Publications

This paper introduces a novel dual-purpose transmission system that integrates power transmission and energy storage using hydrogen ammonia and compressed air—an area largely unexplored in the literature. Unlike conventional cable transmission which requires separate storage infrastructure the proposed approach leverages the transmission medium itself as an energy storage solution enhancing system efficiency and reducing costs. By incorporating a defined storage allocation factor this study examines the delivery of offshore-generated power to onshore locations calculating the necessary media flow rates and evaluating the required transportation infrastructure including tunnels and pipelines. A comparative cost-effectiveness analysis is conducted to determine optimal conditions under which storage-integrated transmission outperforms conventional cable transmission. Various transmission powers storage fractions pressures and distances are analysed to assess feasibility and economic viability. The findings indicate that for a 75 % storage allocation factor compressed air can transmit up to 450 MW over 300 km more cost-effectively than cables while hydrogen enables 230 MW transmission beyond 310 km. Ammonia proves to be the most efficient facilitating the transmission of over 2000 MW across distances exceeding 140 km at a lower cost than cables all without requiring onshore storage. Moreover for a 500-km transmission line compressed air hydrogen and ammonia can store the equivalent of 62 58 and 152 h of wind farm output respectively significantly reducing the need for additional onshore storage. This study fills a critical research gap by optimizing offshore wind power delivery through an innovative cost-effective and scalable transmission and storage approach.

This study focuses on the power systems of inland waterway vessels in Chinese Yangtze River systematically outlining the low-carbon technology pathways for different power system types. A comparative analysis is conducted on the technical feasibility emission reduction potential and economic viability of LNG methanol ammonia pure electric and hybrid power systems revealing the bottlenecks hindering the large-scale application of each system. Key findings indicate that: (1) LNG and methanol fuels offer significant short-term emission reductions in internal combustion engine power systems yet face constraints from methane slip and insufficient green methanol production capacity respectively; (2) ammonia enables zero-carbon operations but requires breakthroughs in combustion stability and synergistic control of NOX; (3) electric vessels show high decarbonization potential but battery energy density limits their range while PEMFC lifespan constraints and SOFC thermal management deficiencies impede commercialization; (4) hybrid/range-extended power systems with superior energy efficiency and lower retrofitting costs serve as transitional solutions for existing vessels though challenged by inadequate energy management strategies and multi-equipment communication protocol interoperability. A phased transition pathway is proposed: LNG/methanol engines and hybrid systems dominate during 2025–2030; ammonia-powered systems and solid-state batteries scale during 2030–2035; post-2035 operations achieve zero-carbon shipping via green hydrogen/ammonia.

To reduce fossil fuel dependency in shipping adopting alternative fuels and innovative propulsion systems is essential. Solid Oxide Fuel Cells (SOFC) powered by hydrogen carriers represent a promising solution. This study investigates a multi-fuel SOFC system for ocean-going vessels capable of operating with ammonia methanol or hydrogen thus enhancing bunkering flexibility. A thermodynamic model is developed to simulate the performance of a 3 kW small-scale system subsequently scaling up to a 10 MW configuration to meet the power demand of a container ship used as the case study. Results show that methanol is the most efficient fueling option reaching a system efficiency of 58% while ammonia and hydrogen reach slightly lower values of about 55% and 51% respectively due to higher auxiliary power consumption. To assess technical feasibility two installation scenarios are considered for accommodating multiple fuel tanks. The first scenario seeks the optimal fuel share equivalent to the diesel tank’s chemical energy (17.6 GWh) minimizing mass increase. The second scenario optimizes the fuel share within the available tank volume (1646 m3 ) again minimizing mass penalties. In both cases feasibility results have highlighted that changes are needed in terms of cargo reduction equal to 20.3% or alternatively in terms of lower autonomy with an increase in refueling stops. These issues can be mitigated by the benefits of increased bunkering flexibility

Green hydrogen is likely to play a major role in decarbonising the aviation industry. It is crucial to understand the effects of microstructure on hydrogen redistribution which may be implicated in the embrittlement of candidate fuel system metals. We have developed a multiscale finite element modelling framework that integrates micromechanical and hydrogen transport models such that the dominant microstructural effects can be efficiently accounted for at millimetre length scales. Our results show that microstructure has a significant effect on hydrogen localisation in elastically anisotropic materials which exhibit an interesting interplay between microstructure and millimetre-scale hydrogen redistribution at various loading rates. Considering 316L stainless steel and nickel a direct comparison of model predictions against experimental hydrogen embrittlement data reveals that the reported sensitivity to loading rate may be strongly linked with rate-dependent grain scale diffusion. These findings highlight the need to incorporate microstructural characteristics in hydrogen embrittlement models.

Thermal energy storage (TES) technologies are emerging as key enablers of sustainable energy systems by providing flexibility and efficiency in managing thermal resources across diverse applications. This review comprehensively examines the latest advancements in TES mechanisms materials and structural designs including sensible heat latent heat and thermochemical storage systems. Recent innovations in nano-enhanced phase change materials (PCMs) hybrid TES configurations and intelligent system integration are highlighted. The role of advanced computational methods such as digital twins and AI-based optimization in enhancing TES performance is also explored. Applications in renewable energy systems industrial processes district heating networks and green hydrogen production are discussed along with associated challenges and future research directions. This review aims to synthesize current knowledge while identifying pathways for accelerating the development and practical deployment of next-generation TES technologies.

With the increasing adoption of renewable energy sources such as solar and wind power it is essential to achieve carbon neutrality. However several shortcomings including their intermittence pose significant challenges to the stability of the electrical grid. This study explores hydrogen-based technologies such as fuel cells and water electrolysis systems as an effective solution to improve frequency stability and address the problems of power grid reliability. Using power system analysis programs modeling and simulations performed on IEEE-25 Bus and Jeju Island systems demonstrate the potential of these technologies to mitigate reductions reduce transmission constraints and stabilize frequencies. The results show that hydrogen-based systems are important factors enabling sustainable energy transition.

Hydrogen energy one of the renewable energy sources plays a crucial role in combating climate change since its usage aims to reduce carbon emissions and enhance energy security. As the global energy trend moves toward cleaner alternatives countries start to adapt their energy strategies. In this transition hydrogen is one of the energy sources with the potential to increase long-term energy security. Developing countries face challenges such as high energy import dependency rising industrial demand and the need for infrastructure modernization making hydrogen valleys one of the viable solutions since they integrate hydrogen production storage distribution and utilization at one facility. However establishing small-scale hydrogen valleys requires a comprehensive decision-making strategy consisting of technical financial environmental social and political factors while addressing uncertainties in the system. To systematically manage the process this study proposes a Z-numberbased fuzzy cognitive mapping approach which models the interdependencies among success factors supported by Z-number Decision-Making Trial and Evaluation Laboratory for structured prioritization with a multi-expert perspective. The results indicate that Financial Factors emerged as the most critical category with Government Incentives Infrastructure Investment Cost and Land Acquisition Cost ranking as the top three sub-success factors. Availability of Skilled Workforce and Regional Energy Supply followed in importance which demonstrates the importance of social and technical dimensions in the hydrogen valley development. These findings demonstrate the critical role of policy support infrastructure readiness and workforce availability in the design process. Sensitivity analyses are also conducted to present robustness of the given decisions for the analysis of the results. Based on the results and analyses possible implications based on the policy and practical dimensions are also discussed. By integrating fuzzy logic and Z-numbers the study aims to minimize loss of information enhances the analytical background for decision-making and provides a strategic roadmap for hydrogen valley development.

Against the backdrop of the global transition towards clean energy deep-sea wind-power hydrogen production integrates offshore wind with green hydrogen technology. Addressing the technical coupling complexity and the impact of uncertain hydrogen prices this paper develops a capacity optimization model. The model incorporates floating wind turbine output the technical distinctions between alkaline (ALK) electrolyzers and proton exchange membrane (PEM) electrolyzers and the synergy with energy storage. Under three hydrogen price scenarios the results demonstrate that as the price increases from 26 CNY/kg to 30 CNY/kg the optimal ALK capacity decreases from 2.92 MW to 0.29 MW while the PEM capacity increases from 3.51 MW to 5.51 MW. Correspondingly the system’s Net Present Value (NPV) exhibits an upward trend. To address the limitations of traditional methods in handling multi-dimensional benefit correlations and information ambiguity a comprehensive benefit evaluation framework encompassing economic technical environmental and social synergies was constructed. Sensitivity analysis indicates that the comprehensive benefit level falls within a relatively high-efficiency interval. The numerical characteristics an entropy value of 3.29 and a hyper-entropy of 0.85 demonstrate compact result distribution and robust stability validating the applicability and stability of the proposed offshore wind–hydrogen benefit assessment model.

Under the dual pressures of global climate change and energy structure transition the offshore wind–solar–current energy coupling hydrogen production (OCWPHP) system has emerged as a promising integrated energy solution. However its complex multi-energy structure and harsh marine environment introduce systemic risks that are challenging to assess comprehensively using traditional methods. To address this we develop a novel risk assessment framework based on hesitant fuzzy sets (HFS) establishing a multidimensional risk criteria system covering economic technical social political and environmental aspects. A hybrid weighting method integrating AHP entropy weighting and consensus adjustment is proposed to determine expert weights while minimizing risk information loss. Two aggregation operators—AHFOWA and AHFOWG—are applied to enhance uncertainty modeling. A case study of an OCWPHP project in the East China Sea is conducted with the overall risk level assessed as “Medium.” Comparative analysis with the classical Cumulative Prospect Theory (CPT) method shows that our approach yields a risk value of 0.4764 closely aligning with the CPT result of 0.4745 thereby confirming the feasibility and credibility of the proposed framework. This study provides both theoretical support and practical guidance for early-stage risk assessment of OCWPHP projects.

This review provides a comprehensive examination of artificial intelligence methods applied to the design optimization and performance prediction of composite-based hydrogen storage vessels with a focus on composite overwrapped pressure vessels. Targeted at researchers engineers and industrial stakeholders in materials science mechanical engineering and renewable energy sectors the paper aims to bridge traditional mechanical modeling with evolving AI tools while emphasizing alignment with standardization and certification requirements to enhance safety efficiency and lifecycle integration in hydrogen infrastructure. The review begins by introducing HSV types their material compositions and key design challenges including high-pressure durability weight reduction hydrogen embrittlement leakage prevention and environmental sustainability. It then analyzes conventional approaches such as finite element analysis multiscale modeling and experimental testing which effectively address aspects like failure modes fracture strength liner damage dome thickness winding angle effects crash behavior crack propagation charging/discharging dynamics burst pressure durability reliability and fatigue life. On the other hand it has been shown that to optimize and predict the characteristics of hydrogen storage vessels it is necessary to combine the conventional methods with artificial intelligence methods as conventional methods often fall short in multi-objective optimization and rapid predictive analytics due to computational intensity and limitations in handling uncertainty or complex datasets. To overcome these gaps the paper evaluates hybrid frameworks that integrate traditional techniques with AI including machine learning deep learning artificial neural networks evolutionary algorithms and fuzzy logic. Recent studies demonstrate AI’s efficacy in failure prediction design optimization to mitigate structural risks structural health monitoring material property evaluation burst pressure forecasting crack detection composite lay-up arrangement weight minimization material distribution enhancement metal foam ratio optimization and optimal material selection. By synthesizing these advancements this work underscores AI’s potential to accelerate development reduce costs and improve HSV performance while advocating for physics-informed models robust datasets and regulatory alignment to facilitate industrial adoption.