Energy Engineering - Doctoral Theses
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Item A geospatial economic analysis of hydrogen production from offshore wind using electrolysers(University College Cork, 2024) Vu Dinh, Quang; Leahy, Paul; Dinh, Nguyen; Wall, David; Science Foundation IrelandThe transition to renewable energy has driven growing interest in green hydrogen as an energy carrier. Green hydrogen refers to hydrogen produced without generating carbon emissions. Typically, it’s produced from renewable energy sources. Combined with the advantages of offshore wind, green hydrogen production from offshore wind energy has emerged as a potential versatile zero-carbon energy vector. Exploiting offshore wind energy to produce hydrogen not only opens up a new direction in the use of renewable energy but also positively contributes to global sustainable development goals. A comprehensive literature review highlights advancements in green hydrogen production, geospatial methods for renewable energy, and the optimisation of hydrogen systems. This thesis focuses on researching the potential of the technical and economic aspects of hydrogen production from offshore wind energy. Two concepts for combining electrolysers with offshore wind farms to produce hydrogen are considered. In the first concept, the centralised electrolyser is located offshore, while the second considers an onshore centralised electrolyser. The first aim is to develop a model to calculate the cost of hydrogen production from offshore wind farms. Then, the cost model is used to construct a LCOH map and applied to Irish waters. The area off the west coast has more robust wind resources than the east coast. However, the east coast has shallower water depths and seaports that are more convenient. Nearshore areas suitable for cheaper foundations can produce hydrogen at a lower cost in the two hydrogen production concepts considered. The second aim is to minimise hydrogen production costs by optimising installed electrolyser capacity. In a 600 MW offshore wind farm case study, the optimal offshore electrolyser capacity is about 83% of the wind farm capacity. In the onshore electrolyser concept, the optimal ratio is about 79%. This research also conducts sensitivity analyses to examine the influence of technical parameters on the optimal electrolyser capacity, providing valuable insights into system design and operational efficiency. Beyond hydrogen production aspects, a general assessment to identify potential export markets for hydrogen from Ireland was also investigated. Countries with high hydrogen demand and close to Ireland, such as the UK, Germany, France, and the Netherlands, could be potential export markets in the future. Hydrogen trade among markets will require a suitable hydrogen transport method. Hydrogen can be transported directly or in other forms. The offshore transportation of hydrogen in the form of ammonia was investigated. From the results, suitable transportation methods can be selected based on transportation distance and electrolyser capacity. The goal of this thesis is to provide a comprehensive and in-depth view of the process and potential of hydrogen production from offshore wind energy, thereby contributing to general knowledge and supporting planners, policymakers, scientists, and engineers in promoting the application and development of green hydrogen production from offshore wind energy. The results and recommendations will be the basis for new steps in the journey towards a sustainable energy future. Sensitivity analyses demonstrate that the projections of hydrogen demand and levelised cost presented in this thesis are dependent on input data and assumptions which will need to be continually updated as the technology and markets develop.Item Depicting energy service demands as a mitigation lever in energy systems models(University College Cork, 2024) Gaur, Ankita Singh; Daly, Hannah E.; Curtis, John; Science Foundation IrelandEnergy Systems Optimisation Models (ESOMs) and Integrated Assessment Models (IAMs) are integral to informing climate change mitigation strategies and the associated policymaking processes. However, the techno-economic nature of these models restricts the representation of demand-side mitigation measures, which have been recognised to have substantial potential in meeting climate goals. Specifically, measures that can bring about a reduction in the level or structure of energy demand are often missing. Even when such measures \emph{are} included—often as exogenous scenarios—they are typically not well-connected to empirical evidence and lack spatial and demographic granularity. Moreover, these demand reduction measures are more relevant to developed countries like Ireland. In developing countries, access to energy services is disproportionately low, despite the higher growth in demand for energy relative to developed countries. Further, the representation of Global South regions is generally oversimplified in large-scale IAMs, often relying on stylised assumptions based on observations from Global North. This thesis seeks to address these gaps by adopting a dual approach of addressing demand-side mitigation while incorporating a particular perspective from the Global South region. This thesis develops bottom-up projections of energy service demand for the TIMES-Ireland Model (TIM). The baseline energy service demands are driven by growth in population and economy. TIM is set up to allow for alternate scenarios for demand drivers that result in different energy service demand projections in the end-use sectors. The thesis develops the `Irish Low Energy Demand' (ILED) scenario where the impact of reducing and restructuring energy service demand on the whole energy systems is analysed using TIM. The results indicate that the ILED pathway is especially valuable in meeting near-term deep mitigation targets and lowers reliance on novel fuels and technologies. For policymakers, the recommendation is to expand the arsenal of mitigation measures, beyond policies that promote renewable energy deployment and energy efficiency. It was observed that a pivotal driver of such Low Energy Demand (LED) scenarios both, at national and global level, is spatial settlement patterns. The LED scenarios heavily depend on future compact development. Hence, to improve the granularity and empirical basis of such scenarios, this thesis quantifies the relationship between population density and the energy as well as carbon intensity of the residential and transport energy service demands. Analysis reveals that the energy and carbon intensity of populations living in dense parts of Ireland are significantly lower than those living in sparsely populated areas. The future growth of cities and town in Ireland, whether compact or dispersed, will heavily determine emissions trajectory and mitigation options. This exercise allows future modelling studies to include spatial settlement patterns as a mitigation lever. This thesis also analyses the impact of electrifying spatially dispersed residential heating demand on the Irish power system. This analysis is particularly important for Ireland: given the lack of a district heating network, electrification is the central heat decarbonisation policy. Utilizing a generation and transmission expansion planning model, the analysis finds that the spatial distribution of demand drives the investments in the power system. This again highlights the role of future spatial settlement patterns in decarbonising the Irish energy system. Further, electrifying residential heating via heat pumps leads to greater utilization of renewable energy, when combined with thermal energy storage. In the next part, this thesis explores the gaps and challenges associated with representing Global South regions in a global IAM. With a focus on passenger mobility in South Asian countries, this thesis develops an evidence based framework to improve the quantification of sustainable mobility scenarios in global IAMs. A comprehensive literature review is conducted to identify measured causal relationship between various phenomena, such as urbanisation and passenger mobility patterns, placing an emphasis on literature focusing on South Asia and the Global South. These phenomena are incorporated into a novel mobility projection model designed to interface with an IAM. This is an innovative and adaptable framework, that can be applied to other Global South regions and other IAMs. Using this framework, four distinct mobility scenarios are developed, each reflecting alternate visions for mobility based on explicit futures for these phenomena. Through the various research chapters, this thesis demonstrates the value of including demand-side measures in ESOMs and IAMs. It highlights how energy service demands can be managed to reduce GHG emissions, particularly in the residential and transport sectors. Further, by incorporating empirical evidence of the phenomena that drive energy service demands, the research develops methodologies to improve the quantification of decarbonisation scenarios in ESOMs and IAMs. And lastly, the inclusion of region-specific data for the Global South is a step towards more equitable practices within IAM frameworks.Item Enhanced modelling of transport decarbonisation and policy pathways for Ireland(University College Cork, 2023) O'Riordan, Vera; Rogan, Fionn; Daly, Hannah E.; O'Gallachoir, Brian; Climate and Energy Modelling ServicesThe release of increasing human-induced greenhouse gas emissions and the corresponding global temperature rise has prompted a growing political consensus on a decarbonised future to prevent any sustained economic or environmental harm. Many countries are using energy system modelling tools to develop strategies and policy measures to deliver timely and effective reductions of harmful greenhouse gas emissions across all energy-related sectors. Ireland, with ambitious legally binding carbon budgets, and decarbonisation targets for transport, is a country in the process of assessing and addressing key transport decarbonisation challenges faced by high-emitting countries. This thesis - with its scientific contributions on transport emissions, methodological advancements for transport and multi-sector energy systems simulation modelling, and policy recommendations on how effective measures have been in the past or could be in the future - serves as a small, but novel, piece of this process. The thesis updates the Irish Car Stock Model to investigate the importance of taxation policy using a novel bottom-up stock simulation approach. The simulation model evaluates the 2008 car tax policy in Ireland and finds that while the policy was effective at reducing CO2 emissions, it had a high cost of carbon abatement, between €1,500 – 2,200 per tCO2. The thesis develops the Irish Passenger Transport Emissions and Mobility (IPTEM) model, which for the first time, calculates the overall passenger transport demand in Ireland by trip purpose, trip distance, and mode type. The methodological advancement is in the combination of passenger transport demand from all modes of transport and information from the National Travel Survey, national transport providers, and the Irish Car Stock Model. The study finds that 82% of passenger transport demand is met by cars in Ireland, and the main reason for travel is for work (30%), shopping (19%), and companion journeys (16%). The study also finds that 40% of emissions come from journeys less than 8 kilometres. In Chapter 4, this thesis develops a new model, the LEAP Ireland ASI (Avoid-Shift-Improve) model which projects emissions and demand for passenger and freight transport up to 2030. It is novel in its application of the Avoid-Shift-Improve framework for scenarios focused on reducing the need to travel in the first instance (“Avoid”), then on modal shifting towards increased public transport use and active travel (“Shift”), and then on scenarios focused on improving the fuels used to ones with a lower carbon intensity (“Improve). These scenarios are modelling in combination with one another and the interaction between the policies is also determined. In Chapter 5, the thesis develops a new methodology for simulation modelling to project carbon dioxide emissions, how different scenarios could reduce carbon dioxide emissions, and how these fit in with sectoral emissions ceilings within carbon budgets. The thesis tracks past sectoral emissions and simulates the mitigation potential of a suite of scenarios for transport, residential, electricity, services, and industry sectors. The LEAP Ireland model developed in Chapter 5 can simulate the impact of additional policies, track policy performance, and simulate mitigation potential. The data sources, methodology, and carbon budget analysis are outlined in this novel simulation modelling framework designed to support countries with their carbon budgeting commitments. This thesis also examines the interaction effect between these policy scenarios and discusses their combinations' synergistic and antagonistic effects. The contribution of this thesis is the improvements made to the modelling methods and more robust evidence base for developing sound decarbonisation transport policy measures by shifting the focus beyond car efficiency and electrification.Item Multi–scale simulation of hybrid inorganic–organic films(University College Cork, 2023) Muriqi, Arbresha; Nolan, Michael; Horizon 2020The discovery of novel materials and associated process chemistries is crucial for the realization of higher performance electronic devices and the progress of nanotechnology in general. Hybrid materials are a special class of materials with unusual features which are attracting great interest for a wide range of applications. The unique properties of hybrid materials arise from the combination of advantages of both building blocks, i.e., inorganic and organic, which allow material functionalities that are not present in the individual components to be engineered. The properties of these materials can be also tuned depending on the requirements of the application by the choice of the components. Hybrid films are fabricated using molecular layer deposition (MLD) technique, a variant of the widely used atomic layer deposition (ALD) technique, which enables precision and control at the atomistic scale. In recent years, many MLD processes for hybrid films have been developed. However, much less is known about the growth mechanism of hybrid MLD films. In my thesis I used first principles density functional theory (DFT) simulations to investigate the key steps in the mechanism of hybrid film deposition through MLD, to address open questions around earlier MLD experiments and to predict the most suitable precursors for deposition processes. We build up an atomistic level understanding of the growth chemistry of different types of hybrid films by modelling the relevant MLD deposition processes. In particular, deep investigations on how precursor atomic structure determines film growth, stability and flexibility is carried out. We focus on the key MLD process chemistries, namely alucone and titanicone films, both of high interest for passivation layers in batteries. We assist the interpretation of experimental findings by showing for the first time why the ethylene glycol precursor performs poorly in making stable alucone films and why glycerol is better. For titaiocone films we highlight the role of the substrate and the titanium containing precursors on the initial MLD steps and in film production. We have also predicted that aromatic molecules are a good choice for stable hybrid films and their chemistry can be manipulated without impacting on the stability and this has been borne out by experimental work.Furthermore, we predict suitable MLD chemistries for production of hybrid antibacterial materials. We also study the diffusion phenomena of MLD precursors into polymeric substrates with the vapour phase infiltration (VPI) technique to understand the chemical interactions and corroborate the experimental data on Ru nanostructures and self-healing materials. Finally, we provide atomic level understanding around novel organometallic precursors and predict their applicability for deposition of oxide and hybrid thin films. The work in my thesis illustrates the key role of atomistic simulations in materials and process development.Item Applications of big data and machine learning in global energy system modelling(University College Cork, 2022) Joshi, Siddharth; O'Gallachoir, Brian; Holloway, Paul; Glynn, James; Science Foundation IrelandGlobal efforts to limit atmospheric warming well below 2 degree celcius above pre-industrial levels form the backbone of our response to mitigate the detrimental effects of climate change. The energy sector contributes circa 75% of global GHG emissions, amongst which the Electricity and Heat sectors each contribute ~40%, and the Transport sector contributes ~20% to the total global energy-related GHG emissions. The recent IPCC AR6 report finds that in nearly all possible emission scenarios considered, the world is heading towards a 1.5 degree celcius global temperature rise by the early 2030s. Pursuant to this, Energy Systems Models (ESMs) and Integrated Assessment Models (IAMs) are essential tools that provide energy system pathways to limit global warming below the temperature threshold. Thus, improving the accuracies of ESMs and IAMs will lead to measurable improvement in energy policy formulation and evaluation,thereby increasing the likelihood of meeting the commitments under the Paris Climate Agreement. This thesis develops and applies novel frameworks and methods that use a big data and machine learning driven strategy to improve the technology potential assessment of global decentralised solar PV technology and projection of transport energy service demand. The frameworks and methods developed in this thesis are presented in a format of methodological design principles followed by a case study using them. Specifically, on the supply side, the thesis investigates the global high-resolution spatiotemporal technical potential of rooftop solar PV for 2015 and further growth in the technical potentials from 2020-2050. For this assessment case study, the developed framework utilises a suite of GIS derived geospatial metrics in conjunction with a custom machine learning framework to calculate the global rooftop area at a high spatial resolution. Further using an IAM, the role of decentralised solar PV in global future energy transitions is explored. On the demand side, the thesis introduces a new machine learning model called ‘TrebuNet’ that is capable of high accuracy in estimating future energy service demand in the transport sector. The thesis thus provides the first development of machine learning and GIS based methods to improve the accuracy of global ESMs and IAMs. Particular attention is also paid towards the reproduction and transparency of the methods and the frameworks developed in this thesis for cross- disciplinary research. The thesis contributes to the important task of climate change mitigation by providing a bridge between mature IAM and ESM modelling and emerging machine learning-big data-driven tools. In doing so, this thesis demonstrates how the emerging methods in conjunction with large geospatial open source data, can aid in improving the technology representation of variable renewable energy technology in energy systems. The thesis also lays the foundation for providing solutions to energy system related tasks that are currently limited by high computational costs and data. The datasets and analysis generated by this thesis are presently assisting in unlocking the global role of decentralised renewable energy technologies in future energy systems and are also encouraging shifts in national decarbonisation pathways.