China, People’s Republic

Electrolysis of water to generate hydrogen fuel is an attractiverenewable energy storage technology. However grid-scale fresh-water electrolysis would put a heavy strain on vital water re-sources. Developing cheap electrocatalysts and electrodes that cansustain seawater splitting without chloride corrosion could ad-dress the water scarcity issue. Here we present a multilayer anodeconsisting of a nickel–iron hydroxide (NiFe) electrocatalyst layeruniformly coated on a nickel sulfide (NiSx) layer formed on porousNi foam (NiFe/NiSx-Ni) affording superior catalytic activity andcorrosion resistance in solar-driven alkaline seawater electrolysisoperating at industrially required current densities (0.4 to 1 A/cm2)over 1000 h. A continuous highly oxygen evolution reaction-active NiFe electrocatalyst layer drawing anodic currents towardwater oxidation and an in situ-generated polyatomic sulfate andcarbonate-rich passivating layers formed in the anode are respon-sible for chloride repelling and superior corrosion resistance of thesalty-water-splitting anode.

In response to environmental concerns related to carbon and nitrogen emissions hydrogen-powered aircraft (HPA) are poised for significant development over the coming decades driven by advances in power electronics technology. However HPA systems present complex multi-domain challenges encompassing electrical hydraulic mechanical and chemical disciplines necessitating efficient modeling and robust validation platforms. This paper introduces a physics-informed machine learning (PIML) approach for multi-domain HPA system modeling enhanced by hardware accelerated parallel hardware emulation to construct a real-time digital twin. It delves into the physical analysis of various HPA subsystems whose equations form the basis for both traditional numerical solution methods like Euler’s and Runge-Kutta methods (RKM) as well as the physics-informed neural networks (PINN) components developed herein. By comparing physics-feature neural networks (PFNN) and PINN with conventional neural network strategies this paper elucidates their advantages and limitations in practical applications. The final implementation on the Xilinx® UltraScale+™ VCU128 FPGA platform showcases the PIML method’s high efficiency accuracy data independence and adherence to established physical laws demonstrating its potential for advancing real-time multi-domain HPA emulation.

Hydrogen energy represents an ideal medium for energy storage. By integrating hydrogen power conversion utilization and storage technologies with distributed wind and photovoltaic power generation techniques it is possible to achieve complementary utilization and synergistic operation of multiple energy sources in the form of microgrids. However the diverse operational mechanisms varying capacities and distinct forms of distributed energy sources within hydrogen-coupled microgrids complicate their operational conditions making fine-tuned scheduling management and economic operation challenging. In response this paper proposes an energy management method for hydrogen-coupled microgrids based on the deep deterministic policy gradient (DDPG). This method leverages predictive information on photovoltaic power generation load power and other factors to simulate energy management strategies for hydrogen-coupled microgrids using deep neural networks and obtains the optimal strategy through reinforcement learning ultimately achieving optimized operation of hydrogen-coupled microgrids under complex conditions and uncertainties. The paper includes analysis using typical case studies and compares the optimization effects of the deep deterministic policy gradient and deep Q networks validating the effectiveness and robustness of the proposed method.

The introduction of several alternative marine fuels is considered an important strategy for maritime decarbonization. These alternative marine fuels include liquefied natural gas (LNG) liquefied biogas (LBG) hydrogen ammonia methanol ethanol hydrotreated vegetable oil (HVO) etc. In some studies nuclear power and electricity are also included in the scope of alternative fuels for merchant ships. However the operation of alternative-fuel-powered ships has some special risks such as fuel spills vapor dispersion and fuel pool fires. The existing international legal framework does not address these risks sufficiently. This research adopts the method of legal analysis to examine the existing international legal regime for regulating the development of alternative-fuel-powered ships. From a critical perspective it evaluates and predicts the consequences of these policies together with their shortcomings. Also this research explores the potential solutions and countermeasures that might be feasible to deal with the special marine environmental risks posed by alternative-fuel-powered ships in the future.

In this study we developed a new hybrid mathematical model that combines wind-speed range with the log law to derive the wind energy potential for wind-generated hydrogen production in Pakistan. In addition we electrolyzed wind-generated power in order to assess the generation capacity of wind-generated renewable hydrogen. The advantage of the Weibull model is that it more accurately reflects power generation potential (i.e. the capacity factor). When applied to selected sites we have found commercially viable hydrogen production capacity in all locations. All sites considered had the potential to produce an excess amount of wind-generated renewable hydrogen. If the total national capacity of wind-generated was used Pakistan could conceivably produce 51917000.39 kg per day of renewable hydrogen. Based on our results we suggest that cars and other forms of transport could be fueled with hydrogen to conserve oil and gas resources which can reduce the energy shortfall and contribute to the fight against climate change and global warming. Also hydrogen could be used to supplement urban energy needs (e.g. for Sindh province Pakistan) again reducing energy shortage effects and supporting green city programs.

Because the new energy is intermittent and uncertain it has an influence on the system’s output power stability. A hydrogen energy storage system is added to the system to create a wind light and hydrogen integrated energy system which increases the utilization rate of renewable energy while encouraging the consumption of renewable energy and lowering the rate of abandoning wind and light. Considering the system’s comprehensive operation cost economy power fluctuation and power shortage as the goal considering the relationship between power generation and load assigning charging and discharging commands to storage batteries and hydrogen energy storage and constructing a model for optimal capacity allocation of wind–hydrogen microgrid system. The optimal configuration model of the wind solar and hydrogen microgrid system capacity is constructed. A particle swarm optimization with dynamic adjustment of inertial weight (IDW-PSO) is proposed to solve the optimal allocation scheme of the model in order to achieve the optimal allocation of energy storage capacity in a wind–hydrogen storage microgrid. Finally a microgrid system in Beijing is taken as an example for simulation and solution and the results demonstrate that the proposed approach has the characteristics to optimize the economy and improve the capacity of renewable energy consumption realize the inhibition of the fluctuations of power reduce system power shortage and accelerate the convergence speed.

This paper uses established and recently introduced methods from the applied mathematics and statistics literature to study trends in the end-use sector and the capacity of low-carbon hydrogen projects in recent and upcoming decades. First we examine distributions in plants over time for various end-use sectors and classify them according to metric discrepancy observing clear similarity across all industry sectors. Next we compare the distribution of usage sectors between different continents and examine the changes in sector distribution over time. Finally we judiciously apply several regression models to analyse the association between various predictors and the capacity of global hydrogen projects. Across our experiments we see a welcome exponential growth in the capacity of zero-carbon hydrogen plants and significant growth of new and planned hydrogen plants in the 2020’s across every sector.

A distributed power system with renewable energy sources is very popular in recent years due to the rapid depletion of conventional sources of energy. Reasonable sizing for such power systems could improve the power supply reliability and reduce the annual system cost. The goal of this work is to optimize the size of a stand-alone hybrid photovoltaic (PV)/wind turbine (WT)/battery (B)/hydrogen system (a hybrid system based on battery and hydrogen (HS-BH)) for reliable and economic supply. Two objectives that take the minimum annual system cost and maximum system reliability described as the loss of power supply probability (LPSP) have been addressed for sizing HS-BH from a more comprehensive perspective considering the basic demand of load the profit from hydrogen which is produced by HS-BH and an effective energy storage strategy. An improved ant colony optimization (ACO) algorithm has been presented to solve the sizing problem of HS-BH. Finally a simulation experiment has been done to demonstrate the developed results in which some comparisons have been done to emphasize the advantage of HS-BH with the aid of data from an island of Zhejiang China.

When offshore wind farms are connected to a hydrogen plant with dedicated transmission lines for example high-voltage direct current the fluctuation of wind speed will influence the efficiency of the alkaline electrolyzer and deteriorate the techno-economic performance. To overcome this issue firstly an additional heating process is adopted to achieve insulation for the alkaline solution when power generated by wind farms is below the alkaline electrolyzer minimum power threshold while the alkaline electrolyzer overload feature is used to generate hydrogen when wind power is at its peak. Then a simplified piecewise model-based alkaline electrolyzer techno-economic analysis model is proposed. The improved economic performance of the islanded green hydrogen system with the proposed operation strategy is verified based on the wind speed data set simulation generated by the Weibull distribution. Lastly the sensitivity of the total return on investment to wind speed parameters was investigated and an islanded green hydrogen system capacity allocation based on the proposed analysis model was conducted. The simulation result shows the total energy utilization increased from 62.0768% to 72.5419% and the return on investment increased from 5.1303%/month to 5.9581%/month when the proposed control strategy is adopted.

The primary methods for hydrogen transportation include gaseous storage and transport liquid hydrogen storage and transport via organic liquid carriers. Among these pipeline transportation offers the lowest cost and the greatest potential for large-scale long-distance transport. Although the construction and operation costs of dedicated hydrogen pipelines are relatively high blending hydrogen into existing natural gas networks presents a viable alternative. This approach allows hydrogen to be transported to the end-users where it can be either separated for use or directly combusted thereby reducing hydrogen transport costs. This study based on the GERG-2008 equation of state conducts experimental tests on the compressibility factor of hydrogen-doped natural gas mixtures across a temperature range of −10 ◦C to 110 ◦C and a pressure range of 2 to 12 MPa with hydrogen blending ratios of 5% 10% 20% 30% and 40%. The results indicate that the hydrogen blending ratio temperature and pressure significantly affect the compressibility factor particularly under low-temperature and high-pressure conditions where an increase in the hydrogen blending ratio leads to a notable rise in the compressibility factor. These findings have substantial implications for the practical design of hydrogen-enriched natural gas pipelines as changes in the compressibility factor directly impact pipeline operational parameters compressor characteristics and other system performance aspects. Specifically the introduction of hydrogen alters the compressibility factor of the transported medium thereby affecting the pipeline’s flowability and compressibility which are crucial for optimizing and applying the performance of hydrogen-enriched natural gas in transportation channels. The research outcomes provide valuable insights for understanding combustion reactions adjusting pipeline operational parameters and compressor performance characteristics facilitating more precise decision-making in the design and operation of hydrogen-enriched natural gas pipelines.