3,675 research outputs found

    Energy storage design and integration in power systems by system-value optimization

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    Energy storage can play a crucial role in decarbonising power systems by balancing power and energy in time. Wider power system benefits that arise from these balancing technologies include lower grid expansion, renewable curtailment, and average electricity costs. However, with the proliferation of new energy storage technologies, it becomes increasingly difficult to identify which technologies are economically viable and how to design and integrate them effectively. Using large-scale energy system models in Europe, the dissertation shows that solely relying on Levelized Cost of Storage (LCOS) metrics for technology assessments can mislead and that traditional system-value methods raise important questions about how to assess multiple energy storage technologies. Further, the work introduces a new complementary system-value assessment method called the market-potential method, which provides a systematic deployment analysis for assessing multiple storage technologies under competition. However, integrating energy storage in system models can lead to the unintended storage cycling effect, which occurs in approximately two-thirds of models and significantly distorts results. The thesis finds that traditional approaches to deal with the issue, such as multi-stage optimization or mixed integer linear programming approaches, are either ineffective or computationally inefficient. A new approach is suggested that only requires appropriate model parameterization with variable costs while keeping the model convex to reduce the risk of misleading results. In addition, to enable energy storage assessments and energy system research around the world, the thesis extended the geographical scope of an existing European opensource model to global coverage. The new build energy system model ‘PyPSA-Earth’ is thereby demonstrated and validated in Africa. Using PyPSA-Earth, the thesis assesses for the first time the system value of 20 energy storage technologies across multiple scenarios in a representative future power system in Africa. The results offer insights into approaches for assessing multiple energy storage technologies under competition in large-scale energy system models. In particular, the dissertation addresses extreme cost uncertainty through a comprehensive scenario tree and finds that, apart from lithium and hydrogen, only seven energy storage are optimizationrelevant technologies. The work also discovers that a heterogeneous storage design can increase power system benefits and that some energy storage are more important than others. Finally, in contrast to traditional methods that only consider single energy storage, the thesis finds that optimizing multiple energy storage options tends to significantly reduce total system costs by up to 29%. The presented research findings have the potential to inform decision-making processes for the sizing, integration, and deployment of energy storage systems in decarbonized power systems, contributing to a paradigm shift in scientific methodology and advancing efforts towards a sustainable future

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

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    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals

    Contactless excitation for electric machines: high temperature superconducting flux pumps

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    With the intensification of global warming and climate change, the pace of transformation to a neutral-emission society is accelerating. In various sectors, electrification has become the absolute tendency to promote such a movement, where electric machines play an important role in the current power generation system. It is widely convinced that electric machines with very high power density are essential for future applications, which, however, can be hardly achieved by conventional technologies. Owing to the maturation of the second generation (2G) high temperature superconducting (HTS) technologies, it has been recognized that superconducting machine could be a competitive candidate to realize the vision. One significant obstacle that hinders the implementation of superconducting machines is how to provide the required magnetic fields, or in other words, how to energise them appropriately. Conventional direct injection is not suitable for HTS machines, because the current leads would bridge ambident temperature to the cryogenic environment, which can impose considerable heat load on the system and increase the operational cost. Thus, an efficient energisation method is demanded by HTS machines. As an emerging technology that can accumulate substantial flux in a closed loop without any physical contact, HTS flux pumps have been proposed as a promising solution. Among the existing developed HTS flux pumps, rotary HTS flux pumps, or so-called HTS dynamo, can output non-zero time-averaged DC voltage and charge the rest of the circuit if a closed loop has been formed. This type of flux pump is often employed together with HTS coils, where the HTS coils can potentially work in the persistent current mode, and act like electromagnets with a considerable magnetic field, having a wide range of applications in industry. The output characteristics of rotary HTS flux pumps have been extensively explored through experiments and finite element method (FEM) simulations, yet the work on constructing statistical models as an alternative approach to capture key characteristics has not been studied. In this thesis, a 2D FEM program has been developed to model the operation of rotary HTS flux pumps and evaluate the effects of different factors on the output voltage through parameter sweeping and analysis of variance. Typical design considerations, including the operating frequency, air gap, HTS tape width, and remanent flux density have been investigated, in particular, the bilateral effect of HTS tape width has been discovered and explained by looking at the averaged integration of the electric field over the HTS tape. Based on the data obtained from various simulations, regression analysis has been conducted through a collection of machine learning methods. It has been demonstrated that the output voltage of a rotary HTS flux pump can be obtained promptly with satisfactory accuracy via Gaussian process regression, aiming to provide a novel approach for future research and a powerful design tool for industrial applications using rotary HTS flux pumps. To enhance the applicability of the proposed statistical models, an updated FEM program has been built to take more parameters into account. The newly added parameters, namely the rotor radius and the width of permanent magnet, together with formerly included ones, should have covered all the key design parameters for a rotary HTS flux pump. Based on data collected from the FEM model, a well-trained semi-deep neural network (DNN) model with a back-propagation algorithm has been put forward and validated. The proposed DNN model is capable of quantifying the output voltage of a rotary HTS flux pump instantly with an overall accuracy of 98% with respect to the simulated values with all design parameters explicitly specified. The model possesses a powerful ability to characterize the output behaviour of rotary HTS flux pumps by integrating all design parameters, and the output characteristics of rotary HTS flux pumps have been successfully demonstrated and visualized using this model. Compared to conventional time-consuming FEM-based numerical models, the proposed DNN model has the advantages of fast learning, accurate computation, as well as strong programmability. Therefore, the DNN model can greatly facilitate the design and optimization process for rotary HTS flux pumps. An executable application has been developed accordingly based on the DNN model, which is believed to provide a useful tool for learners and designers of rotary HTS flux pumps. A new variant inspired by the working principles of rotary HTS flux pumps has been proposed and termed as stationary wave HTS flux pumps. The superiority of this type is that it has a simple structure without any moving components, and it utilises a controllable current-driven electromagnet to provide the required magnetic field. It has been demonstrated that the origin of the output voltage is determined by the asymmetric distribution of the dynamic resistance in the HTS tape, for which the electromagnet must be placed at such a position that its central line is not aligned with that of the HTS tape. A numerical model has been built to simulate the operation of a stationary wave HTS flux pump, based on which the output characteristics and dynamic resistance against various parameters have been investigated. Besides, accurate and reliable statistical models have been proposed to predict the open circuit voltage and effective dynamic resistance by adapting the previously developed machine learning techniques. The work presented in this PhD thesis can bring more insight into HTS flux pumps as an emerging promising contactless energisation technology, and the proposed statistical models can be particularly useful for the design and optimization of such devices

    Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging

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    Peering into the Dark: Investigating dark matter and neutrinos with cosmology and astrophysics

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    The LCDM model of modern cosmology provides a highly accurate description of our universe. However, it relies on two mysterious components, dark matter and dark energy. The cold dark matter paradigm does not provide a satisfying description of its particle nature, nor any link to the Standard Model of particle physics. I investigate the consequences for cosmological structure formation in models with a coupling between dark matter and Standard Model neutrinos, as well as probes of primordial black holes as dark matter. I examine the impact that such an interaction would have through both linear perturbation theory and nonlinear N-body simulations. I present limits on the possible interaction strength from cosmic microwave background, large scale structure, and galaxy population data, as well as forecasts on the future sensitivity. I provide an analysis of what is necessary to distinguish the cosmological impact of interacting dark matter from similar effects. Intensity mapping of the 21 cm line of neutral hydrogen at high redshift using next generation observatories, such as the SKA, would provide the strongest constraints yet on such interactions, and may be able to distinguish between different scenarios causing suppressed small scale structure. I also present a novel type of probe of structure formation, using the cosmological gravitational wave signal of high redshift compact binary mergers to provide information about structure formation, and thus the behaviour of dark matter. Such observations would also provide competitive constraints. Finally, I investigate primordial black holes as an alternative dark matter candidate, presenting an analysis and framework for the evolution of extended mass populations over cosmological time and computing the present day gamma ray signal, as well as the allowed local evaporation rate. This is used to set constraints on the allowed population of low mass primordial black holes, and the likelihood of witnessing an evaporation

    Solid Solution Tetrelides and Pnictides for Thermoelectric Applications

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    Recent major advancements in thermoelectric material performance center around the development and understanding of band structure engineering techniques and phonon scattering mechanisms. Solid solution materials have the potential to access these major strategies simultaneously within a single system. In this thesis, solid solutions of tetrels (C, Si, Ge, Sn, and Pb) and pnictogens (N, P, As, Sb, and Bi) as thermoelectric materials are explored. Electronic structures are examined to understand established materials and propose band engineering strategies. New synthesis approaches for established materials are designed while established methods are utilized to synthesize novel solid solutions. Thermoelectric properties are measured and discussed in terms of the underlying chemistry of the materials. Future work is proposed for the systems studied where improvements can be suggested. Chapter 1 discusses the various principles and strategies underlying the design and application of thermoelectric materials with a focus on solid solution materials. An overview of current high-performance materials and the principles which provide their status is presented. Finally, the classes of materials which are experimentally studied are discussed to provide background and motivation for the research conducted. Chapter 2 reviews the principles and practices for the experimental methods and instrumentation utilized throughout the course of this study. The first solid solution material focused on in Chapter 3 is the tetrelide Mg2Si0.3Sn0.67Bi0.03; a high performance, nontoxic, and inexpensive thermoelectric material. A scaled-up reaction process was developed providing the first steps towards large scale applications. Large, condensed pieces of material were pressed on a scale which had not been achieved previously. Statistical analysis of measured thermoelectric properties is performed on the material using samples cut at various positions and orientations. Over 1 kg of material was prepared which displayed a zTmax above 1.2 reliably. These methods are used to assure a consistent quality of the process and material which is the first step towards establishing device applications. Pnictide-tetrel chalcopyrite solid solutions are investigated in Chapters 4 and 5, with ZnGe1-xSnxP2 explored in the former, while ZnSnP2-yAsy and ZnGe1-xSnxP2-yAsy are explored in the latter. A robust synthesis method for end members and solid solutions was developed using ball milling techniques followed by hot pressing. Successful synthesis and full miscibility of end members and solid solutions are confirmed with powder X-ray diffraction followed by Rietveld refinements. The synthesis method is primarily discussed in Chapter 4 which is further developed for synthesis of higher order solid solutions in Chapter 5. The methods developed provide a useful tool for low temperature synthesis of solid solutions with differently melting and difficult to synthesize end members. Structural investigations conducted on resulting ZnGe1-xSnxP2 (x = 0, 0.25, 0.5, 0.75, and 1), show a tendency for tetragonality (c/(2a)=1) which maintained high Seebeck coefficients for the Sn rich and equal substituted members. Electronic structure calculations with Boltzmann transport analysis and experimental lattice thermal conductivities were used to predict thermoelectric performance. Doping ZnSnP2 with p-type carriers was predicted to give zT = 1 at 0.002 carriers per formula unit and 900 K (such as with ZnSn0.998-In0.002P2), and 1.3 at 0.007 carriers per formula unit. Measured thermoelectric performance was most improved by decreased thermal conductivity due to alloy phonon scattering at equal Ge and Sn substitution (x = 0.5) while maintaining a large Seebeck coefficient. The end members displayed thermal conductivity of 4.4 W m-1 K-1 and 2.5 W m-1 K-1 for Ge and Sn respectively which decreased to 1.8 W m-1 K-1 for x = 0.5 at 875 K. Improvements from zT = 3.9∙10-4 and 2.0∙10-3 for Ge and Sn end members respectively were achieved to zT = 5.5∙10-3 for x = 0.5 at 800 K while increased thermal stability allowed greater performance at higher temperatures. Chapter 5 focuses on improving the carrier concentration of ZnSnP2 and ZnGe1-xSnxP2 by substitution of As for P. The first half of the chapter explores ZnSnP2-yAsy substitutions (y = 0, 0.5, 1, 1.5, and 2) where full miscibility of the solid solutions is achieved. The measured electrical conductivity shows exponential increase with As substitution from 0.03 S cm-1 for ZnSnP2 to 10.3 S cm-1 for ZnSnAs2 at 715 K. Band gaps as calculated from the activation energies showed a steady decrease with increasing As concentration from 1.4 eV for ZnSnP2 to 0.7 eV for ZnSnAs2. The Seebeck coefficient decreases significantly with As substitution from nearly 1000 μV K-1 for the P end member to -100 μV K-1 for the As end member at 650 K. Indications of bipolar conductivity are seen starting with the ZnSnP0.5As1.5 member which decreases down to 100 μV K-1 at 650 K. Thermal conductivity is decreased due to alloy phonon scattering with y = 1 and y = 0.5 showing the lowest values of 1.4 W m-1 K-1 at 825 K. Figure of merit values are increased at lower temperatures when compared to the ZnGe1-xSn¬xP2 series due to increased electrical conductivity, with y = 1 reaching zT = 2.1∙10-3 and y = 2 reaching 2.8∙10-3 at 700 K. The ZnSnP2-yAsy series displayed lower thermal stability and therefore overall lower figures of merit were found. The higher order quinary solid solutions ZnGe1-xSnxP2-yAsy (x = 0.5, 0.75, and y = 0, 0.5, 1, 1.5, and 2) are also studied in Chapter 5. Successful synthesis and structural refinements of the solid solutions were performed with a preference for tetragonality again observed. The alloy phonon scattering effect shows additive behavior which decreased the thermal conductivity further to 0.8 W m-1 K-1 at 775 K for x = 0.75, y = 1 to within the glasslike regime. Transport properties for the x = 0.5 (y = 0, 0.5, 1.5, and 2) series were measured which showed significant improvements compared to properties obtained for quaternary series. Large Seebeck coefficients were maintained despite exponential increase of electrical conductivities with increasing As substitution displaying characteristics similar to high entropy alloys. For ZnGe0.5Sn0.5P0.5As1.5 electrical conductivity increases from 0.02 S cm-1 to 2 S cm-1 while Seebeck coefficient also increases from 500 μV K-1 to 575 μV K-1 between 325 K and 775 K. The resulting thermoelectric performance of ZnGe0.5Sn0.5P0.5As1.5, zT = 0.038, is increased by more than 30-fold of the highest performing end member ZnSnAs2 with greater thermal stability. The final solid solutions explored are the pnictides Ca11Sb10-xBix and Ca11Sb10-yAsy series in Chapter 6. A direct liquid solid synthesis method is performed which succeeds for many attempted samples while some contained elemental impurities. Single crystals of Ca11Sb10-xBix were obtained and structures solved which display coloring substitution effects. A correlation parameter using electronic structure calculations was developed which predicted the substitution effects well. The highest thermoelectric performance was found for Ca11Sb10, with zT = 0.093 at 1000 K, which showed improvement compared to other literature studies of the compound. Evidence of intrastructural suppression of bipolar conductivity is observed resulting in simultaneous increase in Seebeck coefficient and electrical conductivity with increasing temperatures. Bi substitution tended to increase electrical conductivity while decreasing the Seebeck coefficient due to increasing bipolar conductivity. Low thermal conductivity values were measured for all samples with the lowest Ca11Sb10 displayed phonon glass electron crystal like behavior of 0.6 W m-1 K-1 to 0.7 W m-1 K-1 at 300 K and 1050 K respectively

    Resolving particle acceleration and transport in the jets of the microquasar SS 433 with H.E.S.S. and HAWC

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    The microquasar SS 433 offers a unique laboratory to study the physics of mildly relativistic jets and the associated non-thermal processes. It hosts a compact binary system, from which a pair of counter-propagating jets is observed to emanate. The jets are resolved by observations out to distances of approximately 0.1 pc from the central source, but further out, they remain dark until they abruptly reappear at around 25 pc as bright X-ray sources. These outer jets were recently reported to be sources of TeV gamma-rays by the High Altitude Water Cherenkov (HAWC) observatory. This thesis presents a complete picture of the TeV emission from the jets of SS 433 including new data from the High Energy Stereoscopic System (H.E.S.S.) and the HAWC observatory. To fully exploit the capabilities of the H.E.S.S. observations, a new approach to background rejection is presented. It is based on the detection of Cherenkov light from muons by large Imaging Atmospheric Cherenkov Telescopes (IACTs), such as the telescope located at the center of the H.E.S.S. array. The application of this technique leads to a factor four reduction in background above several tens of TeV in the H.E.S.S. stereoscopic analysis. This thesis presents the detection of the SS 433 outer jets for the first time with an IACT array using H.E.S.S.. The superior angular and energy resolution of H.E.S.S. compared to HAWC allow for a detailed study of the emission from the jets, including a measurement of the physical extension of the emission and of the spectra out to tens of TeV. These observations also reveal the presence of striking energy- dependent morphology, ruling out a hadronic origin for the bulk of the gamma-ray emission. Photons above 10 TeV are observed only close to the base of the outer jets, implying efficient particle acceleration to very-high energies at that location. Evidence suggests that the acceleration is due to a shock, thus providing a clue to the long-standing question of the reappearance of the jets. The observed energy-dependent morphology is modeled as a consequence of the particle cooling times and the advection flow of the jet, which constrains the jet dynamics and, in particular, results in an estimate of the velocity of the outer jets at their base. This solves several issues concerning the non-thermal processes occurring in the jets and their dynamics, but also opens up new questions that highlight our incomplete understanding of the SS 433 system. A joint analysis of the H.E.S.S. and HAWC data would provide insights on the system across the entire range of TeV energies. To make this possible, a tool capable of reading and analyzing the data from both instruments is required. This thesis presents the extension and validation of an existing data format and analysis tool shared among IACTs to the data from particle detector arrays such as the HAWC observatory. This framework is then used to revisit the HAWC observations of the SS 433 region with the inclusion of additional data taken since the first detection was reported. The existence of this framework enables for the first time the joint analysis of the H.E.S.S. and HAWC data, the preliminary results of which are presente
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