Computational Methods, Artificial Intelligence, Modeling, and Simulation Applications in Green Hydrogen Production Through Water Electrolysis: A Review

Abstract

Green hydrogen production is emerging as a crucial component in global decarbonization efforts. This review focuses on the role of computational approaches and artificial intelligence (AI) in optimizing green hydrogen technologies. Key approaches to improving electrolyzer efficiency and scalability include computational fluid dynamics (CFD), thermodynamic modeling, and machine learning (ML). As an instance, CFD has achieved over 95% accuracy in estimating flow distribution and polarization curves, but AI-driven optimization can lower operational expenses by up to 24%. Proton exchange membrane electrolyzers achieve efficiencies of 65–82% at 70–90 °C, but solid oxide electrolyzers reach up to 90% efficiency at temperatures ranging from 650 to 1000 °C. According to studies, combining renewable energy with hydrogen production reduces emissions and improves grid reliability, with curtailment rates of less than 1% for biomass-driven systems. This integration of computational approaches and renewable energy ensures a long-term transition to green hydrogen while also addressing energy security and environmental concerns.

1. Introduction

Green hydrogen is a carbon-free, clean fuel produced by water electrolysis using renewable sources of energy like solar, waterfall, or wind energy. Consuming fuel from conventional sources of oil and natural gas has detrimental effects on the environment by emitting a considerable amount of carbon dioxide (CO₂) into the atmosphere that causes the greenhouse effect and ecological hazards [1]. On the other hand, the product of the combustion reaction of hydrogen is only water; accordingly, it is nominated for decarbonizing heavy industry as it can substitute carbon as the reducing agent in the steel industry; replace fossil fuel in the ammonia (NH₃) and methanol (CH₃OH) chemical industry; and ensure a constant hydrogen supply for kiln operation in the cement industry and powering the whole production process [2]. Furthermore, in renewable energy production, green hydrogen is a promising energy storage medium during surplus production durations; for example, it can store energy generated during daylight hours from solar power production. As it has the privilege of long-term storage and high energy density over batteries, it enables the stabilization of energy grids and improves their efficiency [3]. The transition to green hydrogen production is critical for supporting the world’s burgeoning hydrogen infrastructure and uses. For example, Japan has already produced over 10,000 Toyota Mirai vehicles, deployed over 300,000 Ene-Farm cogeneration systems, and set up 100 hydrogen refueling stations (HRSs). Similarly, China plans to increase the number of hydrogen-powered buses in Foshan to 5000 each year. To sustain and improve such programs, it is critical to focus on green hydrogen production technologies, which ensure that the hydrogen utilized in these applications is sourced from renewable energy sources, thus aligning with global sustainability goals and lowering carbon emissions [4].

The green hydrogen production procedure is built on electrolysis process of water (reactant) into oxygen (O₂) (produced at the anode) and hydrogen (H⁺) (produced at the cathode) because of the electrical current powered by a renewable source of energy in the electrolyzer device. Hydrogen gas (H₂) is formed when the protons (H⁺) combine with electrons (e⁻) from the external circuit, where the electrolyte plays the role of ion transport to allow the proton to transfer to the cathode. In between the electrodes, there is a membrane that has two flow channels to ultimately maintain gas separation and anode cathode separation [5]. Green hydrogen electrolyzers can be distinguished into three types according to electrolyte type and cathode/anode material, where each has its own properties and advantages as shown in Figure 1. Firstly, the proton exchange membrane (PEM) electrolyzer has an electrolyte as a solid polymer membrane, a cathode made of platinum-group metals, and an anode of iridium. In this electrolyzer, PEM permits only H⁺ ions to pass to the cathode while preventing gases and other ions from passing through. A PEM is characterized by flexibility for varying sources of renewable energy; also, it works at relatively low temperatures (50–80 °C), has a fast time response, its water uptake is 34.2–78.3%, it has a proton conductivity of up to 203.1 mS/cm at 90 °C; here, the alternative low efficiency of a PEM is a matter of research and optimization. Secondly, alkaline water electrolyzers (AWEs) have an electrolyte that is an alkaline solution (e.g., potassium hydroxide), a cathode, and an anode made of nickel alloys. In this electrolyzer, the hydroxide ions (OH⁻) work as an ion-transport mechanism through the alkaline solution. Alkaline electrolyzers are characterized by low cost and high durability, but their low current density due to their ohmic resistance is still under improvement. Finally, solid oxide electrolyzers (SOEs) have an electrolyte that is a solid ceramic electrolyte (e.g., yttria-stabilized zirconia), a cathode, and an anode made of ceramic materials which enable it to operate at high temperatures. In this electrolyzer, oxygen ions (O²⁻) pass from anode to cathode through the electrolyte, where steam is converted into hydrogen. An SOE is characterized by high efficiency as it is working at a high temperature (500–850 °C), merging electrical and thermal reactions, but the limitation of high temperature requires particular operating settings [1]. A comparison between the three types of electrolyzers is summarized in

Table 1. Comparison of various hydrogen production electrolyzers [5,6].

Electrolyzer	Proton Exchange Membrane (PEM)	Alkaline Water Electrolyzer (AWE)	Solid Oxide Electrolyzer (SOE)	Anion Exchange Membrane Electrolysis (AEM)
Characteristics	- Compact; - Pure gas; - Low durability.	- Low gas purity; - Low operating cost; - Corrosive electrolyte.	- Uses steam; - Can be used as a fuel cell; - Unstable electrode.	- Suitable for load fluctuation; - Unstable membrane.
Electrolyte	Solid polymer.	Aqueous solution KOH/NaOH.	Solid ceramic.	Solid polymer.
Working temperature °C	70–90	65–100	650–1000	50–70
Working pressure (bar)	15–30	2–10	Above 30	up to 35
Efficiency Range	65% to 82%	59% to 70%	Around 90%
Cell voltage (V)	1.80 to 2.4	1.80 to 2.4	0.95 1.30	1.85
Current density A/cm2	0.6 to 2	0.2 to 0.4	0.3 to 1	0.1 to 0.5
Startup duration (min)	Above 15	15	Above 30
Stack lifetime (h)	Above 40,000	Above 90,000	Above 40,000	Less 10,000
Energy consumption kWh/Nm3	4.5–7.5	4.5–7	2.5–3.5	~4.8

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Hydrogen production from seawater, with a focus on electrolysis and desalination procedures to support sustainable energy and water supplies, takes place simultaneously with three commercial desalination methods: reverse osmosis (RO), thermal desalination, and membrane desalination. One must take into account investment and operating costs, energy requirements, and environmental impact. The benefits and challenges of integrating hydrogen production with saltwater desalination can be analyzed to offer insights into the energy, economic, and environmental impacts of this approach. Key research gaps can be identified, and recommendations can be proposed for advancing hydrogen production technologies and their sustainable integration with water desalination in the future [6]. Computational, numerical, and artificial intelligence techniques improve the efficiency, dependability, and cost-effectiveness of seawater-based hydrogen production, particularly when electrolysis is combined with desalination operations. They provide predictive insights, optimize operations, and allow for real-time modifications, making the entire system more sustainable and adaptable to changing conditions.

Fuel cells are devices that convert chemical energy from hydrogen or other fuels into electricity using an electrochemical process. They play an important role in providing clean, efficient, and dependable energy solutions. Unlike combustion engines, fuel cells produce only water as a byproduct, making them an important technology for lowering greenhouse gas emissions. Their adaptability allows for applications in transportation (e.g., fuel cell vehicles), stationary power generation, portable devices, and industrial energy systems [4]. A hydrogen-based energy system, as shown in Figure 2, that integrates renewable energy with components for hydrogen generation, storage, and usage is composed of the following elements: The first unit processes water and produces hydrogen. A demineralizer eliminates contaminants from water, preparing it for electrolysis. The electrolyzer converts water (H₂O) into hydrogen (H₂), which is then refined in a purifier to ensure its quality for future usage. The second unit handles and stores hydrogen. A compressor pressurizes the hydrogen for efficient storage, while the storage system keeps it available for future use. The third unit focuses on energy conversion and distribution. A fuel cell transforms stored hydrogen into energy that may be distributed to the grid or local loads, as well as heat that can be used for thermal applications [7].

The produced hydrogen is stored in three forms: compressed hydrogen tanks, liquid hydrogen, or underground storage, where it is transported to the local distribution network through pipelines and trucks and can be converted to liquid organic hydrogen carriers (LOHCs) such as ammonia to facilitate its transportation. The hydrogen grid system is usually integrated with the electrical grid of renewable energy to compensate for intermittencies in the supply and demand of electricity to ensure grid stabilization. This integration, transportation, and storage system claims to have developed system planning and management approaches that appeal to energy system modeling, which includes forecasting, load balancing, and demand response [5].

Green hydrogen production methods focus on sustainable and environmentally friendly energy processes. Key techniques include electrolysis, where electricity from renewable sources like solar or wind split water into hydrogen and oxygen. Photocatalysis stands out as an innovative process that utilizes sunlight to split water molecules into hydrogen and oxygen. This process is driven by semiconductor photocatalysts, which absorb photons to create electron–hole pairs that facilitate the redox reactions required for water splitting. Materials like titanium dioxide (TiO₂), graphitic carbon nitride (g-C₃N₄), and molybdenum-based catalysts are frequently employed for their efficiency and stability under solar irradiation [8]. Meanwhile, thermochemical cycles use heat from solar concentrators or nuclear reactors to achieve water splitting. Biomass gasification transforms organic matter into hydrogen through high-temperature reactions. The optimization of photocatalysts and electrocatalysts is greatly aided by computational methods such as density functional theory (DFT) and molecular dynamics (MDs). While DFT provides an atomic-level insight of electrical structures and reaction mechanisms, MD simulations forecast how materials will behave under different circumstances. By forecasting reaction routes, assessing stability, and determining ideal configurations, these technologies make it easier to develop high-performance materials. When combined, they hasten the creation of economical and effective hydrogen production systems.

The type of electrolyte used by fuel cells determines their operating temperature and suitability for specific applications. Within the cell, the separation of electrons and ions produces an electrical current, with only water and heat as byproducts. Each fuel cell type is intended to satisfy specific operating conditions and application needs that are summarized in

Table 2. Working theory and key components of different types of fuel cells [4,7].

Fuel Cell Type	Working Theory	Key Components	System Efficiency	Operating Temperatures
Proton exchange membrane fuel cell (PEMFC)	Hydrogen splits into protons (H+) and electrons (e−) at the anode. Protons pass through the membrane to the cathode, while electrons flow through an external circuit, generating electricity. At the cathode, oxygen (O2) combines with protons and electrons to form water.	- Electrolyte: Proton-conducting membrane (e.g., Nafion). - Catalysts: Platinum-based catalyst for hydrogen oxidation and oxygen reduction. - Gas diffusion layer (GDL): Distributes gases and removes water. - Bipolar plates: Transport gases and collect current.	43–68%	−40 to 90 °C
Alkaline fuel cell (AFC)	Hydrogen reacts with hydroxide ions (OH−) at the anode, producing water and electrons. Electrons flow to the cathode, where oxygen reacts with water to regenerate hydroxide ions.	- Electrolyte: Potassium hydroxide solution. - Catalysts: Nickel-based. - Gas diffusion electrodes: Facilitate gas exchange and reactions.	60–70%	65–220 °C
Phosphoric acid fuel cell (PAFC)	Hydrogen oxidizes at the anode, producing protons and electrons. Protons pass through the phosphoric acid electrolyte to the cathode, where they combine with oxygen to form water.	- Electrolyte: Phosphoric acid. - Catalysts: Platinum-based. - Electrodes: Carbon-based electrodes coated with a catalyst.	40–55%	150–200 °C
Molten carbonate fuel cell (MCFC)	Hydrogen reacts with carbonate ions (CO32−) at the anode, producing water, carbon dioxide, and electrons. At the cathode, oxygen reacts with carbon dioxide to regenerate carbonate ions.	- Electrolyte: Molten lithium–potassium carbonate mixture. - Catalysts: Nickel-based. - Porous Electrodes: Allow gas diffusion and reactions.	55–65%	650–700 °C
Solid oxide fuel cell (SOFC)	Oxygen molecules split into oxygen ions (O2−) at the cathode. Oxygen ions migrate through the ceramic electrolyte to the anode, where they react with hydrogen or carbon monoxide to produce water or carbon dioxide and electrons.	- Electrolyte: Yttria-stabilized zirconia (YSZ), a ceramic. - Anode: Nickel-based. - Cathode: Perovskite materials. - Interconnects: Distribute gases and collect current.	55–65%	600–1000 °C

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Systems using artificial intelligence (AI) can perform tasks normally requiring human intelligence, including logic, if–then rules, decision trees, and visual perception for decision making and problem solving. As a subset of AI, machine learning (ML) uses statistics techniques to build experience based on data over time, identify patterns, and make decisions with minimal human input. As a branch of machine learning, deep learning (DL) utilizes artificial neural networks with multiple layers to model complex patterns and relationships. Electric grids integrating renewable energy sources and hydrogen generation require the use of artificial intelligence (AI) in decision making and control, machine Learning (ML) in data-driven models for demand forecasting, predictive maintenance, and cost optimization, and deep learning (DL) in powers complex, real-time optimizations that over all optimize performance, minimize costs, and enhance renewable integration into the grid [9]. As smart grids become more prevalent, six main categories of AI applications can be identified: 1—improved weather prediction for increasing renewable energy integration into the electric grid; 2—using AI for supply and demand forecasting; 3—improve cost-effectiveness while also increasing demand-side management and efficiency; 4—advancements in demand response energy storage technologies, and overall energy performance; 5—reduced local power consumption prices while increasing storage device owners’ returns; 6—maximize close to real-time business operations [10].

Green hydrogen production experiences difficulties over fusel fuel in forecasting energy supply and demand, optimizing electrolyzer operations, and controlling energy flow and storage systems [11]. The objective of this review is to give a complete examination of computational, AI-driven, and simulation approaches for green hydrogen production via electrolysis in conjunction with renewable energy sources. The study examines how these strategies help to improve manufacturing processes, energy efficiency, predictive maintenance, and overall system reliability, making green hydrogen a more realistic option for decarbonizing energy systems and heavy industries. This study will focus on the potential of modeling and simulation in the fluid dynamics in production cells, thermodynamics and kinetic models in the electrochemical reaction, smart grid management, and cost-effectiveness, in addition to exploring the role of AI in predictive analytics and real-time optimization in the hydrogen production process and the future directions for scaling all computational methods into green hydrogen production technology.

2. Computational Methods in Green Hydrogen Production

Computational methods have a vital role in green hydrogen production through simulating and optimizing the production processes like electrolysis, energy conversion efficiency, energy cycle, reactor design, and material selection. Computational methods study the production process controllable parameters like process temperature and pressure, ohmic resistance, electrodes, catalysts, and membrane materials to improve energy yield and reduce losses.

2.1. Computational Fluid Dynamics (CFDs)

Computational fluid dynamics (CFDs) simulations study electrolyzer fluid flow, thermal, and electrochemical cycles with the aim of improving the design, performance, and energy cycle of the production process. The efficiency of power to hydrogen transformation is affected by electrolyte flow distribution, as it is supposed to be uniform and overcomes the possibility of hotspots or dead zones and reduces pressure drops where CFD simulation is working on the design geometry of the flow field plates and optimization of flow channels in the separation membrane. Furthermore, two-phase flow modeling in CFD simulation could be utilized to improve gas bubble detachment and transport with the electrolyte away from the electrodes, as during operation, gas bubbles accumulate on the electrode and raise its ohmic resistance. Thermal and pressure simulation and stress analysis can be constructed for electrolyzers under elevated working temperatures to identify areas of overheating and propose the optimum cooling system and balance pressure distribution. Electrochemical models for electrolyzers to improve energy cycle efficiency and raise membrane transport efficiency to maintain ion exchange during reaction. Figure 3 provides an overview of applications that have utilized CFD for water electrolyzers. CFD achieves a correlation coefficient of 0.99, indicating near-perfect accuracy in flow predictions. These strategies improve operational efficiency by resolving degradation issues and improving component design [12].

To study the electrolyte flow distribution, T. Wang et al. [15] investigated the fluid flow in the concave–convex bipolar flow plate through 3D CFD simulation CFD and coupled particle tracing (CFD-PT) method. The study found that the research methodology is applicable and can be extended to both PEM and SOE systems. Additionally, the flow field exhibits clear non-uniformity, leading to the proposal of a uniformity criterion. Wong et al. [16] investigated gas bubble dynamics in a zero-gap AWE with fixed gas sources. Their study focused on bubble resistance and examined the impact of flow channel structural parameters on the gas–liquid two-phase flow pattern. They found that the model helped in studying bubble resistance economically under the simulated conditions; they found that a rounded bend has 13% more pressure stability than a squared bend and has a smaller low-velocity area. A.S. Tijani et al. [17] studied flow plate design and computer models of several available flow plate designs and evaluated both the hydrodynamic properties they exhibited: the velocity field and pressure gradients. In this investigation, an important contribution to the assessment of flow channel designs in PEM electrolyzers was made by the constructed CFD models. Parallel flow channel designs appear to be the most promising of the three models taken into consideration, as they can sustain a steady pressure distribution above the pressure threshold for high-pressure electrolysis. Y. Tu et al. [18] developed a new Y-shaped flow channel interconnector and conducted a comparison study between it and the conventional straight channel. Component distribution, temperature field, electrolyte current density, and thermal stress are all covered by SOEC models. The model presented patterns of thermal stress distribution and a detailed 3D model was created that integrates several physical processes, such as mass transfer, heat transfer, electrochemistry, and electron/ion transport. The newly developed Y-shaped channel improves the SOEC’s hydrogen production efficiency, and this is attributed to the elongated channel design, which promotes uniform gas diffusion, decreases concentration losses, and more efficiently and widely distributes electrolyte current density.

By comparing numerical results with experimental data under various settings across many investigations, CFD simulations have demonstrated their accuracy and dependability in modeling water electrolysis processes.

Table 3. Comparison of the accuracy and experimental validation of CFD.

Reference	Aim of the Study	Key Results	Validation Accuracy
[19]	Plate structure designs with a focus on flow uniformity are developed for alkaline water electrolyzers (AWE).	The proposed innovative designs (wedge and rhombus) were validated with a visualization/performance testing platform. Flow uniformity increased, with experimental results exceeding 95% agreement with CFD forecasts.	- The experimental and CFD findings demonstrated a strong correlation, with errors below 5%. - The proposed designs demonstrated performance gains of 10–15% in flow distribution, as proven by an experimental dwell time distribution analysis.
[20]	PEM water electrolyzer performance was evaluated using a 3D CFD model.	The CFD model predicted polarization curves and the impact of variables such as temperature and pressure. Experimental validation yielded high agreement with modeled expectations.	When comparing experimental polarization data to CFD simulations, the error was less than 2%.
[21]	CFD modeling of multiphase flow in AWE, with emphasis on gas fraction and bubble curtain width.	The addition of turbulence dispersion improved model estimates for gas layer spreading. Experimental validation against gas fraction data revealed high agreement. The study highlighted the relevance of turbulent forces in making credible forecasts.	Experimental validation yielded errors of less than 5% for high current densities. The addition of turbulence dispersion forces considerably improved accuracy, reducing errors from experimental data to 5–7% at higher current densities.
[22]	Two-dimensional CFD simulation of AWE including electrochemical and fluid dynamics effects.	Polarization curves and gas profiles were predicted with high accuracy across a range of operating situations.	Polarization curve comparisons with experimental data yielded a mean relative error of less than 1%.

provides a comparison of the accuracy and experimental validation of CFD in modeling water electrolysis processes.

When paired with rigorous experimental validation, CFD models accurately predict both electrochemical and fluid dynamic events in water electrolyzers. Validation errors typically vary between 1 and 7%, with the best cases falling under 3%. This accuracy allows for informed design modifications that can boost performance by up to 15%. Despite small differences at higher complexity levels (e.g., turbulence effects), CFD remains an important tool for improving electrolyzer efficiency and dependability.

2.2. Thermodynamic and Kinetic Modeling

Thermodynamic modeling studies the water electrolysis process in a changeable optimal thermodynamic condition to ensure high efficiency. It determines the energy requirements using the minimum energy required to break the oxygen–hydrogen bond at a certain temperature and pressure, which is named Gibbs free energy (ΔG). In addition, it calculates the value of electrical potential and overpotential needed to operate the electrolysis cell at mutable cell efficiency to overcome elevated resistance in the cell as the activation barriers. Kinetic modeling examines reaction mechanisms, including ion adsorption and desorption, while also analyzing the reaction rate by identifying the “Rate-Determining Step” (RDS). The RDS is the slowest phase in the electrochemical process and is influenced by catalyst performance.

Numerous studies employ thermodynamics and kinetic modeling to study and propose solutions to hydrogen production and storage processes. For instance, Y. Zhu et al. [23] investigated the impact of operating pressure, temperature, and membrane thickness on the rate and efficiency of hydrogen production using MATLAB/Simulink (version 8) to solve the thermodynamic model for PEM electrolyzers. It deduces that increasing the operating temperature, decreasing the operating pressure, and reducing the proton membrane thickness can all enhance hydrogen production efficiency. In addition, temperature and pressure variations have a significant impact on hydrogen production rates. In another research article, a thermodynamic model was built for a system of hydrogen-fueled compressed air energy storage systems combined with a water electrolysis hydrogen generator by R. Cao et al. [24]. It was found that the thermodynamic model was helpful in presenting energy analysis, exergy analysis, and sensitivity analysis of the proposed new system. It also suggested points of improvement that should be achieved, such as increasing the outlet hydrogen combustor temperature and the claim to ensure that the value of operating pressure optimizes the energy storage density and roundtrip efficiency. Moreover, building upon thermodynamic and electrochemical modeling, Ruiz Diaz et al. [25] studied hydrogen production under high temperatures in SOEC electrolyzers and estimated using MATLAB/Simulink the principal sources for the performance loss in the temperature range of 800–1000 °C, which is validated against experimental data from the literature. The model analysis reveals that ohmic polarization is a key factor in the reported difference in SOEC performance and proposes that integrating a machine learning (ML) model can provide a chance to improve system optimization in a complicated dynamic setting. In another thermodynamics model was built by Ulleberg [26] to study advanced alkaline electrolyzer and estimate cell voltage, generation of hydrogen, efficiency, and working temperature. To demonstrate the model’s applicability, a 1-year simulation of a photovoltaic–hydrogen system was run. The results suggest that the created system simulation model can help identify better electrolyzer operating methods. Overall, thermodynamic and kinetic modeling are significant techniques for studying hydrogen production in water electrolysis by improving and optimizing the electrochemical processes involved. These models enable the creation of more efficient and effective electrolyzers, hence improving both energy efficiency and reaction kinetics.

2.3. Material Investigations for Electrodes and Catalysts

Developing abundant, cost-effective electrode materials and catalysts is essential for scalable, economical water electrolysis. Computational methods can submit a systematic approach to simulate and predict new materials application in the electrolysis process at both atomic and macro scales; Figure 4 explores material simulation and modeling research techniques and applications in hydrogen production by water electrolysis. One of the atomic-scale computational methods is density functional theory (DFT), which studies the electronic structure of materials and different dopants to simulate its catalytic activity and interactions of electrode materials. Molecular dynamics (MDs) is computational method that models the dynamics of atoms at different pressures and temperatures; it can predict the degradation rate of a material. Kinetic Monte Carlo (KMC) simulation is a stochastic method based on random sampling of possible events used to model the time evolution of intricate systems at the atomic or molecular level, capturing both the kinetics and mechanisms of reactions over time. KMC contributes to the development of hydrogen production by providing an understanding of reaction kinetics, catalyst behavior, and system optimization [27,28]. On a macroscale, finite element analysis (FEA) is simulation tool that is used to simulate the mechanical, thermal, and electrical behavior of electrodes in the electrolysis process. A comparison of various approaches is given in

Table 4. Comparison between DFT, MD, and KMC computational and simulation applications in hydrogen production by water electrolysis.

Aspect	Density Functional Theory (DFT)	Molecular Dynamic (MD)	Kinetic Monte Carlo (KMC)
Nature of simulation	Based on first-principles quantum mechanical technique relies on solving the electronic structure of materials [29].	Based on classical or quantum mechanics a simulation of atomic and molecular motion over time; based on solving Newton’s equations of motion [30].	Based on probabilistic events simulating reaction dynamics over longer time scales [31].
Focuses	Reaction mechanisms and electronic properties [29].	Atomic-scale dynamics, studying how atoms and molecules interact and move over time [30].	Kinetic events, such as adsorption, desorption, and diffusion, over large system sizes and longtime scales [31].
Computational and simulating capabilities in hydrogen production	- Calculations of electronic structure and energy reaction energies, adsorption energies, and activation barriers. - Exploring new catalysts like transition metals, alloys, and 2D materials. - Can calculate the Gibbs free energy of hydrogen adsorption (ΔGH: a critical parameter for evaluating catalyst efficiency in the HER. - Hydrogen or water molecules interact with catalyst surfaces [32].	- Study the dynamics of hydrogen adsorption, surface diffusion, and desorption. - Study the interactions between electrolytes, solvents, and electrodes to explore molecular interactions affect reaction kinetics near the anode or cathode. - Simulate systems under various temperature and pressure. - Explore dynamic changes over short–medium time scales, allowing researchers to see how systems evolve in real time [32].	- Track events such as hydrogen adsorption, surface diffusion, desorption, and catalytic turnover rates over long periods. - Simulating large-scale hydrogen production rates reactions over extended time scales (seconds to hours) that would be impossible for MD or DFT. - Study defects, impurities, or surface roughness in catalysts to study how they impact the overall hydrogen production efficiency [32].
Application in hydrogen production	Catalyst design, reaction pathways, and adsorption.	Dynamics of adsorption/desorption and electrolyte interactions.	Surface reaction rates, long-term kinetics, and catalyst performance over time.
Key limitation	Computationally expensive, no time evolution [29].	Limited to shorter time scales and depends on force fields [33].	Requires accurate input data, and has no electronic structure [34].
Key strength	High accuracy in energy calculations [33].	Real-time atomic movements and dynamic behavior [33].	Long-term reaction dynamics and surface kinetics.
Time/length scale	Short time scales, small systems [35].	Short–medium time scales, medium systems [35].	Long time scales, large systems [34].

, which also highlights their accuracy, computing needs, and common uses in regard to hydrogen production through water electrolysis. The purpose of this comparison is to help researchers select the best computational approach for their particular goals, whether those goals be long-term reaction kinetics, system dynamics, or catalyst discovery.

The use of the discussed computational techniques and simulations has been examined in a variety of research studies. To improve the reaction active sites and electrical conductivity while also optimizing intermediate adsorption energy, X. Fan et al. [36] synthesize free-standing N-doped C electrocatalyst for oxygen evolution reaction trough one-step calcination. A model of the catalytic mechanism via Vienna ab initio simulation package (VASP) was built based on density functional theory (DFT), where the atomic process was demonstrated, and the adsorption energy on the surface of the electrocatalyst was precisely calculated. The results show that the N dopant was discovered to have a considerable influence on the conspicuous oxygen evolution reaction performance of our target electrocatalyst. In another study, a finite element model (FEA) for a high-pressure PEM water electrolyzer paired with a photovoltaic multi-junction solar cell mounted on a solar concentrator is created by D. Ferrero et al. [37]. The proposed model studied the electrochemical, fluidic, and thermal properties of the repeating unit of a PEM electrolyzer stack, and it is validated using experimental data from an operational prototype. The results showed that, while high-pressure operation of the PEM can reduce energy consumption for hydrogen compression, it does not represent a major advantage for the overall system’s efficiency, which is more affected by cell performance than by auxiliaries. To examine the elementary chemical stages of bulk water electrolysis. F. Hofbauer et al. [38] used Car–Parrinello molecular dynamics (MDs) simulations to imitate reactions near the anode and cathode; they offered insight into the atomic-scale processes that govern these reactions. The simulations indicate that the electrochemistry of oxygen production without direct electrode contact is provided by radical reactions in a solvent. These reactions could involve the creation of intermediate ions. Hydrogen production is governed by fast proton exchanges among water molecules. In another study, using KMC, I. Pašti et al. [39] studied the hydrogen evolution reaction to enhance H₂ formation by the H spillover in the favor of improvement and specialization of the catalyst deposit. The study investigates the impact of elementary process rates, catalyst dispersion, and morphology, leading the researchers to suggest broad guidelines for selecting the catalyst support combination that can be applied to the creation of novel, cutting-edge HER catalysts.

Material development for electrodes and catalysts in producing hydrogen by water electrolysis is crucial for increasing efficiency, lowering costs, and expanding sustainable hydrogen production. Platinum catalysts remain prohibitively expensive, preventing the widespread adoption of fuel cell technologies. To address this constraint, it is critical to investigate alternate materials or methods for replacing platinum while preserving or enhancing performance. Current research aims to reduce platinum loading to 0.147 mg/cm² or develop non-platinum-based catalysts to solve cost challenges and ensure the economic feasibility of fuel cells in large-scale applications [4]. The development of effective electrocatalysts for the hydrogen evolution process (HER) is crucial for developing sustainable hydrogen production. Mo-based materials, including Mo₂C, MoO₂, and MoS₂, provide intriguing non-platinum alternatives due to their electrical characteristics and abundance. Metal–organic frameworks (MOFs) can be used as precursors improve the structure, porosity, and catalytic effectiveness of these catalysts, hence overcoming the problems faced using HER [40]. Modeling and simulation research cover the optimization of the materials utilized in both the anode (oxygen evolution reaction, OER) and the cathode (hydrogen evolution reaction, HER) to improve performance, stability, and cost. DFT, MD, KMC, and FEA are complementary computational tools for studying hydrogen production from water electrolysis in different scale. DFT provides highly accurate electronic-level details, MD allows the study of real-time dynamics, and KMC excels at long-term kinetics on larger scales, while FEA studies the electrolysis process parameters in the macroscale.

3. Smart Grids

In order to increase and optimize energy efficiency, smart grids are made up of simulation tools and modern communication and automation systems that allow the study of the integrated whole electric network elements of power generation, transmission, distribution, and storage; they also consider consumers and the production, storage, and transportation of green hydrogen. It is critical to improve the integration between green hydrogen production and renewable energy sources to overcome the alternating fluctuations in production of renewable energy and balance the electricity supply and demand [10]. Smart grids can reduce hydrogen production during peak times, making electricity more efficient when it is cheaper and more plentiful. The smart grid that is illustrated in Figure 5 consists of a control center that monitors and manages grid operations using advanced control systems like “Supervisory Control and Data Acquisition” (SCADA) that enable the collection and analysis of data and can remotely adjust imbalances, faults, or inefficiencies. Meanwhile, communication networks, regional controller microgrids, and chain-blocks support real-time data exchange between smart meters, sensors, and control centers, and the ensure the coordination of distributed energy resources (DERs) to maintain grid resilience [9].

There is a critical need to use simulation and computational methods as well as AI technologies to administer smart systems efficiently, allowing them to respond more quickly to grid needs and market conditions as well as improve their energy efficiency at the same time.

3.1. Demand Response System

Demand response (DR) programs are an intelligent scheduling tool used to promote changes in customers’ power usage habits in accordance with incentives related to energy costs as it aims to stabilize the electric system by allowing customers to reduce their loads in response to grid needs and economic signals. Classification and definition of (DR) programs and the reliance on computational methods for their prediction and adaption is shown in Figure 6. Through demand response, a grid is more reliable and stable through load management, and blackouts and brownouts are less likely to happen during times of high demand. AI helps to improve efficiency, reduces costs, increase grid stability, and better integrate renewable energy sources. Through the optimization of hydrogen manufacturing processes and the alignment of energy demand patterns with renewable generation, DR programs give networks flexibility and stability. As energy systems move toward incorporating larger percentages of hydrogen and renewable energy sources, their function becomes more crucial in maintaining steady and effective operation [9,41].

Numerous research studies propose solutions for demand response systems, focusing on the analysis of methodologies, scheduling strategies, and optimization techniques for practical field applications. These studies employ a wide range of computational, numerical, and AI-based methods. Additionally, a significant body of research explores the integration of renewable energy sources with hydrogen production, highlighting innovative approaches to enhance system efficiency and sustainability. To address the challenges associated with large-scale wind energy production, L. Yang et al. [42] have developed an optimal scheduling approach for electro-thermal systems that integrates source–load–storage cooperative hydrogen production. This method enhances demand responsiveness by coordinating energy sources, loads, and storage systems to optimize hydrogen production. By effectively managing the variability inherent in wind energy, this approach aims to improve system efficiency and reliability. To mitigate wind power variations on hydrogen production, a “wind-storage” combined hydrogen production module based on hydrogen energy characteristics is presented to ensure safety. Additionally, price and incentive recommendations are used to optimize the electric load curve and increase “source–load” coordination flexibility in a revised demand response model. Next, a bi-level scheduling model of source–load–storage cooperative operation is created to maximize wind power tracking while protecting both sides from the benefit conflicts of system operations and hydrogen production. According to simulation results, the suggested approach can increase hydrogen profit by 24% while balancing the costs of system operation and the advantages of hydrogen production. Furthermore, it is possible to accomplish 100% wind power consumption even with a low-capacity hydrogen generation plant. Another study by N. Eghbali et al. [43] proposed a management algorithm that seeks to determine the best day-ahead operation for renewable energy sources, comprising wind turbines, photovoltaic (PV) units, fuel cells (FCs), electrolyzers, microturbines, and energy storage, while also taking smart home participation in demand response programs into account. Two distinct energy storage devices—a battery and a hydrogen storage tank—were modeled and their functions were compared using a mixed-integer linear programming (MILP) issue that was resolved using a stochastic programming technique. It has been demonstrated that microgrids may be economical, implementing both the demand response program and hydrogen storage, which lowers the overall cost significantly.

In a proposed study by M. Guo et al. [28], a new renewable hybrid energy power system is constructed using hydrogen gas turbines, wind turbines, photovoltaic, electric energy storage, and hydrogen energy storage. By including numerous inputs and objective functions into the optimization method, three scenarios are created for simulation trials, with the goal of meeting the regional load while attaining a stable and cost-effective system and power grid operation. The findings demonstrate that, under the same circumstances, the scheduling approach suggested in this research can reduce operational costs by 22.8%. A new scenario is created in the assessment of renewable energy efficiency, and the outcome is still optimal. In order to benefit the transportation industry as well as the operator of the energy market, a further study by N. El-Taweel et al. [29] develops a novel methodology for the best scheduling of privately held hydrogen storage facilities. The model’s primary goals are as follows: (1) take advantage of the reduced energy market prices to lower the cost of purchasing power; and (2) support the capacity-based demand response program to increase the investment’s economic viability. According to numerical analyses, the suggested model’s stacking profit from the two previously indicated revenue streams would result in a higher rate of return.

Demand response systems can help to reduce energy costs, improve grid reliability, and increase the use of renewable energy sources. Dynamic energy consumption can be proposed based on real-time grid conditions by ramping hydrogen production up or down and absorbing renewable energy during over-supply periods. In addition, it proposes a cost-efficient system by controlling time-of-use pricing through shifting hydrogen production to times of energy abundance. AI can optimize charging and discharging, forecast demand, and control energy flow in response to market prices and grid conditions.

3.2. Energy Storage System

Energy storage systems (ESSs) store energy from external sources and release it as needed. An energy storage integration system (ESIS) integrates storage technologies such as batteries, thermal storage, pumped hydro, and hydrogen storage into energy networks for generation, distribution, and consumption. This integration is critical for systems that rely on intermittent renewable energy, allowing excess energy from peak production periods to be stored for later use. Hydrogen storage provides a long-term solutions through a variety of technologies, including compressed gas, cryogenic liquid, and chemical storage with metal hydrides or ammonia. Metal hydrides, liquid organic hydrogen carriers (LOHC), and high-pressure systems are examples of storage advancements that aim to lower prices, increase storage density, and improve safety. Hydrogen may be transformed into electricity through fuel cells or used in turbines [44,45]. The power-to-gas system plays a crucial role in energy storage integration by offering a method to convert intermittent renewable electricity into green hydrogen. In many sectors, such as steel production, chemical manufacture, and heavy transportation, where direct electrification is difficult, power-to-gas (P2G) systems transform surplus renewable electricity into hydrogen, promoting energy resilience and decarbonization [46].

The broad use of hydrogen as a clean energy carrier with uses in industry, power generation, and transportation depends on hydrogen storage technology. Compressed gas, liquefied hydrogen, and metal hydrides are some of the most important storage techniques; each has advantages and disadvantages of its own.

Table 5. Comparison between different hydrogen storage systems [7].

Storage Type	Working Principal	Key Characteristics
Compressed gas	- Hydrogen stored in high-pressure gas cylinders. - Commonly used for industrial and vehicle applications. - Energy density depends on gas pressure. - Efficient but requires large volumes for significant storage.	- Operating pressure: 20 MPa (industrial), 10,000 psi (vehicles). - Historical storage: 12 MPa in wrought-iron vessels (1880). - Type I vessels: Store 1 wt% of hydrogen. - Type III/IV vessels: Use lightweight composites.
Liquefied hydrogen	- Stored at cryogenic temperatures (−253 °C) in insulated vessels. - Requires intensive energy for liquefaction. - Provides higher storage density than compressed gas. - Not suitable for on-board applications due to boil-off.	- Energy loss during liquefaction: 40%. - Tank capacity: Exceeds 60,000 L for transport. - Requires advanced insulation with materials like perlite or aluminum film.
Metal hydrids	- Stores hydrogen in solid form by chemical absorption. - Promising for high-density storage. - Requires heat to release hydrogen (desorption). - Includes light and complex hydrides for improved properties.	- Storage capacity: 5–7 wt% hydrogen. - Desorption temperature: 120–200 °C. - Operating temperature for some hydrides: 250 °C or above. - Examples: Mg-based hydrides with up to 7 wt% hydrogen capacity.

presents a comparison between them. By enhancing material discovery, simulating hydrogen behavior, and forecasting performance under various circumstances, artificial intelligence (AI) is essential to the optimization of these technologies.

Computational, numerical, and artificial intelligence technologies are critical for improving ESIS’s performance, efficiency, and dependability. They offer solutions for modeling, simulation, control, and prediction, enabling better decision making and system integration. Figure 7 summarizes methods and applications of computational and AI in ESIS. Numerus research articles have developed and simulated computational methods in ESIS, integrated with renewable energy sources and hydrogen production. R. Carapellucci et al. [47] established a simulation tool built on a hybrid genetic–simulated annealing algorithm and objects to reduce the unit cost of electricity. The proposed system is composed renewable energy islands, which include various electricity-generation technologies (photovoltaic modules, wind turbines, and micro-hydroelectric plants) and a hydrogen storage system consisting of an electrolyzer, a hydrogen storage tank, and a fuel cell. The investigation revealed that decreased energy flows do not reduce the size of the hydrogen storage tank, since the time gap between inlet and output hydrogen flows increases. In another study, García-Triviño et al. [48] offers a new energy management system based on multi-objective optimization problem using the weight aggregation approach with the particle swarm optimization (PSO) method. For optimizing the energy sources of a grid-connected hybrid renewable energy system (wind turbine and solar) with battery and hydrogen system (fuel cell and electrolyzer), three control objective functions must be addressed: (1) operating cost, (2) efficiency, and (3) device lifetime. The results show that this energy management system provides acceptable operating costs, efficiency, and device deterioration.

G. Zini [49] developed and simulated a hydrogen energy storage system using real data from a photovoltaic plant. The study aimed to evaluate the key operating parameters and assess the financial feasibility of implementing hydrogen storage systems, providing valuable insights for institutional investors considering such installations. in infrastructure. The findings indicate that such a system is capable of ensuring a consistent energy input into the grid, making the solar power plant as dependable as typical fossil fuel power plants. In further study, to specify and evaluate a hydrogen storage and off-grid hybrid renewable energy system, A. Khosravi et al. [50] conducted an exergy and economic analysis. Using a dynamic model of solar and wind energy, the pattern of power generated by the photovoltaic (PV) system and wind turbine was established. The size of the suggested system’s components was also established. According to the PV system’s energy and exergy studies, the average energy and exergy efficiencies were 12% and 16%, respectively. The wind turbine’s average energy and exergy efficiencies were roughly 32% and 25%, respectively. The PV system achieved a maximum exergy destruction of about 65%. Additionally, 50% of the total investment was allocated to an energy storage system based on economic analysis.

The energy storage integration system (ESIS), which stores excess energy and makes it available when needed, is essential in maximizing the potential of renewable energy sources. ESIS allows for flexibility in a variety of energy sectors and longer-duration storage through the integration of green hydrogen. ESIS performs better thanks to computational, numerical, and artificial intelligence techniques that optimize operations, offer predictive insights, and guarantee a smooth transition to hydrogen and renewable energy sources. As a result, the energy network becomes more stable overall and more cost-effective.

3.3. Distributed Energy Resources (DERs)

Distributed energy resources (DERs) are small–medium-sized energy generation or storage devices that are positioned near the point of consumption (usually at residences, companies, or industrial sites) rather than being centralized in big power plants. DERs are renewable energy systems and storage units that can work independently or as part of a larger, integrated grid system. Smart grids can swiftly react to fluctuations in supply and demand on account of DERs, which also promote consumer participation and decentralized energy markets. This reduces transmission losses by generating energy closer to where it is used and lowers the cost of infrastructure investments like large-scale transmission improvements or the construction of new centralized power plants [37]. Computational methods, numerical modeling, and AI-based techniques are crucial in optimizing the operation of DER systems, allowing effective management of renewable energy, improving storage strategies, and ensuring reliable energy decentralize generation. Figure 8 summarizes modeling techniques for distributed energy resources (DERs) that are vital for better monitoring, control, and stability in the face of unpredictability when DERs are integrated into distribution networks.

Many studies model and optimize the distributed energy resources in smart grid applications. K. Sasaki et al. [51] suggest a plan that uses time-of-use (TOU) electricity pricing as a motivator to encourage home prosumers to control the DERs and provide them with more flexibility. Specifically, we created a model for an energy management system that uses model predictive control. An aggregator who dealt with markets and prosumers created the TOU tariffs. Batteries, CO₂ heat pump water heaters, or fuel cells with combined heat and electricity were the DERs used by residential prosumers. To assess the effectiveness of the created model, numerical simulations were conducted using measured energy demand and insolation data. The results showed that the created model could incorporate prediction mistakes and give the grid flexibility. In order to credit the reserve resources and charge the uncertainty producers, a unique pricing technique known as the uncertainty marginal price, or UMP, is suggested by Y. Xia et al. [52]. To linearize the power flow model, voltage sensitivity coefficients, power transfer distribution factors, and loss sensitivity factors are added. The entire issue can be broken down into two smaller issues and formulated as a strong model. The two-stage robust model is solved using the CCG algorithm. The IEEE-33 and 69 bus systems are used to validate this new pricing approach. According to numerical results, the suggested price rises as the degree of uncertainty grows. The inflection points of the operating cost curves under varying degrees of uncertainty can be selected as the system robustness reference points.

DERs assist in managing the unpredictability of renewable energy sources and decentralizing power generation by integrating with them. Their combination with green hydrogen provides long-term energy storage options that aid in decarbonization and grid stability.

3.4. Energy Management Systems (EMSs)

Energy management systems (EMSs) are complex frameworks for monitoring, controlling, and optimizing energy generation, distribution, and consumption in a specific facility or network. EMS is intended to improve energy efficiency, save costs, and increase system reliability by coordinating multiple energy sources and storage systems. Advanced EMSs use artificial intelligence and machine learning to estimate demand and make real-time decisions on energy storage usage.

Numerous studies have focused on modeling and optimizing energy management systems for smart grid applications. C. Ziogou et al. simulated an adaptable energy management strategy (EMS) for a sustainable hydrogen production unit using water electrolysis and solar electricity by a supervisory control system. The suggested method formalizes system functioning with a finite-state machine (FSM), which is then paired with propositional-based logic to characterize transitions between different process stages. The operating rules for the integrated system are derived in a systematic manner, taking into account both the operational limitations and the interaction of the individual subsystems. The optimal control system parameter values are determined in order to satisfy a system performance requirement that includes efficient and economical operation. The resulting EMS has been integrated into the industrial automation system, which monitors and manages a small-scale experimental solar hydrogen production unit. The overall performance of the proposed EMS in the experimental unit has been assessed throughout both short and long working durations, resulting in smooth and efficient hydrogen production. In alternative research based on wind energy, C. Ziogou et al. [53] presents an energy management system (EMS) based on model predictive control (MPC) to control power consumption for a group of electrolysis units on an offshore platform. The platform produces sustainable hydrogen utilizing power from local wind turbines and wave energy converters, with all generated power going to the electrolyzers. The MPC manages each electrolysis unit’s operation using a mixed-integer quadratic programming algorithm, balancing power by altering operating points and controlling connections/disconnections. This strategy takes into account the expected power supply and consumption, which improves stability and reduces the frequency of switching activities. Two case studies using real data confirm the performance of the EMS, demonstrating its ability to sustain balanced operation in offshore wind and wave energy facilities.

To manage an isolated powered microgrid, G. Cau et al. [54] built a system of a solar array and a wind turbine and supplied with two distinct energy storage systems: electric batteries and a hydrogen production and storage system. In particular, efficient scheduling of storage devices is used to optimize the benefits of available renewable resources by running solar systems and wind turbines at their peak power points while lowering overall utilization costs. According to the data, the average energy storage efficiency has increased, and the utilization costs have decreased by almost 15% when compared to traditional state-of-charge (SOC)-based EMSs.

4. Role of AI in Green Hydrogen Production

4.1. Machine Learning in Process Optimization

Machine learning (ML) models are particularly valuable in analyzing the complex datasets generated during electrolysis, which include variables such as electricity input, water quality, temperature, pressure, and electrochemical properties. By uncovering patterns and relationships within these datasets, ML algorithms can predict optimal operational conditions and make real-time adjustments, maximizing hydrogen production while minimizing energy consumption [55]. Numerous studies have explored the application of machine learning in green hydrogen production, contributing valuable insights to this emerging field. These research efforts address various concepts and methodologies for developing machine learning models aimed at optimizing green hydrogen production processes. By leveraging advanced AI techniques, these studies enhance efficiency, reduce costs, and promote the scalability of green hydrogen technologies. Their contributions are instrumental in advancing the adoption of sustainable energy solutions and accelerating progress toward global decarbonization goals [56]. AI algorithms are utilized to process diverse inputs such as real-time energy demand, renewable energy generation forecasts, and system constraints. These inputs enable the optimization of outputs, including energy distribution, demand response, and hydrogen production efficiency. Figure 9 illustrates the interaction between the inputs and outputs of AI algorithms in smart grids, highlighting their crucial role in managing and optimizing integrated renewable energy and hydrogen production systems.

To improve hydrogen production efficiency, multiple machine learning (ML) models, such as regression analysis, neural networks, and reinforcement learning, are developed and validated. These models are trained on historical data from electrolysis processes and experimental setups to ensure high levels of accuracy and reliability. Advanced feature selection and engineering methods are utilized to pinpoint the key factors that significantly impact efficiency and cost. The findings reveal that AI-driven optimization can lead to notable improvements in the energy efficiency of hydrogen production, with potential energy savings of up to 20%. This research provides proof that regression analysis demonstrates effectiveness in predicting the efficiency of the electrolysis process by examining input variables such as electricity supply, water quality, temperature, and pressure. By analyzing how these variables interact and influence efficiency, regression models offer critical insights into the factors affecting electrolyzer performances [57].

The trend of scientific publications related to artificial intelligence (AI) in green hydrogen production from 2010 to 2024, as indexed by Google Scholar, is illustrated in Figure 10. There has been a consistent rise in the number of publications over the years, demonstrating the increasing interest and research efforts in the intersection of AI and renewable energy. The data highlight a notable peak in 2023, which represents the year with the largest number of publications. This surge in 2023 could be attributed to advancements in AI technologies and the increased focus on green hydrogen as a crucial component of sustainable energy solutions. The continuous upward trend suggests that the integration of AI in green hydrogen production is an emerging and rapidly expanding field.

4.2. AI-Driven Control Systems

Data-driven approaches play a crucial role in green hydrogen technology, enabling the optimization of production, cost reduction, and improvements in overall process efficiency. These methods rely on the use of vast datasets in conjunction with advanced analytical techniques, such as artificial intelligence (AI), to generate valuable insights and facilitate informed decision making. Studies have shown that the flexible operation of wind–hydrogen systems is achievable due to the strong dynamic response of electrolysis, allowing system operators to participate strategically in day-ahead power markets to minimize hydrogen production costs. However, uncertainties arising from inaccurate predictions of fluctuating market prices and wind power availability limit the effectiveness of these strategies. To address the challenge of uncertainties in wind power and electricity market prices, Yi Zheng et al. [58] proposed a decision-making framework based on data-driven robust chance-constrained programming (DRCCP). Additionally, the framework integrates a multi-layer perceptron neural network (MLPNN) for more accurate prediction of wind power generation and spot electricity prices. The results indicate that, over a 30-day period, the operation strategy derived from this framework reduces overall operational costs by 24.36% compared to strategies based on imperfect predictions.

The day-ahead operational problem is formulated using mixed-integer linear programming (MILP), which incorporates uncertain parameters. They used a case study based on a real-world application at Green Lab Skive, an industrial park in Denmark that features wind turbines, electrolyzers, and chemical plants. In this case study, the data-driven approach is employed to improve decision making in the face of imperfect predictions by considering historical observations of prediction errors. This approach generates new operational strategies that consider not only day-ahead forecasts but also available data on past prediction errors. Also, authors addressed uncertainties using the DRCCP scheme. The system operator’s objective in the wind–hydrogen system is to minimize the total daily cost of producing a specific quantity of hydrogen, utilizing robust data-driven techniques to ensure cost-effective and reliable outcomes.

In the early 2020s, a growing number of countries have committed to achieving net-zero emissions within the next two–three decades, reflecting a global effort to combat climate change. Although some nations have not yet adopted formal commitments, many others have introduced ambitious plans to achieve carbon neutrality. Achieving these net-zero emission goals requires a comprehensive transformation of sectors that are significant contributors to CO₂ emissions, such as power generation, industry, transportation, and buildings.

Du. Wen et al. [59] introduce the concept of an energy hub that employs hydrogen and ammonia as energy carriers in order to increase renewable energy usage and system flexibility. Two approaches are investigated: P2X2P (power-to-gas-to-power) and B2X2P (biomass-to-gas-to-power). Over the course of a year, the study optimizes operations using both model-based (MILP) and model-free (DRL) techniques. The most profitable scenario, B2A2P (biomass-to-ammonia-to-power), has a net present value (NPV) of $550.76 million USD and a discounted payback period of 5 years, beating the other options. B2H2P (biomass-to-hydrogen-to-power) likewise delivers impressive results, with an NPV of $378.18 million USD and a 9-year payback period. Under certain conditions, P2H2P and P2A2P scenarios have negative NPVs, with payback times lasting more than 17 years. Ammonia storage (capacity of 35,000 kg) is more cost-effective and scalable than hydrogen storage (7000 kg). DRL displays flexibility by reducing fossil fuel use while increasing curtailment, illustrating the trade-off between profitability and sustainability.

To manage the flexibility and profitability of the energy hub efficiently, a scheduling approach was developed using a combination of model-based mixed-integer linear programming (MILP) and model-free deep reinforcement learning (DRL) methods. Over recent years, reinforcement learning (RL) has become increasingly popular for the formulation of energy management systems due to its ability to handle complex, human-like decision-making processes. For this study, the MERRA-2 database, provided by the Global Modeling and Assimilation Office at the Goddard Space Flight Center and NASA, was utilized to gather essential weather data, including solar irradiance, wind speed, and ambient temperature, which are critical for optimizing the renewable energy input into the system.

Furthermore, a model-free method based on DRL was developed to optimize the planning and scheduling of the energy hub for different scenarios, considering the uncertainties in both supply and demand. One of the key advantages of this approach is its ability to respond dynamically to changes in supply and demand without requiring an exact model of the system. This makes it a more flexible solution compared to traditional methods. Additionally, this DRL-based method considers multiple objectives, including profitability and system flexibility, ensuring a more balanced and effective optimization. The numerical results obtained from the study show that the use of this approach significantly reduces reliance on fossil fuels, which is a key step toward meeting net-zero emission goals. However, this reduction in fossil fuel dependence does come with a trade-off, as it slightly reduces profitability. Despite this, the DRL method has been shown to be generally applicable to similar scheduling problems, particularly in complex, multi-task optimization scenarios, where it performs well in balancing competing objectives.

By integrating these elements, this study demonstrates how advanced data-driven techniques, such as DRL, can play a crucial role in optimizing the operation of renewable energy systems, paving the way for more sustainable and economically viable energy hubs. This connection between flexible scheduling, renewable integration, and the use of hydrogen and ammonia as energy carriers represents a significant step toward achieving long-term sustainability in energy management.

4.3. Predictive Maintenance and Fault Detection

Predictive maintenance algorithms use historical and real-time data to find patterns that indicate possible equipment problems. This proactive strategy reduces downtime and increases the lifespan of components like electrolyzers. Predictive maintenance is especially important in energy systems since it improves both efficiency and reliability. Similarly, the increasing complexity of the smart grid necessitates interoperability research to assure the seamless integration of developing technologies. The predictive maintenance process involves several key stages, as shown in Figure 11. A feedback loop promotes continuous improvement by reintegrating post-maintenance data into prediction algorithms, hence improving their future accuracy and performance. This comprehensive strategy reduces downtime, improves reliability, and allows for smarter, more efficient energy systems.

Predictive maintenance is crucial for guaranteeing the efficient operation of proton exchange membrane (PEM) electrolyzers by recognizing and resolving defects before they worsen. Studies have shown that combining advanced computational methods, such as machine learning (ML) and artificial intelligence (AI), can improve fault detection and prediction capabilities.

• Fault detection: Model-based approaches have proven to be successful in detecting frequent failures such as membrane degradation and catalyst fouling, both of which have a substantial impact on electrolyzer performance. AI-enabled algorithms, such as deep reinforcement learning (DRL) and long short-term memory (LSTM) neural networks, achieve 99% correlation between predicted and real sensor readings, with a root mean square error (RMSE) of 0.1351 [12,60].
• Fault isolation: Data-driven models can successfully isolate problems including hydrogen leakage, overheating, and diaphragm compressor defects. Non-invasive diagnostic techniques, such as thermography and acoustic emission signals, can improve dependability and reduce downtime by up to 30% [60].
• Maintenance optimization: Predictive solutions, such as AI-based scheduling, improve maintenance intervals. This saves money on personnel and spare parts, ensuring that the electrolyzer lasts as long as it should. Computational frameworks optimize system efficiency, resulting in 15–20% energy savings [12,60].

For example, DRL and LSTM networks may successfully pick features for predictive maintenance, with RMSEs as low as 0.1351 [12]. These models allow for the early diagnosis of component failures, which reduces downtime and operational expenses.

4.4. Optimization of Electrolyzer Operations

AI-driven optimization greatly improves system performance. Machine learning models used with computational fluid dynamics (CFD) increase the accuracy of operational predictions by up to 95%. These approaches improve power allocation at peak renewable energy availability, lowering manufacturing costs by 24% and reducing energy waste. Hybrid systems that use AI-based scheduling optimize resource consumption and increase equipment lifespan. Furthermore, decision-support systems ensure smooth operation by forecasting maintenance requirements, increasing reliability, and lowering downtime. Operational flexibility is also a key focus of optimization efforts. PEMWE systems are the most adaptable, working within a load range of 5–120% with response rates reaching 10% per second. In comparison, SOEC systems reach efficiencies of up to 90% but require longer ramp times due to high operating temperatures. PEMWE capital expenditures are estimated from $1100 to $1800 USDper kW, whereas SOEC systems cost between USD 2800 and USD 5600 per kW. Incorporating renewable energy reduces the levelized cost of hydrogen (LCOH) while improving system economics. Optimization methods seek to reduce operational expenses while increasing hydrogen generation efficiency. AI-based scheduling strategies, such as mixed-integer programming, can lower hydrogen production costs by up to 9.2%, while increasing renewable energy consumption by 19% in wind–electrolyzer systems. Furthermore, sophisticated optimization frameworks improve electrolyzer performance, resulting in ramp speeds of more than 10% per second for PEMWE. Such computational advancements improve coupling with renewable energy, boosting sustainability and economic feasibility [61].

Smart grids are emerging as a key solution for clean, sustainable, efficient, and reliable energy generation, distribution, and consumption. Ensuring stable and secure operation is essential for the smart grid, which requires effective stability analysis and control mechanisms. As smart grids evolve with increasing interconnections, greater integration of renewable energy sources, the widespread use of direct current power transmission systems, and the liberalization of electricity markets, their stability characteristics have become more complex than they were in the past. Conventional stability analysis and control methods struggle to keep up with these changes, facing limitations in terms of speed, effectiveness, and cost-efficiency. In contrast, emerging artificial intelligence (AI) techniques offer powerful and promising tools for stability analysis and control, attracting growing attention in the field [62,63,64].

Empowering smart grids is crucial for dynamically balancing energy supply and demand, detecting and isolating faults, minimizing energy waste, and optimizing the integration of renewable sources such as solar and wind. These capabilities are essential for managing the increasing complexity of modern energy grids, particularly with the growing share of decentralized and variable energy sources. The integration of artificial intelligence (AI) techniques with optimization algorithms, particularly metaheuristic methods, plays a pivotal role in improving the performance of hybrid renewable energy systems in smart grid environments. These AI-driven approaches are crucial for efficiently managing key functions such as demand response, load balancing, and renewable energy forecasting. By optimizing the energy flow and system operations, AI techniques help to enhance both the efficiency and reliability of these systems, ensuring that renewable energy sources are utilized effectively, and that the smart grid operates with minimal disruptions, as summarized in Figure 12. Using supervised and unsupervised learning algorithms, ML applications in predictive maintenance for industry showed notable benefits in one study, including a 25% increase in equipment lifespan and a 50% decrease in maintenance expenditures [65]. Another study on intelligent energy management systems (EMSs) described AI-driven methods for optimizing appliance scheduling in smart homes to reduce electricity bills by 15%. These methods used weather forecasting through enhanced differential evolution (EDE) models that were integrated with artificial neural networks (ANNs) [66,67]. ConvLSTM-BDGRU, a hybrid AI model for building energy management, achieved a 12% lower mean absolute error (MAE) and a 20% higher accuracy than classic ML techniques. This improved forecasts for renewable energy generation and load prediction [67]. AI-powered dynamic load management in smart grids can cut peak load demand by up to 30%, allowing for effective integration of renewable sources like solar and wind [68]. These findings highlight the importance of AI in improving operating efficiency, lowering expenses, and decreasing energy consumption. The incorporation of modern AI frameworks improves predictive skills, hence facilitating the global shift to sustainable energy systems and smart manufacturing.

5. Mathematical Modeling in Green Energy Production

Mathematical models serve as powerful tools for simulating, predicting, and optimizing renewable energy systems, offering valuable insights into addressing challenges in green hydrogen technology, especially when integrated with AI. By leveraging mathematical modeling, we can tackle the technical, economic, and policy obstacles that hinder the development and implementation of green hydrogen. These challenges include optimizing production processes, improving efficiency, reducing costs, and ensuring regulatory compliance. The following figure illustrates how mathematical modeling can help overcome these hurdles and advance green hydrogen technology toward broader adoption and sustainability [69]. Figure 13 provides an overview of the current status, challenges, and potential solutions for green hydrogen production technologies.

Some models are designed to either maximize or minimize specific objectives, such as cost, emissions, or energy efficiency, while operating within a set of constraints. These constraints can include resource limitations, technical specifications, or regulatory requirements. Other models incorporate stochastic techniques, which account for uncertainty in the system. Approaches such as Markov chains and Monte Carlo simulations are commonly used in these cases to model randomness and variability more accurately, enabling better decision making under uncertain conditions. Sanchez et al. [70] introduce a sustainable integrated renewable energy system (SIRES), developed to meet the energy requirements of an off-grid village in Chile. The cost of energy for each resource, such as solar, wind, biomass, and biogas, was calculated by considering the availability of these resources and the specific energy needs of the village. The study thoroughly evaluates various renewable energy systems, including solar photovoltaic panels, wind energy turbines, biomass gasifiers, biogas production, and solar thermal collectors, to assess their potential for providing a reliable energy supply. A real-world case study is presented in this research to demonstrate the feasibility of implementing such systems. A mathematical optimization model was used to determine the optimal energy production while minimizing the overall cost of energy (COE) for the village. In this process, different scenarios were analyzed to understand how energy availability changes throughout the year and to assess the economic viability of each resource. For example, the study looks at the feasibility of using biomass and biogas as energy sources and explores the challenges associated with wind power, given its relatively high costs. Lastly, the research also considers the environmental benefits of using renewable energy, specifically in terms of reducing CO₂ emissions. This aspect highlights the broader positive impact of renewable energy systems on both the economy and the environment. In addition, hybrid models are increasingly being used to combine the strengths of both deterministic and stochastic approaches. These hybrid models can address uncertainty more effectively by leveraging the predictive power of deterministic models while accounting for the variability and randomness captured by stochastic techniques. This allows for a more robust and comprehensive modeling of complex systems.

On the other hand, a study introduced by A. Ahmed et al. [71] aimed to identify the best operating conditions to maximize hydrogen production via wastewater electrolysis. To achieve this, response surface methodology (RSM) was combined with genetic algorithms (GA) and particle swarm optimization (PSO) to create predictive models and optimize the process parameters. The parameters examined included the catalyst amount (g), electrode voltage (V), and electrolysis duration (min), while the rate of oxyhydrogen (HHO) gas generation (L/min) served as the key response variable. The results demonstrated the effectiveness of using a hybrid approach of RSM, GA, and PSO for accurate prediction and optimization of the wastewater electrolysis process, particularly for producing green hydrogen. By determining optimal conditions, the study improved efficiency and increased hydrogen output, thereby supporting the development of more sustainable energy solutions. The desirability technique was employed to refine control variables to further enhance the response.

A noteworthy finding was that RSM led to a time saving of 6.83 min compared to GA and PSO, highlighting its efficiency in delivering faster optimization results. Analysis of variance (ANOVA) indicated significant interactions between catalyst amount and electrode voltage, as well as between catalyst amount and electrolysis time, both of which were critical to maximizing HHO yield. Other interactions, however, were found to have no significant influence. Furthermore, the use of solar energy for the electrolysis process contributed to the system’s sustainability and environmental benefits.

Another emerging trend is the use of computational algorithms, such as genetic algorithms and machine learning techniques. These algorithms are particularly effective when dealing with many variables and parameters, as they can optimize multiple responses simultaneously. By applying these advanced algorithms, researchers can find optimal solutions in high-dimensional spaces, making them suitable for handling the complexity of real-world problems in renewable energy and other fields [72]. This combination of methods and models allows for more precise optimization, addressing the multifaceted challenges in energy systems, including those related to resource management, operational efficiency, and environmental sustainability [73].

6. Hydrogen Production from Seawater and Water Desalination

The system for producing hydrogen from saltwater consists of multiple interconnected processes, beginning with water desalination and progressing to electrolysis. Seawater, which contains significant levels of salts and pollutants, must first be desalinated to generate fresh water appropriate for electrolysis. Desalination is often accomplished by techniques such as reverse osmosis (RO) or multi-effect distillation (MED). Seawater is pumped via a semi-permeable membrane that filters out salts and impurities, resulting in fresh water with minimal energy use, particularly when powered by renewable energy. MED, on the other hand, uses thermal energy to evaporate and condense seawater in numerous stages, making it perfect for large-scale desalination when waste heat from other processes (such as concentrated solar power) is available. When driven by renewables, the entire system delivers a closed-loop, emission-free solution for green hydrogen generation, using seawater—a readily available resource—as feedstock while addressing water scarcity and carbon emissions [6].

Computational modeling allows for thorough simulations of electrolysis cells and desalination systems, which serve to enhance efficiency and design by studying aspects such as fluid dynamics, temperature, and pressure. Numerical approaches aid in optimization by determining the ideal operating parameters, decreasing energy consumption, and estimating the costs associated with various electrolysis and desalination setups. AI and machine learning improve these processes by forecasting maintenance requirements, optimizing real-time operations, and modifying settings in response to dynamic conditions like variable renewable energy inputs [74]. AI algorithms also offer predictive analysis of energy demand, allowing for more effective integration of renewable sources. These strategies work together to lower costs, enhance efficiency, and enable the scalability of seawater-based hydrogen production technology [75]. M. Kabir et al. [76] performs a techno-economic and environmental feasibility analysis for green hydrogen production (GHP), identifying proton exchange membrane (PEM) and dark fermentation (DF) as the most promising and environmentally benign technologies. ML models, including K-nearest neighbor for DF and random forest for PEM, are used to forecast and optimize GHP using metrics such as R², RMSE, and MAE. Key characteristics impacting GHP have been identified: for DF, they include chemical oxygen demand (COD), butyrate, and pH; for PEM, critical components include cell temperature, area, pressure, and voltage. Partial dependence analysis identifies ideal COD and temperature ranges for both processes, with cell temperatures up to 35 °C and cell size of 40–70 cm² improving GHP for PEM. This study illustrates the potential of machine learning and artificial intelligence to overcome scalability difficulties.

Varies studies investigate the capabilities of computation, simulation, and deep learning in optimizing the integration of renewable energy source electrolysis and water desalination while attaining high performance and cost effectiveness. W. Sheta et al. [77] investigated the potential for hydrogen production in Egypt’s north coast and Red Sea Zone to convert renewable energy to hydrogen (power to gas). The main goal is to calculate the amount of power required to produce one kilogram of hydrogen from seawater using renewable energy, as well as the amount of desalinated seawater required to cover this cost. We will cover various possibilities, with the major two being photovoltaic (PV) solar with reverse osmosis (RO) desalination and concentrating solar power (CSP) with multi-effect desalination (MED). In another study, M. Saeedi Zadegan et al. [78] presented a hybrid system for hydrogen production from seawater that includes parabolic trough collectors, a reverse osmosis (RO) desalination unit, and a thermochemical water decomposition process based on the cuprous chloride (Cu-Cl) cycle. The system’s components are designed for maximum efficiency, with RO and Cu-Cl processes simulated using ROZA and Aspen HYSYS software, which is coupled to MATLAB for further system aspects. The key features include an optimum collector area of 2964.6 m² and two cooling towers with heights of 40 and 20 m and base diameters of 50 and 35 m, respectively. The hybrid system has an overall energy efficiency of 18% and an exergy efficiency of 30%, showing excellent energy usage and thermodynamic efficacy. In a novel design D. Wen et al. [79] introduce a renewable seawater desalination system that uses hydrogen as an energy carrier to maintain the electricity supply, meet demand, and generate heat for desalination. Using a water–hydrogen nexus structure, the system optimally provides power, drinkable water, and green hydrogen. A heuristic algorithm selects the optimal design while accounting for seasonal fluctuations in renewable energy. The system has an average efficiency of 38.9%, which exceeds prior research, with curtailment rates ranging from 5% to 14% due to energy storage restrictions. A net-zero integration of electrolysis and fuel cells is presented as a sustainable and profitable alternative to fossil fuel desalination plants, with water electrolysis systems operating at 40–60% and fuel cells at <20% utilization.

In summary, computational approaches and artificial intelligence (AI) play critical roles in increasing the efficiency, reliability, and scalability of hydrogen generation and water desalination systems. Computational modeling and numerical approaches allow for precise simulations of complicated processes like electrolysis and desalination, optimizing factors such as energy input, temperature, and pressure to improve efficiency and reduce operational costs. AI enhances these systems by performing predictive maintenance, optimizing real-time operations, and responding to variable renewable energy supply, thereby increasing overall system resilience. These advanced methodologies, when combined, allow for the construction of sustainable, high-performance water–hydrogen nexus systems, paving the way for clean energy solutions and effective water resource management in an increasingly renewable-energy-dependent future.

7. Conclusions

Green hydrogen production is rapidly emerging as a significant solution to worldwide carbon reduction efforts, supported by computational methodologies and artificial intelligence. The combination of artificial intelligence (AI), computational fluid dynamics (CFD), and thermodynamic modeling has considerably increased the efficiency, scalability, and cost-effectiveness of hydrogen production systems. The following are the important results and statements that summarize the influence of these advancements:

• Computational methods like CFD have achieved 95% accuracy in estimating flow distribution and polarization curves, with errors as low as 1–7%, allowing for design modifications that increase electrolyzer performance by up to 15%.
• Thermodynamic modeling has improved operational parameters such as temperature, pressure, and membrane thickness, resulting in hydrogen generation efficiencies of up to 90% in solid oxide electrolyzers (SOE) and 65–82% in proton exchange membrane electrolyzers (PEMs).
• AI-driven techniques, like machine learning (ML), have shown the capacity to optimize electrolyzer operations in renewable hydrogen production hubs, lowering energy usage by up to 20% and operational expenses by 24%.
• Hydrogen storage technologies such as compressed gas and metal hydrides have variable capacities. Compressed hydrogen runs at 10,000 pressure, whereas metal hydrides have storage capacities of 5–7 wt% hydrogen and desorption temperatures ranging from 120 to 200 °C.
• Integrating renewable energy with green hydrogen generation improves grid stability dramatically, reducing curtailment rates to less than 1% in biomass-driven systems while increasing renewable energy use.
• AI integration in demand response systems increased energy efficiency and lowered operational costs, with curtailment rates of less than 9% for power-to-hydrogen paths.

The convergence of computational methodologies, artificial intelligence, and renewable energy integration has proven green hydrogen as a scalable and cost-effective solution to global decarbonization. These developments are critical for optimizing hydrogen production systems, lowering costs, and ensuring long-term energy sustainability.