Дисертації з теми "Gas network modelling"

The large scale integration of wind generation capacity into an electricity network poses technical as well as economic challenges. In this research, three major challenges introduced by wind including non-correlated power output from geographically dispersed wind farms, wind variability and wind uncertainty were studied. In order to address each of the aforementioned challenges an appropriate modelling approach and case studies were used. The impacts of power output from dispersed wind farms on the Great Britain transmission reinforcement were studied using an optimal DC load flow combined with a power generation model. It was shown that Western and Eastern HVDC links play a crucial role to bypass the Scotland to England transmission bottleneck. The impacts of wind variability on the GB gas and electricity network were investigated through application of the Combined gas and Electricity Network (CGEN) Model. Additional gas storage capacity was shown to be an efficient option to compensate for wind variability. Two-stage and multi-stage stochastic programming models were developed to examine the impact of wind forecast uncertainty on the GB electricity and gas networks. Stochastic modelling approaches were shown to be efficient methods for scheduling and operating the system under wind uncertainty. The key contributions of this thesis are the investigation of the impacts of wind generation variability on the gas network, and development of twostage and multi-stage stochastic programming models of integrated gas and electricity network.

This thesis investigates novel methodologies for modelling, simulation and control of gas turbines using ANNs. In the field of modelling and simulation, two different types of gas turbines are modelled and simulated using both Simulink and neural network based models. Simulated and operational data sets are employed to demonstrate the capability of neural networks in capturing complex nonlinear dynamics of gas turbines. For ANN-based modelling, the application of both static (MLP) and dynamic (NARX) networks are explored. Simulink and NARX models are set up to explore both steady-state and transient behaviours. To develop an offline ANN-based system identification methodology for a low-power gas turbine, comprehensive computer program code including 18720 different ANN structures is generated and run in MATLAB to create and train different ANN models with feedforward multi-layer perceptron (MLP) structure. The results demonstrate that the ANN-based method can be applied accurately and reliably for the system identification of gas turbines. In this study, Simulink and NARX models are created and validated using experimental data sets to explore transient behaviour of a heavy-duty industrial power plant gas turbine (IPGT). The results show that both Simulink and NARX models successfully capture dynamics of the system. However, NARX approach can model gas turbine behaviour with a higher accuracy compared to Simulink approach. Besides, a separate complex model of the start-up operation of the same IPGT is built and verified by using NARX models. The models are set up and verified on the basis of measured time-series data sets. It is observed that NARX models have the potential to simulate start-up operation and to predict dynamic behaviour of gas turbines. In the area of control system design, a conventional proportional-integral-derivative (PID) controller and neural network based controllers consisting of ANN-based model predictive (MPC) and feedback linearization (NARMA-L2) controllers are designed and employed to control rotational speed of a gas turbine. The related parameters for all controllers are tuned and set up according to the requirements of the controllers design. It is demonstrated that neural network based controllers (in this case NARMA-L2) can perform even better than conventional controllers. The settling time, rise time and maximum overshoot for the response of NARMA-L2 is less than the corresponding factors for the conventional PID controller. It also follows the input changes more accurately than the PID. Overall, it is concluded from this thesis that in spite of all the controversial issues regarding using artificial neural networks for industrial applications, they have a high and strong potential to be considered as a reliable alternative to the conventional modelling, simulation and control methodologies. The models developed in this thesis can be used offline for design and manufacturing purposes or online on sites for condition monitoring, fault detection and trouble shooting of gas turbines.

A Proton Exchange Membrane Fuel Cell (PEMFC) is a device converting hydrogen into electricity thanks to an electrochemical reaction called reverse electrolysis. Like every fuel cell or battery, PEMFCs are made of a series of layers. We are interested in the gas diffusion layer (GDL) on the cathode side. The GDL is made of carbon fibers treated hydrophobic. It can be seen as a thin porous medium with a mean pore size of few tens of microns. A key question in this system is the management of the water produced by the reaction. In this context, the main objective of the thesis is the development of a numerical tool aiming at simulating the liquid water formation within the GDL. A pore network approach is used. We concentrate on a scenario where liquid water forms in the GDL by condensation. Comparisons between simulations and experiments performed with a two-dimensional microfluidic device are first presented for different wettability conditions, temperature distributions and inlet relative humidity in order to validate the model. A sensitivity study is then performed to better characterize the parameters controlling the water invasion. Finally, simulations are compared with in situ experimental water distributions obtained by X-ray micro-tomography as well as with experimental distributions from the literature obtained by neutron imaging.

Growing environmental concerns are causing a large transformation within the energy industry. Within the gas turbine industry, there is a large drive to develop improved modern dry-low emission combustion systems. The aim is to enable gas turbines to run on green fuels like hydrogen, while still keeping emission as NOx down. To design these systems, a thorough understanding of the aerothermal and kinetic processes within the combustion system of a gas turbine is essential. The goal of the thesis was to develop a one-dimensional general network model of the combustion system of Siemens Energy SGT-700, which accurately could predict pressure losses, mass flows, key temperatures, and emissions. Three models were evaluated and a code that emulated some aspects of the control system was developed. The models and the code were evaluated and compared to each other and to test data from earlier test campaigns performed on SGT-700 and SGT-600. Simulations were also carried out with hydrogen as the fuel.  In the end, a model of the SGT-700 combustion chamber was developed and delivered to Siemens Energy. The model had been verified against test data and predictions made by other Siemens Energy thermodynamic calculation software, for a range of load conditions. The preforms of the model, when hydrogen was introduced into the fuel mixture, were also tested and compared to test data
En växande medvetenhet kring klimatfrågan, har medfört stora förändringar i energibranschen. I och med detta behöver även gasturbinindustrin förbättra de nuvarande dry-low emissions systemen och göra det möjligt för gasturbiner att förbränna gröna bränslen som väte. Samtidigt måste också utsläppen av NOx hållas nere. För att kunna utforma dessa system behövs en fullständig förståelse för de aerotermiska och kinetiska processerna i en gasturbins förbränningskammare. Målet med detta examensarbete var att utveckla en endimensionell generell nätverksmodell för förbränningssystemet i Siemens Energys SGT-700. Modellen skulle noggrant kunna förutsäga tryckförluster, massflöden, viktiga temperaturer samt utsläpp. Tre modeller utvärderades och en kod som emulerade vissa aspekter av styrsystemet utvecklades också. Modellerna och koden utvärderades och jämfördes mot varandra och även mot testdata från tidigare testserier som utfördes på SGT-700 och SGT-600. Simuleringar utfördes också med väte som bränsle. Slutligen levererades en modell av SGT-700 förbränningskammaren till Siemens Energy. Modellen har verifierats för en rad olika lastfall, mot testdata och data som genererats av andra termodynamisk beräkningsprogram som utvecklats av Siemens Energy. Hur modellen uppförde sig när väte var introducerat in i olika lastfall jämfördes också mot testdata

Renewable energy sources (RESs), such as wind, solar and biomass, are the keystone of the energy policy of the EU to fulfil the target of a carbon neutral economy by 2050. However, the integration of a significant share of RESs pose significant challenges to the EU energy systems as it requires, on the one hand, storing large energy volumes to match intermittent renewable supply with the pattern of energy consumption and, on the other hand, transporting renewable energy from where it can be most efficiently and feasibly produced to where it is consumed. To overcome such challenges, it will be effective to consider the reuse of the existing natural gas (NG) infrastructure. The latter will play a crucial role in the development of a decarbonized energy system based on a large usage of RESs due to its widespread presence and its capacity to provide a cost-effective option for transporting and storing large amounts of energy for long-term period exploiting the NG transportation and distribution networks, as well as the storage complexes of the existing NG infrastructure. This thesis aims to address, with a multi-thematic approach, the issue of innovative uses of, as well as the development of innovative technologies on, NG transport and distribution networks. To this aim, a number of five case studies were investigated to: i) evaluate the fault management strategies for NG distribution networks to minimize the disservice and to define possible structural improvement measures; ii) assess the technical feasibility of the Power to Gas concept to store intermittent RESs; iii) analyse the impact of hydrogen injection on NG networks; iv) evaluate the effectiveness of the equilibrium gasification models as a modelling tool for the design and optimization of biomass gasification systems integrated into polygenerative plants coupled with energy networks. Results of this thesis provide useful insights to researchers, designers and policy makers, filling some of the gaps highlighted in the existing scientific literature in all the analysed areas.

This thesis describes the development of a pore network model and its application to the analysis of the underlying physical mechanisms governing gas flow behaviour in porous media. The main focus of the study is CO2 and CH4 injection for EOR and storage applications as well as the evolution of solution gas following depressurization of hydrocarbon-saturated porous media. The model incorporates algorithms that dynamically track interface movements during both steady and unsteady-state flow under the coupled influence of capillary, gravity, and viscous forces. The model has been validated against laboratory experiments and the roles played by key system parameters have been identified. For injection processes, simulation results show that gravity-driven regimes fall into two broad categories of quasi-stable and migratory regimes, depending on the governing Bond number. The transition from non-dispersive to dispersive migratory flow was found to be largely independent of injection rate but a strong function of pore size distribution variance and system connectivity. CO2 and CH4 regimes in brine were found to exhibit striking similarities, suggesting that CH4-brine relative permeability curves could be used to accurately parameterize simulation models of CO2 storage in aquifers. Decreasing the interfacial tension was found to dampen viscous fingering but exacerbates gravity override which suggests that standard laboratory methods for analysing CO2 EOR processes are likely to overestimate displacement efficiency. Other sensitivity studies highlight the pore to core scale variables that control caprock sealing mechanisms, and residual and solubility trapping during CO2 injection for storage, and their implications at the reservoir scale. For depressurization, simulations performed on a pore network anchored to measured petrophysical properties of a 0.23mD fractured chalk core from a North Sea reservoir show a very weak correlation between depletion rate and critical gas saturation, contrary to conventional belief. Depressurization oil recovery efficiency was found to increase with increase in initial water saturation but the presence of fractures caused the critical saturation to decrease by approximately 60%.

La gestion de l’eau dans les piles à combustible à membrane d’échange de proton (PEMFC) est une problématique principale pour assurer leur efficacité et leur durabilité. La couche de diffusion côté cathode est considérée comme l’un des composants critiques concernant cette problématique. Dans ce contexte, l’objectif principal de la thèse est d’améliorer la compréhension des mécanismes intervenants dans la formation et le transport de l’eau dans la couche dediffusion côté cathode. Pour ce faire, un modèle en réseau de pores d’injection mixte liquidevapeur (MIPNM) est développé. Ce nouveau modèle permet de simuler la formation et le transport de l’eau dans la couche de diffusion côté cathode sur une plus large gamme de conditions de fonctionnement de la pile (température, densité de courant et humidité relative dans le canal) qu’avec les modèles des travaux précédents. Différents régimes de formation et de transport del’eau sont identifiés et décrits. Dans une seconde partie, le travail de thèse se focalise sur l’impact du traitement hydrophobe de la couche de diffusion. Les couches de diffusion actuellement commercialisées sont rendues hydrophobes en déposant une couche de polytétrafluoroéthylène (PTFE) sur les fibres de carbones hydrophiles. Il a été observé que le revêtement peut être non uniforme sur des couches de diffusion neuves et que le revêtement peut se dégrader au cours dufonctionnement de la pile. L’impact de ces deux phénomènes sur la distribution de l’eau liquide et sur l’accès du gaz réactif jusqu’à la couche catalytique est étudié en utilisant le modèle MIPNM pour des réseaux à mouillabilité mixte. Dans une troisième partie, un travail visant à l’amélioration de l’efficacité des piles est réalisé. Le but est d’optimiser l’accès du gaz réactif jusqu’à la couche catalytique en modifiant la microstructure des couches de diffusion. Ce travail est réalisé en couplant le modèle en réseau de pore avec un algorithme génétique. En complément, la modification des propriétés de mouillabilité des couches de diffusion est étudiée dans le but d’améliorer l’accès du gaz réactif. Enfin, un modèle 1D de tout l’assemblage anode-cathode est développé pour prendre en compte à la fois les conditions de fonctionnement à la cathode et à l’anode. Ce modèle 1D est couplé au MIPNM afin d’évaluer l’impact des conditions de fonctionnement côté anode sur la distribution d’eau liquide dans la couche de diffusion côté cathode
Water management is considered as a key issue in order to improve Proton Exchange Membrane Fuel Cells efficiency and durability. One of the critical components regarding this issue is the athode Gas Diffusion Layer (GDL). In this context, the main goal of the PhD work is to improve the understanding of the mechanisms responsible for the liquid water formation and transport in the cathode GDL. To this end, a Mixed liquid-vapour Injection Pore Network Model (MIPNM) is developed. This new model enables one to simulate the liquid water formation and transport in the cathode GDL for a larger range of operating conditions (temperature, current density and channel relative humidity) than in previous works. Different regimes of water formation and transport are identified and described. In a second part, the PhD work focus on the impact of the GDL hydrophobic treatment. Currently commercialized GDLs are rendered hydrophobic by coating Polytetrafluoroethylene (PTFE) onto the hydrophilic carbon fibres. It has been reported that the coating can be nonuniform on fresh GDLs and also that the coating can be altered during the operation of the fuel cell. The impact of these two phenomena on the liquid water distribution and on the reactant gas access to the catalyst layer is studied using the MIPNM for mixed wettability networks. In a third part, a work aiming at the improvement of PEMFC efficiency is developed. The goal is to optimise the reactant gas access to the catalyst layer by modifying the microstructure of GDLs. This is performed by coupling the PNM with a genetic algorithm. In a complementary study, the improvement of the reactant gas access is studied through modifications of the GDL wettability properties. Finally, a 1D model of the whole anode-cathode assembly is developed so as to take into account both anode and cathode operating conditions. This 1D model is coupled with the MIPNM in order to assess the impact of the anode operating conditions on the liquid water distribution in the cathode GDL

The objective of this research is to develop a simulation software tool, GASFLO, which should evaluate pressure, flow and temperature distributions of process gas in pellet induration system networks. Pellet induration systems are complex industrial systems composed of heterogenous components. The magnitude of gas through leaks i.e. the air entering or leaving the system from the points other than the known exits, is substantial and it adversely effects the performance of induration process. These leaks are very difficult to measure because of the hostile environment in the plant. The modelling of such industrial systems requires a notable amount of experimentation so the tool has been designed to enable the user modeller to change the component models and solution algorithms easily. The conventional methods for flow network simulation are based on process centred approach, mostly composed of homogeneous components. For ease of computation, the non-pipe elements are modelled with an approximate linear or non-linear generic equation, whose coefficients can simulate different states of the element. The resulting set of non-linear equations is linearised and solved simultaneously using some iterative method. By contrast, GASFLO is based on device centred or unit based approach, and uses a two level hierarchical solution algorithm. The pellet induration system network is first idealised into a connected graph of streams (sets of serially connected components) and nodes. At the top or coordination level the flow and pressure distributions satisfying the Kirchhoff's laws are evaluated for the connected graph. At the lower or component level the exact mathematical models of components ale computed, in order of their occurrence in respective streams, using coordination variables as parameters. The converged flows are used for the temperature computation. The solution algorithm requires partitioning of the connected graph into forest and coforest structures, for which secondary algorithms have been developed using specific heuristics relevant to the pellet induration systems. The rigorous application of software engineering techniques for the design and implementation of software, enabled the resolution of the complexity of the modelled system, embedded the characteristics of 'quality software' into the resulting code and benefits from object orientation, even though it is implemented in standard FORTRAN 77. GASFLO predicted results are in a good agreement with the measured results, it has been validated for a real life pellet induration system. It has been applied to simulate several practical scenarios, like addition of extra wind boxes to the zones and to determine how the plant production can be increased by certain ratio, such simulations were not feasible otherwise. GASFLO takes less than a minute to simulate a real-life pellet induration system on a 486 PC. The combined simulation with an other software tool, INDSYS, which evaluates the heat distribution in the solids, is also feasible.

The scope of this thesis is an investigation into the interactions between the gas and electrical transmission networks in the context of a low-carbon energy system with explicit considerations of the role played by multi-energy vectors, including heating, in future scenarios. Many energy systems are in a state of transition due to the growing need to reduce their carbon impact while maintaining reliability and reducing costs. The generation capacity of gas-fuelled power stations, as a cleaner alternative to coal, has been continuing to grow in many power systems. Furthermore, their operational characteristics are evolving as they are increasingly used to meet demand when there is a shortfall in renewable generation and are playing a role in contributing to the reliability of the power system. Additionally, changes to the heating sector (e.g., the electrification of heat or the increased use of combined heat-and-power) and the introduction of power-to-gas (to convert excess renewable electricity into hydrogen for successive energy generation) all lead to tighter interactions between the heat, gas and power sectors which require a multi-energy framework to assess. In this work this is achieved, firstly, through the development of integrated gas and power network modelling techniques. The power system modelling incorporates a multi-temporal DC optimal power flow, while gas network models use steady-state and transient flow analysis to allow for an assessment of the pressures and flows around the network. Additionally, a novel heat model is presented for a regional assessment of the heating demands of the British energy system, so that the impacts of changes to heating technologies on the gas and electrical transmission networks can be quantified in whole-energy system case studies. Power-to-gas technologies where (excess renewable) electrical energy is converted to hydrogen and then potentially synthetic natural gas which is then injected into the gas network where it can be stored and transported have the potential to increase the integration of renewable resources and reduce the carbon impact of both electricity and heating sectors. Models are presented which assess the operation of power-to-gas and its impact on the gas and electrical transmission networks as well as the benefits to the energy system. This uses power system modelling to assess the excess renewable energy (from wind and solar resources) that can be used in the power-to-gas process, with the amounts of produced hydrogen and synthetic natural gas also being evaluated considering constraints imposed by the gas network. The resulting case studies evaluate the impact on the gas and electrical networks and the benefit to the energy system by displacing natural gas and reducing carbon emissions. To assess the extent to which gas power stations can change their output to follow changes in renewable generation output, an integrated gas and electrical network flexibility model has been developed. This uses the notion of zonal linepack flexibility and allows for limits to be applied to the ability of gas turbines to change their output over the forthcoming hours with consideration of the intermittent nature of renewable generation. The developed models and methodologies are incorporated into a number of case studies using the British gas and electrical networks and heating sector showing their applicability to assessing future energy system needs.

The olfactory bulb forms the first level of input integration for olfactory receptor neurons that receive stimuli from odorant molecules in the nose. The olfactory bulb is multi channel in nature, with each channel containing its own populations of mitral cells. These channels each handle the input from neurons expressing a single type of olfactory receptor protein tuned to a unique range of odorant structures. I have constructed a mitral cell gap-junction network model with morphologically accurate mitral cells to study the behaviour of mitral cells in a channel population. The passive parameters of each of the mitral cells were determined by fitting to in vitro recordings. Sodium and potassium channels were added to the mitral cells to give the ability to generate action potentials. Gap-junctions were placed in the apical dendrite tufts of the mitral cells and their conductance adjusted to give a coupling ratio between mitral cells consistent with experimental findings. Firing was induced with twenty current injections randomly located in the apical dendrite tuft of two of the mitral cells, mimicking the multiple inputs from the olfactory receptor neurons. A protocol was used to promote an initial asynchrony in firing which was transmitted across the gap-junctions to all six mitral cells. I found that the mitral cell population would overcome this asynchrony, rapidly tending to synchronous firing. Adding calcium and calcium dependent potassium channels to the mitral cells produced burst firing patterns that were different for each of the cells. The gap-junctions did not have enough influence to overcome the asynchrony of the different burst firing patterns. The addition of calcium concentration threshold dependant glutamate release and AMPA auto receptors to the apical dendrite tuft of each mitral cell allowed the burst firing to promote self propagating synchronised firing after an initial period of asynchrony.

This thesis explores the physical behaviour of industrial pressure reducing valves (PRVs) used around the world for effective water distribution and network pressure reduction. The mathematical models developed as part of this project showcase the steady state and dynamic behaviour of PRVs when changes are made to its control system or external variables within water distribution networks (WDNs) acting to disturb the PRV system (used to simulate water demand within the network). Analyses on the performance of the models to quantify any pressure setpoint offsets or low PRV head loss complications as experienced in Western Australia’s Interconnected Water Supply Scheme (IWSS) network by Water Corporation was done and validated with industry contacts alongside a comprehensive literature review to validate general PRV dynamics. Downstream pressure offsets attributed to higher PRV pressure setpoints were related to the low head loss issues of PRVs (i.e. when PRV head loss ≤ 10m) and classified into three distinct categories. Further simulations were done to verify the PRV as an effective disturbance rejection device with equations formulated to predict and quantify any PRV offsets in the downstream pressure, either as a result from pressure setpoint changes or disturbance inputs. Furthermore, work done to relate hydraulically controlled PRVs (i.e. with no power supply) and GHG emissions was completed to enable water service providers to estimate the true financial and environmental costs of PRVs within their WDNs. These costs were quantified within a theoretical WDN based on the IWSS in which the energy analytics related to all PRV operations (that is commonly unaccounted for by water service providers) totalled $672,000/year and 5040 tons CO2/year. The models created can be adapted to any PRV. Whilst further work can be done to refine the models developed within this thesis paper, they provide a strong foundation for academia and industry alike when analysing PRVs and estimating the commonly overlooked GHG emissions associated with water distribution.

Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. This similarity measure gives rise to the possibility of interpolating in the data which has been done successfully in the past. Herein we propose a new stochastic interpolation method for GANs where the interpolation is forced to adhere to the data distribution by implementing a sequential Monte Carlo algorithm for data sampling. The results show that the new method outperforms previously known interpolation methods for the data set LINES; compared to the results of other interpolation methods there was a significant improvement measured through quantitative and qualitative evaluations. The developed interpolation method has met its expectations and shown promise, however it needs to be tested on a more complex data set in order to verify that it also scales well.
Generative adversarial networks (GANs) är ett slags generativ modell som har fått mycket uppmärksamhet de senaste åren sedan de upptäcktes för sin potential att återskapa komplexa högdimensionella datafördelningar. Dessa förser en komprimerad representation av datan där enbart de karaktäriserande egenskaperna är bevarade, vilket följdaktligen inducerar ett avståndsmått på datarummet. Detta avståndsmått möjliggör interpolering inom datan vilket har åstadkommits med framgång tidigare. Häri föreslår vi en ny stokastisk interpoleringsmetod för GANs där interpolationen tvingas följa datafördelningen genom att implementera en sekventiell Monte Carlo algoritm för dragning av datapunkter. Resultaten för studien visar att metoden ger bättre interpolationer för datamängden LINES som användes; jämfört med resultaten av tidigare kända interpolationsmetoder syntes en märkbar förbättring genom kvalitativa och kvantitativa utvärderingar. Den framtagna interpolationsmetoden har alltså mött förväntningarna och är lovande, emellertid fordras att den testas på en mer komplex datamängd för att bekräfta att den fungerar väl även under mer generella förhållanden.

When maintaining the biodiversity and ecological integrity of forests is a goal of management, a primary requirement is to assess the status, condition, conservation value of each forest, and change in forest conditions over time. GIS procedures were used here to compare different map-based surveys and look in detail at changes in woodlands of the Snowdonia National Park, North Wales, from 1970 to 2000. The maps from the 1970s and 1980s generated by Silsoe College did not compare well with the map from the 1990s produced by the Forestry Commission and no meaningful changes could be measured. This was attributed to difficulties with comparison of different interpretations and classification schemes used by the two organisations. A series of landscape surveys using the same minimum mapping unit, classification scheme, and methodology in general is needed. The potential changes in broadleaved and scrub woodland area were modelled based on the two most extreme climate change scenarios, termed the Low and High scenarios. Temperature and rainfall models formed the basis for logistic regressions of woodland type and distribution. A declining trend in probability of presence for both woodland types from the present sites was shown under the UKCIP98 High climate change scenario. The results emphasized the conceptual difficulties in using fragments of woodland within the realised niche rather than the fundamental niche as the basis for environmental modelling of plant community distributions. GIS based models were generated to address the key question in the biodiversity action plan process of where should new woodland be created or plantations restored. Ecological criteria were developed to identify the priority areas for native woodland expansion taking into account of the requirements for successful woodland expansion from the nature point of view and specific policy aims. The results were interesting and suggested that there is ample land potentially suitable in Snowdonia for new native woodland. The models could be used to aid decision-making for new native woodland in the National Park. A further extension of GIS-based modelling was developed for the prediction of individual NVC types and BAP priority woodland types. The environmental spaces occupied by the fragments of NVC woodland types currently present in Snowdonia were defined and used as templates to produce maps of potentially suitable sites for the occurrence of each NVC type. The results were not as clear-cut as had been hoped because of overlaps in the predicted occurrences of various woodland types. Independent verification of the predictions using non-spatial data for 24 sites revealed that the model produced was very poor. This was not, however, a fault of the modelling but a reflection of the fact that some of the environmental data were at too coarse a scale and that NVC types are not solely determined by environmental factors. In spite of some weaknesses in the data, the use of GIS for modelling these scenarios proved useful. Nowadays, forest policies in Wales, Europe and elsewhere are changing rapidly to meet modified global, national, and local objectives. GIS is, and will increasingly be so, proving to be a useful and flexible tool for translating forest policy into practical application on the ground.

Following the introduction of new laws and regulations to ensure data protection in GDPR and PIPEDA, interests in technologies to protect data privacy have increased. A promising research trajectory in this area is found in Generative Adversarial Networks (GAN), an architecture trained to produce data that reflects the statistical properties of its underlying dataset without compromising the integrity of the data subjects. Despite the technology’s young age, prior research has made significant progress in the generation process of so-called synthetic data, and the current models can generate images with high-quality. Due to the architecture’s success with images, it has been adapted to new domains, and this study examines its potential to synthesize financial tabular data. The study investigates a state-of-the-art model within tabular GANs, called CTGAN, together with two proposed ideas to enhance its generative ability. The results indicate that a modified training dynamic and a novel early stopping strategy improve the architecture’s capacity to synthesize data. The generated data presents realistic features with clear influences from its underlying dataset, and the inferred conclusions on subsequent analyses are similar to those based on the original data. Thus, the conclusion is that GANs has great potential to generate tabular data that can be considered a substitute for sensitive data, which could enable organizations to have more generous data sharing policies.
Med striktare förhållningsregler till hur data ska hanteras genom GDPR och PIPEDA har intresset för anonymiseringsmetoder för att censurera känslig data aktualliserats. En lovande teknik inom området återfinns i Generativa Motstridande Nätverk, en arkitektur som syftar till att generera data som återspeglar de statiska egenskaperna i dess underliggande dataset utan att äventyra datasubjektens integritet. Trots forskningsfältet unga ålder har man gjort stora framsteg i genereringsprocessen av så kallad syntetisk data, och numera finns det modeller som kan generera bilder av hög realistisk karaktär. Som ett steg framåt i forskningen har arkitekturen adopterats till nya domäner, och den här studien syftar till att undersöka dess förmåga att syntatisera finansiell tabelldata. I studien undersöks en framträdande modell inom forskningsfältet, CTGAN, tillsammans med två föreslagna idéer i syfte att förbättra dess generativa förmåga. Resultaten indikerar att en förändrad träningsdynamik och en ny optimeringsstrategi förbättrar arkitekturens förmåga att generera syntetisk data. Den genererade datan håller i sin tur hög kvalité med tydliga influenser från dess underliggande dataset, och resultat på efterföljande analyser mellan datakällorna är av jämförbar karaktär. Slutsatsen är således att GANs har stor potential att generera tabulär data som kan betrakatas som substitut till känslig data, vilket möjliggör för en mer frikostig delningspolitik av data inom organisationer.

The successful delivery of low-carbon housing (both new and retrofitted) is a key aspect of the UK s commitment to an 80% reduction in carbon emissions by 2050. In this context, the inclusion of small-scale building-integrated renewable energy technologies is an important component of low carbon design strategies, and is subject to numerous regulation and incentive schemes (including the Renewable Heat Incentive (RHI)) set up by government to encourage uptake and set minimum performance benchmarks. Unfortunately there are numerous examples of in-use energy and carbon performance shortfalls for new and retrofitted buildings this is termed the performance gap . Technical and human factors associated with building subsystem performance, which are often not considered in design tools used to predict performance, are the root cause of performance uncertainty. The research presented in this doctoral thesis aims to develop and apply a novel probabilistic method of evaluating the performance uncertainty of solar thermal systems installed in the UK. Analysis of measured data from a group of low carbon retrofitted dwellings revealed that the majority of buildings failed to meet the designed-for carbon emissions target with an average percentage difference of 60%. An in-depth case study technical evaluation of one of these dwellings showed significant dysfunction associated with the combined ASHP/solar thermal heating system, resulting in a performance gap of 94%, illustrating that the performance gap can be regarded as a whole-system problem, comprising a number of subsystem causal factors. Using a detailed dataset obtained from the UK s largest field trial of domestic solar thermal systems, a cross-cutting evaluation of predicted vs. measured performance similarly revealed a discrepancy with a mean percentage difference in predicted and measured annual yield of -24%. Having defined the nature and extent of underperformance for solar thermal technology in the UK, causal factors influencing performance were mapped and the associated uncertainty quantified using a novel knowledge-based Bayesian network (BN). In addition, the BN approach along with Monte Carlo sampling was applied to the well-established BREDEM model in order to quantify performance uncertainty of solar thermal systems by producing distributions of annual yield. As such, the modified BN-based BREDEM model represents a significant improvement in the prediction of performance of small-scale renewable energy technologies. Finally, financial analysis applied to the probabilistic predictions of annual yield revealed that the current UK RHI scheme is unlikely to result in positive returns on investment for solar thermal systems unless the duration of the payments is extended or electricity is the primary source of heating.

The 2050 long-term strategy, defined by the European Commission, leads towards zero greenhouse gas emissions by 2050. Reduction of carbon dioxide emissions can be achieved substituting high carbon fossil fuels (coal and oil) with natural gas, renewable sources and green fuels. In the next years, the gas system will play a central and crucial role in the global energy market. Due to modifications of international gas trade flows and rise of demand, the existing gas infrastructures will necessarily have to be expanded, upgraded and renovated in the immediate future. Furthermore, power-to-gas technology is a potential solution to support and accelerate the penetration of renewable sources and the decarbonization of the energy sector. The excess of power generated by renewable energy sources is used by power-to-gas facilities to produce alternative green fuels. The resulting gas, such as hydrogen or synthetic natural gas, can be injected and stored into the existing gas grid. Subsequently, the green low/zero-carbon fuel blended with the traditional natural gas would enable to reduce carbon dioxide emission of industrial, commercial and residential gas customers. In this new scenario, it is essential to study, model and simulate the integration and operation of gas networks in the energy system. It is also very important to evaluate the impact of alternative fuel injections on the properties and composition of the gas delivered to the users connected to the gas grid. In this thesis, a steady-state and dynamic one-dimensional gas network tool, named "Gas Network Solver", is developed. The research focuses on mathematical modelling of city gate station (source), pipe, reducing station, valve, demand node and interchange node elements, which compose a gas distribution network. Particular attention is dedicated to the implementation of the mathematical model of the gas and the algorithm for quality tracking in order to analyse and simulate multiple types of gas sources. The tool proposed is validated by comparing results of three test cases to solutions obtained with a commercial software application, named "Scenario Analysis Interface for Energy Systems" (SAInt), and data from other models available in the literature. Finally, a case study considering a real medium-pressure and low-pressure gas distribution network, composed by about 2289 elements and located in a hilly area of central Italy, is analysed. After the simulation and analysis of the network in the actual scenario, a possible solution to decarbonize the network is carried out. The installation of a power-to-gas facility, associated effects on behaviour of the network and quality of the gas delivered are studied. The investigation also aims to evaluate the maximum amount of hydrogen injectable respecting gas standards defined by the Italian Regulatory Authority for Energy, Networks and Environment.