China, People’s Republic

Transitioning to renewable energy is vital to achieving decarbonization at the global level but energy storage is still a major challenge. This review discusses the role of energy storage in the energy transition and the blue economy focusing on technological development challenges and directions. Effective storage is vital for balancing intermittent renewable energy sources like wind solar and marine energy with the power grid. The development of battery technologies hydrogen storage pumped hydro storage and emerging technologies like sodium-ion and metal-air batteries is discussed for their potential for large-scale deployment. Shortages in critical raw materials environmental impact energy loss and costs are some of the challenges to large-scale deployment. The blue economy promises opportunities for offshore energy storage notably through ocean thermal energy conversion (OTEC) and compressed air energy storage (CAES). Moreover the capacity of datadriven optimization and artificial intelligence to enhance storage efficiency is discussed. Policy interventions and economic incentives are necessary to spur the development and deployment of sustainable energy storage technology. Education and workforce training are also important in cultivating future researchers engineers and policymakers with the ability to drive energy innovation. Merging sustainability training with an interdisciplinary approach can potentially establish an efficient workforce that is capable of addressing energy issues. Future work needs to focus on higher energy density efficiency recyclability and cost-effectiveness of the storage technologies without sacrificing their environmental sustainability. The study underlines the need for converging technological economic and educational approaches to enable a sustainable and resilient energy future.

Hydrogen fuel cell vessels represent a vital direction for green shipping but the risk of large-scale hydrogen leakage and diffusion in their enclosed compartments is particularly prominent. To enhance safety a simplified three-dimensional model of the deck-level cabins of the “Water-Go-Round” passenger ship was established using SolidWorks (2023) software. Based on a hydrogen leakage and diffusion model the effects of leakage location leakage aperture and initial ambient temperature on the diffusion patterns and distribution of hydrogen within the cabins were investigated using FLUENT software. The results show that leak location significantly affects diffusion direction with hydrogen leaking from the compartment ceiling diffusing horizontally much faster than from the floor. When leakage occurs at the compartment ceiling hydrogen can reach a maximum horizontal diffusion distance of up to 5.04 m within 540 s; the larger the leak aperture the faster the diffusion with a 10 mm aperture exhibiting a 40% larger diffusion range than a 6 mm aperture at 720 s. The study provides a theoretical basis for the safety design and risk prevention of hydrogen fuel cell vessels.

Amid escalating global climate crises and the urgent imperative to meet the Paris Agreement’s carbon neutrality targets the steel industry—a leading contributor to global greenhouse gas emissions—confronts unprecedented challenges in driving sustainable industrial transformation through innovative low-carbon steelmaking technologies. This paper examines decarbonization technologies across three stages (source process and end-of-pipe) for two dominant steel production routes: the long process (BF-BOF) and the short process (EAF). For the BF-BOF route carbon reduction at the source stage is achieved through high-proportion pellet charging in the blast furnace and high scrap ratio utilization; at the process stage carbon control is optimized via bottom-blowing O2-CO2-CaO composite injection in the converter; and at the end-of-pipe stage CO2 recycling and carbon capture are employed to achieve deep decarbonization. In contrast the EAF route establishes a low-carbon production system by relying on green and efficient electric arc furnaces and hydrogen-based shaft furnaces. At the source stage energy consumption is reduced through the use of green electricity and advanced equipment; during the process stage precision smelting is realized through intelligent control systems; and at the end-of-pipe stage a closed-loop is achieved by combining cascade waste heat recovery and steel slag resource utilization. Across both process routes hydrogen-based direct reduction and green power-driven EAF technology demonstrate significant emission reduction potential providing key technical support for the low-carbon transformation of the steel industry. Comparative analysis of industrial applications reveals varying emission reduction efficiencies economic viability and implementation challenges across different technical pathways. The study concludes that deep decarbonization of the steel industry requires coordinated policy incentives technological innovation and industrial chain collaboration. Accelerating large-scale adoption of low-carbon metallurgical technologies through these synergistic efforts will drive the global steel sector toward sustainable development goals. This study provides a systematic evaluation of current low-carbon steelmaking technologies and outlines practical implementation strategies contributing to the industry’s decarbonization efforts.

To address the collaborative optimization challenge in multi-microgrid systems with significant renewable energy integration this study presents a dual-layer optimization model incorporating power-hydrogen coupling. Firstly a hydrogen energy system coupling framework including photovoltaics storage batteries and electrolysis hydrogen production/fuel cells was constructed at the architecture level to realize the flexible conversion of multiple energy forms. From a modeling perspective the upper-layer optimization aims to minimize lifecycle costs by determining the optimal sizing of distributed PV systems battery storage hydrogen tanks fuel cells and electrolyzers within the microgrid. At the lower level a distributed optimization framework facilitates energy sharing (both electrical and hydrogen-based) across microgrids. This operational layer maximizes yearly system revenue while considering all energy transactions—both inter-microgrid and grid-to-microgrid exchanges. The resulting operational boundaries feed into the upper-layer capacity optimization with the optimal equipment configuration emerging from the iterative convergence of both layers. Finally the actual microgrid in a certain area is taken as an example to verify the effectiveness of the proposed method.

Hydrogen production from water electrolysis can not only reduce greenhouse gas emissions but also has abundant raw materials which is one of the ideal ways to produce hydrogen from new energy. The hydrogen production power supply is the core component of the new energy electrolytic water hydrogen production device and its characteristics have a significant impact on the efficiency and purity of hydrogen production and the service life of the electrolytic cell. In essence the DC/DC converter provides the large current required for hydrogen production. For the converter its input still needs the support of a DC power supply. Given the maturity and technical characteristics of new energy power generation integrating energy storage into offshore energy systems enables stable power supply. This configuration not only mitigates energy fluctuations from renewable sources but also further reduces electrolysis costs providing a feasible pathway for large-scale commercialization of green hydrogen production. First this paper performs a simulation analysis on the wind–solar hybrid energy storage power generation system to demonstrate that the wind–solar–storage system can provide stable power support. It places particular emphasis on the significance of hydrogen production power supply design—this focus stems primarily from the fact that electrolyzers impose specific requirements on high operating current levels and low current ripple which exert a direct impact on the electrolyzer’s service life hydrogen production efficiency and operational safety. To suppress the current ripple induced by high switching frequency and high output current traditional approaches typically involve increasing the output inductor. However this method substantially increases the volume and weight of the device reduces the rate of current change and ultimately results in a degradation of the system’s dynamic response performance. To this end this paper focuses on developing a virtual impedance control technology aiming to reduce the ripple amplitude while avoiding an increase in the filter inductor. Owing to constraints in current experimental conditions this research temporarily relies on simulation data. Specifically a programmable power supply is employed to simulate the voltage output of the wind–solar–storage hybrid system thereby bringing the simulation as close as possible to the actual operating conditions of the wind–solar–storage hydrogen production system. The experimental results demonstrate that the proposed method can effectively suppress the ripple amplitude maintain high operating efficiency and ultimately meet the expected research objectives. That makes it particularly suitable as a high-quality power supply for offshore hydrogen production systems that have strict requirements on volume and weight.

Hydrogen blending in natural gas pipelines facilitates renewable energy integration and cost-effective hydrogen transport. Due to hydrogen’s lower density and higher leakage potential compared to natural gas understanding hydrogen concentration distribution is critical. This study employs ANSYS Fluent 2022 R1 with a realizable k-ε model to analyze flow dynamics of hydrogen–methane mixtures in horizontal and undulating pipelines. The effects of hydrogen blending ratios pressure (3–8 MPa) and pipeline geometry were systematically investigated. Results indicate that in horizontal pipelines hydrogen concentrations stabilize near initial values across pressure variations with minimal deviation (maximum increase: 1.6%). In undulating pipelines increased span length of elevated sections reduces maximum hydrogen concentration while maintaining proximity (maximum increase: 0.65%) to initial levels under constant pressure. Monitoring points exhibit concentration fluctuations with changing pipeline parameters though no persistent stratification occurs. However increasing the undulating height elevation difference leads to an increase in the maximum hydrogen concentration at the top of the pipeline rising from 3.74% to 9.98%. The findings provide theoretical insights for safety assessments of hydrogen–natural gas co-transport and practical guidance for pipeline design optimization.

A simulation study was conducted on the hydrogen leakage diffusion process and influencing factors of fuel cell vehicles in enclosed spaces. The results indicate that when hydrogen leakage flows towards the rear of the vehicle it mainly flows along the rear wall of the space and diffuses to the surrounding areas. Setting ventilation openings of different areas on the top of the carriage did not significantly improve the spatial diffusion speed of the leaked hydrogen and the impact on the concentration of leaked hydrogen was limited to the vicinity of the ventilation openings. The ventilation opening at the rear can accelerate the diffusion of hydrogen gas to the external environment significantly reducing the concentration of hydrogen and rate of gas rise. When the leaked hydrogen gas flows towards the front of the vehicle and above the space the concentration of hydrogen mainly increases along the height direction of the space. The research results have significant safety implications for the use of fuel cell semi-trailer trucks.

The erosion behavior and the service life of a hydrogen transmission tube with high velocity suitable for a hydrogen fuel aviation engine are not clear which is the bottleneck for its application. In this study a coupled model considering the fluid flow field of hydrogen and discrete motion of particles was established. The effects of the geometry parameters and erosion parameters on the hydrogen erosion behavior were investigated. The maximum erosion rate increased exponentially with the increased hydrogen velocity and increased linearly with the increased erosion time. The large bend radius and inner diameter of the bend tube contributed to the decreased erosion rate. There was an optimized window of the bend angle for a small erosion rate. The relationship between the accumulated thickness loss and maximum erosion rate was established. The prediction model of the service life was established using fourth strength theory. The service life of the tube was sensitive to the hydrogen velocity and erosion time. The experiments were conducted and the variations in thickness and hardness were measured. The simulated models agreed with the experiments and could provide guidance for the parameter selection and prediction of the service life of a bend tube.

Accurate and efficient prediction of thermal radiant of hydrogen jet fire is important to schedule safety design and emergency rescue program for hydrogen pipelines. In response this paper proposes a novel Optuna-improved back propagation neural network (Optuna-BPNN) to estimate hydrogen jet flame radiation. A linear integral approach incorporating leakage rate and jet flame length is theoretically derived to establish dataset for machine learning. Then the Optuna tool is employed to optimize the initial weights and thresholds of the BP neural network. Input matrix of the Optuna-BPNN model includes pipeline diameter leakage aperture size and hydrogen pressure. 8 sets of experimental data are employed to verify its correctness. When the abnormal data is excluded the predicted thermal radiation of hydrogen jet fire agrees quite well with experimental results with average and maximum deviations being 12.4% and 24.4% respectively. Using the linear integral approach 32670 thermal radiation data points are generated to train and test the Optuna-BPNN model. The maximum deviation between predicted and theoretical radiant heat flux for training and testing sets are only 4.5% and 6.2% respectively. Parallel comparison trials using 6 different machine learning algorithms show that the Optuna-BPNN model gives the best mean absolute error root mean square error and determination coefficient which proves the effectiveness and feasibility of the developed OptunaBPNN model in predicting thermal radiation of hydrogen pipeline jet fires.

With the global energy structure shifting towards clean and efficient hydrogen energy the safety management issues of hydrogen refueling stations are becoming increasingly prominent. To address these issues a hydrogen leak localization algorithm for hydrogen refueling stations based on a combination of reinforcement learning and hidden Markov models is proposed. This method combines hidden Markov model to construct a probability distribution model for hydrogen leakage and diffusion simulates the propagation probability of hydrogen in different grid cells and uses reinforcement learning to achieve fast and accurate localization of hydrogen leakage events. The outcomes denoted that the training accuracy reached 95.2% with an F1 value of 0.961 indicating its high accuracy in hydrogen leak localization. When the wind speed was 0.8 m/s the mean square error of the raised method was 0.03 and when the wind speed was 1.0 m/s the mean square error of the raised method was 0.04 proving its good robustness. After 50 localization experiments the proposed algorithm achieves a localization success rate of 93.7% and an average computation time of 42.8 s further demonstrating its high accuracy and computational efficiency. The proposed hydrogen leakage location algorithm has improved the accuracy and efficiency of hydrogen leakage location providing scientific basis and technical guarantee for the safe operation of future hydrogen refueling stations.