Applications & Pathways

Dual-fuel engines are a way of transitioning the marine sector to carbon-neutral fuels like hydrogen and methanol. For the development of these engines accurate simulation of the combustion process is needed for which calculating the pilot’s ignition delay is essential. The present work investigates novel methodologies for calculating this. This involves the use of chemical kinetic schemes to compute the ignition delay for various operating conditions. Machine learning techniques are used to train models on these data sets. A neural network model is then implemented in a dual-fuel combustion model to calculate the ignition delay time and is compared using a lookup table or a correlation. The numerical results are compared with experimental data from a dual-fuel medium-speed marine engine operating with hydrogen or methanol from which the method with best accuracy and fastest calculation is selected.

Renewable energy systems are at the core of global efforts to reduce greenhouse gas (GHG) emissions and to combat climate change. Focusing on the role of energy storage in enhancing dependability and efficiency this paper investigates the design and optimization of a completely sustainable hybrid energy system. Furthermore hybrid storage systems have been used to evaluate their viability and cost-benefits. Examined under a 100% renewable energy microgrid framework three setup configurations are as follows: (1) photovoltaic (PV) and Battery Storage System (BSS) (2) Hybrid PV/Wind Turbine (WT)/BSS and (3) Integrated PV/WT/BSS/Electrolyzer/ Hydrogen Tank/Fuel Cell (FC). Using its geographical solar irradiance and wind speed data this paper inspires on an industrial community in Neom Saudi Arabia. HOMER software evaluates technical and economic aspects net present cost (NPC) levelized cost of energy (COE) and operating costs. The results indicate that the PV/ BSS configuration offers the most sustainable solution with a net present cost (NPC) of $2.42M and a levelized cost of electricity (LCOE) of $0.112/kWh achieving zero emissions. However it has lower reliability as validated by the provided LPSP. In contrast the PV/WT/BSS/Elec/FC system with a higher NPC of $2.30M and LCOE of $0.106/kWh provides improved energy dependability. The PV/WT/BSS system with an NPC of $2.11M and LCOE of $0.0968/kWh offers a slightly lower cost but does not provide the same level of reliability. The surplus energy has been implemented for hydrogen production. A sensitivity analysis was performed to evaluate the impact of uncertainties in renewable resource availability and economic parameters. The results demonstrate significant variability in system performance across different scenarios

Electricity in rural Alaska is provided by more than 200 standalone microgrid systems powered predominantly by diesel generators. Incorporating renewable energy generation and storage to these systems can reduce their reliance on costly imported fuel and improve sustainability; however uncertainty remains about optimal grid architectures to minimize cost including how and when to incorporate long-duration energy storage. This study implements a novel multi-pronged approach to assess the techno-economic feasibility of future energy pathways in the community of Kotzebue which has already successfully deployed solar photovoltaics wind turbines and battery storage systems. Using real community load resource and generation data we develop a series of comparison models using the HOMER Pro software tool to evaluate microgrid architectures to meet over 90% of the annual community electricity demand with renewable generation considering both battery and hydrogen energy storage. We find that near-term planned capacity expansions in the community could enable over 50% renewable generation and reduce the total cost of energy. Additional build-outs to reach 75% renewable generation are shown to be competitive with current costs but further capacity expansion is not currently economical. We additionally include a cost sensitivity analysis and a storage capacity sizing assessment that suggest hydrogen storage may be economically viable if battery costs increase but large-scale seasonal storage via hydrogen is currently unlikely to be cost-effective nor practical for the region considered. While these findings are based on data and community priorities in Kotzebue we expect this approach to be relevant to many communities in the Arctic and Sub-Arctic regions working to improve energy reliability sustainability and security.

Heavy-duty fuel cell trucks are a promising approach to reduce the CO2 emissions of logistic fleets. Due to their higher powertrain energy density in comparison to battery-electric trucks they are especially suited for long-haul applications while transporting high payloads. Despite these great advantages the fleet integration of such vehicles is made difficult due to high costs and limited performance in thermally critical environmental conditions. These challenges are addressed in the European Union (EU) funded project ESCALATE which aims to demonstrate high-efficiency zero-emission heavy-duty vehicle (zHDV) powertrains that provide a range of 800 km without refueling or recharging. Powertrain components and their corresponding thermal components account for a large part of the production costs. For vehicle users higher costs are only acceptable if a significantly higher benefit can be achieved. Therefore it is important to size these components for the actual vehicle mission to avoid oversizing. In this paper an optimization method which determines the optimum component sizes for a given mission scenario under consideration of multiple criteria (e.g. costs performance and range) is presented.

Hydrogen fuel cell vehicles (HFCVs) are vital for advancing the hydrogen economy and decarbonizing the transportation sector. However research on HFCV market dynamics in passenger vehicles is limited especially incorporating both market competition from other vehicle types and the comprehensive supply–demand market dynamics. To bridge this gap our study proposed a spatial agent-based model to simulate the HFCV market evolution with the aim of finding effective strategies and policy implications for breaking the diffusion dilemma of the HFCV market. We calibrated the model using survey data (N=1065) collected from Beijing and evaluated its performance across five “What-If” scenarios. Results indicate that HFCVs and hydrogen stations are difficult to penetrate under the current conditions despite HFCV applicants and market share growing by 37.5% and 15.63% respectively. Consumer perceptions on cost social and environment have greater impacts on HFCV proliferation than facility availability. The HFCV purchase subsidy has much greater impact than the technological learning rate greatly accelerating its market emergence timing. Finally HFCVs’ diffusion significantly influences the market of battery electric vehicles.

This study introduces the Smart Grid Hybrid Electrolysis-and-Combustion System (SGHE-CS) designed to seamlessly integrate hydrogen production storage and utilization within smart grid operations to maximize renewable energy use and maintain grid stability. The system achieves a hydrogen production efficiency of 98.5% indicating the effective conversion rate of electrical energy to hydrogen via PEM electrolysis. Combustion efficiency reaches 98.1% reflecting the proportion of hydrogen energy successfully converted into usable power through advanced staged combustion. Storage and transportation efficiency is 96.3% accounting for energy losses during hydrogen compression storage and delivery. Renewable integration efficiency is 97.3% representing the system’s capacity to utilize variable renewable energy inputs without curtailment. Operational versatility is 99.3% denoting the system’s ability to maintain high performance across load demands and grid conditions. Real-time monitoring and adaptive control strategies ensure reliability and resilience positioning SGHE-CS as a promising solution for sustainable low-carbon energy infrastructure.

The transportation industry significantly contributes to greenhouse gas (GHG) emissions. Federal and provincial governments have implemented strategies to decrease dependence on gasoline and diesel fuels. This encompasses promoting the adoption of electric cars (EVs) and biofuel alternatives investing in renewable energy sources and enhancing public transit systems. There is a growing focus on enhancing infrastructure to facilitate active transportation modes like cycling and walking which provide the combined advantages of decreasing emissions and advancing public health. In this paper we propose a System Dynamics simulation model for evaluating factors for the adoption of alternative-fuel vehicles such as EVs biofuel vehicles bus bikes and hydrogen vehicles. Five factors— namely customer awareness government initiatives cost of vehicles cost of fuels and infrastructure developments—to increase the adoption of alternative-fuel vehicles are studied. Two scenarios are modeled: A baseline scenario that follows the existing trends in transportation (namely the use of gasoline vehicles) Scenario 1 which prioritizes greater adoption of electric vehicles (EVs) and biofuel-powered vehicles and Scenario 2 which prioritizes hydrogen fuel-based vehicles and improves biking culture. The simulation findings show that all scenarios achieve reductions in GHG emissions compared to the baseline with Scenario 2 showing the lowest emissions. The proposed work is useful for transport decision makers and municipal administrators in devising policies for reducing overall GHG emissions and this also aligns with Canada’s net zero goals.

This paper provides a comprehensive review of novel aviation technologies analyzing the advancements and challenges associated with the transition to sustainable air transport. The study explores three key pillars: unconventional aerodynamic configurations novel propulsion systems and advanced materials. Unconventional airframe architectures such as box-wing blended-wing-body and truss-braced wings demonstrate potential for improved aerostructural efficiency and reduced fuel consumption compared to traditional tube-and-wing designs. Aeropropulsive innovations as distributed propulsion boundary layer ingestion and advanced turbofan configurations are also promising in this regard. Significant progress in propulsion technologies including hybrid-electric hydrogen and extensive use of sustainable aviation fuels (SAF) plays a pivotal role in reducing air transport greenhouse gas emissions. However energy storage limitations and infrastructure constraints remain critical challenges and hence in the near future SAF could represent the most feasible solution. The introduction of advanced lightweight materials could further enhance aircraft overall performance. The results presented and discussed in this paper show that there is no a unique solution to the problem of the sustainability of air transport but a combination of all the novel technologies is necessary to achieve the ambitious environmental goals for the air transport of the future.

The global steel industry accounts for 8–10 % of global CO2 emissions and requires deep decarbonisation for achieving the targets set in the Paris Agreement. However no low-emission primary steel production technology has yet been commercially feasible or deployed. Through analysing revisions and additions of European Union climate policy we show that green hydrogenbased steelmaking in competitive locations achieves cost-competitiveness on the European market starting 2026. If the deployment of competitive lowemission steelmaking is insufficient we show that the European steel industry loses competitiveness vis-à-vis countries with access to low-cost renewable energy. Therefore we assess the options for the European steel industry to relocate the energy-intensive ironmaking step and trade Hot Briquetted Iron for rapid deep decarbonisation of the European steel industry. Lastly we discuss complementing policy options to enhance the Carbon Border Adjustment Mechanism’s strategic value through European Union-lead global climate cooperation and the possibility of sparking an international decarbonisation race.

The escalating global demand for primary energy—still predominantly met by conventional carbon-based fuels—has led to increased atmospheric pollution. This underscores the urgent need for alternative energy strategies capable of reducing carbon emissions while meeting global energy requirements. Hydrogen as a clean combustible fuel offers a promising alternative to hydrocarbons producing neither soot CO2 nor unburned hydrocarbons. Although nitrogen oxides (NOx) are the primary combustion by-products their formation can be mitigated by controlling flame temperature. This study investigates the viability of hydrogen as a clean energy vector by simulating an unsteady turbulent non-premixed hydrogen jet flame interacting with an air co-flow. The numerical simulations employ the Unsteady Reynolds-Averaged Navier–Stokes (URANS) framework for efficient and accurate prediction of transient flow behavior. Turbulence is modeled using the Shear Stress Transport (SST k-ω) model which enhances accuracy in high Reynolds number reactive flows. The combustion process is described using a presumed Probability Density Function (PDF) model allowing for a statistical representation of turbulent mixing and chemical reaction. The simulation results are validated by comparison with experimental temperature and mixture fraction data demonstrating the reliability and predictive capability of the proposed numerical approach.

With the global energy structure shifting towards clean and efficient hydrogen energy the safety management issues of hydrogen refueling stations are becoming increasingly prominent. To address these issues a hydrogen leak localization algorithm for hydrogen refueling stations based on a combination of reinforcement learning and hidden Markov models is proposed. This method combines hidden Markov model to construct a probability distribution model for hydrogen leakage and diffusion simulates the propagation probability of hydrogen in different grid cells and uses reinforcement learning to achieve fast and accurate localization of hydrogen leakage events. The outcomes denoted that the training accuracy reached 95.2% with an F1 value of 0.961 indicating its high accuracy in hydrogen leak localization. When the wind speed was 0.8 m/s the mean square error of the raised method was 0.03 and when the wind speed was 1.0 m/s the mean square error of the raised method was 0.04 proving its good robustness. After 50 localization experiments the proposed algorithm achieves a localization success rate of 93.7% and an average computation time of 42.8 s further demonstrating its high accuracy and computational efficiency. The proposed hydrogen leakage location algorithm has improved the accuracy and efficiency of hydrogen leakage location providing scientific basis and technical guarantee for the safe operation of future hydrogen refueling stations.

Infrastructure projects can act as niches for innovation development contribute to strategic goals of network owners and drive broader systemic transitions. However limited research has examined how sustainability transitions are shaped through narratives and counternarratives around infrastructure projects. Using a case study of the port of Rotterdam we analyze how three embedded projects - Maasvlakte 2 RDM Campus and the Hydrogen Pipeline - reflected and shaped evolving narratives and counter-narratives over a 20-year sustainability transition. Grounded in the Multi-Level Perspective (MLP) the study demonstrates how an infrastructure owner like the Port of Rotterdam Authority (PoRA) strategically mobilized narrative framing to reshape existing regimes over time. The study contributes to the debate on project management and transition studies by highlighting how infrastructure project owners respond to transition-related tensions by shaping defending and adapting project narratives over time thereby influencing sustainability trajectories.

The carbon emission reduction mission requires a multifaceted approach in which green hydrogen is expected to play a key role. The accelerated adoption of green hydrogen technologies is vital to this journey towards carbon neutrality by 2050. However the energy transition involving green hydrogen requires a data-driven approach to ensure that the benefits are realised. The introduction of testing sites for green hydrogen technologies will be crucial in enabling the performance testing of various components within the green hydrogen value chain. This study involves an areal assessment of a selected test site for the installation of a grid-tied solar PV-green hydrogen-battery storage microgrid system at a factory facility in South Africa. The evaluation includes a site energy audit to determine the consumption profile and an analysis of the location’s weather pattern to assess its impact on the envisaged microgrid. Lastly a design of the microgrid is conceptualised. A 39 kW photovoltaic system powers the microgrid which comprises a 22 kWh battery storage system 10 kW of electrolyser capacity an 8 kW fuel cell and an 800 L hydrogen storage capacity between 30 and 40 bars.

This paper presents TwinP2G a software application for optimal planning of investments in power-to-gas (PtG) systems. TwinP2G provides simulation and optimization services for the techno-economic analysis of user-customized energy networks. The core of TwinP2G is based on power flow simulation; however it supports energy sector coupling including electricity green hydrogen natural gas and synthetic methane. The framework provides a user-friendly user interface (UI) suitable for various user roles including data scientists and energy experts using visualizations and metrics on the assessed investments. An identity and access management mechanism also serves the security and authorization needs of the framework. Finally TwinP2G revolutionizes the concept of data availability and data sharing by granting its users access to distributed energy datasets available in the EnerShare Data Space. These data are available to TwinP2G users for conducting their experiments and extracting useful insights on optimal PtG investments for the energy grid.

In the pursuit of zero-emission mobility hydrogen represents a promising fuel for internal combustion engines. However its low volumetric energy density poses challenges especially for high-performance applications where compactness and lightweight design are crucial. This study investigates the feasibility of an innovative hydrogen-fueled two-stroke opposed-piston (2S-OP) engine targeting a specific power of 130 kW/L and an indicated thermal efficiency above 40%. A detailed 3D-CFD analysis is conducted to evaluate mixture formation combustion behavior abnormal combustion and water injection as a mitigation strategy. Innovative ring-shaped multi-point injection systems with several designs are tested demonstrating the impact of injector channels’ orientation on the final mixture distribution. The combustion analysis shows that a dual-spark configuration ensures faster combustion compared to a single-spark system with a 27.5% reduction in 10% to 90% combustion duration. Pre-ignition is identified as the main limiting factor strongly linked to mixture stratification and high temperatures. To suppress it water injection is proposed. A 55% evaporation efficiency of the water mass injected lowers the in-cylinder temperature and delays pre-ignition onset. Overall the study provides key design guidelines for future high-performance hydrogen-fueled 2S-OP engines.

This study expresses energy scheduling in intelligent distribution grid with renewable resources charging stations and hydrogen stations for electric vehicles and integrated energy systems. In deterministic model objective function minimizes total operating energy losses and environmental costs of grid. Constraints are power flow equations network operating and voltage security limits operating model of renewable resources electric vehicle stations and integrated energy systems. Scheme includes uncertainties in load renewable resources charging and hydrogen stations and energy prices. Robust optimization uses to obtain an operation that is robust against the forecast error of the aforementioned uncertainties. Modeling electric vehicles station and aforementioned integrated energy systems considering economic operational and environmental objectives of network operator as objective function extracting a robust model of aforementioned uncertainties in order to extract a solution that is robust against the uncertainty prediction error and examining ability of energy management to improve voltage security of grid are among innovations of this paper. Numerical results obtained from various cases prove the aforementioned advantages and innovations. Energy management of resources charging and hydrogen stations and aforementioned integrated systems lead to scheme being robust against 35% of the prediction error of various uncertainties. In these conditions scheme has improved economic operational environmental and voltage security conditions by about 33.6% 7%- 37.4% 44.4% and 24.7% respectively compared to load flow studies. By applying optimal penalty price for energy losses and pollution pollution and energy losses in the network are reduced by about 45.15% and 34.1% respectively.

Decarbonising aquaculture support vessels is pivotal to reducing greenhouse gas (GHG) emissions across both the aquaculture and maritime sectors. This study evaluates the technical and economic feasibility of deploying hydrogen as a marine fuel for a 14.95 m net cleaning vessel (NCV) operating in Tasmania Australia. The analysis retains the vessel’s original layout and subdivision to enable a like-for-like comparison between conventional diesel and hydrogen-based systems. Two options are evaluated: (i) replacing both the main propulsion engines and auxiliary generator sets with hydrogen-based systems— either proton exchange membrane fuel cells (PEMFCs) or internal combustion engines (ICEs); and (ii) replacing only the diesel generator sets with hydrogen power systems. The assessment covers system sizing onboard hydrogen storage integration operational constraints lifecycle cost and GHG abatement. Option (i) is constrained by the sizes and weights of PEMFC systems and hydrogen-fuelled ICEs rendering full conversion unfeasible within current spatial and technological limits. Option (ii) is technically feasible: sixteen 700 bar cylinders (131.2 kg H2 total) meet one day of onboard power demand for net-cleaning operations with bunkering via swap-and-go skids at the berth. The annualised total cost of ownership for the PEMFC systems is 1.98 times that of diesel generator sets while enabling annual CO2 reductions of 433 t. The findings provide a practical decarbonisation pathway for small- to medium-sized service vessels in niche maritime sectors such as aquaculture while clarifying near-term trade-offs between cost and emissions.

Fuel-cell flying vehicles suffer from limited endurance while ammonia decomposed onboard to supply hydrogen offers a carbon-free high-density solution to extend flight missions. However the system’s performance is governed by a multi-scale coupling between propulsion and control systems. To this end this paper introduces a novel optimization paradigm termed physics-informed gradient-enhanced multi-objective optimization (PIGEMO) to simultaneously optimize the ammonia decomposition unit (ADU) catalyst composition powertrain sizing and flight control parameters. The PI-GEMO framework leverages a physics-informed neural network (PINN) as a differentiable surrogate model which is trained not only on sparse simulation data but also on the governing differential equations of the system. This enables the use of analytical gradient information extracted from the trained PINN via automatic differentiation to intelligently guide the evolutionary search process. A comprehensive case study on a flying vehicle demonstrates that the PIGEMO framework not only discovers a superior set of Pareto-optimal solutions compared to traditional methods but also critically ensures the physical plausibility of the results.

By integrating Renewable Energy Sources (RES) and storage devices Hybrid Energy Systems (HESs) represent a promising solution for decarbonizing isolated and remote communities. Proper sizing and management of systems comprising a variety of components requires however more advanced methods than conventional energy systems. This study proposes a novel Mixed Integer Linear Programming (MILP) framework for the simultaneous design of a grid-connected HES supported by renewable generators. Unlike the standard design approach based on parametric dispatch strategies this framework simultaneously optimizes the energy management of each system configuration under analysis. The novel approach is applied to size a combination of Li-Ion batteries an alkaline electrolyzer H2 tanks and a PEM fuel cell to maximize the NPV of a system including a wind turbine and a photovoltaic field. Managing thousands of variables at the same time the framework simultaneously optimizes how all components are used to fulfill the load and balance the input/export of power within a limited electrical network. Results show that the combination of BESS and H2 can provide for both the need for short- and long-term energy storage and that the MILP optimization can effectively allocate the energy flows and produce 558 k€ of revenues per year 15.5% of the initial investment cost of 3.6 M€. The investment cost of the system is recovered in six years and presents an NPV of 5.51 M€ after 20 years. Results from the proposed method are also compared to common approaches based on rule-based parametric dispatch strategies demonstrating the superiority of MILP for the design and management of complex HESs.

Air travel demand is rising rapidly and the aviation sector is relying on technology to decouple environmental impacts from its growth. Using Sweden as a case study we assessed the absolute environmental sustainability of medium-distance air travel in 2050 positioning the aviation sector's environmental impacts in relation to the planetary limits. We employed a novel framework that integrates prospective life cycle assessment and absolute environmental sustainability assessment methodologies. Our findings suggest that projected medium-distance air travel powered by e-kerosene or liquid hydrogen could have life cycle environmental impacts that overshoot global climate change and biodiversity loss thresholds by several orders of magnitude. Based on our case results for Sweden for aviation to develop within the planetary limits we recommend cross-sector collaboration to address environmental impacts from fossil-free energy supplies and the establishment of integrated targets that incorporate broader environmental issues. Given the unlikelihood of decoupling growth from environmental impacts policymakers and the aviation sector should consider concurrently supporting technological development and implementing measures to manage air travel demand.

The integration of electric vehicles (EVs) and fuel-cell electric vehicles (FCEVs) requires accessible and profitable facilities for fast charging. To promote fast-charging stations (FCSs) a systematic analysis that encompasses both planning and operation is required including the incorporation of multi-energy resources and uncertainty. This paper presents an optimization framework that addresses a joint strategy for the configuration and operation of an EV/FCEV fast-charging station (FCS) integrated with distributed energy resources (DERs) and hydrogen systems. The framework incorporates uncertainties related to solar photovoltaic (PV) generation and demand for EVs/FCEVs. The proposed joint strategy comprises a four-phase decision-making framework. Phase 1 involves modeling EV/FECE demand while Phase 2 focuses on determining an optimal long-term infrastructure configuration. Subsequently in Phase 3 the operator optimizes daily power scheduling to maximize profit. A real-time uncertainty update is then executed in Phase 4 upon the realization of uncertainty. The proposed optimization framework formulated as mixed-integer quadratic programming (MIQP) considers configuration investment operational maintenance and penalty costs for excessive grid power usage. A heuristic algorithm is proposed to solve this problem. It yields good results with significantly less computational complexity. A case study shows that under the most adverse conditions the proposed joint strategy increases the FCS owner’s profit by 3.32% compared with the deterministic benchmark.

In this study we investigate the dual-fuel operation of compression ignition engines using biodiesel at varying concentrations in combination with biogas with and without hydrogen enrichment. A response surface methodology based on a central composite experimental design was employed to optimize energy efficiency and minimize pollutant emissions. The partial substitution of diesel with gaseous fuel substantially reduces the specific fuel consumption achieving a maximum decrease of 21% compared with conventional diesel operation. Enriching biogas with hydrogen accounting for 13.3% of the total flow rate increases the thermal efficiency by 0.8% compensating for the low calorific value and reduced volumetric efficiency of biogas. Variations in biodiesel concentration exhibits a nonlinear effect yielding an additional average efficiency gain of 0.4%. Regarding emissions the addition of hydrogen to biogas contributes to an average reduction of 5% in carbon monoxide emissions compared to the standard dual-fuel operation. However dual-fuel operation leads to higher unburned hydrocarbon emissions relative to neat diesel; hydrogen enrichment mitigates this drawback by reducing hydrocarbon emissions by 4.1%. Although NOx emissions increase by an average of 26.6% with hydrogen addition dual-fuel strategies achieve NOx reductions of 11.5% (hydrogen-enriched mode) and 33.3% (pure biogas mode) relative to diesel-only operation. Furthermore the application of response surface methodology is robust and reliable with experimental validation showing errors of 0.55–8.66% and an overall uncertainty of 4.84%.

The transport sector is a major contributor to greenhouse gas emissions largely due to its dependence on fossil fuels. Electrifying transport through Battery Electric Vehicles (BEVs) and Hydrogen Fuel Cell Electric Vehicles (FCEVs) is widely recognized as a key pathway to reducing emissions. While both BEVs and FCEVs are zero-emission during operation they still require electricity to function. Sourcing this electricity from solar energy presents a promising opportunity for sustainable operation. The novelty of this work lies in exploring how solar energy can be effectively integrated into both BEV and FCEV systems. The paper examines the potential scope and infrastructure requirements of these vehicle types as well as innovative charging and refuelling strategies. For BEVs charging options include fixed charging stations battery swapping stations and wireless charging. In the context of solar integration photovoltaic (PV) systems can be mounted directly on the vehicle body or used to power charging stations. While current PV efficiency and reliability are insufficient to meet the full energy demand of BEVs they can provide valuable auxiliary power. For FCEVs solar energy can be utilized for hydrogen production enabling the concept of solar-powered FCEVs. Refuelling options include onsite and offsite hydrogen production facilities as well as mobile refuelling units. In both cases land requirements for PV installations are significant. Alternatives to ground-mounted PV such as floating PV or agrivoltaics (agriPV) should be considered to optimize land use. While solar-powered charging or refuelling stations are technically feasible complete reliance on solar power alone is not yet practical. A hybrid approach with grid connections energy storage or backup generation remains necessary to ensure consistent energy availability. For BEVs the cost of charging particularly for long-distance travel where rapid charging is required remains a barrier. For FCEVs challenges include the high cost of hydrogen production and the limited availability of refuelling infrastructure despite their advantage of fast refuelling times. Government policies and incentives are playing a critical role in overcoming these barriers fostering investment in infrastructure and accelerating the transition toward a cleaner transport sector. In summary integrating solar energy into BEV and FCEV infrastructure can advance sustainable mobility by reducing lifecycle emissions. While current PV efficiency storage and hydrogen production limitations require hybrid energy solutions ongoing technological improvements and supportive policies can enable broader adoption. A balanced renewable energy mix with solar as a key component will be essential for realizing truly sustainable zero-emission transport.

Against the dual backdrop of intensifying carbon emission constraints and the large-scale integration of renewable energy integrated electricity–hydrogen energy systems (EH-ESs) have emerged as a crucial technological pathway for decarbonising energy systems owing to their multi-energy complementarity and cross-scale regulation capabilities. This paper proposes an operational optimisation and carbon reduction capability assessment framework for EH-ESs focusing on revealing their operational response mechanisms and emission reduction potential under multi-disturbance conditions. A comprehensive model encompassing an electrolyser (EL) a fuel cell (FC) hydrogen storage tanks and battery energy storage was constructed. Three optimisation objectives—cost minimisation carbon emission minimisation and energy loss minimisation—were introduced to systematically characterise the trade-offs between economic viability environmental performance and energy efficiency. Case study validation demonstrates the proposed model’s strong adaptability and robustness across varying output and load conditions. EL and FC efficiencies and costs emerge as critical bottlenecks influencing system carbon emissions and overall expenditure. Further analysis reveals that direct hydrogen utilisation outperforms the ‘electricity–hydrogen–electricity’ cycle in carbon reduction providing data support and methodological foundations for low-carbon optimisation and widespread adoption of electricity–hydrogen systems.

Hydrogen is the key to decarbonising heavy-duty transport. Understanding the distribution of hydrogen demand is crucial for effective planning and development of infrastructure. However current data on future hydrogen demand is often coarse and aggregated limiting its utility for detailed analysis and decision-making. This study developed a spatial disaggregation approach to estimating hydrogen demand for heavy-duty trucks and mapping the spatial distribution of hydrogen demand across multiple scales in Australia. By integrating spatial datasets with economic factors market penetration rates and technical specifications of hydrogen fuel cell vehicles the approach disaggregates the projected demand into specific demand centres allowing for the mapping of regional hydrogen demand patterns and the identification of key centres of hydrogen demand based on heavy-duty truck traffic flow projections under different scenarios. This approach was applied to Australia and the findings offered valuable insights that can help policymakers and stakeholders plan and develop hydrogen infrastructure such as optimising hydrogen refuelling station locations and support the transition to a low-carbon heavy-duty transport sector.