collective behavior of electrical systems, hydrogen conversion pathways, and AI control structures. Instead of an inventory of components, this work examines the effects of architectural decisions on power electronics that bound hydrogen system dynamics, storage, and conversion pathways on control and forecasting needs, and AI orchestration on grid interaction, reliability, and techno-economic performance. Thus, the review organizes along a set of thematic axes—electrical architecture, hydrogen integration pathway, AI-based orchestration, grid services and interactivity, system reliability and resilience, and policy/technology readiness considerations—to enable a critical assessment of couplings across various interdependencies/trade-offs/deployment maturity scales. This integrative framing sets the review apart from descriptive surveys and may provide a clearer connection between technological innovation, operational feasibility, and commercialization readiness [35].

The paper is structured as follows. Section 2 gives an overview of the technological background within hybrid wind energy systems with a focus on (1) electrical architectures; (2) converter topologies, and (3) sector-coupling interfaces. Section 3 addresses hydrogen-enabling pathways from electrolyzer packaging to energy storage options to reconversion schemes. In Section 4, we describe AI-driven optimization and control, and AI-enabled optimization and digital-twin-controlled approaches, with their predictive data analytics and reliability-performance characteristics. In Section 5, we also describe both grid-interactive and storage-integrated architectures, including head-end smart-grid interfacing, hybrid storage cooperation, and demand response aspects. Power-electronic advances, protection, and reliability are presented in Section 6, and potential emerging trends and future research activities are reported in Section 7. Section 8 discusses the policy implications of sustainable electrification and global decarbonization in the final remarks.

2  Technological Foundations of Hybrid Wind Systems

2.1 Evolution of Wind-Turbine Architectures

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.

Figure 1: Layered architecture of an integrated hybrid wind-energy system.

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.

2.2 Electrical Topologies

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

2.2.1 AC-Coupled Systems

These follow a traditional back-to-back converter arrangement, in which the turbine generator supplies an AC link that is synchronized with the grid. There are separate rectifiers or inverters for the electrolyzers of storage systems. The topology is compatible with the existing infrastructure, but introduces several conversion stages (up to three cascading ones), leading to cumulative losses of about 8%–10% and complex harmonic mitigation [47,48].

2.2.2 DC-Coupled Systems

All of the subsystems (turbines, electrolyzers, and batteries) are linked with respect to a standard direct current (DC) bus, meaning a minimum number of conversions. This setup allows direct connection of DC loads such as those used for electrolysers and fuel-cell stacks, while also allowing for an easy connection to offshore HVDC transmission, thereby enhancing power management efficiency. As a side benefit, DC systems increase energy management efficiency by 6%–9% over pure AC architectures. The DC-bus of modular MMCs is also connected in parallel, but they need to be equipped with accurate and fast DC-fault detection and protection systems [49].

2.2.3 Hybrid AC/DC Architectures

These integrate local DC distribution on the wind farm with AC export over HVDC lines to realize benefits from both domains. The advantage of the hybrid model is that the regional microgrids can be operated under an islanded mode with synchronized AC output to the transmission grid [50]. The multi-stage power-electronic conversion process illustrated in Fig. 2 is conceptualized based on widely reported AC/DC and DC-coupled hybrid wind systems.

Figure 2: Advanced multi-stage power-electronic conversion chain in a hybrid wind-energy system with integrated energy storage.

A multiple-stage conversion chain of a hybrid system is shown in Fig. 2. The chain consists of an AC/DC rectifier, a DC-link with controllable energy buffers, and a DC/AC inverter that is connected to the grid of the electrolyzer bus. Table 1: A comparison summary of three topologies. “Conversion efficiency, harmonic distortion, control complexity, and protection need” are compared in quantitative terms between the three topologies. The hybrid AC/DC topology achieves the optimum of efficiency and controllability, especially for multi-terminal offshore networks.

The reported efficiency and harmonic distortion ranges are compiled from heterogeneous literature sources, including simulation models, laboratory prototypes, and pilot-scale demonstrations. Values are not directly comparable unless normalized to rated power, converter topology, switching frequency, filter configuration, and grid short-circuit ratio (SCR). Accordingly, the ranges should be interpreted as indicative performance envelopes, with evidence levels classified by deployment scale. In such hybrid configurations, medium-voltage DC (MVDC) operation (+5–20 kV) is going to be a routine for cluster-link interfacing. On the other hand, a high-voltage DC (HVDC) transmission system (>+300 kV) for long-distance export. Furthermore, the wide-bandgap (WBG) semiconductors used in SiC and GaN converter modules also improves conversion efficiency and thermal management for down-sizing [50]. Note that the reported efficiency and harmonic distortion values are literature envelopes, i.e., drawn from heterogeneous sources ranging from simulation studies to laboratory scale prototypes or pilot/early field deployments. These variations stem from differences in rated power, converter topology (two-level or MMC), switching frequency, filter design, grid short-circuit ratio (SCR), ambient operating conditions, and measurement definitions. The dynamic nomograms are not always directly formalizable to other studies, although values are estimated at nominal operating points when possible.

2.3 Energy-Management Interfaces

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.

2.4 Cross-Study Synthesis and Applicability by Scale

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].

3  Hydrogen-Enabled Pathways

3.1 Wind-to-Hydrogen Conversion Chain

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].

Figure 3: Power-electronic interface for hybrid DC link with electrolyzer.

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].

3.2 Electrical Interface and Control

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.

3.3 Hydrogen Storage and Utilization

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.

Figure 4: Power-to-hydrogen-to-power (P2H2P) system architecture with combined heat and power (CHP) in a hybrid wind plant.

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].

3.4 Comparative Synthesis: Electrolyzer Choice vs. Dynamics, Degradation, and System-Level Efficiency

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).

4  AI-Orchestrated Optimization and Forecasting

4.1 Forecasting and Scheduling

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.

Figure 5: AI-orchestrated supervisory control hierarchy for hybrid energy system.

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].

4.2 Reinforcement-Learning Control

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.

4.3 Digital-Twin Fusion and Fault Diagnosis

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.

Figure 6: Digital-twin integration for AI-orchestrated fault diagnosis and predictive maintenance in hybrid wind–hydrogen systems.

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].

4.4 Methodological Rigor: Data Requirements, Reward Design, Convergence, and Safety Constraints

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.

5  Grid-Interactive and Storage-Integrated Architectures

5.1 Smart Grid Interfacing

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.

Figure 7: Multi-layer control structure of a grid-interactive hybrid wind-hydrogen system.

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].

5.2 Energy-Storage Integration

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.

Figure 8: Enhanced AI-supervised hierarchical control framework for wind–hydrogen grid interaction.

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.

5.3 Demand Response and Market Participation (DRMP)

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].

5.4 Comparative Synthesis: Grid Services vs. Architecture Complexity and Deployment Maturity

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].

6  Power Electronics, Protection, and Reliability

6.1 Converter-Level Innovations

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.

Figure 9: Integrated DC protection framework with solid-state circuit–hydrogen system.

6.2 Protection Strategies

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.

6.3 Reliability and Prognostics

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:

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].

6.4 Cybersecurity and Data Integrity for AI-Controlled Hybrid Wind–Hydrogen Plants

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].

7  Emerging Trends and Future Research Directions

7.1 Superconducting and Magnetless Generators

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].

7.2 Solid-State Transformer and Power-Flow Control

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].

7.3 6G-First Enabled Edge Control and Quantum Optimization

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].

7.4 Policy and Techno-Economic Perspectives

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.

Figure 10: Technology roadmap (2025–2050) for advanced hybrid wind–hydrogen systems.

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.

7.4.1 Hydrogen Certification and Guarantees of Origin (GO)

The commercialization of hybrid wind–hydrogen systems is increasingly conditioned by the availability of robust hydrogen certification and guarantees of origin (GO) schemes that verify the renewable provenance, carbon intensity, and production pathway of hydrogen. Certification frameworks play a critical role in enabling cross-border trade, market transparency, and eligibility for policy incentives. However, in practice, schemes vary significantly in terms of system boundary conditions, accounting methods, and the temporal alignment between renewable power generation and hydrogen production. For wind-based electrolytic hydrogen, certification complexity stems from the intermittent nature of generation and its hybrid interaction with the grid (and the partial dependence on grid electricity during low-wind periods). Tight time-coupling requirements can either limit the use of the electrolyzer or require flexibility that would dilute the environmental credibility of certified hydrogen. These trade-offs have immediate implications for system design options, electrolyzer scale, and storage options. Accordingly, certification criteria must be aligned, and more straightforward guidelines for hybrid renewable–grid operation must be formulated to reduce investment risk and facilitate upscaling [45–140].

7.4.2 Grid-Code Compliance for Grid-Forming Converter Plants

As converter-based wind plants transition to grid-forming, compliance with evolving grid codes is a key regulatory hurdle. It is anticipated that the grid-forming converters will furthermore: support sub-synchronous FRT, provide synthetic inertia, support voltage and frequency regulation, and prevent cascading failures, including coordinated fast-response protection in case of disturbance events, such as with a weak grid and offshore. As these uses are more frequently demonstrated at pilot scale, standardized test protocols and certification routes are still being developed in many regions. In hybrid wind–hydrogen systems, new levels of complexity emerge from the fact that electrolyzers and storage interfaces can act as fast power-moderating devices when the grid is subjected to voltage disturbances. Coordinated response of GF converters, protection relays, and subsystems is essential to prevent adverse interactions or cascading events. Consequently, clarity in the regulation of feasible control modes, inertia supply bounds, and protection coordination is a prerequisite for the widespread adoption of grid-forming hybrid plants. Until standards develop to the point where these are widely adopted, most deployments remain limited to demonstration and conservative grid-following installations.

7.4.3 Market Regulation and Permitting Barriers

In addition to technical norms, market regulation and permitting have a significant effect on the feasibility of hybrid wind–hydrogen systems. From the coast to offshore, systems of offshore wind and hydrogen have increased lead times for consenting due to marine spatial planning processes, EIA’s, and the co-location of hydrogen production facilities on the UKCS or in coastal zonal waters. Storage, compression, transport, and proximity to population centres trigger hydrogen-specific safety requirements for land-based installations. Foreign-market design also influences investment incentives. Electricity and hydrogen markets are often regulated in a disconnected fashion. Hereby, the potential of cross-linking energy vectors for hybrid systems is limited from both economic and system perspectives. The use of electrolyzers or hydrogen-based storage, with no precise mechanism for compensating for grid services, provides less revenue certainty. Overcoming these barriers will require coordinated regulatory reform to ensure that the electricity market rules, hydrogen safety standards, and permitting procedures work together to encourage integrated multi-energy systems.

7.4.4 Techno-Economic Sensitivity and Bankability (CAPEX/OPEX, LCOH, Market Coupling)

The economic feasibility of hybrid wind–hydrogen systems is determined by an interconnected set of techno-economic drivers that go beyond nominal component efficiencies. Levelized cost of hydrogen (LCOH) and levelized cost of storage (LCOS) results are susceptible to the electrolyzer utilization factor, which is influenced by wind resource quality, curtailment potential, grid interaction rules, and hydrogen storage volume. Low utilization rates, such as those experienced in purely off-grid or temporally limited certification systems, even with a high conversion efficiency, are shown to result in LCOH levels that skyrocket (note the two orders of magnitude gap between the y-axes), thereby demonstrating the significance of system-level co-design. From a cost breakdown perspective, CAPEX is mainly represented by electrolyzers, power electronic interfaces, hydrogen storage, and borehole components, whereas OPEX is due to offshore (O&M), stack replacement, auxiliary consumption, and inspection and safety regulations. In offshore or remote installations, logistics-heavy O&M can outweigh marginal efficiency gains, shifting the economic optimum towards robust, maintainable configurations rather than the promise of theoretical maximum performance. Finally, the economic value of curtailed wind energy, once channeled into hydrogen production, also shapes project economics and varies with local market rules and inflation patterns, as shown throughout the section. The bankability is determined by market coupling and policy stimuli. Such structural mechanisms include carbon pricing, contracts for difference, hydrogen purchase agreements and new revenue streams for grid services that can all drastically change risk profiles and the stacks of revenue. In places with weak hydrogen-electricity coupling, hybrid systems offer limited flexibility to arbitrage between the two vectors and are over-exposed to both price volatility and use risk. Lastly, harmonized market designs have the potential to internalize grid-supporting capabilities of electrolysis and storage, resulting in more stable cash flows and improved attractiveness for investments. It is therefore important to interpret as of this techno-economic context results offering cost-savings for the supply chain rendered possible by AI. Estimates of cost savings often depend on high data availability and robust communication infrastructure that require the models to run continuously, conditions usually met in a simulation or digital twin environment. The actual and potential cost reduction by implementing high-quality AI will vary based on data quality, frequency of retraining the model, maintenance policy and deployment and validation monetary costs. Nonetheless, AI-augmented optimization should be viewed as a risk-mitigating and efficiency-enhancing layer—rather than a substitute for good system design, conservative financial assumptions, or favorable market conditions [160].

In general, the bankability of hybrid wind–hydrogen projects depend heavily on appropriately aligning technical design decisions, control sophistication, and market participation strategies with realistic expectations regarding utilization (i.e., capacity factors), O&M intensity, and policy support. Thus, sensitivity-aware techno-economic assessment is critical for differentiating performance advancements at the research stage from commercially actionable value.

7.5 Technology Readiness and Commercialization Gaps (TRL Lens)

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.

8  Conclusion

It is a monumental step forward in connected global energy systems, demonstrating the transition from conventional electricity systems to multisector, agile energy systems. This can be integrated with wind power hydrogen production (and the subsequent combined production-storage-AI-based control to convert the otherwise intrinsic intermittency into manageable flexibility and thus ensure stable and scalable renewable deployment). Proper, steerable/pumped, resilient, e-energy infrastructure, digital-optimized based on advanced sensorics and electrochemical processing. The findings from Figs. 1–10 and Tables 1–8 demonstrate how electric architecture, power electronics, and machine intelligence work together to create grid-connected hybrid networks that are self-biased and closer to self-tuning. Such coupled systems enable online control of generation, conversion, and storage entities to dynamically dispatch them, given the variability of their renewable inputs. Additionally, autonomous optimization, adaptive fault response, and predictive maintenance—all essential tools in our fight against complexity for decentralized hybrid grids—can be facilitated by AI-driven controllers. A few issues still hamper the widespread adoption of this technology. The primary challenges are interoperability (where a variety of communications protocols and vendor-specific architectures slow down a system-wide integration), standardization (which is still rather fragmented at the international and at least regional regulator level), and cost parity for initial capital outlays towards the power electronics, electrolyzers, and AI infrastructure in comparison to conventional solutions. To overcome these challenges, particularly in integrating use-option tools within New Grid architectures, institutional and competing policy tools aligned with new grid engineering technological changes will be necessary. Therefore, future research will concentrate on creating stable control structures for HC microgrids that can easily connect to other platforms. Cyber-physical security in AI-based energy networks needs to be improved to safeguard energy-critical infrastructure from the newly created attack surfaces introduced by digitalization. Furthermore, if TEA included carbon pricing, lifecycle accounting, and emissions costing, it would be much simpler to evaluate the system’s long-term sustainability and overall economic viability. These multidisciplinary initiatives will propel the creation of tomorrow’s hybrid wind systems and ultimately enable them to function as the foundation for environmentally friendly electrification. Through the development of smart control systems, renewable energy storage, and policy, the hybrid wind-hydrogen system is expected to make a significant contribution to global decarbonization goals. In addition to being a technological achievement, their creation represents a shift toward an interconnected system moving toward the ideal of zero-carbon circular energy.

Acknowledgement: Not applicable.

Funding Statement: The authors received no specific funding for this study.

Author Contributions: The authors confirm contribution to the paper as follows: Conceptualization: Jalpa Thakkar and Mohan Kolhe; Methodology: Siddharth Shankar Mishra and V. Shanmugapriya; Investigation: Siddharth Shankar Mishra and Mohan Kolhe; Formal analysis: Siddharth Shankar Mishra and V. Shanmugapriya; Resources: Jalpa Thakkar and Mohan Kolhe; Data curation: Siddharth Shankar Mishra; Writing—original draft preparation: Siddharth Shankar Mishra; Writing—review and editing: Jalpa Thakkar, V. Shanmugapriya and Mohan Kolhe; Visualization: V. Shanmugapriya; Supervision: Jalpa Thakkar; Project administration: Jalpa Thakkar. All authors reviewed and approved the final version of the manuscript.

Availability of Data and Materials: Not applicable. This article is a review study based on previously published literature and does not involve the generation or analysis of new datasets.

Ethics Approval: Not applicable. This study does not involve human participants, animals, or any form of personal or sensitive data.

Conflicts of Interest: The authors declare no conflicts of interest.

References

1. Vergara PP, Rey JM, da Silva LCP, Ordóñez G. Comparative analysis of design criteria for hybrid photovoltaic/wind/battery systems. IET Renew Power Gener. 2017;11(3):253–61. doi:10.1049/iet-rpg.2016.0250. [Google Scholar] [CrossRef]

2. Praswanto DH, Djiwo S, Palevi BRPD. Analysis of hydrogen gas production results in water electrolysis process on genset characteristics. J Sci Appl Eng. 2023;6(1):50–7. doi:10.31328/jsae.v6i1.4236. [Google Scholar] [CrossRef]

3. Zeng Z, Bliznakov S, Bonville LJ, Maric R. Advanced porous transport electrode for high-performance proton exchange membrane water electrolyzers. Meet Abstr. 2024;MA2024-01(34):1896. doi:10.1149/ma2024-01341896mtgabs. [Google Scholar] [CrossRef]

4. Hu G, Chen C, Lu HT, Wu Y, Liu C, Tao L, et al. A review of technical advances, barriers, and solutions in the power to hydrogen (P2H) roadmap. Engineering. 2020;6(12):1364–80. doi:10.1016/j.eng.2020.04.016. [Google Scholar] [CrossRef]

5. Glenk G, Reichelstein S. Economics of converting renewable power to hydrogen. Nat Energy. 2019;4(3):216–22. doi:10.1038/s41560-019-0326-1. [Google Scholar] [CrossRef]

6. Osman AI, Nasr M, Mohamed AR, Abdelhaleem A, Ayati A, Farghali M, et al. Life cycle assessment of hydrogen production, storage, and utilization toward sustainability. Wires Energy Environ. 2024;13(3):e526. doi:10.1002/wene.526. [Google Scholar] [CrossRef]

7. Shaya N, Glöser-Chahoud S. A review of life cycle assessment (LCA) studies for hydrogen production technologies through water electrolysis: recent advances. Energies. 2024;17(16):3968. doi:10.3390/en17163968. [Google Scholar] [CrossRef]

8. Di Q, Xu T, Wang J, Cheng H, Qiao J, Liang Z. Research on optimal scheduling of wind-hydrogen integrated energy system considering wind power uncertainty. In: Proceedings of the 2024 6th International Conference on Energy, Power and Grid (ICEPG); 2024 Sep 27–29; Guangzhou, China. p. 183–7. doi:10.1109/ICEPG63230.2024.10775980. [Google Scholar] [CrossRef]

9. Nnabuife SG, Hamzat AK, Whidborne J, Kuang B, Jenkins KW. Integration of renewable energy sources in tandem with electrolysis: a technology review for green hydrogen production. Int J Hydrogen Energy. 2025;107(73):218–40. doi:10.1016/j.ijhydene.2024.06.342. [Google Scholar] [CrossRef]

10. Ghaib K, Ben-Fares FZ. Power-to-methane: a state-of-the-art review. Renew Sustain Energy Rev. 2018;81(3):433–46. doi:10.1016/j.rser.2017.08.004. [Google Scholar] [CrossRef]

11. Talab OA, Avci I. Energy management in microgrids using model-free deep reinforcement learning approach. IEEE Access. 2025;13(2):5871–91. doi:10.1109/ACCESS.2025.3525843. [Google Scholar] [CrossRef]

12. Zhang Z, Zhang D, Qiu RC. Deep reinforcement learning for power system applications: an overview. CSEE J Power Energy Syst. 2020;6(1):213–25. doi:10.17775/CSEEJPES.2019.00920. [Google Scholar] [CrossRef]

13. Abu-Siada A, Budiri J, Abdou AF. Solid state transformers topologies, controllers, and applications: state-of-the-art literature review. Electronics. 2018;7(11):298. doi:10.3390/electronics7110298. [Google Scholar] [CrossRef]

14. Huber JE, Kolar JW. Applicability of solid-state transformers in today’s and future distribution grids. IEEE Trans Smart Grid. 2019;10(1):317–26. doi:10.1109/tsg.2017.2738610. [Google Scholar] [CrossRef]

15. Chandran DR, Kumar S, Sanath D. Solid state transformers: a comprehensive review of technology, topologies, applications, research gaps, and future directions. J Power Energy Eng. 2025;13(6):30–64. doi:10.4236/jpee.2025.136003. [Google Scholar] [CrossRef]

16. Williams J, Jones G, Berg T, Birt L, Stowe A, Li X. Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models. Comput Ind Eng. 2026;215(2):111914. doi:10.1016/j.cie.2026.111914. [Google Scholar] [CrossRef]

17. De Kooning JDM, Stockman K, De Maeyer J, Jarquin-Laguna A, Vandevelde L. Digital twins for wind energy conversion systems: a literature review of potential modelling techniques focused on model fidelity and computational load. Processes. 2021;9(12):2224. doi:10.3390/pr9122224. [Google Scholar] [CrossRef]

18. Stadtmann F, Rasheed A, Kvamsdal T, Johannessen KA, San O, Kölle K, et al. Digital twins in wind energy: emerging technologies and industry-informed future directions. IEEE Access. 2023;11:110762–95. doi:10.1109/ACCESS.2023.3321320. [Google Scholar] [CrossRef]

19. Shadoul M, Ahshan R, AlAbri RS, Al-Badi A, Albadi M, Jamil M. A comprehensive review on a virtual-synchronous generator: topologies, control orders and techniques, energy storages, and applications. Energies. 2022;15(22):8406. doi:10.3390/en15228406. [Google Scholar] [CrossRef]

20. Zhao R, Yang X, Wei A, Zhang J. Impedance modeling and stability analysis of grid-forming converters. In: Proceedings of the 2024 IEEE Transportation Electrification Conference and Expo (ITEC); 2024 Jun 19–21; Chicago, IL, USA. p. 1–6. doi:10.1109/ITEC60657.2024.10598845. [Google Scholar] [CrossRef]

21. Rosso R, Wang X, Liserre M, Lu X, Engelken S. Grid-forming converters: control approaches, grid-synchronization, and future trends—a review. IEEE Open J Ind Appl. 2021;2:93–109. doi:10.1109/OJIA.2021.3074028. [Google Scholar] [CrossRef]

22. Emon BA. Grid-forming (GFM) converters in modern power systems: a review of control methods, pilot projects, and strategic roles in renewable energy integration. Dhaka, Bangladesh: University of Asia Pacific; 2025. doi:10.13140/RG.2.2.32475.07206. [Google Scholar] [CrossRef]

23. Du W, Liu Y, Huang R, Tuffner FK, Xie J, Huang Z. Positive-sequence phasor modeling of droop-controlled, grid-forming inverters with fault current limiting function. In: Proceedings of the 2022 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT); 2022 Apr 24–28; New Orleans, LA, USA. p. 1–5. doi:10.1109/ISGT50606.2022.9817530. [Google Scholar] [CrossRef]

24. Li R, Xu L. Review of DC fault protection for HVDC grids. Wires Energy Environ. 2018;7(2):e278. doi:10.1002/wene.278. [Google Scholar] [CrossRef]

25. Radwan M, Azad SP. Protection of multi-terminal HVDC grids: a comprehensive review. Energies. 2022;15(24):9552. doi:10.3390/en15249552. [Google Scholar] [CrossRef]

26. Mohammadi F, Azizi N, Moradi CheshmehBeigi H, Rouzbehi K. Stability and control of VSC-based HVDC systems: a systematic review. e-Prime Adv Electr Eng Electron Energy. 2024;8(1):100503. doi:10.1016/j.prime.2024.100503. [Google Scholar] [CrossRef]

27. Li B, He J, Li Y, Li B. A review of the protection for the multi-terminal VSC-HVDC grid. Prot Control Mod Power Syst. 2019;4(1):21. doi:10.1186/s41601-019-0136-2. [Google Scholar] [CrossRef]

28. Xiang W, Yang S, Adam GP, Zhang H, Zuo W, Wen J. DC fault protection algorithms of MMC-HVDC grids: fault analysis, methodologies, experimental validations, and future trends. IEEE Trans Power Electron. 2021;36(10):11245–64. doi:10.1109/TPEL.2021.3071184. [Google Scholar] [CrossRef]

29. Abedrabbo M, Leterme W, Van Hertem D. DC-side fault current estimation approach for HVDC circuit breaker sizing. Electr Power Syst Res. 2022;212(9):108310. doi:10.1016/j.epsr.2022.108310. [Google Scholar] [CrossRef]

30. Perez-Molina MJ, Larruskain DM, Lopez PE, Buigues G, Valverde V. Review of protection systems for multi-terminal high voltage direct current grids. Renew Sustain Energy Rev. 2021;144(1):111037. doi:10.1016/j.rser.2021.111037. [Google Scholar] [CrossRef]

31. Arefaynie AD, Kim CH. A time-domain fault location scheme using voltage gradient and traveling wave analysis in MMC-based HVDC lines. Int J Electr Power Energy Syst. 2025;172(6):111298. doi:10.1016/j.ijepes.2025.111298. [Google Scholar] [CrossRef]

32. Arooj Q, Ahmad Taj I. A hybrid multi-port HVDC circuit breaker that improves interruption and reset time through shared energy dissipation and fault current regeneration. Results Eng. 2024;23:102701. doi:10.1016/j.rineng.2024.102701. [Google Scholar] [CrossRef]

33. He J, Xue L, Li B, Li Y, Prieto-Araujo E, Gomis-Bellmunt O. Protection and fault ride-through techniques of VSC-HVDC systems for offshore wind power transmission—research status, challenges, and prospects. Renew Sustain Energy Rev. 2024;200(2):115138. doi:10.1016/j.rser.2024.115138. [Google Scholar] [CrossRef]

34. Zhang S, Zou G, Gao F, Wei X, Zhou C. A comprehensive review of multiport DC circuit breakers for MTdc grid protection. IEEE Trans Power Electron. 2023;38(7):9100–15. doi:10.1109/TPEL.2023.3264534. [Google Scholar] [CrossRef]

35. Zhang S, Zou G, Wei X, Sun C. Diode-bridge multiport hybrid DC circuit breaker for multiterminal DC grids. IEEE Trans Ind Electron. 2021;68(1):270–81. doi:10.1109/TIE.2020.2965459. [Google Scholar] [CrossRef]

36. Fletcher SDA, Norman PJ, Fong K, Galloway SJ, Burt GM. High-speed differential protection for smart DC distribution systems. IEEE Trans Smart Grid. 2014;5(5):2610–7. doi:10.1109/TSG.2014.2306064. [Google Scholar] [CrossRef]

37. Astan S, Hajizadeh A. Predictive cooperative terminal agents for decentralised self-healing in multi-terminal HVDC grids. Electr Power Syst Res. 2026;255:112747. doi:10.1016/j.epsr.2026.112747. [Google Scholar] [CrossRef]

38. Taveras-Cruz AJ, Mariano-Hernández D, Jiménez-Matos E, Aybar-Mejia M, Mendoza-Araya PA, Molina-García A. Adaptive protection based on multi-agent systems for AC microgrids: a review. Appl Energy. 2025;377:124673. doi:10.1016/j.apenergy.2024.124673. [Google Scholar] [CrossRef]

39. Shamsoddini M, Vahidi B, Razani R, Mohamed YAI. A novel protection scheme for low voltage DC microgrid using inductance estimation. Int J Electr Power Energy Syst. 2020;120:105992. doi:10.1016/j.ijepes.2020.105992. [Google Scholar] [CrossRef]

40. Bhalla A, Bhalja BR. A new fault detection and relay coordination technique for low voltage DC microgrid. Electr Power Syst Res. 2024;236(1):110907. doi:10.1016/j.epsr.2024.110907. [Google Scholar] [CrossRef]

41. Jayamaha DKJS, Lidula NWA, Rajapakse AD. Protection and grounding methods in DC microgrids: comprehensive review and analysis. Renew Sustain Energy Rev. 2020;120:109631. doi:10.1016/j.rser.2019.109631. [Google Scholar] [CrossRef]

42. Javed W, Chen D, Farrag ME, Xu Y. System configuration, fault detection, location, isolation and restoration: a review on LVDC microgrid protections. Energies. 2019;12(6):1001. doi:10.3390/en12061001. [Google Scholar] [CrossRef]

43. Pérez-Molina MJ, Eguia P, Larruskain DM, Torres E, Abarrategi O, Sarmiento-Vintimilla JC. Single-ended limiting inductor voltage-ratio-derivative protection scheme for VSC-HVDC grids. Int J Electr Power Energy Syst. 2023;147:108903. doi:10.1016/j.ijepes.2022.108903. [Google Scholar] [CrossRef]

44. Yao Z, Lei X, Du X. A comprehensive review of condition monitoring technologies for modular multilevel converter (MMC) HVDC systems. Electronics. 2025;14(17):3462. doi:10.3390/electronics14173462. [Google Scholar] [CrossRef]

45. Barros LAM, Martins AP, Pinto JG. A comprehensive review on modular multilevel converters, submodule topologies, and modulation techniques. Energies. 2022;15(3):1078. doi:10.3390/en15031078. [Google Scholar] [CrossRef]

46. Sakib MN, Azad SP, Kazerani M. A critical review of modular multilevel converter configurations and submodule topologies from DC fault blocking and ride-through capabilities viewpoints for HVDC applications. Energies. 2022;15(11):4176. doi:10.3390/en15114176. [Google Scholar] [CrossRef]

47. Chen Y, Ren C, Sheng J, Wang J, Zhou Y, Cao W, et al. An optimized fault-ride-through control strategy of hybrid MMC with fewer FBSMs. Electronics. 2024;13(10):1797. doi:10.3390/electronics13101797. [Google Scholar] [CrossRef]

48. Mousaei A, Gheisarnejad M, Khooban MH. Challenges and opportunities of FACTS devices interacting with electric vehicles in distribution networks: a technological review. J Energy Storage. 2023;72(5):108860. doi:10.1016/j.est.2023.108860. [Google Scholar] [CrossRef]

49. Zhang Y, Yu Y, Yang G. An ultra-fast MMC-HVDC fault location algorithm based on transient voltage features and regression neural network. Int J Electr Power Energy Syst. 2024;159(4):110249. doi:10.1016/j.ijepes.2024.110249. [Google Scholar] [CrossRef]

50. Qin Y, Mao C, Yang H, Wang D. Fault identification algorithm for transmission line integrated with MMC-HVDC converter station. Recent Pat Electr Eng. 2025;18:1–12. doi:10.2174/0123520965398960250612125727. [Google Scholar] [CrossRef]

51. Awal MA, Yu H, Tu H, Lukic SM, Husain I. Hierarchical control for virtual oscillator based grid-connected and islanded microgrids. IEEE Trans Power Electron. 2020;35(1):988–1001. doi:10.1109/TPEL.2019.2912152. [Google Scholar] [CrossRef]

52. Shi Z, Li J, Nurdin HI, Fletcher JE. Comparison of virtual oscillator and droop controlled islanded three-phase microgrids. IEEE Trans Energy Convers. 2019;34(4):1769–80. doi:10.1109/TEC.2019.2922447. [Google Scholar] [CrossRef]

53. Shi Z, Li J, Nurdin HI, Fletcher JE. Transient response comparison of virtual oscillator controlled and droop controlled three-phase inverters under load changes. IET Gener Transm Distrib. 2020;14(6):1138–47. doi:10.1049/iet-gtd.2018.5612. [Google Scholar] [CrossRef]

54. Ali M, Nurdin HI, Fletcher JE. Dispatchable virtual oscillator control for single-phase islanded inverters: analysis and experiments. IEEE Trans Ind Electron. 2020;68(6):4812–26. doi:10.1109/tie.2020.2991996. [Google Scholar] [CrossRef]

55. Johnson BB, Dhople SV, Cale JL, Hamadeh AO, Krein PT. Oscillator-based inverter control for islanded three-phase microgrids. IEEE J Photovolt. 2014;4(1):387–95. doi:10.1109/JPHOTOV.2013.2280953. [Google Scholar] [CrossRef]

56. Raisz D, Thai TT, Monti A. Power control of virtual oscillator controlled inverters in grid-connected mode. IEEE Trans Power Electron. 2019;34(6):5916–26. doi:10.1109/TPEL.2018.2868996. [Google Scholar] [CrossRef]

57. Dorfler F, Dhople SV, Johnson BB, Hamadeh A. Synchronization of nonlinear circuits in dynamic electrical networks. In: 2014 European Control Conference (ECC); 2014 Jun 24–27; Strasbourg, France. p. 552–7. doi:10.1109/ecc.2014.6862501. [Google Scholar] [CrossRef]

58. Lu M, Dutta S, Purba V, Dhople S, Johnson B. A grid-compatible virtual oscillator controller: analysis and design. In: 2019 IEEE Energy Conversion Congress and Exposition (ECCE); 2019 Sep 29–Oct 3; Baltimore, MD, USA. p. 2643–9. doi:10.1109/ecce.2019.8913128. [Google Scholar] [CrossRef]

59. Afshar Z, Mollayousefi M, Bathaee SM, Bina MT, Gharehpetian GB. A novel accurate power sharing method versus droop control in autonomous microgrids with critical loads. IEEE Access. 2019;7:89466–74. doi:10.1109/access.2019.2927265. [Google Scholar] [CrossRef]

60. Ali O, Mohammed OA. A review of multi-microgrids operation and control from a cyber-physical systems perspective. Computers. 2025;14(10):409. doi:10.3390/computers14100409. [Google Scholar] [CrossRef]

61. Gao X, Du Y, Wang Q, Wang C, Hou Y, Wu M, et al. Synergistic hydrogen-heat utilization in alkali-catalyzed aluminum-water systems for high-efficiency energy conversion and storage. J Power Sources. 2026;676:239870. doi:10.1016/j.jpowsour.2026.239870. [Google Scholar] [CrossRef]

62. Leon-Medina JX, Tibaduiza DA, Parés N, Pozo F. Digital twin technology in wind turbine components: a review. Intell Syst Appl. 2025;26(8):200535. doi:10.1016/j.iswa.2025.200535. [Google Scholar] [CrossRef]

63. Zhao S, Yang H, Kareck TL, Khan F, Wang Q. Data-driven fault detection and diagnosis in industrial process systems: a systematic review and perspective. Reliab Eng Syst Saf. 2026;270:112159. doi:10.1016/j.ress.2025.112159. [Google Scholar] [CrossRef]

64. Kandemir E, Hasan A, Kvamsdal T, Abdel-Afou Alaliyat S. Predictive digital twin for wind energy systems: a literature review. Energy Inform. 2024;7(1):68. doi:10.1186/s42162-024-00373-9. [Google Scholar] [CrossRef]

65. Zhu L, Li R, Pang Y, Chen J, Xu W, Yang S, et al. Multivariable model predictive control of PEM electrolysis system based on improved gray wolf optimization algorithm. J Renew Sustain Energy. 2025;17(5):054702. doi:10.1063/5.0288089. [Google Scholar] [CrossRef]

66. Mohandes B, El Moursi MS, Hatziargyriou N, El Khatib S. A review of power system flexibility with high penetration of renewables. IEEE Trans Power Syst. 2019;34(4):3140–55. doi:10.1109/TPWRS.2019.2897727. [Google Scholar] [CrossRef]

67. Hu K, Fang J, Ai X, Zhong Z, Huang D, Wang C, et al. Grid-oriented multiphysics model of power-to-hydrogen electrolyzers. Energy Convers Manag. 2022;270(1):116264. doi:10.1016/j.enconman.2022.116264. [Google Scholar] [CrossRef]

68. Zarghami A, Deen NG, Vreman AW. CFD modeling of multiphase flow in an alkaline water electrolyzer. Chem Eng Sci. 2020;227:115926. doi:10.1016/j.ces.2020.115926. [Google Scholar] [CrossRef]

69. Zain Ul Abideen F, Khalid HA, Hasan A, Saud Khan M, Rehman H. Direct model predictive control of a hybrid energy storage system for electric vehicles. IEEE Access. 2025;13:209247–65. doi:10.1109/ACCESS.2025.3641883. [Google Scholar] [CrossRef]

70. Qiu Y, Zhou Y, Chen S, Zang T, Zhou B. Flexibility assessment and aggregation of alkaline electrolyzers considering dynamic process constraints for energy management of renewable power-to-hydrogen systems. Renew Energy. 2024;235:121261. doi:10.1016/j.renene.2024.121261. [Google Scholar] [CrossRef]

71. Deng L, Jin L, Yang L, Feng C, Tao A, Jia X, et al. Bubble evolution dynamics in alkaline water electrolysis. eScience. 2025;5(4):100353. doi:10.1016/j.esci.2024.100353. [Google Scholar] [CrossRef]

72. Wang W, Ma H, Wang B, Zheng J, Liu Z, Li T, et al. Scheduling of electricity-hydrogen integrated system under renewable energy sources uncertainty: non-anticipativity and robust feasibility. Appl Energy. 2026;404:126996. doi:10.1016/j.apenergy.2025.126996. [Google Scholar] [CrossRef]

73. Sheikholeslami M. Integrating electrolyzer with photovoltaic systems for hydrogen production. In: Innovations in solar thermal energy: techniques to enhance photovoltaic-thermal system efficiency. Amsterdam, The Netherlands: Elsevier; 2025. p. 235–77. doi:10.1016/B978-0-443-33698-0.00012-1. [Google Scholar] [CrossRef]

74. Gao Y, Yu H, Wang W, Chi P, Liu S, Rao H, et al. Roadmap toward next-generation proton exchange membrane water electrolyzers. Joule. 2026;10:102337. doi:10.1016/j.joule.2026.102337. [Google Scholar] [CrossRef]

75. Lei J, Zhang H, Pan J, Zhuo Y, Chen A, Chen W, et al. Techno-economic assessment of a full-chain hydrogen production by offshore wind power. Energies. 2024;17(11):2447. doi:10.3390/en17112447. [Google Scholar] [CrossRef]

76. Bos MJ, Kersten SRA, Brilman DWF. Wind power to methanol: renewable methanol production using electricity, electrolysis of water and CO2 air capture. Appl Energy. 2020;264(1):114672. doi:10.1016/j.apenergy.2020.114672. [Google Scholar] [CrossRef]

77. McDonagh S, Ahmed S, Desmond C, Murphy JD. Hydrogen from offshore wind: investor perspective on the profitability of a hybrid system including for curtailment. Appl Energy. 2020;265:114732. [Google Scholar]

78. McDonagh S, Deane P, Rajendran K, Murphy JD. Are electrofuels a sustainable transport fuel? Analysis of the effect of controls on carbon, curtailment, and cost of hydrogen. Appl Energy. 2019;247:716–30. doi:10.1016/j.apenergy.2019.04.060. [Google Scholar] [CrossRef]

79. Hou P, Enevoldsen P, Eichman J, Hu W, Jacobson MZ, Chen Z. Optimizing investments in coupled offshore wind-electrolytic hydrogen storage systems in Denmark. J Power Sources. 2017;359(3):186–97. doi:10.1016/j.jpowsour.2017.05.048. [Google Scholar] [CrossRef]

80. Loisel R, Baranger L, Chemouri N, Spinu S, Pardo S. Economic evaluation of hybrid off-shore wind power and hydrogen storage system. Int J Hydrogen Energy. 2015;40(21):6727–39. doi:10.1016/j.ijhydene.2015.03.117. [Google Scholar] [CrossRef]

81. Schmidt O, Gambhir A, Staffell I, Hawkes A, Nelson J, Few S. Future cost and performance of water electrolysis: an expert elicitation study. Int J Hydrogen Energy. 2017;42(52):30470–92. doi:10.1016/j.ijhydene.2017.10.045. [Google Scholar] [CrossRef]

82. Blanco H, Nijs W, Ruf J, Faaij A. Potential for hydrogen and Power-to-Liquid in a low-carbon EU energy system using cost optimization. Appl Energy. 2018;232:617–39. doi:10.1016/j.apenergy.2018.09.216. [Google Scholar] [CrossRef]

83. Badawi MN, Agrawal N, Luqman M, Ramesh S, Ramesh K, Khan M, et al. Developments and challenges in batteries, and hydrogen as a future fuel, and storage and carrier devices. Int J Hydrogen Energy. 2025;105:1242–60. doi:10.1016/j.ijhydene.2024.10.367. [Google Scholar] [CrossRef]

84. Dehghani-Sanij AR, Tharumalingam E, Dusseault MB, Fraser R. Study of energy storage systems and environmental challenges of batteries. Renew Sustain Energy Rev. 2019;104:192–208. doi:10.1016/j.rser.2019.01.023. [Google Scholar] [CrossRef]

85. Guandalini G, Campanari S, Romano MC. Power-to-gas plants and gas turbines for improved wind energy dispatchability: energy and economic assessment. Appl Energy. 2015;147(1):117–30. doi:10.1016/j.apenergy.2015.02.055. [Google Scholar] [CrossRef]

86. Frank E, Gorre J, Ruoss F, Friedl MJ. Calculation and analysis of efficiencies and annual performances of power-to-gas systems. Appl Energy. 2018;218:217–31. doi:10.1016/j.apenergy.2018.02.105. [Google Scholar] [CrossRef]

87. Parra D, Zhang X, Bauer C, Patel MK. An integrated techno-economic and life cycle environmental assessment of power-to-gas systems. Appl Energy. 2017;193:440–54. doi:10.1016/j.apenergy.2017.02.063. [Google Scholar] [CrossRef]

88. Zhang X, Witte J, Schildhauer T, Bauer C. Life cycle assessment of power-to-gas with biogas as the carbon source. Sustain Energy Fuels. 2020;4(3):1427–36. doi:10.1039/c9se00986h. [Google Scholar] [CrossRef]

89. Pérez-Fortes M, Schöneberger JC, Boulamanti A, Tzimas E. Methanol synthesis using captured CO2 as raw material: techno-economic and environmental assessment. Appl Energy. 2016;161(11):718–32. doi:10.1016/j.apenergy.2015.07.067. [Google Scholar] [CrossRef]

90. Xiao P, Hu W, Xu X, Liu W, Huang Q, Chen Z. Optimal operation of a wind-electrolytic hydrogen storage system in the electricity/hydrogen markets. Int J Hydrogen Energy. 2020;45(46):24412–23. doi:10.1016/j.ijhydene.2020.06.302. [Google Scholar] [CrossRef]

91. Kaoud OG, Al-Oweiti QN, Elbassoussi MH, Rehman S, Zubair SM. Battery versus hydrogen storage for renewable-powered RO desalination: a technoeconomic and environmental analysis in coastal Saudi Arabia. Int J Hydrogen Energy. 2025;50:152466. doi:10.1016/j.ijhydene.2025.152466. [Google Scholar] [CrossRef]

92. Staffell I, Scamman D, Velazquez Abad A, Balcombe P, Dodds PE, Ekins P, et al. The role of hydrogen and fuel cells in the global energy system. Energy Environ Sci. 2019;12(2):463–91. doi:10.1039/c8ee01157e. [Google Scholar] [CrossRef]

93. Chadha S, Jain V, Chandna VK. Hybrid optimization algorithm based demand side management and energy scheduling in EV charging station. J Energy Storage. 2025;89(1):120076. doi:10.1016/j.est.2025.120076. [Google Scholar] [CrossRef]

94. Wei S, Du W, Xia C. Power quality disturbances categorization using identity feature vector and extreme learning machine. Intell Syst Appl. 2024;22:200446. doi:10.1016/j.iswa.2024.200446. [Google Scholar] [CrossRef]

95. Kim D, Sun HI, Kim H. Public perception and VSC-HVDC power transmission adoption: experiences and lessons learned from past cases. Energy Policy. 2025;202:114574. doi:10.1016/j.enpol.2025.114574. [Google Scholar] [CrossRef]

96. Fang J, Hu W, Liao J, Chen X, Mo H, Jin C, et al. Artificial intelligence-based design optimization for wind turbines: a comprehensive review of its methodologies, applications, and challenges. Renew Sustain Energy Rev. 2025;210(1):116697. doi:10.1016/j.rser.2025.116697. [Google Scholar] [CrossRef]

97. Gong M, Na Y. Louver and step flow field design to increase the water content of the membrane of an electrochemical hydrogen compressor. Int J Hydrogen Energy. 2024;78:513–23. doi:10.1016/j.ijhydene.2024.06.194. [Google Scholar] [CrossRef]

98. Holtwerth A, Xhonneux A, Müller D. Model predictive control of a stand-alone hybrid battery-hydrogen energy system: a case study of the PHOEBUS energy system. Energies. 2024;17(18):4720. doi:10.3390/en17184720. [Google Scholar] [CrossRef]

99. Du P, Yuan L, Bao T, Xi Y, Li J, Bi Y, et al. Facet effect of metal-organic frameworks on supporting co-catalysts for photocatalytic hydrogen peroxide production. Chin Chem Lett. 2025;36(11):110472. doi:10.1016/j.cclet.2024.110472. [Google Scholar] [CrossRef]

100. Zhou A, Khodayar ME, Wang J. Distributionally robust optimal scheduling with heterogeneous uncertainty information: a framework for hydrogen systems. IEEE Trans Sustain Energy. 2024;15(3):1933–45. doi:10.1109/TSTE.2024.3388388. [Google Scholar] [CrossRef]

101. Gong J, Sun C, Shi H, Tan W. Response behaviour of proton exchange membrane water electrolysis to hydrogen production under dynamic conditions. Int J Hydrogen Energy. 2023;48(79):30642–52. doi:10.1016/j.ijhydene.2023.04.223. [Google Scholar] [CrossRef]

102. Khatib FN, Wilberforce T, Ijaodola O, Ogungbemi E, El-Hassan Z, Durrant A, et al. Material degradation of components in polymer electrolyte membrane (PEM) electrolytic cell and mitigation mechanisms: a review. Renew Sustain Energy Rev. 2019;111(4):1–14. doi:10.1016/j.rser.2019.05.007. [Google Scholar] [CrossRef]

103. Rakousky C, Reimer U, Wippermann K, Kuhri S, Carmo M, Lueke W, et al. Polymer electrolyte membrane water electrolysis: restraining degradation in the presence of fluctuating power. J Power Sources. 2017;342:38–47. doi:10.1016/j.jpowsour.2016.11.118. [Google Scholar] [CrossRef]

104. Westman J, Hadidi R. Overload mitigation for grid-forming inverters in islanded microgrids with synchronous generators. Electr Power Syst Res. 2025;239:111231. doi:10.1016/j.epsr.2024.111231. [Google Scholar] [CrossRef]

105. Ma Z, Jasni J, Ab Kadir MZA, Azis N, Ma Y. Improved electrogeometric model for shielding failure evaluation of double-circuit UHVAC transmission lines based on leader propagation simulations. Electr Power Syst Res. 2025;239:111240. doi:10.1016/j.epsr.2024.111240. [Google Scholar] [CrossRef]

106. Li C, Yang Y, Mao X, Xiong X, Dragicevic T. Modeling, control and stabilization of virtual synchronous generator in future power electronics-dominated power systems: a survey of challenges, advances, and future trends. Int J Electr Power Energy Syst. 2025;171(1):111001. doi:10.1016/j.ijepes.2025.111001. [Google Scholar] [CrossRef]

107. Zhao Y, Liang T, Cheng M, Chen L, Mei C, Dai S, et al. Recent advances in CO2 mineralization by alkaline industrial solid waste: mechanisms, applications and perspectives. Renew Sustain Energy Rev. 2026;226:116355. doi:10.1016/j.rser.2025.116355. [Google Scholar] [CrossRef]

108. Kabalci E. Internal model control framework for power and current regulation of grid-connected inverters with LCL-Filter. Measurement. 2026;268:120617. doi:10.1016/j.measurement.2026.120617. [Google Scholar] [CrossRef]

109. Hasan MM, Razmi D, Babayomi O, Davidson I, Terzija V, Zhang Z. Advanced control and protection strategies for grid-forming inverters in microgrids—a review. Int J Electr Power Energy Syst. 2025;172:111297. doi:10.1016/j.ijepes.2025.111297. [Google Scholar] [CrossRef]

110. Shang X, Wang N, Shi Y, Zuo J. Research on frequency coupling factors and stability influencing factors of grid-forming inverters. Int J Electr Power Energy Syst. 2026;174:111506. doi:10.1016/j.ijepes.2025.111506. [Google Scholar] [CrossRef]

111. Mishra M, Pragati A, Gadanayak DA, Parida T. Signal processing and artificial intelligence based HVDC network protection: a systematic and state-up-the-art review. Prot Control Mod Power Syst. 2024;8:100606. doi:10.1016/j.prime.2024.100606. [Google Scholar] [CrossRef]

112. Soomro JB, Kumar D, Chachar FA, Isik S, Alharbi M. An enhanced AC fault ride through scheme for offshore wind-based MMC-HVDC system. Sustainability. 2023;15(11):8922. doi:10.3390/su15118922. [Google Scholar] [CrossRef]

113. Zhang Y, Ravishankar J, Fletcher J, Li R, Han M. Review of modular multilevel converter based multi-terminal HVDC systems for offshore wind power transmission. Renew Sustain Energy Rev. 2016;61:572–86. doi:10.1016/j.rser.2016.01.108. [Google Scholar] [CrossRef]

114. Xiong M, Yao Y, Pei Z, Wu A, Zhou X, Wang L, et al. Dynamic response characteristics of solid oxide cell stacks for seawater/CO2 co-electrolysis under strong fluctuation electricity. Int J Hydrogen Energy. 2024;63:937–42. doi:10.1016/j.ijhydene.2024.03.208. [Google Scholar] [CrossRef]

115. Dai Y, Shu H, Tang Y, Gong F. Adaptive reclosing scheme for flexible HVDC grid with single-pole ground fault based on residual voltage zero-crossing detection. Int J Electr Power Energy Syst. 2026;175:111519. doi:10.1016/j.ijepes.2025.111519. [Google Scholar] [CrossRef]

116. Muniappan M. A comprehensive review of DC fault protection methods in HVDC transmission systems. Prot Control Mod Power Syst. 2021;6(1):1. doi:10.1186/s41601-020-00173-9. [Google Scholar] [CrossRef]

117. Zhou Y, Liu Y, Huang S, Guo C, Cao Y, Li Y. A review of HVDC-based transmission congestion alleviation strategies for modern power systems. Electr Power Syst Res. 2026;255:112748. doi:10.1016/j.epsr.2026.112748. [Google Scholar] [CrossRef]

118. Barnes M, Beddard A. Voltage source converter HVDC links—the state of the art and issues going forward. Energy Proc. 2012;24:108–22. doi:10.1016/j.egypro.2012.06.092. [Google Scholar] [CrossRef]

119. Psaras V, Tzelepis D, Burt G. HVDC grid fault location method using genetic algorithm on reconstructed frequency-domain voltage profiles. Int J Electr Power Energy Syst. 2023;144(1):108429. doi:10.1016/j.ijepes.2022.108429. [Google Scholar] [CrossRef]

120. Mokhberdoran A, Van Hertem D, Silva N, Leite H, Carvalho A. Multiport hybrid HVDC circuit breaker. IEEE Trans Ind Electron. 2018;65(1):309–20. doi:10.1109/TIE.2017.2719608. [Google Scholar] [CrossRef]

121. Tong N, Zeng X, Tan Z, Li G, Li D, Wu X. Adaptive fault nature identification and soft restart criterion for hybrid multiterminal UHVDCs. Int J Electr Power Energy Syst. 2024;160:110332. doi:10.1016/j.ijepes.2024.110332. [Google Scholar] [CrossRef]

122. Fang J, Liu J, Wu H, Chen J, Blaabjerg F. Emerging applications: opportunities and challenges. In: Grid-forming converters: principles, control, and applications in modern power systems. Amsterdam, The Netherlands: Elsevier; 2024. p. 241–95. doi:10.1016/B978-0-443-23735-5.00006-9. [Google Scholar] [CrossRef]

123. Zhang S, Zou G, Li B, Xu B, Li J. Fault property identification method and application for MTDC grids with hybrid DC circuit breaker. Int J Electr Power Energy Syst. 2019;110(6):136–43. doi:10.1016/j.ijepes.2019.02.048. [Google Scholar] [CrossRef]

124. Shivadasan AE, Shanmugam PK. Advanced fault detection methodologies and communication protocols for DC micro grid—a technical review. Results Eng. 2025;25(6):104398. doi:10.1016/j.rineng.2025.104398. [Google Scholar] [CrossRef]

125. Sharma S, Tripathy M, Wang L. Transient power polarity based fault detection in DC microgrid with localized backup scheme. Electr Power Syst Res. 2022;209(4):108008. doi:10.1016/j.epsr.2022.108008. [Google Scholar] [CrossRef]

126. do Amaral JVS, dos Santos CH, Montevechi JAB, de Queiroz AR. Energy digital twin applications: a review. Renew Sustain Energy Rev. 2023;188:113891. doi:10.1016/j.rser.2023.113891. [Google Scholar] [CrossRef]

127. Koirala B, Cai H, Khayatian F, Munoz E, An JG, Mutschler R, et al. Digitalization of urban multi-energy systems–Advances in digital twin applications across life-cycle phases. Adv Appl Energy. 2024;16:100196. doi:10.1016/j.adapen.2024.100196. [Google Scholar] [CrossRef]

128. Gordon M, De Wolf C. Optimisation goals for efficient construction from reused materials towards a circular built environment. Dev Built Environ. 2024;19(3):100509. doi:10.1016/j.dibe.2024.100509. [Google Scholar] [CrossRef]

129. Kumar K, Kwon S, Bae S. Deep reinforcement learning-based control strategy for integration of a hybrid energy storage system in microgrids. J Energy Storage. 2025;108:114936. doi:10.1016/j.est.2024.114936. [Google Scholar] [CrossRef]

130. Manjula A, Kute UT, Reddy CVK, Mallala B. Power quality improvement of microgrid for photovoltaic ev charging station with hybrid energy storage system using RPO-ADGAN approach. J Energy Storage. 2025;108(5):114970. doi:10.1016/j.est.2024.114970. [Google Scholar] [CrossRef]

131. Heckmann R. Optimal control of a hybrid microgrid for hydrogen-based heat supply using deep reinforcement learning. Clean Energy. 2023;7(5):940–51. doi:10.1093/ce/zkad038. [Google Scholar] [CrossRef]

132. Urs RR, Chadly A, Al Sumaiti A, Mayyas A. Techno-economic analysis of green hydrogen as an energy-storage medium for commercial buildings. Clean Energy. 2023;7(1):84–98. doi:10.1093/ce/zkac083. [Google Scholar] [CrossRef]

133. Awad M, Said A, Saad MH, Farouk A, Mahmoud MM, Alshammari MS, et al. A review of water electrolysis for green hydrogen generation considering PV/wind/hybrid/hydropower/geothermal/tidal and wave/biogas energy systems, economic analysis, and its application. Alex Eng J. 2024;87:213–39. doi:10.1016/j.aej.2023.12.032. [Google Scholar] [CrossRef]

134. Lin R, Lu S, Yang A, Shen W, Ren J. Multi-criteria sustainability assessment and decision-making framework for hydrogen pathways prioritization: an extended ELECTRE method under hybrid information. Int J Hydrogen Energy. 2021;46(24):13430–45. doi:10.1016/j.ijhydene.2021.01.018. [Google Scholar] [CrossRef]

135. Shadfar H, Ghorbani Pashakolaei M, Akbari Foroud A. Solid-state transformers: an overview of the concept, topology, and its applications in the smart grid. Int Trans Electr Energy Syst. 2021;31(9):e12996. doi:10.1002/2050-7038.12996. [Google Scholar] [CrossRef]

136. Lahooti Eshkevari A, Mosallanejad A, Sepasian M. In-depth study of the application of solid-state transformer in design of high-power electric vehicle charging stations. IET Electr Syst Transp. 2020;10(3):310–9. doi:10.1049/iet-est.2019.0106. [Google Scholar] [CrossRef]

137. O’Dwyer E, Pan I, Charlesworth R, Butler S, Shah N. Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems. Sustain Cities Soc. 2020;62:102412. doi:10.1016/j.scs.2020.102412. [Google Scholar] [CrossRef]

138. Dincer I, Ezzat MF. 5.24 sectoral energy and exergy management. In: Dincer I, editor. Comprehensive energy systems. Amsterdam, The Netherlands: Elsevier; 2018. p. 896–926. doi:10.1016/b978-0-12-809597-3.00545-9. [Google Scholar] [CrossRef]

139. Huang W, Zhang Y, Zeng W. Development and application of digital twin technology for integrated regional energy systems in smart cities. Sustain Comput Inform Syst. 2022;36(2):100781. doi:10.1016/j.suscom.2022.100781. [Google Scholar] [CrossRef]

140. Cao H, Zhang D, Yi S. Real-time machine learning-based fault detection, classification, and locating in large scale solar energy-based systems: digital twin simulation. Sol Energy. 2023;251(6):77–85. doi:10.1016/j.solener.2022.12.042. [Google Scholar] [CrossRef]

141. Mourtzis D, Angelopoulos J, Panopoulos N. Development of a PSS for smart grid energy distribution optimization based on digital twin. Procedia CIRP. 2022;107(1):1138–43. doi:10.1016/j.procir.2022.05.121. [Google Scholar] [CrossRef]

142. Kim G, Park S. Analysis of household power consumption data for social safety net services. Energy Rep. 2023;9(1):18–24. doi:10.1016/j.egyr.2023.04.259. [Google Scholar] [CrossRef]

143. Stansberry JM, Brouwer J. Experimental dynamic dispatch of a 60 kW proton exchange membrane electrolyzer in power-to-gas application. Int J Hydrogen Energy. 2020;45(16):9305–16. doi:10.1016/j.ijhydene.2020.01.228. [Google Scholar] [CrossRef]

144. Bhutto JK, Kumar A, Kamangar S, Ali Arabi AI, Hakami H. Design and performance assessment of a green hydrogen and renewable integrated hybrid industrial microgrid with advanced control strategies considering uncertainties of renewable energy. Sustain Comput Inform Syst. 2025;48(2):101267. doi:10.1016/j.suscom.2025.101267. [Google Scholar] [CrossRef]

145. Kumar S, Dwivedy SK. Assessment of reaching laws for linear and nonlinear sliding surfaces: improving performance of pipe crack sealing manipulator. Expert Syst Appl. 2026;296(6):128935. doi:10.1016/j.eswa.2025.128935. [Google Scholar] [CrossRef]

146. Zhu W, Wen S, Zhao Q, Zhang B, Huang Y, Zhu M. Deep reinforcement learning based optimal operation of low-carbon island microgrid with high renewables and hybrid hydrogen–energy storage system. J Mar Sci Eng. 2025;13(2):225. doi:10.3390/jmse13020225. [Google Scholar] [CrossRef]

147. Campobasso MS, Rose MS, Shende S, Adirosi E, Pace G, De Silvestri L, et al. Development, performance and energy trade-off analyses of wind turbine precipitation-reactive control at offshore and onshore sites in Western Europe. Renew Energy. 2026;262(9):125357. doi:10.1016/j.renene.2026.125357. [Google Scholar] [CrossRef]

148. Wang Y, Warder SC, Benmoufok EF, Wynn A, Buxton ORH, Staffell I, et al. Geographic variability in reanalysis wind speed biases: a high-resolution bias correction approach for UK wind energy. Energy Convers Manag. 2026;352:121066. doi:10.1016/j.enconman.2026.121066. [Google Scholar] [CrossRef]

149. Potisomporn P, Adcock TAA, Vogel CR. Evaluating ERA5 reanalysis predictions of low wind speed events around the UK. Energy Rep. 2023;10:4781–90. doi:10.1016/j.egyr.2023.11.035. [Google Scholar] [CrossRef]

150. Zhou Y, Chen L, Zhao J, Liu X, Ye X, Wang F, et al. Power and dynamic performance of a floating multi-functional platform: an experimental study. Energy. 2023;285:129367. doi:10.1016/j.energy.2023.129367. [Google Scholar] [CrossRef]

151. Alatawi MN. EdgeGuard-IoT: 6G-enabled edge intelligence for secure federated learning and adaptive anomaly detection in Industry 5.0. Comput Mater Contin. 2025;85(1):695–727. doi:10.32604/cmc.2025.066606. [Google Scholar] [CrossRef]

152. Chu W, Zhang Y. The efficiency and economic feasibility study on wind-hydrogen system. In: Proceedings of the 2020 IEEE Sustainable Power and Energy Conference (iSPEC); 2020 Nov 23–25; Chengdu, China. doi:10.1109/ispec50848.2020.9350988. [Google Scholar] [CrossRef]

153. Sarma RN, Kumar V, Lal SRS, Reghunath M, Jayan A, Suryan A. Wind power resource assessment and wind-hydrogen generation potential: a case study. Energy Sources Part A Recovery Util Environ Eff. 2025;47(2):2026537. doi:10.1080/15567036.2022.2026537. [Google Scholar] [CrossRef]

154. Hassan Q, Algburi S, Sameen AZ, Salman HM, Jaszczur M. A review of hybrid renewable energy systems: solar and wind-powered solutions: challenges, opportunities, and policy implications. Results Eng. 2023;20:101621. doi:10.1016/j.rineng.2023.101621. [Google Scholar] [CrossRef]

155. Ding Z, Hou H, Yu G, Hu E, Duan L, Zhao J. Performance analysis of a wind-solar hybrid power generation system. Energy Convers Manag. 2019;181:223–34. doi:10.1016/j.enconman.2018.11.080. [Google Scholar] [CrossRef]

156. Odenweller A, Ueckerdt F, Nemet GF, Jensterle M, Luderer G. Probabilistic feasibility space of scaling up green hydrogen supply. Nat Energy. 2022;7(9):854–65. doi:10.1038/s41560-022-01097-4. [Google Scholar] [CrossRef]

157. Pandey I, Kumari S. Indian green hydrogen framework: an assessment. EAI Endorsed Trans Energy Web. 2023;10:1–8. doi:10.4108/ew.4701. [Google Scholar] [CrossRef]

158. Cao F, Wang C, Chen S. Mapping the green aviation: a bibliometric analysis of green aviation research (1900–2025). Transp Policy. 2026;180:104014. doi:10.1016/j.tranpol.2026.104014. [Google Scholar] [CrossRef]

159. Alshammari S, Serret V, Tiwari S, Mohammed KS. Industry 4.0 and AI amid economic uncertainty: implications for sustainable markets. Res Int Bus Financ. 2025;75:102773. doi:10.1016/j.ribaf.2025.102773. [Google Scholar] [CrossRef]

160. Upreti H, Bartwal P. Mapping the evolution of nanofluid-based parabolic trough solar collector research: insights from bibliometric analysis. Results Eng. 2026;29:109896. doi:10.1016/j.rineng.2026.109896. [Google Scholar] [CrossRef]