Australia

Replacing the energy density and convenience of diesel fuel for all forms of fossil fuel-powered trains presents significant challenges. Unlike the traditional evolutions of rail which has largely self-optimised to different fuels and cost structures over 150 years the challenges now present with a timeline of just a few decades. Fortunately unlike the mid-1800s simulation and modelling tools are now quite advanced and a full range of scenarios of operations and train trips can be simulated before new traction systems are designed. Full trip simulations of large heavy haul trains or high speed passenger trains are routinely completed controlled by emulations of human drivers or automated control systems providing controls of the “virtual train”. Recent developments in digital twins can be used to develop flexible and dynamic models of passenger and freight rail systems to support the new complexities of decarbonisation efforts. Interactions between many different traction components and the train multibody system can be considered as a system of systems. Adopting this multi-modelling paradigm enables the secure and integrated interfacing of diverse models. This paper demonstrates the application of the multi-modelling approach to develop digital twins for rail decarbonisation traction and it presents physics-based multi-models that include key components required for studying rail decarbonisation problems. Specifically the challenge of evaluating zero-emission options is addressed by adding further layers of modelling to the existing fully detailed multibody dynamics simulations. The additional layers detail control options energy storage the alternate traction system components and energy management systems. These traction system components may include both electrical system and inertia dynamics models to accurately represent the driveline and control systems. This paper presents case study examples of full trip scenarios of both long haul freight trains and higher speed passenger trains. These results demonstrate the many complex scenarios that are difficult to anticipate. Flowing on from this risks can be assessed and practical designs of zero-emission systems can be proposed along with the required recharging or refuelling systems.

The experimental performance of an autothermal hydrogen release unit comprising a perhydro benzyltoluene (H12-BT) dehydrogenation chamber and a catalytic hydrogen combustion (CHC) chamber in thermal contact is discussed. In detail the applied set-up comprised a multi-tubular CHC heating based on seven parallel tubes with the reactor shell containing a commercial dehydrogenation catalyst. In this way the CHC heated the endothermal LOHC dehydrogenation using a part of the hydrogen generated in the dehydrogenation. The proposed heating concept for autothermal LOHC dehydrogenation offers several advantages over state-of-the-art heating concepts including minimized space consumption high efficiency and zero NOx emissions. During performance tests the process reached a minimum hydrogen combustion fraction of 37 % while the minimum heat requirement for the dehydrogenation reaction for industrial scale plants is 33 %. The reactor orientation (vertical vs horizontal) and the flow configuration (counter-current vs. co-current) showed very little influence on the performance demonstrating the robustness of the proposed reactor design.

Green hydrogen has become a core element of Europe’s energy transition to assist in lowering carbon emissions. However the transition to green hydrogen faces challenges including the cost of production availability of renewable energy sources public opposition and the need for supportive government policies and financial initiatives. While there are other alternatives for producing low-carbon hydrogen for example blue hydrogen German funding favours projects that involve hydrogen production via electrolysis. Beyond climate goals it is anticipated that a green hydrogen industry will create economic benefits and a wide-range of collaborative opportunities with key international partnerships increasing energy security if done appropriately. Germany a leader in green hydrogen technology will need to rely on imports to meet long-term demand due to limited renewable energy capacity. Despite the current obstacles to transitioning to green hydrogen it is felt that ultimately the benefits of this industry and reducing emissions will outweigh the associated costs of production. This study analyses the hydrogen transition in Germany by interviewing 37 European experts guided by the research question: What are the key perceived barriers and opportunities influencing the successful adoption and integration of hydrogen technologies in Germany’s hydrogen transition?

The global energy sector is aiming to substantially reduce CO2 emissions to meet the UN climate goals. Among the proposed strategies underground storage solutions such as radioactive disposal CO2 NH3 and underground H2 storage (UHS) have emerged as promising options for mitigating anthropogenic emissions. These approaches require rigorous research and development (R&D) often involving laboratory-scale experiments to establish their feasibility before being scaled up to pilot plant operations. Microorganisms which are ubiquitous in laboratory environments can significantly influence geochemical reactions under variable experimental conditions of porous media and a salt cavern. We have selected a consortium composed of Bacillus sp. Enterobacter sp. and Cronobacter sp. bacteria which are typically present in the laboratory environment. These microorganisms can contaminate the rock sample and develop experimental artifacts in UHS experiments. Hence it is pivotal to sterilize the rock prior to conduct experimental research related to effects of microorganisms in the porous media and the salt cavern for the investigation of UHS. This study investigated the efficacy of various disinfection and sterilization methods including ultraviolet irradiation autoclaving oven heating ethanol treatments and gamma irradiation in removing the microorganisms from silica sand. Additionally the consideration of their effects on mineral properties are reviewed. A total of 567 vials each filled with 9 mL of acid-producing bacteria (APB) media were used to test killing efficacy of the cleaning methods. We conducted serial dilutions up to 10−8 and repeated them three times to determine whether any deviation occurred. Our findings revealed that gamma irradiation and autoclaving were the most effective techniques for eradicating microbial contaminants achieving sterilization without significantly altering the mineral characteristics. These findings underscore the necessity of robust cleaning protocols in hydrogeochemical research to ensure reliable reproducible data particularly in future studies where microbial contamination could induce artifacts in laboratory research.

Virtual power plants (VPPs) are gaining significance in the energy sector due to their capacity to aggregate distributed energy resources (DERs) and optimize energy trading. However their effectiveness largely depends on accurately modeling the uncertain parameters influencing optimal bidding strategies. This paper proposes a deep learning-based forecasting method to predict these uncertain parameters including solar irradiation temperature wind speed market prices and load demand. A stochastic programming approach is introduced to mitigate forecasting errors and enhance accuracy. Additionally this research assesses the flexibility of VPPs by mapping the flexible regions to determine their operational capabilities in response to market dynamics. The study also incorporates power-to‑hydrogen (P2H) and hydrogen-to-power (H2P) conversion processes to facilitate the integration of hydrogen fuel cell vehicles (HFCVs) into VPPs enhancing both technical and economic aspects. A network-aware VPP connected to generation resources storage facilities demand response programming (DRP) vehicle-to-grid technology (V2G) P2H and H2P is used to evaluate the proposed method. The problem is formulated as a convex model and solved using the GUROBI optimizer. Results indicate that a hydrogen refueling station can increase profits by approximately 49 % compared to the base case of directly selling surplus generation from renewable energy sources (RESs) to the market and profits can further increase to roughly 86 % when other DERs are incorporated alongside the hydrogen refueling station.

As the global push for carbon neutrality accelerates energy efficiency has become essential for sustainable development especially for nations like Nigeria that face rising energy demands and significant environmental challenges. This study explores how integrating energy efficiency with carbon neutrality can support Nigeria’s strategic energy goals while offering global lessons for other countries facing similar challenges focusing on key sectors including industry transport and power generation. The study systematically examines the impacts of renewable energy (RE) technologies like solar wind and hydropower—alongside policy reforms technological innovations and demand-side management strategies to advance energy efficiency in Nigeria. Key findings include the identification of strategic policy frameworks technological solutions and the transformative role of green hydrogen in decarbonizing hard-to-electrify sectors. The study also emphasizes the importance of international climate finance decentralized RE systems like solar mini-grids for improving energy access and economic opportunities for job creation in the RE sector. Furthermore it highlights the need for behavioral changes community engagement and consistent policy implementation to address infrastructure gaps and drive energy efficiency goals. The novelty of this research lies in its scenario-based analysis of Nigeria’s low-carbon transition detailing both the opportunities and challenges such as policy inconsistencies infrastructure deficits and financial constraints. The findings stress the importance of international collaboration technological advancements and targeted investments to overcome these challenges. By offering actionable insights and strategic recommendations this study provides a roadmap for policymakers industry stakeholders and researchers to drive Nigeria towards a sustainable carbon-neutral future by 2050.

The integration of hydrogen technologies into islanded DC microgrids presents significant challenges in maintaining voltage stability and coordinating power flow under highly variable renewable energy conditions. This paper proposes a novel DC-link voltage control (DCVC) framework that incorporates adaptive droop control and autonomous operation algorithms to regulate fuel cells electrolysers and battery systems in a coordinated manner. Unlike conventional fixed-gain or priority-based methods the proposed adaptive control dynamically adjusts the droop coefficient in response to voltage deviations enhancing system stability and responsiveness. The control framework is validated on an industry-standard hydrogen DC microgrid platform developed at Griffith University featuring real-time implementation on a Raspberry Pi controller and comprehensive integration with solar wind wave and hydrogen energy sources. A small-signal stability analysis confirms that the proposed control ensures asymptotic voltage convergence under dynamic operating conditions. Experimental results across five case studies demonstrate that the proposed DCVC strategy ensures fast transient response minimises overshoot and maintains the DC-link voltage near the nominal 380 V under varying load and generation scenarios. The framework facilitates flexible energy sharing while ensuring safe hydrogen production and storage. It is also compatible with low-cost open-source hardware making it a scalable solution for remote and off-grid energy applications.

This study presents a comprehensive framework that integrates high-resolution energy forecasting and technoeconomic modeling to assess green hydrogen production potential in Flanders Belgium. Using 15-min interval data from the Elia Group four deep learning models (LSTM BiLSTM GRU and CNN-LSTM) were developed to forecast regional photovoltaic (PV) and onshore wind energy generation. These forecasts informed the estimation of hydrogen yields and the evaluation of the levelized cost of hydrogen (LCOH) under different configurations. Results show that wind-powered hydrogen production achieves the lowest LCOH (6.63 €/kg) due to higher annual operating hours. Among electrolysis technologies alkaline electrolysis (AEL) offers the lowest cost while proton exchange membrane (PEMEL) provides greater flexibility for intermittent power sources. The hybrid PVwind system demonstrated seasonal complementarity increasing annual hydrogen yield and improving production stability. The proposed framework supports regional planning and highlights strategic investment opportunities for cost-effective green hydrogen deployment.