China, People’s Republic

China’s progress in decarbonizing its transportation particularly vehicle electrification is notable. However the economically effective pathways are underexplored. To find out how much cost is necessary for carbon neutrality for the light-duty vehicle (LDV) sector this study examines twenty decarbonization pathways combining the New Energy and Oil Consumption Credit model and the China-Fleet model. We find that the 2060 zero-greenhouse gas (GHG) emission goal for LDVs is achievable via electrification if the battery pack cost is under CNY483/kWh by 2050. However an extra of CNY8.86 trillion internal subsidies is needed under pessimistic battery cost scenarios (CNY759/kWh in 2050) to eliminate 246 million tonnes of CO2-eq by 2050 ensuring over 80% market penetration of battery electric vehicles (BEVs) in 2050. Moreover the promotion of fuel cell electric vehicles is synergy with BEVs to mitigate the carbon abatement difficulties decreasing up to 34% of the maximum marginal abatement internal investment.

Fuel-cell flying vehicles suffer from limited endurance while ammonia decomposed onboard to supply hydrogen offers a carbon-free high-density solution to extend flight missions. However the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end this paper introduces a novel optimization paradigm termed physics-informed gradient-enhanced multi-objective optimization (PIGEMO) to simultaneously optimize the ammonia decomposition unit (ADU) catalyst composition powertrain sizing and flight control parameters. The PI-GEMO framework leverages a physics-informed neural network (PINN) as a differentiable surrogate model which is trained not only on sparse simulation data but also on the governing differential equations of the system. This enables the use of analytical gradient information extracted from the trained PINN via automatic differentiation to intelligently guide the evolutionary search process. A comprehensive case study on a flying vehicle demonstrates that the PIGEMO framework not only discovers a superior set of Pareto-optimal solutions compared to traditional methods but also critically ensures the physical plausibility of the results.

Europe’s transition toward a low-carbon energy system relies on the deployment of hydrogen produced with minimized carbon emissions; however regulatory requirements increase system costs and create financial barriers. This study investigates the financial implications of enforcing European Commission rules for renewable hydrogen production from 2024 to 2048. Using a scenario-based modeling approach that draws on European power system investments in renewable energy the results show that immediate compliance leads to an additional cost of approximately eighty billion euros over twenty-four years corresponding to a 3.6 percent increase in total system costs. To address this investment gap the study employs a segmentation analysis of support mechanisms based on existing policies and market practices identifying seven categories that range from investment incentives and production subsidies to infrastructure and financial instruments. Among these hydrogen offtake support and infrastructure funding are identified as the most effective measures for reducing risk and enabling private investment. These findings provide strategic insights for policymakers seeking to align their regulatory ambitions with financially viable pathways for integrating renewable energy.

Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper proposes an operational optimisation and carbon reduction capability assessment framework for EH-ESs focusing on revealing their operational response mechanisms and emission reduction potential under multi-disturbance conditions. A comprehensive model encompassing an electrolyser (EL) a fuel cell (FC) hydrogen storage tanks and battery energy storage was constructed. Three optimisation objectives—cost minimisation carbon emission minimisation and energy loss minimisation—were introduced to systematically characterise the trade-offs between economic viability environmental performance and energy efficiency. Case study validation demonstrates the proposed model’s strong adaptability and robustness across varying output and load conditions. EL and FC efficiencies and costs emerge as critical bottlenecks influencing system carbon emissions and overall expenditure. Further analysis reveals that direct hydrogen utilisation outperforms the ‘electricity–hydrogen–electricity’ cycle in carbon reduction providing data support and methodological foundations for low-carbon optimisation and widespread adoption of electricity–hydrogen systems.

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.

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.