China, People’s Republic

This review aims to summarize the recent advancements and prevailing challenges within the realm of hydrogen storage and transportation thereby providing guidance and impetus for future research and practical applications in this domain. Through a systematic selection and analysis of the latest literature this study highlights the strengths limitations and technological progress of various hydrogen storage methods including compressed gaseous hydrogen cryogenic liquid hydrogen organic liquid hydrogen and solid material hydrogen storage as well as the feasibility efficiency and infrastructure requirements of different transportation modes such as pipeline road and seaborne transportation. The findings reveal that challenges such as low storage density high costs and inadequate infrastructure persist despite progress in high-pressure storage and cryogenic liquefaction. This review also underscores the potential of emerging technologies and innovative concepts including metal–organic frameworks nanomaterials and underground storage along with the potential synergies with renewable energy integration and hydrogen production facilities. In conclusion interdisciplinary collaboration policy support and ongoing research are essential in harnessing hydrogen’s full potential as a clean energy carrier. This review concludes that research in hydrogen storage and transportation is vital to global energy transformation and climate change mitigation.

Low-cost alkaline water electrolysis from renewable energy sources (RESs) is suitable for large-scale hydrogen production. However fluctuating RESs lead to poor performance of alkaline water electrolyzers (AWEs) at low loads. Here we explore two urgent performance issues: inefficiency and inconsistency. Through detailed operation process analysis of AWEs and the established equivalent electrical model we reveal the mechanisms of inefficiency and inconsistency of low-load AWEs are related to the physical structure and electrical characteristics. Furthermore we propose a multi-mode self-optimization electrolysis converting strategy to improve the efficiency and consistency of AWEs. In particular compared to a conventional dc power supply we demonstrate using a lab-scale and large-scale commercially available AWE that the maximum efficiency can be doubled while the operation range of the electrolyzer can be extended from 30–100% to 10–100% of rated load. Our method can be easily generalized and can facilitate hydrogen production from RESs.

Sorption-enhanced steam reforming (SorESR) is an advanced thermochemical process integrating in-situ CO2 capture via solid sorbents to significantly enhance hydrogen production and purity. By coupling CO2 adsorption with steam reforming SorESR shifts the reaction equilibrium toward increased H₂ yield surpassing the limitations of conventional steam reforming (SR). The efficacy of SorESR critically depends on the physicochemical properties of the solid CO2 sorbents employed. This review critically evaluates widely studied sorbents including Ca-based Mg-based hydrotalcite-like and alkali ceramic sorbents focusing on their CO2 capture capacity reaction kinetics thermal stability and cyclic durability under SR conditions. Furthermore recent progress in multifunctional sorbent-catalysts that synergistically facilitate catalytic steam reforming alongside CO2 sorption is critically discussed. Moreover the review summarises recent performance achievements and proposes strategies to improve sorbent capacity and reaction kinetics thereby making the SorESR process more appealing for commercial applications. Large-scale SorESR implementation is expected to substantially increase hydrogen production efficiency while concurrently reducing CO2 emissions and advancing sustainable energy technologies. This review offers novel insights into the development of advanced sorbent-catalyst systems and provides new strategies for enhancing SorESR efficiency and scalability for commercial H2 Production.

The inherent randomness and intermittency of offshore renewable energy sources such as wind and solar power pose significant challenges to the stable and secure operation of the power grid. These fluctuations directly affect the performance of grid-connected systems particularly in terms of harmonic distortion and load response. This paper addresses these challenges by proposing a novel harmonic control strategy and load response optimization approach. An integrated three-winding transformer filter is designed to mitigate high-frequency harmonics and a control strategy based on converter-side current feedback is implemented to enhance system stability. Furthermore a hybrid PI-VPI control scheme combined with feedback filtering is employed to improve the system’s transient recovery capability under fluctuating load and generation conditions. Experimental results demonstrate that the proposed control algorithm based on a transformer-oriented model effectively suppresses low-order harmonic currents. In addition the system exhibits strong anti-interference performance during sudden voltage and power variations providing a reliable foundation for the modulation and optimization of offshore wind–solar coupled hydrogen production power supply systems.

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources including remote-controlled switches (RCSs) mobile hydrogen emergency resources (MHERs) and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages including prevention fault isolation and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events.

Currently there are many gaps in the research on the safety of hydrogen-powered trains and the hazardous points vary across different scenarios. It is necessary to conduct safety analysis for various scenarios in order to develop effective accident response strategies. Considering the implementation of hydrogen power in the rail transport sector this paper reviews the development status of hydrogen-powered trains and the hydrogen leak hazard chain. Based on the literature and industry data a thorough analysis is conducted on the challenges faced by hydrogen-powered trains in the scenario of electrified railways tunnels train stations hydrogen refueling stations and garages. Existing railway facilities are not ready to deal with accidental hydrogen leakage and the promotion of hydrogen-powered trains needs to be cautious.

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.

Hydrogen internal combustion engines are pivotal components of the power industry for achieving zero-carbon emissions. However the development of hydrogen engines is still in its infancy and experimental research on their injection strategies lacks systematization. In this study the individual impacts of hydrogen injection pressure (within low-pressure ranges) and injection timing as well as their coupling effects on combustion characteristics engine efficiency and exhaust emissions were experimentally investigated. Results show that under fixed timing an injection pressure of 25–27.5 bar yields the highest and earliest peak in-cylinder pressures whereas at 15 bar the ignition delay increases to 14.7°CA the flame development duration extends to 8.57°CA and the late combustion duration shortens to 41.37°CA; the exhaust gas temperature peaks at 628 K at 20 bar and NOX peaks at 537 ppm at 25 bar. BTE (brake thermal efficiency) exhibits a U-shaped relationship with pressure with the minimum efficiency occurring near 25 bar when timing is held constant; advancing start of injection from 130° BTDC to 170° BTDC reduces both NOX and exhaust gas temperature with the optimal fuel economy at 140° BTDC and a peak in-cylinder pressure that is approximately 7 % higher and occurs 2–3°CA earlier at 130–140° BTDC. In the pressure–timing maps IMEP (indicated mean effective pressure) is maximized at 30 bar and 90° BTDC; BTE reaches 33.5 % at 25 bar and 100° BTDC; NOX attains a minimum at 25 bar and 110° BTDC while the exhaust gas temperature is lowest at 25 bar and 120° BTDC. Injection pressure is the primary lever for regulating fuel economy and emissions while injection timing mainly adjusts combustion phasing and IMEP. The results provide clear guidance for calibrating low-pressure hydrogen injection systems supply benchmark data for model validation and support the development of practical control strategies for hydrogen engines.

Solid oxide electrolysis cells (SOECs) represent a promising technology because they have the potential to achieve greater efficiency than existing electrolysis methods making them a strong candidate for sustainable hydrogen production. SOECs utilize a solid oxide electrolyte which facilitates the migration of oxygen ions while maintaining gas impermeability at temperatures between 600 ◦C and 900 ◦C. This review provides an overview of the recent advancements in research and development at the intersection of machine learning and SOECs technology. It emphasizes how data-driven methods can improve performance prediction facilitate material discovery and enhance operational efficiency with a particular focus on materials for cathode-supported cells. This paper also addresses the challenges associated with implementing machine learning for SOECs such as data scarcity and the need for robust validation techniques. This paper aims to address challenges related to material degradation and the intricate electrochemical behaviors observed in SOECs. It provides a description of the reactions that may be involved in the degradation mechanisms taking into account thermodynamic and kinetic factors. This information is utilized to construct a fault tree which helps categorize various faults and enhances understanding of the relationship between their causes and symptoms.

Model predictive control (MPC) requires high accuracy of the model. However the actual power system has complex dynamic characteristics. There must be unmodeled dynamics in the system modeling process which makes it difficult for MPC to perform the function of optimal control. ESO has the ability to observe and suppress errors combining the both can solve this problem. Thus this paper proposes a coordinated control strategy of hydrogen-energy storage system based on disturbance-rejection model predictive controller. Firstly this paper constructs the state-space model of the system and improves MPC. By connecting ESO and MPC in series this paper designs a matched disturbance-rejection model predictive controller and analyzes the robustness of the research system. Finally this paper verifies the effectiveness and feasibility of the disturbance-rejection model predictive controller under various working conditions. Compared with the method using only MPC the dynamic response time of the system frequency regulation under the proposed strategy in this paper is increased by about 29.9 % and the frequency drop rate is slowed down by 13.5 %. In addition under the AGC command and continuous load disturbance working conditions the maximum frequency deviation of the system under the proposed strategy is reduced by about 54.01 % and 48.96 %. The results clearly show that the proposed strategy in this paper significantly improves the dynamic response ability of the system and reduces the frequency fluctuation of the system after disturbance.