Applications & Pathways

Research on and the demand for hydrogen-powered vehicles have grown significantly over the past two decades as a solution for sustainable transportation. Bibliometric analysis helps to assess research trends key contributions and the impact of studies focused on the safety and reliability of hydrogen-powered vehicles. This study provides a novel methodology for bibliometric analysis that systematically evaluates the global research landscape on hydrogen-powered vehicle reliability using Scopus-indexed publication data (1965 to 2024). Eighteen key parameters were identified for this study that are often used by researchers for the bibliometric analysis of hydrogen-related studies. Data analytics VOSviewer-based visualization and research impact indicators were integrated to comprehensively assess publication trends key contributors and citation networks. The analysis revealed that hydrogen-powered vehicle reliability research has experienced significant growth over the past two decades with leading contributions from high-impact journals renowned institutions and influential authors. The present study emphasizes the significance of greater funding as well as open-access distribution. Furthermore while major worldwide institutions have significant institutional relationships there are gaps in real-world hydrogen infrastructure evaluations large-scale experimental validation and policy-driven research.

Ocean islands possess abundant renewable energy resources providing favorable conditions for developing offshore clean energy microgrids. However geographical isolation poses significant challenges for direct energy transfer between islands. Recent electrolysis and hydrogen storage technology advancements have created new opportunities for distributed energy utilization in these remote areas. This paper presents a low-carbon economic dispatch strategy designed explicitly for distant oceanic islands incorporating energy self-sufficiency rates and seasonal hydrogen storage (SHS). We propose a power supply model for offshore islands considering hydrogen production from offshore wind power. The proposed model minimizes operational and carbon emission costs while maximizing energy self-sufficiency. It considers the operational constraints of the island’s energy system the offshore transportation network the hydrogen storage infrastructure and the electricityhydrogen-transportation coupling of hydrogen storage (HS) and seasonal hydrogen storage (SHS) services. To optimize the dispatch process this study employs an improved Grey Wolf Optimizer (IGWO) combined with the Differential Evolution method to enhance population diversity and refine the position updating mechanism. Simulation results demonstrate that integrating HS and SHS effectively enhances energy self-sufficiency and reduces carbon emissions. For instance hydrogenation costs decreased by 21.4% after optimization and the peak-valley difference was reduced by 16%. These findings validate the feasibility and effectiveness of the proposed approach.

Most remote and northern communities rely on diesel for their electrical and thermal energy needs. Communities and governments are working toward diesel exit strategies but the role of hydrogen technologies has not been explored. These could serve both electrical and thermal demand reduce emissions and enhance energy security and community ownership. Here we determine the installed capacities costs hydrogen storage needs and water resource requirements of hydrogen microgrids across a large diverse sample of communities. We also compare the cost of hydrogen microgrids to that of diesel microgrids. Our results optimize resource deployment demonstrate how sub-components must operate to serve both demand types and yield insights on storage and resource needs. We find that hydrogen microgrids are cheaper in levelized cost terms than diesel systems in 28 of 37 communities investigated; if wind power capital costs escalate to CAD 20000/kW as recently seen in one project only 3 of the 37 communities net hydrogen microgrids that are cheaper than diesel variants. Hydrogen storage plays a large role in maintaining reliability and reducing cost—both it and water needs are modest. The former can be met with current technologies.

The decarbonization of remote energy systems presents both technical and economic challenges due to their dependance on fossil fuels and the variability of renewable energy sources. This study introduces a Two-Stage Stochastic Programming approach to optimize Hybrid Renewable Energy Systems under uncertainty in renewable energy production. The methodology is applied to the island of Pantelleria aiming to minimize Total Annualized Costs and CO2 emissions using an ε-constraint approach. Results show that within the set of optimized configurations stricter CO2 emissions constraints increase costs due to the need for oversized components to ensure supply reliability. Nevertheless even the zeroemissions scenario offers significant economic benefits compared to the current diesel-based system. Total Annualized Costs are reduced from 15.5 M€ to 8.10 M€ in the deterministic case and to 9.37 M€ in the stochastic one. The additional cost in the stochastic configuration is offset by improved reliability ensuring demand is met under all scenarios. A sensitivity analysis on electricity demand reveals the necessity of further larger components leading to a 27.0% cost increase in a fully renewable scenario with stochastic optimization for a 10% demand increase. These findings highlight the importance of stochastic optimization in designing cost-effective off-grid renewable energy systems.

This study presents a novel multi-objective optimization framework supporting nations sustainability 2030–2040 visions by enhancing renewable energy integration green hydrogen production and emission reduction. The framework evaluates a range of energy storage technologies including battery pumped hydro compressed air energy storage and hybrid configurations under realistic system constraints using the IEEE 9-bus test system. Results show that without storage renewable penetration is limited to 28.65% with 1538 tCO2/day emissions whereas integrating pumped hydro with battery (PHB) enables 40% penetration cuts emissions by 40.5% and reduces total system cost to 570 k$/day (84% of the baseline cost). The framework’s scalability is confirmed via simulations on IEEE 30- 39- 57- and 118-bus systems with execution times ranging from 118.8 to 561.5 s using the HiGHS solver on a constrained Google Colab environment. These findings highlight PHB as the most cost-effective and sustainable storage solution for large-scale renewable integration.

The Net Zero Scenario driven by the imperative of carbon neutrality demands a major reduction in reliance on fossil fuel-based hydrogen production. Another challenge is hydrogen’s storage and transport due to its low volumetric energy density. These issues have elevated hydrogen carriers—particularly methanol—to a prominent position. Methanol’s favorable H/C ratio liquid state under ambient conditions and renewable production potential establish it as a compelling hydrogen carrier. Already essential in vehicle fuels and chemical production methanol’s role is poised to expand further. Among conversion routes methanol steam reforming (MSR) stands out for its high hydrogen yield and low CO production. This review outlines strategies for lowering the MSR reaction temperature enabling integration with proton exchange membrane fuel cells (PEMFC) and leveraging the thermal synergy between the two systems. The review highlights the critical roles of catalysts and reactor design in optimizing MSR–PEMFC integration. A detailed evaluation of Cu-based and group 8–10 metal catalysts provides insight into their suitability for PEMFC applications. Reactor configurations including conventional membrane and micro-channeled designs are assessed for their integration potential. Finally the review synthesizes these findings into design-oriented insights for optimizing MSR–PEMFC systems emphasizing catalyst selection reactor configuration and system-level integration offering practical pathways for implementation.

Developing new injection systems and combustion chambers for hydrogen is a central topic for the new generation of engines. In this effort simulations take a central role but methods developed for conventional hydrocarbons (methane kerosene) must be revisited for hydrogen. Validation then becomes an essential part and clean well documented experiments are needed to guaranty that computational fluid dynamics solvers are as predictive and accurate as expected. In this framework the HYLON case is a swirled hydrogen/air burner used by multiple groups worldwide to validate simulation methods for hydrogen combustion in configurations close to gas turbine burners with experimental data available through the TNF web site. The present study compares recent Raman spectroscopy and Particle Image Velocimetry measurements and Large Eddy Simulations (LES). The LES results are evaluated against a dataset comprising mean and RMS measurements of H2 N2 O2 H2O molar fractions temperature and velocity fields offering new insights into flame stabilization mechanisms. The simulations incorporate conjugate heat transfer to predict the combustor wall temperatures and are conducted for two atmospheric-pressure operating conditions each representing distinct combustion regimes diffusion and partially premixed. Novelty and significance statement Data on confined hydrogen flames in burner similar as industrial ones are limited. This work aims to fill this gap by performing multiple and simultaneous diagnostics on the swirled hydrogen-air flame called HYLON. For the first time in such a swirled configuration mean and RMS fields of temperature main species and velocities are compared to LES allowing new insight into the potential and limits of the models as well as the physics of these flames. These experimental results will be made available on TNF as over 30 research groups worldwide have expressed interest in using them.

The integration of renewable resources with advanced storage technologies is critical for sustainable energy systems. In this paper a planning framework for an energy hub incorporating hydrogen and renewable energy systems is developed with the objective of minimizing operational costs while participating in flexible ramping product (FRP) markets. The energy hub is designed to utilize a hybrid storage system comprising multi-type battery energy storage (BESS) accounting for diverse chemistries and degradation behaviors and hydrogen storage (HS) to meet concurrent electric and hydrogen demands. To address uncertainties in renewable generation and market prices a stochastic optimization model is developed to determine the optimal investment capacities while optimizing operational decisions under uncertainty using scenario-based stochastic programming. Financial risks associated with price and renewable variability are mitigated through the Conditional Value-at-Risk (CVaR) metric. Case studies demonstrate that hybrid storage systems including both BESS and HS can reduce total costs by 23.62% compared to single-storage configurations that rely solely on BESS. Based on the results BESS participates more in providing flexible ramp-up services while HS plays a major role in providing flexible ramp-down services. The results emphasize the critical role of co-optimized hydrogen and multi-type BESS in enhancing grid flexibility and economic viability.

Reducing CO2 emissions in general aviation is a critical challenge where battery electric and fuel cell technologies face limitations in energy density cost and robustness. As a result hydrogen (H2) dual-fuel combustion is a promising alternative but its practical implementation is constrained by abnormal combustion phenomena such as knocking and pre-ignition which limit the achievable H2 energy share. In response to these challenges this paper focuses on strategies to mitigate these irregular combustion phenomena while effectively increasing the H2 energy share. Experimental evaluations were conducted on an engine test bench using a one-cylinder dual-fuel H2 kerosene (Jet A-1) engine utilizing two strategies including water injection (WI) and rising the air–fuel ratio (AFR) by increasing the boost pressure. Additionally crucial combustion characteristics and emissions are examined and discussed in detail contributing to a comprehensive understanding of the outcomes. The results indicate that these strategies notably increase the maximal possible hydrogen energy share with potential benefits for emissions reduction and efficiency improvement. Finally through the use of 0D/1D simulations this paper offers critical thermodynamic and efficiency loss analyses of the strategies enhancing the understanding of their overall impact.

Introduction: With the increasing demand for energy utilization efficiency and minimization of environmental carbon emissions in industrial parks optimizing the configuration and scheduling of integrated energy systems has become crucial. This study focuses on integrated energy systems with massive flexible load resources aiming to maximize energy utilization efficiency while reducing environmental impact. Methods: To model the uncertainties in wind and solar power outputs we employed three-parameter Weibull distribution models and Beta distribution models. Flexible loads were categorized into three types to match different electricity consumption patterns. Additionally an enhanced Kepler Optimization Algorithm (EKOA) was proposed incorporating chaos mapping and adaptive learning rate strategies to improve search scope convergence speed and solution efficiency. The effectiveness of the proposed optimization scheduling and configuration methods was validated through a case study of an industrial park located in a coastal area of southeastern China. Results: The results show that using three-parameter Weibull distribution models and Beta distribution models more accurately reflects the variations in actual wind speeds and solar irradiance levels achieving peak shaving and valley filling effects and enhancing renewable energy utilization. The EKOA algorithm significantly reduced curtailment rates of wind and solar power generation while achieving substantial economic benefits. Compared with other operation modes of hydrogen the daily average cost is reduced by 12.92% and external electricity purchases are reduced by an average of 20.2 MW h/day. Discussion: Although our approach shows potential in improving energy utilization efficiency and economic gains this paper only considered hydrogen energy for single-use pathways and did not account for the economic benefits from selling hydrogen in the market. Future research will further incorporate hydrogen demand response mechanisms and optimize the output of integrated energy systems from the perspective of spot markets. These findings provide valuable references for relevant engineering applications.