China, People’s Republic

The yield of photovoltaic hydrogen production systems is influenced by a number of factors including weather conditions the cleanliness of photovoltaic modules and operational efficiency. Temporal variations in weather conditions have been shown to significantly impact the output of photovoltaic systems thereby influencing hydrogen production. To address the inaccuracies in hydrogen production capacity predictions due to weather-related temporal variations in different regions this study develops a method for predicting photovoltaic hydrogen production capacity using the long short-term memory (LSTM) neural network model. The proposed method integrates meteorological parameters including temperature wind speed precipitation and humidity into a neural network model to estimate the daily solar radiation intensity. This approach is then integrated with a photovoltaic hydrogen production prediction model to estimate the region’s hydrogen production capacity. To validate the accuracy and feasibility of this method meteorological data from Lanzhou China from 2013 to 2022 were used to train the model and test its performance. The results show that the predicted hydrogen production agrees well with the actual values with a low mean absolute percentage error (MAPE) and a high coefficient of determination (R2 ). The predicted hydrogen production in winter has a MAPE of 0.55% and an R2 of 0.985 while the predicted hydrogen production in summer has a slightly higher MAPE of 0.61% and a lower R2 of 0.968 due to higher irradiance levels and weather fluctuations. The present model captures long-term dependencies in the time series data significantly improving prediction accuracy compared to conventional methods. This approach offers a cost-effective and practical solution for predicting photovoltaic hydrogen production demonstrating significant potential for the optimization of the operation of photovoltaic hydrogen production systems in diverse environments.

This paper presents a quantitative study on electric-thermal collaborative system for hydrogen-powered train reutilising the waste heat from fuel cell system for Heating Ventilation and Air Conditioning (HVAC). Firstly a hybrid train simulator is developed to simulate the train’s motion state. Heat generation from fuel cell is estimated using a fuel cell model while a detailed thermodynamic model for railway passenger coach is established to predict the heat demand. Furthermore an electric-thermal collaborative energy management strategy (ETCEMS) is proposed for the system to comprehensively optimise the on-train power distribution considering traction and auxiliary power. Finally comparative analysis is performed among the train with electric heater (EH) heat pump (HP) and heat pump-heat reuse (HP-HR). The results demonstrate that over a round trip the proposed HP-HR with ETC-EMS recovers over 22.88% residual heat and saves 16.17% of hydrogen consumption. For the daily operation it reduces hydrogen and energy consumption by 12.06% and 12.82 % respectively. The findings indicate that collaborative optimisation brings significant improvements on the global energy utilisation. The proposed design with ETC-EMS is potential to further enhance the economic viability of hydrail and contributes to the rail decarbonisation.

Pilot-diesel-ignition ammonia combustion engines have attracted widespread attentions from the maritime sector but there are still bottleneck problems such as high unburned NH3 and N2O emissions as well as low thermal efficiency that need to be solved before further applications. In this study a concept termed as in-cylinder reforming gas recirculation is initiated to simultaneously improve the thermal efficiency and reduce the unburned NH3 NOx N2O and greenhouse gas emissions of pilot-diesel-ignition ammonia combustion engine. For this concept one cylinder of the multi-cylinder engine operates rich of stoichiometric and the excess ammonia in the cylinder is partially decomposed into hydrogen then the exhaust of this dedicated reforming cylinder is recirculated into the other cylinders and therefore the advantages of hydrogen-enriched combustion and exhaust gas recirculation can be combined. The results show that at 3% diesel energetic ratio and 1000 rpm the engine can increase the indicated thermal efficiency by 15.8% and reduce the unburned NH3 by 89.3% N2O by 91.2% compared to the base/traditional ammonia engine without the proposed method. At the same time it is able to reduce carbon footprint by 97.0% and greenhouse gases by 94.0% compared to the traditional pure diesel mode.

Hydrogen holds an important strategic position in the energy systems of many countries. Many studies have analyzed the acceptance of hydrogen energy technology from the public’s perspective but few have examined it from the corporate perspective. This paper establishes a technology acceptance model and employs structural equation modeling to investigate the factors affecting the acceptance of hydrogen energy technology within enterprises. After conducting questionnaire surveys among employees of energy enterprises electric power companies and new energy vehicle manufacturers the results indicate that while most of the interviewed enterprises have positive attitudes towards hydrogen technology their willingness to develop hydrogen business does not appear to be correspondingly positive. In addition government trust perceived benefit and social influence positively impact corporate acceptability indirectly whereas perceived risk exhibits a negative indirect effect on corporate acceptance. Finally this paper discusses the results of the above studies and makes corresponding policy recommendations.

Accidental hydrogen releases from pipelines pose significant risks particularly with the expanding deployment of hydrogen infrastructure. Despite this there has been a lack of thorough investigation into hydrogen leakage from pipelines especially under complex real-world conditions. This study addresses this gap by modeling hydrogen gas dispersion jet fires and explosions based on practical scenarios. Various factors influencing accident consequences such as leak hole size wind speed wind direction and trench presence were systematically examined. The findings reveal that both hydrogen dispersion distance and jet flame thermal radiation distance increase with leak hole size and wind speed. Specifically the longest dispersion and radiation distances occur when the wind direction aligns with the trench which is 110 m where the hydrogen concentration is 4% and 76 m where the radiation is 15.8 kW/m2 in the case of a 325 mm leak hole and wind under 10 m/s. Meanwhile pipelines lacking trenching exhibit the shortest distances 0.17 m and 0.98 m at a hydrogen concentration of 4% and 15.8 kW/m2 radiation with a leak hole size of 3.25 mm and no wind. Moreover under relatively higher wind speeds hydrogen concentration stratification occurs. Notably the low congestion surrounding the pipeline results in an explosion overpressure too low to cause damage; namely the highest overpressure is 8 kPa but this lasts less than 0.2 s. This comprehensive numerical study of hydrogen pipeline leakage offers valuable quantitative insights serving as a vital reference for facility siting and design considerations to eliminate the risk of fire incidents.

To address the issue of imbalanced electricity and hydrogen supply and demand in the flexible multi-energy port area system a multi-regional operational optimization and energy storage capacity allocation strategy considering the working status of flexible multi-status switches is proposed. Firstly based on the characteristics of the port area system models for system operating costs generation equipment energy storage devices flexible multi-status switches and others are established. Secondly the system is subjected to a first-stage optimization where different regions are optimized individually. The working periods of flexible multi-status switches are determined based on the results of this first-stage optimization targeting the minimization of the overall daily operating costs while ensuring 100% integration of renewable energy in periods with electricity supply-demand imbalances. Subsequently additional constraints are imposed based on the results of the first-stage optimization to optimize the entire system obtaining power allocation during system operation as well as power and capacity requirements for energy storage devices and flexible multi-status switches. Finally the proposed approach is validated through simulation examples demonstrating its advantages in terms of economic efficiency reduced power and capacity requirements for energy storage devices and carbon reduction.

Thanks to the advantages of cleanliness high efficiency extensive sources and renewable energy hydrogen energy has gradually become the focus of energy development in the world’s major economies. At present the natural gas transportation pipeline network is relatively complete while hydrogen transportation technology faces many challenges such as the lack of technical specifications high safety risks and high investment costs which are the key factors that hinder the development of hydrogen pipeline transportation. This paper provides a comprehensive overview and summary of the current status and development prospects of pure hydrogen and hydrogen-mixed natural gas pipeline transportation. Analysts believe that basic studies and case studies for hydrogen infrastructure transformation and system optimization have received extensive attention and related technical studies are mainly focused on pipeline transportation processes pipe evaluation and safe operation guarantees. There are still technical challenges in hydrogen-mixed natural gas pipelines in terms of the doping ratio and hydrogen separation and purification. To promote the industrial application of hydrogen energy it is necessary to develop more efficient low-cost and low-energy-consumption hydrogen storage materials.

Currently the global energy structure is undergoing a transition from fossil fuels to renewable energy sources with the hydrogen economy playing a pivotal role. Hydrogen is not only an important energy carrier needed to achieve the global goal of energy conservation and emission reduction it represents a key object of the future international energy trade. As hydrogen trade expands nations are increasingly allocating resources to enhance the international competitiveness of their respective hydrogen industries. This paper introduces an index that can be used to evaluate international hydrogen competitiveness and elucidate the most competitive countries in the hydrogen trade. To calculate the competitiveness scores of seven major prospective hydrogen market participants we employed the entropy weight method. This method considers five essential factors: potential resources economic and financial base infrastructure government support and institutional environment and technological feasibility. The results indicate that the USA and Australia exhibit the highest composite indices. These findings can serve as a guide for countries in formulating suitable policies and strategies to bolster the development and international competitiveness of their respective hydrogen industries.

Proton exchange membrane (PEM) based electrochemical systems have the capability to operate in fuel cell (PEMFC) and water electrolyser (PEMWE) modes enabling efficient hydrogen energy utilisation and green hydrogen production. In addition to the essential cell stacks the system of PEMFC or PEMWE consists of four sub-systems for managing gas supply power thermal and water respectively. Due to the system’s complexity even a small fluctuation in a certain sub-system can result in an unexpected response leading to a reduced performance and stability. To improve the system’s robustness and responsiveness considerable efforts have been dedicated to developing advanced control strategies. This paper comprehensively reviews various control strategies proposed in literature revealing that traditional control methods are widely employed in PEMFC and PEMWE due to their simplicity yet they suffer from limitations in accuracy. Conversely advanced control methods offer high accuracy but are hindered by poor dynamic performance. This paper highlights the recent advancements in control strategies incorporating machine learning algorithms. Additionally the paper provides a perspective on the future development of control strategies suggesting that hybrid control methods should be used for future research to leverage the strength of both sides. Notably it emphasises the role of artificial intelligence (AI) in advancing control strategies demonstrating its significant potential in facilitating the transition from automation to autonomy.

The aim of this paper is the design and implementation of an advanced model predictive control (MPC) strategy for the management of a wind–solar microgrid (MG) both in the islanded and grid-connected modes. The MG includes energy storage systems (ESSs) and interacts with external hydrogen and electricity consumers as an extra feature. The system participates in two different electricity markets i.e. the daily and real-time markets characterized by different time-scales. Thus a high-layer control (HLC) and a low-layer control (LLC) are developed for the daily market and the real-time market respectively. The sporadic characteristics of renewable energy sources and the variations in load demand are also briefly discussed by proposing a controller based on the stochastic MPC approach. Numerical simulations with real wind and solar generation profiles and spot prices show that the proposed controller optimally manages the ESSs even when there is a deviation between the predicted scenario determined at the HLC and the real-time one managed by the LLC. Finally the strategy is tested on a lab-scale MG set up at Khalifa University Abu Dhabi UAE.