Applications & Pathways

To reach climate neutrality by 2050 a goal that the European Union set itself it is necessary to change and modify the whole EU’s energy system through deep decarbonization and reduction of greenhouse-gas emissions. The study presents a current insight into the global energy-transition pathway based on the hydrogen energy industry chain. The paper provides a critical analysis of the role of clean hydrogen based on renewable energy sources (green hydrogen) and fossil-fuels-based hydrogen (blue hydrogen) in the development of a new hydrogen-based economy and the reduction of greenhouse-gas emissions. The actual status costs future directions and recommendations for low-carbon hydrogen development and commercial deployment are addressed. Additionally the integration of hydrogen production with CCUS technologies is presented.

Hydrogen is the gas of the moment: an abundant element that can be created using renewable energy transported in gaseous or liquid form and offering the ability to provide energy with only water vapour as an emission. Hydrogen can also be used in a fuel blend in electricity generation gas turbines providing a low carbon option for providing the peak electricity to cover high demand and firming.<br/>While the electricity grid is itself transforming to decarbonising hard-to-abate industries such as cement and bauxite refineries are slower to reduce emissions constrained by their high temperature process requirements. Hydrogen offers a solution allowing onsite production process heat with waste heat recovery supporting blended gas turbine generation for onsite electricity supply.<br/>This article builds on decarbonisation pathway simulation results from an ANEM model of the electricity grid identifying the amount of peak demand energy required from gas turbines. The research then examines the quantity flow rate storage requirements and emissions reduction if this peak generation were supplied by open cycle hydrogen capable gas turbines.

This study addresses cost-optimal sizing and energy management of a grid-integrated solar photovoltaic wind turbine hybrid renewable energy system integrated with electrolyzer and hydrogen storage tank to simultaneously meet electricity and hydrogen demands considering the case study of Dijon France. Mixed Integer Linear Programming optimization problem is formulated to evaluate two objective case scenarios: single objective and multi-objective minimizing total annual costs and grid carbon emission footprint. The study incorporates various technical economic and environmental indicators focusing on the impact of sensitivity lying on various grid electricity purchase rates within the French electricity market prices. The results highlight that rising grid prices drive increased integration of renewable sources while lower prices favor ultimate grid dependency. Constant hydrogen demand necessitates the installation of two electrolyzers. Notably grid electricity prices above 60 e/MWh result increase in the size of the hydrogen tank and electrolyzer operation to prevent renewable energy losses. Grid prices above 140 e/MWh depict 70% of electrical and 80% of electrolyzer demand provided by the renewable generation resulting in a carbon emission below 0.0416 Mt of CO2 and 0.643 kgCO2 /kgH2 . Conversely grid prices below 20 e/MWh lead ultimately to 100% grid dependency with a higher carbon emission of approximately 0.14 Mt of CO2 and 4.13 kgCO2 /kgH2 reducing the total annual cost to 41.63 Million e. Increase in grid prices from 20e/MWh to 180 e/MWh resulted in increase of hydrogen specific costs from 1.23 to 3.58 e/kgH2 . Finally the Pareto front diagram is employed to illustrate the trade-off between total annual cost and carbon emission due to grid imports aiding in informed decision-making.

Aviation is a major contributor to transportation carbon emissions but aims to reduce its carbon footprint. Sustainable and environmentally friendly green hydrogen fuel is essential for decarbonization of this industry. Using the extremely low temperature of liquid hydrogen in aviation sector unlocks the opportunity for cryoelectric aircraft concept which exploits the advantageous properties of superconductors onboard. A significant barrier for green hydrogen adoption relates to its high cost and the immediate need for large-scale production which Proton Exchange Membrane Water Electrolyzers (PEMWE) can address through optimal dynamic performance high lifetimes good efficiencies and importantly scalability. In PEMWE the cell is a crucial component that facilitates the electrolysis process and consists of a polymer membrane and electrodes. To control the required production rate of hydrogen the output power of cell should be monitored which usually is done by measuring the cell’s potential and current density. In this paper five different machine learning (ML) models based on different algorithms have been developed to predict this parameter. Findings of the work highlight that the model based on Cascade-Forward Neural Network (CFNN) is investigated to accurately predict the cell potential of PEMWE under different anodic material and working conditions with an accuracy of 99.998 % and 0.001884 in terms of R2 and root mean square error respectively. It can predict the cell potential with a relative error of less than 0.65 % and an absolute error of below 0.01 V. The Standard deviation of 0.000061 for 50 iterations of stability analysis indicated that this model has less sensitivity to the random selection of training data. By accurately estimating different cell’s output with one model and considering its ultra-fast response CFNN model has the potential to be used for both monitoring and the designing purposes of green hydrogen production.

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.

Logistics is becoming more cost competitive while customers and regulatory bodies pressure businesses to disclose their carbon footprints creating interest in alternative fuels as a decarbonization strategy. This paper provides a thematic review of the role of alternative fuels in sustainable air land and sea logistics their challenges and potential mitigations. Through an extensive literature survey we determined that biofuels synthetic kerosene natural gas ammonia alcohols hydrogen and electricity are the primary alternative fuels of interest in terms of environmental sustainability and techno-economic feasibility. In air logistics synthetic kerosene from hydrogenated esters and fatty acids is the most promising route due to its high technical maturity although it is limited by biomass sourcing. Electrical vehicles are favorable in road logistics due to cheaper green power and efficient vehicle designs although they are constrained by recharging infrastructure deployment. In sea logistics liquified natural gas is advantageous owing to its supply chain maturity but it is limited by methane slip control and storage requirements. Overall our examination indicates that alternative fuels will play a pivotal role in the logistics networks of the future.

This study aims to carry out a techno-economic analysis of different hydrogen supply chain designs coupled with the Swedish electricity system to study the inter-dependencies between them. Both the hydrogen supply chain designs and the electricity system were parameterized with data for 2030. The supply chain designs comprehend centralised production decentralised production a combination of both and with/without seasonal variation in hydrogen demand. The supply chain design is modelled to minimize the overall cost while meeting the hydrogen demands. The outputs of the supply chain model include the hydrogen refuelling stations’ locations the electrolyser’s locations and their respective sizes as well as the operational schedule. The electricity system model shows that the average electricity prices in Sweden for zones SE1 SE2 SE3 and SE4 will be 4.28 1.88 8.21 and 8.19 €/MWh respectively. The electricity is mainly generated from wind and hydropower (around 42% each) followed by nuclear (14%) solar (2%) and then bio-energy (0.3%). In addition the hydrogen supply chain design that leads to a lower overall cost is the decentralised design with a cost of 1.48 and 1.68 €/kgH2 in scenarios without and with seasonal variation respectively. The seasonal variation in hydrogen demand increases the cost of hydrogen regardless of the supply chain design.

In the light of a future decarbonized power grid based primarily on non-dispatchable renewable energy sources the operation of industrial plants should be decarbonized and flexible. An innovative novel concept combining industrial plants with (i) a water electrolysis unit (ii) a hydrogen storage unit and (iii) a fuel cell unit would enable seasonal supply-demand balancing in the local power grid and storage of surplus energy in the form of stable solid products. The feasibility of this concept was demonstrated in a case study taking into account the overall energy balance and economics. The characteristics of the local power grid and the hydrogen round-trip efficiency must be carefully considered when dimensioning the hydrogen units. It was found that industries producing iron and steel cement ceramics glass aluminum paper and other metals have the potential for seasonal operation. Future research efforts in the fields of technology economics and social sciences should support the sustainable flexibility transition of energy-intensive industries with solid products.

Hydrogen price significantly impacts its potential as a viable alternative in the sustainable energy transition. This study introduces a synergy-based Hydrogen Pricing Mechanism (HPM) within an integrated framework. The HPM leverages synergy between a Renewable-Penetrated Electric Power System (RP-EPS) and a Hydrogen Energy System (HES). Utilizing the Alternating Direction Method of Multipliers (ADMM) it facilitates data exchange quantifying integration levels and simplifying the complexities. The study assesses the HPM’s operational sensitivity across various scenarios of hydrogen generation transportation and storage. It also evaluates the benefits of synergy-based versus stand-alone HPMs. Findings indicate that the synergy-based HPM effectively integrates infrastructure and operational improvements from both EPS and HES leading to optimized hydrogen pricing.

If hydrogen fuel is available to support the transportation sector decarbonization its usage can be placed either directly onboard in a fuel cell vehicle or indirectly off-board by using a fuel cell power station to produce electricity to charge a battery electric vehicle. Therefore in this work the direct and indirect conversion scenarios of hydrogen to vehicle propulsion were investigated regarding energy efficiency. Thus in the first scenario hydrogen is the fuel for the onboard electricity production to propel a fuel cell vehicle while in the second hydrogen is the electricity source to charge the battery electric vehicle. When simulated for a drive cycle results have shown that the scenario with the onboard fuel cell consumed about 20% less hydrogen demonstrating higher energy efficiency in terms of driving range. However energy efficiency depends on the outside temperature when heat loss utilization is considered. For outside temperatures of − 5 ◦C or higher the system composed of the battery electric vehicle fueled with electricity from the off-board fuel cell was shown to be more energyefficient. For lower temperatures the system composed of the onboard fuel cell again presented higher total (heat + electricity) efficiency. Therefore the results provide valuable insights into how hydrogen fuel can be used for vehicle propulsion supporting the hydrogen economy development.