Publications

A surging demand for sustainable energy and the urgency to lower greenhouse gas emissions is driving industrial systems towards more eco-friendly and cost-effective models. Biogas from agricultural and municipal organic waste is gaining momentum as a renewable energy source. Concurrently the European Hydrogen Strategy focuses on green hydrogen for decarbonising the industrial and transportation sectors. This paper presents a multi-objective network design model for urban–industrial symbiosis incorporating anaerobic digestion cogeneration photovoltaic and hydrogen production technologies. Additionally a Bayesian best-worst method is used to evaluate the weights of the sustainability aspects by decision-makers integrating these into the mathematical model. The model optimises industrial plant locations considering economic environmental and social parameters including the net present value energy consumption and carbon footprint. The model’s functionalities are demonstrated through a real-world case study based in Emilia Romagna Italy. It is subject to sensitivity analysis to evaluate how changes in the inputs affect the outcomes and highlights feasible trade-offs through the exploration of the ϵ-constraint. The findings demonstrate that the model substantially boosts energy and hydrogen production. It is not only economically viable but also reduces the carbon footprint associated with fossil fuels and landfilling. Additionally it contributes to job creation. This research has significant implications with potential future studies intended to focus on system resilience plant location optimisation and sustainability assessment.

Co-optimizing energy and water resources in a microgrid can increase efficiency and improve economic performance. Energy-water storage (EWS) devices are crucial components of a high-efficient energy-water microgrid (EWMG). The state of charge (SoC) at the end of the first day of operation is one of the most significant variables in EWS devices since it is used as a parameter to indicate the starting SoC for the second day which influences the operating cost for the second day. Hence this paper examines the benefits and applicability of a lookahead optimization strategy for an EWMG integrated with multi-type energy conversion technologies and multienergy demand response to supply various energy-water demands related to electric/hydrogen vehicles and commercial/residential buildings with the lowest cost for two consecutive days. In addition a hybrid info-gap/robust optimization technique is applied to cover uncertainties in photovoltaic power and electricity prices as a tri-level optimization framework without generating scenarios and using the probability distribution functions. Duality theory is also used to convert the problem into a single-level MILP so that it can be solved by CPLEX. According to the findings the implemented energy-water storage systems and look-ahead strategy accounted for respectively 4.03% and 0.43% reduction in the total cost.

This research delineates a pivotal advancement in the domain of sustainable energy systems with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore the paper elucidates the construction of the forecasting model revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.

Fuel cell hybrid electric vehicles have the advantages of zero emission high efficiency and fast refuelling etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power distribution. The traditional power following strategy has the advantage of a real-time operation but the power correction is usually based only on the state of charge of a lithium battery which causes the operating point of the fuel cell to be in the region of a low efficiency. To solve this problem this paper proposes a hybrid power-following-fuzzy control strategy where a fuzzy logic control strategy is used to optimise the correction module based on the power following strategy which regulates the state of charge while correcting the output power of the fuel cell towards the efficient operating point. The results of the joint simulation with Matlab + Advisor under the Globally Harmonised Light Vehicle Test Cycle Conditions show that the proposed strategy still ensures the advantages of real-time energy management and for the hydrogen fuel cell the hydrogen consumption is reduced by 13.5% and 4.1% compared with the power following strategy and the fuzzy logic control strategy and the average output power variability is reduced by 14.6% and 5.1% respectively which is important for improving the economy of the whole vehicle and prolonging the lifetime of fuel cell.

The urgency to mitigate greenhouse gas emissions from maritime vessels has intensified due to the increasingly stringent directives set forth by the International Maritime Organization (IMO). These directives specifically address energy efficiency enhancements and emissions reduction within the shipping industry. In this context hydrogen is the much sought after fuel for all the global economies and its applications for transportation and propulsion in particular is crucial for cutting down carbon emissions. Nevertheless the realization of hydrogen-powered vessels is confronted by substantial technical hurdles that necessitate thorough examination. This study undertakes a comprehensive analysis encompassing diverse facets including distinct variations of hydrogen fuel cells hydrogen internal combustion engines safety protocols associated with energy storage as well as the array of policies and commercialization endeavors undertaken globally for the advancement of hydrogen-propelled ships. By amalgamating insights from these multifaceted dimensions this paper adeptly encapsulates the myriad challenges intrinsic to the evolution of hydrogen-fueled maritime vessels while concurrently casting a forward-looking gaze on their prospective trajectory.

The development of a Clean Hydrogen Standard based on life-cycle greenhouse gas (GHG) emissions is gaining prominence on the international agenda. Thus a framework for assessing life-cycle GHG emissions for clean hydrogen pathways is necessary. In this study the comprehensive datasets and effects of various scenarios encompassing hydrogen production carriers (liquid hydrogen ammonia methylcyclohexane) carbon capture and storage (CCS) target analysis year (2021 2030) to reflect trends of greening grid electricity and potential import countries on aggregated life-cycle GHG emissions were presented. South Korea was chosen as a case study region and the low-carbon alternatives were suggested for reducing aggregated emissions to meet the Korean standard (5 kgCO2e/kgH2). First capturing and storing nearly entire (>90%) CO2 from fossil- and waste-based production pathways is deemed essential. Second when repurposing the use of hydrogen that was otherwise used internally applying a penalty for substitution is appropriate leading to results notably exceeding the standard. Third for electrolysis-based hydrogen using renewable or nuclear electricity is essential. Lastly when hydrogen is imported in a well-to-point-of-delivery (WtP) perspective using renewable electricity during hydrogen conversion into a carrier and reusing the produced hydrogen for endothermic reconversion reaction are recommended. By implementing the developed calculation framework to other countries' cases it was observed that importing hydrogen to regions having scope of WtP or above (e.g. well-to-wheel) might not meet the threshold due to additional emissions from importation processes. Additionally for hydrogen carriers undergoing the endothermic reconversion the approach to reduce WtP emissions (reusing produced hydrogen) may conflict with the approach to reduce well-to-gate (WtG) emission (using external fossilbased fuel). The discrepancy highlights the need to set a broader scope of emissions assessment to effectively promote the life-cycle emission reduction efforts of hydrogen importers. This study contributes to the field of clean hydrogen GHG emission assessment offering a robust database and calculation framework while addressing the effects of greening grid electricity and CCS implementation proposing low-carbon alternatives and GHG assessment scope to achieve global GHG reduction.

This paper describes a model of hydrogen blowdown dynamics for storage tanks needed for hydrogen safety engineering to accurately represent incident scenarios. Heat transfer through a tank wall affects the temperature and pressure dynamics inside the storage vessel and therefore the characteristics of the resulting hydrogen jet in case of loss of containment. Available non-adiabatic blowdown models are validated only against experiments on hydrogen storages at ambient temperature. Effect of heat transfer for cryo-compressed hydrogen can be more significant due to a larger temperature difference between the stored hydrogen and surrounding atmosphere especially in case of failure of equipment insulation. Previous work by the authors demonstrated that the heat transfer through a discharge pipe wall can significantly affect the mass flow rate of cryogenic hydrogen releases. To the authors’ knowledge thoroughly validated models of non-adiabatic blowdown dynamics for cryo-compressed hydrogen are currently missing. The present work further develops the non-adiabatic blowdown model at ambient temperature using the under-expanded jet theory developed at Ulster University to expand it to cryo-compressed hydrogen storages. The non-ideal behaviour of cryo-compressed hydrogen is accounted through the high-accuracy Helmholtz energy formulations. The developed model includes effect of heat transfer at both the tank and discharge pipe wall. The model is thoroughly validated against sixteen tests performed by Pro-Science on blowdown of hydrogen storage tanks with initial pressure 0.5-20 MPa and temperature 80-310 K through release nozzle of diameter 0.5-4.0 mm. The model well reproduces the experimental pressure and temperature dynamics during the entire blowdown duration.

Safety and reliability have long been recognized as key issues for the development commercialization and implementation of new technologies and infrastructure and hydrogen systems are no exception to this rule. Reliability engineering quantitative risk assessment (QRA) and knowledge exchange each play a key role in proactive addressing safety – before problems happen – and help us learn from problems if they happen. Many international research activities are focusing on both reliability and risk assessment for hydrogen systems. However the element of knowledge exchange is sometimes less visible. To support international collaboration and knowledge exchange the International Energy Agency (IEA) convened a new Technology Collaboration Program “Task 43: Safety and Regulatory Aspects of Emerging Large Scale Hydrogen Energy Applications” started in June 2022. Within Task 43 Subtask E focuses on Hydrogen Systems Safety. This paper discusses the structure of the Hydrogen Systems Safety subtask and the aligned activities and introduces opportunities for future work.

Hydrogen energy is recognized by many European governments as an important part of the development to achieve a more sustainable energy infrastructure. Great efforts are spent to build up a hydrogen supply chain to support the increasing number of hydrogen-powered vehicles. Naturally these vehicles will use the common traffic infrastructure. Thus it has to be ensured these infrastructures are capable to withstand the hazards and associated risks that may arise from these new technologies. In order to have an appropriate assessment tool for hydrogen vehicles transport through tunnels a new QRA methodology is developed and presented here. In Europe the PIARC is a very common approach. It is therefore chosen as a starting point for the new methodology. It provides data on traffic statistics accident frequencies tunnel geometries including certain prevention and protection measures. This approach is enhanced by allowing better identification of hazards and their respective sources for hydrogen vehicles. A detailed analysis of the accident scenarios that are unique for hydrogen vehicles hereunder the initiating events severity of collision types that may result in a release of hydrogen gas in a tunnel and the location of such an accident are included. QRA enables the assessment and evaluation of scenarios involving external fires or vehicles that burst into fire because of an accident or other fire sources. Event Tree Analysis is the technique used to estimate the event frequencies. The consequence analysis includes the hazards from blast waves hydrogen jet fires DDT.

A gas turbine is usually installed inside an acoustic enclosure where the fuel gas supply system is also placed. It is common practice using CFD analysis to simulate the accidental fuel gas release inside the enclosure and the consequent dispersion. These numerical studies are used to properly design the gas detection system according to specific safety criteria which are well defined when the fuel gas is a conventional natural gas. Package design is done to prevent that any sparking items and hot surfaces higher than auto-ignition temperature could be a source of ignition in case of leak. Nevertheless it is not possible to exclude that a leakage from a theoretical point of view could be ignited and for this reason a robust design requires that the enclosure structure is able to withstand the overpressure generated by a gas cloud ignition. Moving to hydrogen as fuel gas makes this design constraint much more relevant for its known characteristics of reactiveness large range of flammability maximum burning velocity etc. In such context gas leak and dispersion analysis become even more crucial because a correct prediction of these scenarios can guide the design to a safe configuration. The present work shows a comparison of the dispersion of different leakages inside a gas turbine enclosure carried out with two different CFD tools Ansys CFX and FLACS. This verification is considered essential since dispersion analysis results are used as initial conditions for gas cloud ignition simulations strictly necessary to predict the consequence in term of overpressure without doing experimental tests.