Applications & Pathways

Despite the number of works on the techno-economics of offshore green hydrogen production there is a lack of research on the design of floating platforms to concomitantly support hydrogen production facilities and wind power generation equipment. Indeed previous studies on offshore decentralised configuration for hydrogen production implicitly assume that a floating platform designed for wind power generation (FOWT) can be also suitable as a floating wind hydrogen system (FWHS). This work proposes a novel design for an offshore decentralised FWHS and analyses the effects of the integration of the hydrogen facilities on the platform’s dynamics and how this in turn affects the performances of the wind turbine and the hydrogen equipment. Our findings indicate that despite the reduction in platform’s stability the performance of the wind turbine is barely affected. Regarding the hydrogen system our results aim at contributing to further assessment and design of this equipment for offshore conditions.

Hydrogen-driven heavy-duty trucks are a promising technology for reducing CO2 emissions in the transportation sector. Thus storing hydrogen efficiently onboard is vital. The three available or currently developed physical hydrogen storage technologies (compressed gaseous subcooled liquid and cryo-compressed hydrogen) are promising solutions. For a profound thermodynamic comparison of these storage systems a universally applicable model is required. Thus this article introduces a generalized thermodynamic model and conducts thermodynamic comparisons in terms of typical drive cycle scenarios. Therefore a model introduced by Hamacher et al. [1] for cryo-compressed hydrogen tanks is generalized by means of an explicit model formulation using the property ��2� from REFPROP [2] which is understood as a generic specific isochoric two-phase heat capacity. Due to an implemented decision logic minor changes to the equation system are automatically made whenever the operation mode or phase of the tank changes. The resulting model can simulate all three storage tank systems in all operating scenarios and conditions in the single- and two-phase region. Additionally the explicit model formulation provides deeper insights into the thermodynamic processes in the tank. The model is applied to the three physical hydrogen storage technologies to compare drive cycles heat requirement dormancy behavior and optimal usable density. The highest driving ranges were achieved with cryo-compressed hydrogen however it also comes with higher heating requirements compared to subcooled liquid hydrogen.

Hydrogen is considered as an attractive alternative to fossil fuels in the transportation sector. However the penetration of Fuel Cell Electric Vehicles (FCEV) is hindered by the lack of hydrogen refueling station infrastructures. In this study the feasibility of a hybrid PV/wind system for hydrogen refueling station is investigated. Refueling events data is collected in different locations including industrial residential highway and tourist areas. Station Occupancy Fractions (SOF) and Social-to-Solar Fraction (STSF) indicators are developed to assess the level of synchronization between the hydrogen demand and solar potential. Then a validated computer code is used to optimize the renewable system components for off/on-grid cases based on minimizing the Net Present Cost (NPC) and the Loss of Hydrogen Supply Probability (LHSP). For off grid cases the results show that STSF attains maximum value in the industrial area where 0.62 fraction of refueling events occur during the sunshine hours and minimum NPC is achieved. It is observed that when STSF attains lower values of 0.52 0.41 and 0.38 for residential highway and tourist areas NPC increases by 8 16 and 31% respectively. This is associated with lower level of coordination between the hydrogen demand and solar potential. The same conclusion can be stated for the on-grid cases. Therefore for green hydrogen production via solar energy utilization it is recommended that a tariff should be applied to encourage refueling hydrogen vehicles during the availability of solar radiation while reducing the environmental impact storage requirements and eventually the cost of hydrogen production.

The spread of renewable energy (RE) generation not only promotes economy and the environmental protection but also brings uncertainty to power system. As the integration of hydrogen and electricity can effectively mitigate the fluctuation of RE generation an electricity-hydrogen integrated energy system is constructed. Then this paper studies the source-load uncertainties and corresponding correlation as well as the electricity-hydrogen price uncertainties and corresponding correlation. Finally an optimal scheduling model considering economy environmental protection and demand response (DR) is proposed. The simulation results indicate that the introduction of the DR strategy and the correlation of electricity-hydrogen price can effectively improve the economy of the system. After introducing the DR the operating cost of the system is reduced by 5.59% 10.5% 21.06% in each season respectively. When considering the correlation of EP and HP the operating cost of the system is reduced by 4.71% 6.47% 1.4% in each season respectively.

Sustainability goals include the utilization of renewable energy resources to supply the energy needs in addition to wastewater treatment to satisfy the water demand. Moreover hydrogen has become a promising energy carrier and green fuel to decarbonize the industrial and transportation sectors. In this context this research investigates a wind-photovoltaic power plant to produce green hydrogen for hydrogen refueling station and to operate an electrocoagulation water treatment unit in Ostrava Czech Republic’s northeast region. The study conducts a techno-economic analysis through HOMER Pro® software for optimal sizing of the power station components and to investigate the economic indices of the plant. The power station employs photovoltaic panels and wind turbines to supply the required electricity for electrolyzers and electrocoagulation reactors. As an offgrid system lead acid batteries are utilized to store the surplus electricity. Wind speed and solar irradiation are the key role site dependent parameters that determine the cost of hydrogen electricity and wastewater treatment. The simulated model considers the capital operating and replacement costs for system components. In the proposed system 240 kg of hydrogen as well as 720 kWh electrical energy are daily required for the hydrogen refueling station and the electrocoagulation unit respectively. Accordingly the power station annually generates 6997990 kWh of electrical energy in addition to 85595 kg of green hydrogen. Based on the economic analysis the project’s NPC is determined to be €5.49 M and the levelized cost of Hydrogen (LCH) is 2.89 €/kg excluding compressor unit costs. This value proves the effectiveness of this power system which encourages the utilization of green hydrogen for fuel-cell electric vehicles (FCVs). Furthermore emerging electrocoagulation studies produce hydrogen through wastewater treatment increasing hydrogen production and lowering LCH. Therefore this study is able to provide practicable methodology support for optimal sizing of the power station components which is beneficial for industrialization and economic development as well as transition toward sustainability and autonomous energy systems.

Population growth and economic development have significantly increased global energy demand. Hence it has raised concerns about the increase in the consumption of fossil fuels and climate change. The present work introduced a new approach to using carbon-free energy sources such as nuclear and renewable to meet energy demand. The idea of using the Nuclear-Renewable Hybrid Energy System (N-R HES) is suggested as a leading solution that couples a nuclear power plant with renewable energy and hydrogen-based storage systems. For this purpose using a meta-heuristic method based on Newton’s laws the configuration of the N-R HES is optimized from an economic and reliability point of view. The optimal system is selected from among six cases with different subsystems such as wind turbine photovoltaic panel nuclear reactor electrolysis fuel cell and hydrogen storage tank. Furthermore the performance of hydrogen-based energy storage systems such as hightemperature electrolysis (HTE) and low-temperature electrolysis (LTE) is evaluated from technical and economic aspects. The results of this work showed that using nuclear energy to supply the base load increases the reliability of the system and reduces the loss of power supply probability to zero. More than 70 % of the power is produced by nuclear reactors which includes more than 80 % of the system costs. The key findings showed that despite HTE’s higher efficiency using LTE as a storage system in N-R HES is more cost-effective. Finally due to recent developments and the safer design of nuclear reactors they can play an important role in combination with renewable energies to support carbon-free energy sectors especially in remote areas for decades to come.

The work carried out in this paper focused on “Machine learning models for the prediction of turbulent combustion speed for hydrogen-natural gas spark ignition engines”. The aim of this work is to develop and verify the ability of machine learning models to solve the problem of estimating the turbulent flame speed for a spark-ignition internal combustion engine operating with a hydrogen-natural gas mixture then evaluate the relevance of these models in relation to the usual approaches. The novelty of this work is the possibility of a direct calculation of turbulent combustion speed with a good precision using only machine learning model. The obtained models are also compared to each other by considering in turn as a comparison criterion: the precision of the result calculation time and the ability to assimilate original data (which has not undergone preprocessing). An important particularity of this work is that the input variables of the machine learning models were chosen among the variables directly measurable experimentally based on the opinion of experts in combustion in internal combustion engines and not on the usual approaches to dimensionality reduction on a dataset. The data used for this work was taken from a MINSEL 380 a 380-cc single-cylinder engine. The results show that all the machine learning models obtained are significantly faster than the usual approach and Random Forest (R2: R-squared = 0.9939 and RMSE: Root Mean Square Error = 0.4274) gives the best results. With a forecasting accuracy of over 90 % both approaches can make reasonable predictions for most industrial applications such as designing engine monitoring and control systems firefighting systems simulation and prototyping tools.

This study explores the performance and emissions characteristics of a dual-fuel internal combustion engine operating on a blend of hydrogen and gasoline. This research began with a baseline simulation of a conventional gasoline engine which was subsequently validated through experimental testing on an AVL testbed. The simulation results closely matched the testbed data confirming the accuracy of the model with deviations within 5%. Building on this validated model a hydrogen–gasoline dual-fuel engine simulation was developed. The predictive simulation revealed an approximately 5% increase in overall engine efficiency at the optimal operating point primarily due to hydrogen’s combustion properties. Additionally the injected gasoline mass and CO2 emissions were reduced by around 30% across the RPM range. However the introduction of hydrogen also resulted in a slight reduction (~10%) in torque attributed to the lower volumetric efficiency caused by hydrogen displacing intake air. While CO emissions were significantly reduced NOx emissions nearly doubled due to the higher combustion temperatures associated with hydrogen. This research demonstrates the potential of hydrogen–gasoline dual-fuel systems in reducing carbon emissions while highlighting the need for further optimization to balance performance with environmental impact.

To expedite the development of the infrastructural expansion for hydrogen applications the research project “THEWA” was founded. Within this project the development of hydrogen-refueling stations is being advanced so that the hydrogen strategy for mobility in Germany can move forward. One development point of the project is to develop an evaluation model that recommends a concept for hydrogen-refueling stations for initial individual situations. In this work an evaluation method is developed that provides an appropriate recommendation. For this purpose basics such as the general structure of hydrogen-refueling stations their classification into functional areas and alreadyexisting evaluation methods for multi-criteria decisions are shown. The method for the evaluation of hydrogen-refueling stations will be developed in a component-based manner for which a selection of influencing factors of hydrogen-refueling stations will be explained and categorized. With the help of an expert workshop these are scaled so that the result is an evaluation method based on an expert assessment and the consideration of individual customer requirements. In addition the method is implemented in a tool so that it can be used more easily.