Publications

This study presents a techno-economic assessment of power-to-gas and power-to-liquid pathways within the Hydrogen Valley concept to support the decarbonization of local energy systems. Using the EnergyPLAN software both business-as-usual and Hydrogen Valley scenarios were analyzed by varying renewable energy electrolyzer capacity and hydrogen storage. The levelized costs of green hydrogen electrofuels and synthetic natural gas were estimated for both scenarios. A sensitivity analysis was conducted to assess the impact of cost parameters on the levelized costs of hydrogen and alternative fuel production. The findings indicate that the Hydrogen Valley scenario results in a 5.9% increase in total annual costs but achieves a 29.5% reduction in CO2 emissions compared to the business-as-usual scenario. Additionally utilizing excess energy for power-to-gas and power-to-liquid conversion in the Hydrogen Valley scenario lowers the levelized cost of electrofuels from 0.28 €·kWh−1 to 0.21 €·kWh−1 . Similarly the levelized cost of synthetic natural gas decreases from 0.33 €·kWh−1 to 0.25 €·kWh−1 when transitioning from the businessas-usual scenario to the Hydrogen Valley scenario. The results highlight that Hydrogen Valleys enable low-emission energy systems with cost-effective alternative fuels underscoring the tradeoffs between deep decarbonization and cost optimization in the transition to clean energy systems.

Integrating carbon capture and storage (CCS) technology into an integrated energy system (IES) can reduce its carbon emissions and enhance its low-carbon performance. However the full CCS of flue gas displays a strong coupling between lean and rich liquor as carbon dioxide liquid absorbents. Its integration into IESs with a high penetration level of renewables results in insufficient flexibility and renewable curtailment. In addition integrating split-flow CCS of flue gas facilitates a short capture time giving priority to renewable energy. To address these limitations this paper develops a carbon capture utilization and storage (CCUS) method into which storage tanks for lean and rich liquor and a two-stage power-to-gas (P2G) system with multiple utilizations of hydrogen including a fuel cell and a hydrogen-blended CHP unit are introduced. The CCUS is integrated into an IES to build an electricity–heat–hydrogen–gas IES. Accordingly a deep low-carbon economic optimization strategy for this IES which considers stepwise carbon trading coal consumption renewable curtailment penalties and gas purchasing costs is proposed. The effects of CCUS the twostage P2G system and stepwise carbon trading on the performance of this IES are analyzed through a case-comparative analysis. The results show that the proposed method allows for a significant reduction in both carbon emissions and total operational costs. It outperforms the IES without CCUS with an 8.8% cost reduction and a 70.11% reduction in carbon emissions. Compared to the IES integrating full CCS the proposed method yields reductions of 6.5% in costs and 24.7% in emissions. Furthermore the addition of a two-stage P2G system with multiple utilizations of hydrogen further amplifies these benefits cutting costs by 13.97% and emissions by 12.32%. In addition integrating CCUS into IESs enables the full consumption of renewables and expands hydrogen utilization and the renewable consumption proportion in IESs can reach 69.23%.

The transition from fossil fuels to renewable energy sources particularly hydrogen has emerged as a central strategy for decarbonization and the pursuit of net-zero carbon emissions. Meeting the demand for large-scale hydrogen storage a crucial component of the hydrogen supply chain has led to the exploration of underground hydrogen storage as an economically viable solution to global energy needs. In contrast to other subsurface storage options such as salt caverns and aquifers which are geographically limited depleted gas reservoirs have garnered increasing attention due to their broader distribution and higher storage capacity. However the safe storage and cycling of hydrogen in depleted gas reservoirs require the preservation of high stability and integrity in the caprock reservoir and wellbore. Nevertheless there exists a significant gap in the current research concerning storage integrity in underground hydrogen storage within depleted gas reservoirs and a systematic approach is lacking. This paper aims to address this gap by reviewing the primary challenges associated with storage integrity including geochemical reactions microbial activities faults and fractures and perspectives on hydrogen cycling. The study comprehensively reviews the processes and impacts such as abiotic and biotic mineral dissolution/precipitation reactivation and propagation of faults and fractures in caprock and host-rock wellbore instability due to cement degradation and casing corrosion and stress changes during hydrogen cycling. To provide a practical solution a technical screening tool has been developed considering controlling variables risks and consequences affecting storage integrity. Finally this paper highlights knowledge gaps and suggests feasible methods and pathways to mitigate these risks facilitating the development of large-scale underground hydrogen storage in depleted gas reservoirs.

This paper presents a method for estimating Well-to-Wheel (WTW) energy use and greenhouse gas (GHG) emissions attributed to the advanced railway propulsion systems implemented in conjunction with different energy carriers and their production pathways. The analysis encompasses diesel-electric multiple unit vehicles converted to their hybrid-electric plug-in hybrid-electric fuel cell hybrid-electric or battery-electric counterparts combined with biodiesel or hydrotreated vegetable oil (HVO) as the first and second generation biofuels liquefied natural gas (LNG) hydrogen and/or electricity. The method is demonstrated using non-electrified regional railway network with heterogeneous vehicle fleet in the Netherlands as a case. Battery-electric system utilizing green electricity is identified as the only configuration leading to emission-free transport while offering the highest energy use reduction by 65–71% compared to the current diesel-powered hybrid-electric system. When using grey electricity based on the EU2030 production mix these savings are reduced to about 27–39% in WTW energy use and around 68–73% in WTW GHG emissions. Significant reductions in overall energy use and emissions are obtained for the plug-in hybrid-electric concept when combining diesel LNG or waste cooking oil-based HVO with electricity. The remaining configurations that reduce energy use and GHG emissions are hybrid-electric systems running on LNG or HVO from waste cooking oil. The latter led to approximately 88% lower WTW emissions than the baseline for each vehicle type. When produced from natural gas or EU2030-mix-based electrolysis hydrogen negatively affected both aspects irrespective of the prime mover technology. However when produced via green electricity it offers a GHG reduction of approximately 90% for hybrid-electric and fuel cell hybrid-electric configurations with a further reduction of up to 92–93% if combined with green electricity in plug-in hybrid-electric systems. The results indicate that HVO from waste cooking oil could be an effective and instantly implementable transition solution towards carbon–neutral regional trains allowing for a smooth transition and development of supporting infrastructure required for more energy-efficient and environment-friendly technologies.

The steel industry which relies heavily on primary energy is one of the industries with the highest CO2 emissions in China. It is urgent for the industry to identify ways to embark on the path to “green steel”. Hydrogen metallurgy technology uses hydrogen as a reducing agent and its use is an important way to reduce CO2 emissions from long-term steelmaking and ensure the green and sustainable development of the steel industry. Previous research has demonstrated the feasibility and emission reduction effects of hydrogen metallurgy technology; however further research is needed to dynamically analyze the overall impact of the large-scale development of hydrogen metallurgy technology on future CO2 emissions from the steel industry. This article selects the integrated MARKAL-EFOM system (TIMES) model as its analysis model constructs a China steel industry hydrogen metallurgy model (TIMES-CSHM) and analyzes the resulting impact of hydrogen metallurgy technology on CO2 emissions. The results indicate that in the business-as-usual scenario (BAU scenario) applying hydrogen metallurgy technology in the period from 2020 to 2050 is expected to reduce emissions by 203 million tons and make an average 39.85% contribution to reducing the steel industry’s CO2 emissions. In the carbon emission reduction scenario applying hydrogen metallurgy technology in the period from 2020 to 2050 is expected to reduce emissions by 353 million tons contributing an average of 41.32% to steel industry CO2 reduction. This study provides an assessment of how hydrogen metallurgy can reduce CO2 emissions in the steel industry and also provides a reference for the development of hydrogen metallurgy technology.

The imperative to address traditional energy crises and environmental concerns has accelerated the need for energy structure transformation. However the variable nature of renewable energy poses challenges in meeting complex practical energy requirements. To address this issue the construction of a multifunctional large-scale stationary energy storage system is considered an effective solution. This paper critically examines the battery and hydrogen hybrid energy storage systems. Both technologies face limitations hindering them from fully meeting future energy storage needs such as large storage capacity in limited space frequent storage with rapid response and continuous storage without loss. Batteries with their rapid response (90%) excel in frequent short-duration energy storage. However limitations such as a selfdischarge rate (>1%) and capacity loss (~20%) restrict their use for long-duration energy storage. Hydrogen as a potential energy carrier is suitable for large-scale long-duration energy storage due to its high energy density steady state and low loss. Nevertheless it is less efficient for frequent energy storage due to its low storage efficiency (~50%). Ongoing research suggests that a battery and hydrogen hybrid energy storage system could combine the strengths of both technologies to meet the growing demand for large-scale long-duration energy storage. To assess their applied potentials this paper provides a detailed analysis of the research status of both energy storage technologies using proposed key performance indices. Additionally application-oriented future directions and challenges of the battery and hydrogen hybrid energy storage system are outlined from multiple perspectives offering guidance for the development of advanced energy storage systems.

The automotive industry is under increasing pressure to develop cleaner and more efficient technologies in response to stringent emission regulations. Hydrogenpowered internal combustion engines represent a promising alternative offering the potential to reduce carbon-based emissions while improving efficiency. However the accurate estimation of in-cylinder pressure is crucial for optimizing the performance and emissions of these engines. While traditional simulation tools such as GT-POWER are widely utilized for these purposes recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure with a particular focus on improvements in computational efficiency. Additionally the artificial neural network is employed to predict the equivalent flame radius thereby obviating the need for repeated tests using dedicated high-speed cameras in optical access research engines due to the resource-intensive nature of data acquisition and post-processing. Experiments were conducted on a single-cylinder research engine operating at low-speed and low-load conditions across three distinct relative air–fuel ratio values with a range of ignition timing settings applied for each air excess coefficient. The findings demonstrate that the artificial neural network model surpasses GT-POWER in predicting in-cylinder pressure with higher accuracy achieving an RMSE consistently below 0.44% across various conditions. In comparison GT-POWER exhibits an RMSE ranging from 0.92% to 1.57%. Additionally the neural network effectively estimates the equivalent flame radius maintaining an RMSE of less than 3% ranging from 2.21% to 2.90%. This underscores the potential of artificial neural network-based approaches to not only significantly reduce computational time but also enhance predictive precision. Furthermore this methodology could subsequently be applied to conventional road engines exhibiting characteristics and performance similar to those of a specific optical engine used as the basis for the machine learning analysis offering a practical advantage in real-time diagnostics.

Fuel cell electric vehicles (FCEVs) have received significant attention in recent times due to various advantageous features such as high energy efficiency zero emissions and extended driving range. However FCEVs have some drawbacks including high production costs; limited hydrogen refueling infrastructure; and the complexity of converters controllers and method execution. To address these challenges smart energy management involving appropriate converters controllers intelligent algorithms and optimizations is essential for enhancing the effectiveness of FCEVs towards sustainable transportation. Therefore this paper presents emerging energy management strategies for FCEVs to improve energy efficiency system reliability and overall performance. In this context a comprehensive analytical assessment is conducted to examine several factors including research trends types of publications citation analysis keyword occurrences collaborations influential authors and the countries conducting research in this area. Moreover emerging energy management schemes are investigated with a focus on intelligent algorithms optimization techniques and control strategies highlighting contributions key findings issues and research gaps. Furthermore the state-of-the-art research domains of FCEVs are thoroughly discussed in order to explore various research domains relevant outcomes and existing challenges. Additionally this paper addresses open issues and challenges and offers valuable future research opportunities for advancing FCEVs emphasizing the importance of suitable algorithms controllers and optimization techniques to enhance their performance. The outcomes and key findings of this review will be helpful for researchers and automotive engineers in developing advanced methods control schemes and optimization strategies for FCEVs towards greener transportation.

Optimally scheduling alkaline electrolyzers (AELs) in a hydrogen-based microgrid (HBM) can greatly unleash the operational flexibility of the HBM. However existing scheduling strategies of AELs mostly utilize a simplified AEL model which ignores the nonlinear coupling of electric-hydrogen-thermal sectors violating the AEL’s security constraints thereby making the scheduling scheme infeasible. This paper proposes an improved model predictive control (MPC) based optimal scheduling framework which incorporates a scheduling correction algorithm into the basic MPC structure. This framework is utilized for implementing economic and resilient scheduling of an HBM under normal and emergency conditions respectively. With the scheduling correction algorithm this framework can be formulated into a computationally efficient mixedinteger linear programming meanwhile guaranteeing the solutions strictly satisfy the security constraints of hydrogen facilities (i.e. AEL and hydrogen tank). Case studies are conducted based on real operating data of a Danish energy island Bornholm. The results demonstrate that the proposed scheduling scheme under normal conditions can contribute to significant comprehensive benefits from the daily operation cost saving of 68% computational time saving of 98% and satisfying the security constraints of hydrogen facilities compared to previous scheduling strategies. Besides it sharply reduces load shedding under emergency conditions by proactively allocating distributed energy sources in the HBM.

This paper mainly introduces the main pain point of China's civil hydrogen energy supply chain - the problem of storage and transportation and analyzes the safety economy and scale effect and other issues of the existing hydrogen energy storage and transportation compares with other storage and transportation technology solutions and comprehensively screens out the storage and transportation technology solution mainly based on liquid hydrogen technology. The liquid hydrogen technology solution has significant advantages over the existing compressed hydrogen system in terms of safety economy and scale effect especially for future large-scale hydrogen energy application scenarios. In addition the future hydrogen energy storage and transportation system based on liquid hydrogen technology can help improve the overall utilization efficiency of country’s renewable energy promote the country's energy transition promote the electrification of the country's transportation sector and help achieve China's carbon emission reduction 2030/2060 target.