Applications & Pathways

This study presents a techno-economic analysis of hydrogen transportation via liquid organic hydrogen carriers by road comparing this option with compressed hydrogen (350 bar) and liquefied hydrogen. The analysis includes the simulation of hydrogenation and dehydrogenation reactors for the dibenzyltoluene/perhydro-dibenzyltoluene system using ASPEN Plus along with a cost assessment of compression liquefaction and trucking. A sensitivity analysis is also carried out evaluating hydrogen transport at varying daily demand levels (1 2 and 4 t/d) and transport distances (50 150 and 300 km) with varying electricity prices and capital expenditures for hydrogenation and dehydrogenation units. Results indicate that compressed hydrogen is the most cost-effective solution for short distances up to 150 km with a levelized cost of transported hydrogen ranging from 1.10 to 1.61 EUR/kg. However LOHC technology becomes more competitive at longer distances with LCOTH values between 1.49 and 1.90 EUR/kg at 300 km across all demand levels. Liquefied hydrogen remains the least competitive option reaching costs up to 5.35 EUR/kg although it requires fewer annual trips due to higher trailer capacity. Notably at 150 km LOHC transport becomes more cost-effective than compressed hydrogen when electricity prices exceed 0.22 EUR/kWh or when the capital costs for hydrogenation and dehydrogenation units are minimized. From an environmental perspective switching from compressed to liquid hydrogen carriers significantly reduces CO2 emissions—by 56% for LOHCs and 78% for liquid hydrogen—highlighting the potential of these technologies to support the decarbonization of hydrogen logistics.

Rapid socio-economic development has made energy application and environmental issues increasingly prominent. Hydrogen energy clean eco-friendly and highly synergistic with renewable energy has become a global research focus. This study using the EnergyPLAN model that includes the electricity transportation and industrial sectors takes Jinan City as the research object and explores how hydrogen penetration changes affect the decarbonization path of the urban integrated energy system under four scenarios. It evaluates the four hydrogen scenarios with the entropy weight method and technique placing them in an order of preference according to their similarity to the ideal solution considering comprehensive indicators like cost carbon emissions and sustainability. Results show the China Hydrogen Alliance potential scenario has better CO2 emission reduction potential and unit emission reduction cost reducing them by 7.98% and 29.39% respectively. In a comprehensive evaluation it ranks first with a score of 0.5961 meaning it is closest to the ideal scenario when cost environmental and sustainability indicators are comprehensively considered. The Climate Response Pioneer scenario follows with 0.4039 indicating that higher hydrogen penetration in terminal energy is not necessarily the most ideal solution. Instead appropriate hydrogen penetration scenarios should be selected based on the actual situation of different energy systems.

In 2021 the global electricity and heat sector recorded the highest increase in carbon dioxide (CO2) emissions in comparison with the previous year highlighting the ongoing challenges in reducing emissions within the sector. Therefore combined heat and power (CHP) plants running on renewable fuels can play an important role in the energy transition by decarbonising a process increasing the efficiency and capacity factor. Since 2003 Luxembourgish CHP plants have been transitioning from natural gas to biomass mainly wood pellets. However even though wood pellets are a renewable alternative the market volatility in 2022 highlighted the vulnerability of a system reliant solely on one type of fuel. This study assesses the feasibility of using hydrogen to decarbonise a cogeneration plant powered by a natural gas-fuelled internal combustion engine. Although the technology to use hydrogen as a fuel for such systems already exists a technical and economic analysis of implementing a hydrogen-ready plant is still lacking. Our results show that from a technical perspective retrofitting an existing power plant to operate with hydrogen is feasible either by adapting or replacing the engine to accommodate hydrogen blends from 0 up to 100%. The costs of making the CHP plant hydrogen-ready vary depending on the scenario ranging from a 20% increase for retrofitting to a 60% increase for engine replacement in the best-case scenarios. However these values remain highly variable due to uncertainties associated with the ongoing technology development. From an economic standpoint as of 2024 running the plant on hydrogen remains more expensive due to significant initial investments and higher fuel costs. Nevertheless projections indicate that rising climate concerns CO2 taxes geopolitical factors and the development of the hydrogen framework in the region—through projects such as MosaHYc and HY4Link— could accelerate the competitiveness of hydrogen making it a more viable alternative to fossil-based solutions in the near future.

Hydrogen has attracted widespread attention due to its zero emissions and high energy density and hydrogen-fueled power systems are gradually emerging. This paper combines the advantages of the high conversion efficiency of fuel cells and strong engine power to propose a hydrogen hybrid energy system architecture based on a mixture of fuel cells and engines in order to improve the conversion efficiency of the energy system and reduce its fuel consumption rate. Firstly according to the topology of the hydrogen hybrid energy system and the circuit model of its core components a state-space model of the hydrogen hybrid energy system is established using the Kirchhoff node current principle laying the foundation for the control and management of hydrogen hybrid energy systems. Then based on the state-space model of the hydrogen hybrid system and Pontryagin’s minimum principle a hydrogen hybrid system management strategy based on adaptive equivalent fuel consumption minimum strategy (A-ECMS) is proposed. Finally a hydrogen hybrid power system model is established using the AVL Cruise simulation platform and a control strategy is developed using matlab 2021b/Simulink to analyze the output power and fuel economy of the hybrid energy system. The results show that compared with the equivalent fuel consumption minimum strategy (ECMS) the overall fuel economy of A-ECMS could improve by 10%. Meanwhile the fuel consumption of the hydrogen hybrid energy system is less than half of that of traditional engines.

As the need to reduce Greenhouse Gas (GHG) emissions and dependence on fossil fuels grows new vehicle concepts are emerging as sustainable solutions for urban mobility. Beyond evaluating tailpipe emissions indirect emissions associated with energy and hydrogen production as vehicle manufacturing must be accounted offering a holistic Lifecycle Assessment (LCA) perspective. This study compares Battery Electric Vehicles (BEVs) Fuel Cell Vehicles (FCVs) Hydrogen Internal Combustion Engine Vehicles (H2ICEVs) and hybrid H2ICEVs analyzing energy efficiency and GHG emissions in urban environment across the European Union. Future scenarios (2030 2050) are examined as well with evolving energy mixes and manufacturing impacts. Findings show BEVs as the most efficient configuration with the lowest GHG emissions in 2024 while FCVs become the best option in future scenarios due to greener hydrogen production and improved manufacturing. This study emphasizes the need for tailored strategies to achieve sustainable urban mobility providing insights for policymakers and stakeholders.

Against the backdrop of ever‑increasing energy demand and growing awareness of en‑ vironmental protection the research and optimization of hydrogen‑related multi‑energy systems have become a key and hot issue due to their zero‑carbon and clean characteristics. In the scheduling of such multi‑energy systems a typical problem is how to describe and deal with the uncertainties of multiple types of energy. Scenario‑based methods and ro‑ bust optimization methods are the two most widely used methods. The first one combines probability to describe uncertainties with typical scenarios and the second one essentially selects the worst scenario in the uncertainty set to characterize uncertainties. The selection of these scenarios is essentially a trade‑off between the economy and robustness of the so‑ lution. In this paper to achieve a better balance between economy and robustness while avoiding the complex min‑max structure in robust optimization we leverage artificial in‑ telligence (AI) technology to generate enough scenarios from which economic scenarios and feasible scenarios are screened out. While applying a simple single‑layer framework of scenario‑based methods it also achieves both economy and robustness. Specifically first a Transformer architecture is used to predict uncertainty realizations. Then a Gener‑ ative Adversarial Network (GAN) is employed to generate enough uncertainty scenarios satisfying the actual operation. Finally based on the forecast data the economic scenar‑ ios and feasible scenarios are sequentially screened out from the large number of gener‑ ated scenarios and a balance between economy and robustness is maintained. On this ba‑ sis a multi‑energy collaborative optimization method is proposed for a hydrogen‑based multi‑energy microgrid with consideration of the coupling relationships between energy sources. The effectiveness of this method has been fully verified through numerical exper‑ iments. Data show that on the premise of ensuring scheduling feasibility the economic cost of the proposed method is 0.67% higher than that of the method considering only eco‑ nomic scenarios. It not only has a certain degree of robustness but also possesses good economic performance.

This paper addresses the challenge of renewable energy curtailment which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation by developing a robust model predictive control (RMPC)-based scheduling strategy for an integrated wind–PV–hydrogen storage multi-energy flow system. By building a “wind– PV–hydrogen storage–fuel cell” collaborative system the time and space complementarity of wind and PV is used to stabilize fluctuations and the electrolyzer–hydrogen production– gas storage tank–fuel cell chain is used to absorb surplus power. A multi-time scale state-space model (SSM) including power balance equation equipment constraints and opportunity constraints is established. The RMPC scheduling framework is designed taking the wind–PV joint probability scene generated by Copula and improved K-means and SSM state variables as inputs and the improved genetic algorithm is used to solve the min–max robust optimization problem to achieve closed-loop control. Validation using real-world data from Xinjiang demonstrates a 57.83% reduction in grid power fluctuations under extreme conditions and a 58.41% decrease in renewable curtailment rates markedly enhancing the local system’s capacity to utilize wind and solar energy.

Aiming at resolving the problem of stable and efficient operation of integrated green hydrogen production storage and supply hydrogen refueling stations at different time scales this paper proposes a multi-time-scale hierarchical energy management strategy for integrated green hydrogen production storage and supply hydrogen refueling station (HFS). The proposed energy management strategy is divided into two layers. The upper layer uses the hourly time scale to optimize the operating power of HFS equipment with the goal of minimizing the typical daily operating cost and proposes a parameter adaptive particle swarm optimization (PSA-PSO) solution algorithm that introduces Gaussian disturbance and adaptively adjusts the learning factor inertia weight and disturbance step size of the algorithm. Compared with traditional optimization algorithms it can effectively improve the ability to search for the optimal solution. The lower layer uses the minute-level time scale to suppress the randomness of renewable energy power generation and hydrogen load consumption in the operation of HFS. A solution algorithm based on stochastic model predictive control (SMPC) is proposed. The Latin hypercube sampling (LHS) and simultaneous backward reduction methods are used to generate and reduce scenarios to obtain a set of high-probability random variable scenarios and bring them into the MPC to suppress the disturbance of random variables on the system operation. Finally real operation data of a HFS in southern China are used for example analysis. The results show that the proposed energy management strategy has a good control effect in different typical scenarios.

Against the backdrop of high investment costs in distributed energy storage systems this paper proposes a bi-level energy management model based on shared multi-type energy storage to enhance system economics and resource utilization efficiency. First an electricity–heat–hydrogen coupled shared storage architecture is developed incorporating hydrogen-blended gas turbines gas boilers and hydrogen loads to achieve deep coupling between the power grid and natural gas network. Then a bi-level game model is formulated with the upper-level objective of minimizing the storage operator’s cost and the lower-level objective of minimizing the cost of the integrated energy microgrid (IEM) aggregator. A cooperative game mechanism is introduced within the microgrids to support peer-to-peer energy trading. Nash bargaining theory is applied to determine benefit allocation and dynamic pricing strategies among microgrids. The model is solved using a genetic algorithm (GA) and the alternating direction method of multipliers (ADMM). Simulation results validate the proposed strategy’s effectiveness and feasibility in reducing system costs improving overall benefits and achieving fair benefit allocation.

Renewing a vision for European aviation: Europe today leads the world in civil aviation and air traffic management (ATM). This success should not be taken for granted particularly as the sector undergoes decarbonisation and digitalisation in today’s challenging geopolitical context. Significant value is at stake and capturing this value – for the sake of Europe’s competitiveness sustainability and sovereignty – is contingent on substantial investment in aviation research and innovation (R&I) and support to market uptake of new technologies to avoid the “valley of death” between technological development and product entry-into-service. Aviation is a major socio-economic contributor to Europe: The aviation industry is a vital component of Europe’s economy contri buting significantly to jobs gross domestic product (GDP) and trade. Overall the European aviation sector supports 15 million jobs and contributes EUR 1.1 trillion to European economic activity. The aviation sector is also critical to the EU single market European integration and global connectivity. It drives innovation and enhances Europe’s global influence and security through its combined focus on sustainability and competitiveness. The importance of aviation in achieving these fundamental goals for Europe is underscored by the findings of the Draghi report.