Iran, Islamic Republic of

The increasing demand for renewable energy sources (RES) to address environmental concerns and reduce fossil fuel dependency highlights the need for efficient energy storage and balancing mechanisms to manage RES output uncertainty. However providing dedicated storage units to RES owners is often infeasible. Additionally the growing interest in hydrogen utilization complicates optimal decision-making for multi-energy systems. To tackle these challenges this paper presents a novel bidding strategy enabling wind farms to participate in dayahead balancing and hydrogen markets through shared multi-energy storage (SMES) systems. These SMES which include both battery and hydrogen storage offer a cost-effective solution by allowing RES owners to rent storage capacity. By optimizing SMES utilization and wind farm management we propose an integrated strategy for day-ahead electrical and real-time balancing markets and also hydrogen markets. The approach incorporates with uncertainties of wind generation bidding by using conditional value at risk (CVaR) to account for different risk-aversion levels. The Dantzig–Wolfe Decomposition (DWD) method is applied to decentralize the problem reduce the calculation burden and enhance the data privacy. The framework is modeled as a mixed-integer linear program (MILP) and solved using CPLEX solver via GAMS software. The results demonstrate the effectiveness of this strategy offering insights into the risk-oriented market participation of wind power plants with the aid of SMES system supporting a more sustainable and resilient energy system. The numerical results show that by utilizing a SMES with only batteries the revenue can be increased by 17.3% and equipping the SMES with hydrogen storage and participating in both markets leads to 36.9% increment in the revenue of the wind power plant.

Developing an effective energy transition roadmap is crucial in the face of global commitments to achieve net zero emissions. While renewable power generation systems are expanding challenges such as curtailments and grid constraints can lead to energy loss. To address this surplus electricity can be converted into green hydrogen serving as a key component in the energy transition. This research explores the use of renewable solar energy for powering a proton exchange membrane electrolyser to produce green hydrogen while a downdraft gasifier fed by municipal solid waste generates hydrogen-enriched syngas. The blended fuel is then used to feed a Solid Oxide Fuel Cell (SOFC) system. The study investigates the impact of hydrogen content on the performance of the fuel cell-based power plant from thermodynamics and exergoeconomic perspectives. Multiobjective optimisation using a genetic algorithm identifies optimal operating conditions for the system. Results show that blending hydrogen with syngas increases combined heat and power efficiency by up to 3% but also raises remarkably the unit product cost and reduces carbon dioxide emissions. Therefore the optimal values for hydrogen content current density temperatures and other parameters are determined. These findings contribute to the design and operation of an efficient and sustainable energy generation system.

Hydrogen is gaining prominence as a sustainable energy source in the UK aligning with the country’s commitment to advancing sustainable development across diverse sectors. However a rigorous examination of the interplay between the hydrogen economy and the Sustainable Development Goals (SDGs) is imperative. This study addresses this imperative by comprehensively assessing the risks associated with hydrogen production storage transportation and utilization. The overarching aim is to establish a robust framework that ensures the secure deployment and operation of hydrogen-based technologies within the UK’s sustainable development trajectory. Considering the unique characteristics of the UK’s energy landscape infrastructure and policy framework this paper presents practical and viable recommendations to facilitate the safe and effective integration of hydrogen energy into the UK’s SDGs. To facilitate sophisticated decision making it proposes using an advanced Decision-Making Trial and Evaluation Laboratory (DEMATEL) tool incorporating regret theory and a 2-tuple spherical linguistic environment. This tool enables a nuanced decision-making process yielding actionable insights. The analysis reveals that Incident Reporting and Learning Robust Regulatory Framework Safety Standards and Codes are pivotal safety factors. At the same time Clean Energy Access Climate Action and Industry Innovation and Infrastructure are identified as the most influential SDGs. This information provides valuable guidance for policymakers industry stakeholders and regulators. It empowers them to make well-informed strategic decisions and prioritize actions that bolster safety and sustainable development as the UK transitions towards a hydrogen-based energy system. Moreover the findings underscore the varying degrees of prominence among different SDGs. Notably SDG 13 (Climate Action) exhibits relatively lower overall distinction at 0.0066 and a Relation value of 0.0512 albeit with a substantial impact. In contrast SDG 7 (Clean Energy Access) and SDG 9 (Industry Innovation and Infrastructure) demonstrate moderate prominence levels (0.0559 and 0.0498 respectively) each with its unique influence emphasizing their critical roles in the UK’s pursuit of a sustainable hydrogen-based energy future.

In this novel conceptual fuel cell vehicle (FCV) an on-board CH4 steam reforming (MSR) membrane reformer (MR) is considered to generate pure H2 for supplying a Fuel Cell (FC) system as an alternative to the conventional automobile engines. Two on-board tanks are forecast to store CH4 and water useful for feeding both a combustion chamber (designed to provide the heat required by the system) and a multi tubes Pd-Ag MR useful to generate pure H2 via methane steam reforming (MSR) reaction. The pure H2 stream is hence supplied to the FC. The flue gas stream coming out from the combustion chamber is used to preheat the MR feed stream by two heat exchangers and one evaporator. Then this theoretical work demonstrates by a 1-D model the feasibility of the MR based system in order to generate 5 kg/day of pure H2 required by the FC system for cruising a vehicle for around 500 km. The calculated CH4 and water consumptions were 50 and 70 kg respectively per 1 kg of pure H2. The on-board MR based FCV presents lower CO2 emission rates than a conventional gasoline-powered vehicle also resulting in a more environmentally friendly solution.

The most challenging aspect of developing a green hydrogen economy is long-distance oceanic transportation. Hydrogen liquefaction is a transportation alternative. However the cost and energy consumption for liquefaction is currently prohibitively high creating a major barrier to hydrogen supply chains. This paper proposes using solid nitrogen or oxygen as a medium for recycling cold energy across the hydrogen liquefaction supply chain. When a liquid hydrogen (LH2) carrier reaches its destination the regasification process of the hydrogen produces solid nitrogen or oxygen. The solid nitrogen or oxygen is then transported in the LH2 carrier back to the hydrogen liquefaction facility and used to reduce the energy consumption cooling gaseous hydrogen. As a result the energy required to liquefy hydrogen can be reduced by 25.4% using N2 and 27.3% using O2. Solid air hydrogen liquefaction (SAHL) can be the missing link for implementing a global hydrogen economy.

Solar energy is important for the future as it provides a clean renewable source of electricity that can help combat climate change by reducing reliance on fossil fuels via implementing various solar-based energy systems. In this study a unique configuration for a parabolic-trough-based solar system is presented that allows energy storage for periods of time with insufficient solar radiation. This model based on extensive analysis in MATLAB utilizing real-time weather data demonstrates promising results with strong practical applicability. An organic Rankine cycle with a regenerative configuration is applied to produce electricity which is further utilized for hydrogen generation. A proton exchange membrane electrolysis (PEME) unit converts electricity to hydrogen a clean and versatile energy carrier since the electricity is solar based. To harness the maximum value from this system additional energy during peak times is used to produce clean water utilizing a reverse osmosis (RO) desalination unit. The system’s performance is examined by conducting a case study for the city of Antalya Turkey to attest to the unit’s credibility and performance. This system is also optimized via the Grey Wolf multi-objective algorithm from energy exergy and techno-economic perspectives. For the optimization scenario performed the energy and exergy efficiencies of the system and the levelized cost of products are found to be approximately 26.5% 28.5% and 0.106 $/kWh respectively.

Hydrogen is emerging as a crucial energy source in the global effort to reduce dependence on fossil fuels and meet climate goals. Integrating hydrogen into Integrated Assessment Models (IAMs) is essential for understanding its potential and guiding policy decisions. These models simulate various energy scenarios assess hydrogen’s impact on emissions and evaluate its economic viability. However uncertainties surrounding hydrogen technologies must be effectively addressed in their modeling. This review examines how different IAMs incorporate hydrogen technologies and their implications for decarbonization strategies and policy development considering underlying uncertainties. We begin by analyzing the configuration of the hydrogen supply chain focusing on production logistics distribution and utilization. The modeling characteristics of hydrogen integration in 12 IAM families are explored emphasizing hydrogen’s growing significance in stringent climate mitigation scenarios. Results from the literature and the AR6 database reveal gaps in the modeling of the hydrogen supply chain particularly in storage transportation and distribution. Model characteristics are critical in determining hydrogen’s share within the energy portfolio. Additionally this study underscores the importance of addressing both parametric and structural uncertainties in IAMs which are often underestimated leading to varied outcomes regarding hydrogen’s role in decarbonization strategies.

Hydrogen energy is essential in the transition to sustainable transportation planning providing a clean and efficient alternative to traditional fossil fuels. As a versatile energy carrier hydrogen facilitates the decarbonization of diverse transportation modes including passenger vehicles heavy-duty trucks trains and maritime vessels. To justify and clarify the role of hydrogen energy in sustainable transportation planning this study conducts a comprehensive techno-economic and environmental assessment of hydrogen production in the USA Europe and China. Utilizing the Shlaer–Mellor method for policy modeling the analysis highlights regional differences and offers actionable insights to inform strategic decisions and policy frameworks for advancing hydrogen adoption. Hydrogen production potential was assessed from solar and biomass resources with results showing that solar-based hydrogen production is significantly more efficient producing 704 tons/yr/km2 compared to 5.7 tons/yr/km2 from biomass. A Monte Carlo simulation was conducted to project emissions and market share for hydrogen and gasoline vehicles from 2024 to 2050. The results indicate that hydrogen vehicles could achieve near-zero emissions and capture approximately 30% of the market by 2050 while gasoline vehicles will decline to a 60% market share with higher emissions. Furthermore hydrogen production using solar energy in the USA yields a per capita output of 330513 kg/yr compared to 6079 kg/yr from biomass. The study concludes that hydrogen particularly from renewable sources holds significant potential for reducing greenhouse gas emissions with policy frameworks in the USA Europe and China focused on addressing energy dependence air pollution and technological development in the transportation sector.

Hydrogen is a promising clean energy source that can be a promising alternative to fossil fuels without toxic emissions. It can be generated from formic acid (FA) through an FA dehydrogenation reaction using an active catalyst. Activated carbon-supported palladium (Pd/C) catalyst has superior activity properties for FA dehydrogenation and can be reused after deactivation. This study focuses on predicting the FA conversion to H2 (%) in the presence of Pd/C using machine learning techniques and experimental data (1544 data points). Six different machine learning algorithms are employed including random forest (RF) extremely randomized trees (ET) decision tree (DT) K nearest neighbors (KNN) support vector machine (SVM) and linear regression (LR). Temperature time FA concentration catalyst size catalyst weight sodium formate (SF) concentration and solution volume are considered as the input data while the FA conversion to H2 (%) is the target value. Based on the train and test outcomes the ET is the most accurate model for the prediction of FA conversion to H2 (%) and its accuracy is assessed by root mean squared error (RMSE) R-squared (R2 ) and mean absolute error (MAE) which are 3.16 0.97 and 0.75 respectively. In addition the results reveal that solution volume is the most significant feature in the model development process that affects the amount of FA conversion to H2 (%). These techniques can be used to assess the efficiency of other catalysts in terms of type size weight percentage and their effects on the amount of FA conversion to H2 (%). Moreover the results of this study can be used to optimize the energy cost and environmental aspects of the FA dehydrogenation process.

Hydrogen storage plays a crucial role in achieving net-zero emissions by enabling large-scale energy storage balancing renewable energy fluctuations and ensuring a stable supply for various applications. This study provides a comprehensive analysis of hydrogen storage technologies with a particular focus on underground storage in geological formations such as salt caverns depleted gas reservoirs and aquifers. These formations offer high-capacity storage solutions with salt caverns capable of holding up to 6 TWh of hydrogen and depleted gas reservoirs exceeding 1 TWh per site. Case studies from leading projects demonstrate the feasibility of underground hydrogen storage (UHS) in reducing energy intermittency and enhancing supply security. Challenges such as hydrogen leakage groundwater contamination induced seismicity and economic constraints remain critical concerns. Our findings highlight the technical economic and regulatory considerations for integrating UHS into the oil and gas industry emphasizing its role in sustainable energy transition and decarbonization strategies.

This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm specifically a convolutional neural network (CNN) to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Square Error (RMSE) and the Correlation Coefficient (R2 ) with comparisons made to a real-world dataset. The CNN model showed outstanding performance with an R2 of 0.96 MSE of 1.97 MAE of 1.003 and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities thereby supporting informed decisionmaking and sustainable energy infrastructure development.

The current study seeks to greenhouse gas emissions reduction in an existing engine under dual-fuel combustion fueled with diesel fuel and natural gas due to great concerns about global warming. This simulation study focuses on the identification of areas prone to the formation of greenhouse gas emissions in engine cylinders. The simulation results of dual-fuel combustion confirmed that the possibility of incomplete combustion and the formation of greenhouse gas emissions in high levels are not far from expected. Therefore an efficient combustion strategy along with replacing natural gas with hydrogen was considered. Only changing the combustion mode to reactivity-controlled compression ignition has led to the improvement of the natural gas burning rate and guarantees a 32 % reduction in unburned methane and 50 % carbon monoxide. To further reduce engine emissions while changing the combustion mode a part of natural gas replacement with hydrogen to the complete elimination of it was evaluated. Increasing the share of hydrogen energy in the intake air-natural gas mixture up to 54 % without exhaust gas recirculation does not lead to diesel knock. Moreover improvement of engine load and efficiency can be achieved by up to 18 % and 6 % respectively. Natural gas consumption can be reduced by up to 67 %. Meanwhile the unburned methane and carbon dioxide mass known as greenhouse gas emissions can be reduced to less than 1 % and up to 50 % respectively. Continued replacement of natural gas with hydrogen until its complete elimination guarantees a reduction of 92000 cubic meters of natural gas per year in one engine cylinder. Although the engine efficiency and load face a decrease of 0.8 % and 5.0 % respectively; the amount of carbon dioxide can be decreased by about 4.5 times. Unburned methane carbon monoxide and nitrogen oxides can be reduced to below the relevant EURO VI range while the amount of unburned hydrogen compared to the hydrogen entering the engine is about 0.5 %.

Hydrogen is anticipated to play a key role in global decarbonization and within the UK’s pathway to achieving net zero targets. However as the production of hydrogen expands in line with government strategies a key concern is where this hydrogen will be stored for later use. This study assesses the different large-scale storage options in geological structures available to the UK and addresses the surrounding uncertainties moving towards establishing a hydrogen economy. Currently salt caverns look to be the most favourable option considering their proven experience in the storage of hydrogen especially high purity hydrogen natural sealing properties low cushion gas requirement and high charge and discharge rates. However their geographical availability within the UK can act as a major constraint. Additionally a substantial increase in the number of new caverns will be necessary to meet the UK’s storage demand. Salt caverns have greater applicability as a good short-term storage solution however storage in porous media such as depleted hydrocarbon reservoirs and saline aquifers can be seen as a long-term and strategic solution to meet energy demand and achieve energy security. Porous media storage solutions are estimated to have capacities which far exceed projected storage demand. Depleted fields have generally been well explored prior to hydrocarbon extraction. Although many saline aquifers are available offshore UK geological characterizations are still required to identify the right candidates for hydrogen storage. Currently the advantages of depleted gas reservoirs over saline aquifers make them the favoured option after salt caverns.

This work presents a feasibility study on the provision of electricity and hydrogen with renewable grid connected and off-the-grid systems for Bandar Abbas City in the south of Iran. The software HOMER Pro® has been used to perform the analysis. A techno-enviro-economic study comparing a hybrid system consisting of the grid/wind turbine and solar cell is done. The wind turbine is analyzed using four types of commercially available vertical axis wind turbines (VAWTs). According to the literature review no similar study has been performed so far on the feasibility of using VAWTs and also no work exists on the use of a hybrid system in the studied area. The results indicated that the lowest price of providing the required hydrogen was $0.496 which was achieved using the main grid. Also the lowest price of the electricity generated was $1.55 which was obtained through using EOLO VAWT in the main grid/wind turbine/solar cell scenario. Also the results suggested that the highest rate of preventing CO2 emission which was also the lowest rate of using the national grid with 3484 kg/year was associated with EOLO wind turbines where only 4% of the required electricity was generated by the national grid.

Hydrogen globally recognized as the most efficient and clean energy carrier holds the potential to transform future energy systems through its use as a fuel and chemical resource. Although progress has been made in reversible hydrogen adsorption and release challenges in storage continue to impede widespread adoption. This review explores recent advancements in hydrogen storage materials and synthesis methods emphasizing the role of nanotechnology and innovative synthesis techniques in enhancing storage performance and addressing these challenges to drive progress in the field. The review provides a comprehensive overview of various material classes including metal hydrides complex hydrides carbon materials metal-organic frameworks (MOFs) and porous materials. Over 60 % of reviewed studies focused on metal hydrides and alloys for hydrogen storage. Additionally the impact of nanotechnology on storage performance and the importance of optimizing synthesis parameters to tailor material properties for specific applications are summarized. Various synthesis methods are evaluated with a special emphasis on the role of nanotechnology in improving storage performance. Mechanical milling emerges as a commonly used and cost-effective method for fabricating intermetallic hydrides capable of adjusting hydrogen storage properties. The review also explores hydrogen storage tank embrittlement mechanisms particularly subcritical crack growth and examines the advantages and limitations of different materials for various applications supported by case studies showcasing real-world implementations. The challenges underscore current limitations in hydrogen storage materials highlighting the need for improved storage capacity and kinetics. The review also explores prospects for developing materials with enhanced performance and safety providing a roadmap for ongoing advancements in the field. Key findings and directions for future research in hydrogen storage materials emphasize their critical role in shaping future energy systems.

The increase in the feasibility of hydrogen-based generation makes it a promising addition to the realm of renewable energies that are being employed to address the issue of electric vehicle charging. This paper presents technical and an economical approach to evaluate a newer off-grid hybrid PV-hydrogen energy-based recharging station in the city of Jamshoro Pakistan to meet the everyday charging needs of plug-in electric vehicles. The concept is designed and simulated by employing HOMER software. Hybrid PV-hydrogen and PV-hydrogenbattery are the two different scenarios that are carried out and compared based on their both technical as well as financial standpoints. The simulation results are evident that the hybrid PV- hydrogen-battery energy system has much more financial and economic benefits as compared with the PV-hydrogen energy system. Moreover it is also seen that costs of energy from earlier from hybrid PV-hydrogen-battery is more appealing i.e. 0.358 $/kWh from 0.412 $/kWh cost of energy from hybrid PV-hydrogen. The power produced by the hybrid PV- hydrogen - battery energy for the daily load demand of 1700 kWh /day consists of two powers produced independently by the PV and fuel cells of 87.4 % and 12.6 % respectively.

Disposing of plastic waste through burial or burning leads to air pollution issues while also contributing to gas emissions and plastic waste spreading underground into seas via springs. Henceforth this research aims at reducing plastic waste volume while simultaneously generating clean energy. Hydrogen energy is a promising fuel source that holds great value for humanity. However achieving clean hydrogen energy poses challenges including high costs and complex production processes especially on a national scale. This research focuses on Iran as a country capable of producing this energy examining the production process along with related challenges and the general supply chain. These challenges encompass selecting appropriate raw materials based on chosen technologies factory capacities storage methods and transportation flow among different provinces of the country. To deal with these challenges a mixed-integer linear programming model is developed to optimize the hydrogen supply chain and make optimal decisions about the mentioned problems. The supply chain model estimates an average cost—IRR 4 million (approximately USD 8)—per kilogram of hydrogen energy that is available in syngas during the initial period; however subsequent periods may see costs decrease to IRR 1 million (approximately USD 2) factoring in return-on-investment rates.

The study examines the methods for producing hydrogen using solar energy as a catalyst. The two commonly recognised categories of processes are direct and indirect. Due to the indirect processes low efficiency excessive heat dissipation and dearth of readily available heat-resistant materials they are ranked lower than the direct procedures despite the direct procedures superior thermal performance. Electrolysis bio photosynthesis and thermoelectric photodegradation are a few examples of indirect approaches. It appears that indirect approaches have certain advantages. The heterogeneous photocatalytic process minimises the quantity of emissions released into the environment; thermochemical reactions stand out for having low energy requirements due to the high temperatures generated; and electrolysis is efficient while having very little pollution created. Electrolysis has the highest exergy and energy efficiency when compared to other methods of creating hydrogen according to the evaluation.

Based on the global efforts to reduce fossil fuel dependence and its environmental concerns green hydrogen has been considered a promising pathway towards sustainable energy transition. Iran is considered a promising location for green hydrogen production due to its considerable solar energy potential. While global interest in green hydrogen continues to grow studies that explore the techno-economic feasibility of small-scale solar-based green hydrogen systems tailored to Iran’s diverse climatic conditions are still relatively limited. This study aims to assess the technical and economic feasibility of small-scale green hydrogen production based on solar energy (photovoltaics) in six cities of Iran including Isfahan Kerman Kermanshah Shiraz Tehran and Zahedan by examining whether such systems can be financially viable despite their relatively high unit costs. The study employs TRNSYS for dynamic simulation of the hydrogen production system and RETScreen for economic analysis. The results indicate that the system has an annual energy production capacity ranging from 831.52 to 1062.22 MWh across the studied locations. The system's hydrogen production rate was between 16800 and 21114 kg/year. Based on the results the lowest levelized cost of hydrogen (LCOH) was recorded in Shiraz at $6.43/kg H₂ while Tehran experienced the highest value ($8.81/kg H₂). Among the evaluated cities Shiraz demonstrated the most favorable financial performance with an internal rate of return (IRR) of 18.5% and a payback period of 8 years. These findings can be useful for policymakers in Iran and the MENA region in investment planning related to the clean energy transition.

This study explores the design and feasibility of a novel fuel cell-powered wind farm for residential electricity hydrogen/oxygen production and cooling/heating via a compression chiller. Wind turbine energy powers Proton Exchange Membrane (PEM) electrolyzers and a compression chiller unit. The proposed system was modeled using EES thermodynamic software and its economic viability was assessed. A case study across seven Italian regions with varying wind potentials evaluated the system’s feasibility in diverse weather conditions. Multi-objective optimization using Response Surface Methodology (RSM) determined the number of wind turbines as optimum number of electrolyzers & fuel cell units. Optimization results indicated that 37 wind turbines 1 fuel cell unit and 2 electrolyzer units yielded an exergy efficiency of 27.98 % and a cost rate of 619.9 $/h. TOPSIS analysis suggested 32 wind turbines 2 electrolyzers and 2 reverse osmosis units as an alternative configuration. Further twelve different scenarios were examined to enhance the distribution of wind farmgenerated electricity among the grid electrolyzers and reverse osmosis systems. revealing that directing 25 % to reverse osmosis 20 % to electrolyzers and 55 % to grid sales was optimal. Performance analysis across seven Italian cities (Turin Bologna Florence Palermo Genoa Milan and Rome) identified Genoa Palermo and Bologna as the most suitable locations due to favorable wind conditions. Implementing the system in Genoa the optimal site could produce 28435 MWh of electricity annually prevent 5801 tons of CO2 emissions (equivalent to 139218 $). Moreover selling this clean electricity to the grid could meet the annual clean electricity needs of approximately 5770 people in Italy

This study presents a comprehensive optimization of a tri-generation system that integrates dual geothermal wells Liquefied Natural Gas (LNG) cold energy recovery and hydrogen production using an advanced Response Surface Methodology (RSM) approach. The system combines two geothermal wells with different temperature profiles power generation via an Organic Rankine Cycle (ORC) and hydrogen production through a Proton Exchange Membrane (PEM) electrolyzer enhanced by integrated LNG regasification for improved energy recovery. The primary novelty of this work lies in the first application of RSM for multi-objective optimization of geothermal-based tri-generation systems moving beyond the conventional single-objective approaches. A 40-run experimental design is employed to simultaneously optimize three critical performance indicators: exergy efficiency power-specific cost and hydrogen production rate considering six key operating parameters. The RSM framework enables systematic exploration of parameter interactions and delivers statistically validated predictive models offering a robust and computationally efficient optimization strategy. The optimized system achieves outstanding performance with an exergy efficiency of 44.60% a competitive power-specific cost of 19.70 $/GJ and a hydrogen production rate of 5.15 kg/hr. Comparative analysis against prior studies confirms the superiority of the RSM-based approach demonstrating a 1% improvement in exergy efficiency (44.60% vs. 44.16%) a significant 44.1% increase in hydrogen production rate (5.15 kg/hr vs. 3.575 kg/hr) and a 0.81% reduction in power-specific cost compared to genetic algorithm-based optimization.

This study discusses energy management in thermal and electrical microgrids while taking heat pumps renewable sources thermal and hydrogen storages into account. The weighted total of the operating cost grid emissions level voltage and temperature deviation function and other factors makes up the objective function of the suggested method. The restrictions include the operationflexibility model of resources and storages micro-grid flexibility limits and optimum power flow equations. Point Estimation Method is used in this work to simulate load energy price and renewable phenomenon uncertainty. A fuzzy decision-making methodology is used to arrive at a compromise solution that satisfies network operators’ operational environmental and financial goals. The innovations of this paper include energy management of various smart microgrids simultaneous modeling of several indicators especially flexibility investigation of optimal performance of resources and storage devices and modeling of uncertainty considering low computational time and an accurate flexibility model. Numerical findings indicate that the fuzzy decision-making approach has the capability to reach a compromise point in which the objective functions approach their minimum values. The integration of the proposed uncertainty modeling with precise flexibility modeling results in a reduction in computational time when compared to stochastic optimization based on scenarios. For the compromise point and uncertainty modeling with PEM by efficiently managing resources and thermal and hydrogen storages scheme is capable of attaining high flexibility conditions. Compared to load flow studies the approach can enhance the operational environmental and economic conditions of smart microgrids by approximately 33–57% 68% and 33–68% respectively under these circumstances.

Achieving global net-zero emissions by 2050 demands integrated and scalable strategies that unite decarbonization technologies across sectors. This review provides a forwardlooking synthesis of carbon capture and storage and hydrogen systems emphasizing their integration through artificial intelligence to enhance operational efficiency reduce system costs and accelerate large-scale deployment. While CCS can mitigate up to 95% of industrial CO2 emissions and hydrogen particularly blue hydrogen offers a versatile low-carbon energy carrier their co-deployment unlocks synergies in infrastructure storage and operational management. Artificial intelligence plays a transformative role in this integration enabling predictive modeling anomaly detection and intelligent control across capture transport and storage networks. Drawing on global case studies (e.g. Petra Nova Northern Lights Fukushima FH2R and H21 North of England) and emerging policy frameworks this study identifies key benefits technical and regulatory challenges and innovation trends. A novel contribution of this review lies in its AI-focused roadmap for integrating CCS and hydrogen systems supported by a detailed analysis of implementation barriers and policy-enabling strategies. By reimagining energy systems through digital optimization and infrastructure synergy this review outlines a resilient blueprint for the transition to a sustainable low-carbon future.

Hydrogen storage is integral to reducing CO2 emissions particularly in the oil and gas industry. However a primary challenge involves the solubility of hydrogen in subsurface environments particularly saline aquifers. The dissolution of hydrogen in saline water can impact the efficiency and stability of storage reservoirs necessitating detailed studies of fluid dynamics in such settings. Beyond its role as a clean energy carrier and precursor for synthetic fuels and chemicals understanding hydrogen’s solubility in subsurface conditions can significantly enhance storage technologies. When hydrogen solubility is high it can reduce reservoir pressure and alter the chemical composition of the storage medium undermining process efficiency. Machine learning techniques have gained prominence in predicting physical and chemical properties across various systems. One of the most complex challenges in hydrogen storage is predicting its solubility in saline water influenced by factors such as pressure temperature and salinity. Machine learning models offer substantial promise in improving hydrogen storage by identifying intricate nonlinear relationships among these parameters. This study uses machine learning algorithms to predict hydrogen solubility in saline aquifers employing techniques such as Bayesian inference linear regression random forest artificial neural networks (ANN) support vector machines (SVM) and least squares boosting (LSBoost). Trained on experimental data and numerical simulations these models provide precise predictions of hydrogen solubility which is strongly influenced by pressure temperature and salinity under a wide range of thermodynamic conditions. Among these methods RF outperformed the others achieving an R2 of 0.9810 for test data and 0.9915 for training data with RMSE values of 0.048 and 0.032 respectively. These findings emphasize the potential of machine learning to significantly optimize hydrogen storage and reservoir management in saline aquifers.

Hydrogen embrittlement (HE) threatens the structural integrity of industrial components exposed to hydrogenrich environments. This review critically explores hydrogen barrier coatings (HBCs) polymeric metallic ceramic and composite their application and assessment focusing on measured effectiveness in limiting hydrogen permeation and hydrogen embrittlement. Also coating application methods and permeation assessment techniques are evaluated. Recent advances in nanostructured and hybrid coatings are emphasized highlighting the pressing need for durable scalable and environmentally sustainable hydrogen barrier coatings to ensure the reliability of emerging hydrogen-based energy solutions. This comprehensive critical review further distinguishes itself by linking coating deposition methods to defect-driven transport behaviour critically assessing permeation test approaches. It also highlights the emerging role of polymeric and hybrid multilayer coatings with direct implications for advanced and reliable hydrogen production storage and transport infrastructure.