Iran, Islamic Republic of

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.