Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems

Computing Optimal Mixed Strategies for Terrorist Plot Detection Games with the Consideration of Information Leakage

The terrorist’s coordinated attack is becoming an increasing threat to western countries. By monitoring potential terrorists, security agencies are able to detect and destroy terrorist plots at their planning stage. Therefore, an optimal monitoring strategy for the domestic security agency becomes necessary. However, previous study about monitoring strategy generation fails to consider the information leakage, due to hackers and insider threat. Such leakage events may lead to failure of watching potential terrorists and destroying the plot, and cause a huge risk to public security. This paper makes two major contributions. Firstly, we develop a new Stackelberg game model for the security agency to generate optimal monitoring strategy with the consideration of information leakage. Secondly, we provide a double-oracle framework DO-TPDIL for calculation effectively. The experimental result shows that our approach can obtain robust strategies against information leakage with high feasibility and efficiency

MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation

Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city. However, with the dramatic and rapid changes in the world environment, the assumption that data obey Independent Identically Distribution is undermined by the subsequent changes in data distribution, known as concept drift, leading to weak replicability and transferability of the model over unseen data. To address the issue, previous approaches typically retrain the model, forcing it to fit the most recent observed data. However, retraining is problematic in that it leads to model lag, consumption of resources, and model re-invalidation, causing the drift problem to be not well solved in realistic scenarios. In this study, we propose a new urban time series prediction model for the concept drift problem, which encodes the drift by considering the periodicity in the data and makes on-the-fly adjustments to the model based on the drift using a meta-dynamic network. Experiments on real-world datasets show that our design significantly outperforms state-of-the-art methods and can be well generalized to existing prediction backbones by reducing their sensitivity to distribution changes.Comment: Accepted by CIKM 202

Techno-economic assessment of wireless charging systems for airport electric shuttle buses

Flightpath 2050, the European Commission's vision for aviation, requires that the aviation industry achieves a 75 % reduction in CO2 emissions per passenger mile and airports become emission-free by 2050. Airport shuttle buses in the airfields are going to be electrified to reduce ground emissions. Simultaneously, the airfield movement space and time schedules are becoming more limited for adopting stationary charging facilities for electrified ground vehicles. Therefore, the dynamic wireless charging technology becomes a promising technology to help improve the stability of electrification of the airfield transport network. This paper proposes a techno-economic assessment of wireless charging, wired charging, and conventional technologies for electrifying airport shuttle buses. A bi-level planning optimisation approach combines the multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-III) and mixed integer linear programming (MILP) algorithm to handle a large number of decision variables and constraints generated from the investigated problem. The airport shuttle bus transport is simulated through a multi-agent-based model (MABM) approach. Four case studies are analysed for illustrating the techno-economic feasibility of wireless charging technology for airport electric shuttle buses. The results show that the wireless charging technology enables the electric shuttle buses to carry smaller batteries while conducting the same as tasks conventional diesel/petrol vehicles and the bi-directional wireless charging technology could help mitigate the impact of electrification of shuttle buses on the distribution network.Engineering and Physical Sciences Research Council (EPSRC): EP/S032053/

Oocytes Selected Using BCB Staining Enhance Nuclear Reprogramming and the In Vivo Development of SCNT Embryos in Cattle

The selection of good quality oocytes is crucial for in vitro fertilization and somatic cloning. Brilliant cresyl blue (BCB) staining has been used for selection of oocytes from several mammalian species. However, the effects of differential oocyte selection by BCB staining on nuclear reprogramming and in vivo development of SCNT embryos are not well understood. Immature compact cumulus–oocyte complexes (COCs) were divided into control (not exposed to BCB), BCB+ (blue cytoplasm) and BCB− (colorless cytoplasm) groups. We found that BCB+ oocytes yielded a significantly higher somatic cell nuclear transfer (SCNT) blastocyst rate and full term development rate of bovine SCNT embryos than the BCB− and control oocytes. BCB+ embryos (embryos developed from BCB+ oocytes) showed increased acetylation levels of histone H3 at K9 and K18 (AcH3K9, AcH3K18), and methylation levels of histone H3 at K4 (H3K4me2) than BCB− embryos (embryos developed from BCB− oocytes) at the two-cell stage. Furthermore, BCB+ embryos generated more total cells, trophectoderm (TE) cells, and inner cell mass (ICM) cells, and fewer apoptotic cells than BCB− embryos. The expression of SOX2, CDX2, and anti-apoptotic microRNA-21 were up-regulated in the BCB+ blastocysts compared with BCB− blastocysts, whereas the expression of pro-apoptotic gene Bax was down-regulated in BCB+ blastocysts. These results strongly suggest that BCB+ oocytes have a higher nuclear reprogramming capacity, and that BCB staining can be used to select developmentally competent oocytes for nuclear transfer

Analysis of Approaches for Modeling the Low Frequency Emission of LED Lamps

Light emitting diode (LED) lamps are now an established lighting technology, which is becoming prevalent in all load sectors. However, LED lamps are non-linear electrical loads, and their impact on distribution system voltage quality must be evaluated. This paper provides a detailed analysis of time domain and frequency domain approaches for developing and evaluating models suitable for use in large scale steady-state harmonic power flow analysis of the low frequency (LF) emission of LED lamps. The considered approaches are illustrated using four general categories of LED lamps, which have been shown to cover the vast majority of LED lamps currently available on the market. The aim is an in-depth assessment of the ability of commonly applied models to represent the specific design characteristics of different categories of LED lamps. The accuracy of the models is quantitatively evaluated by means of laboratory tests, numerical simulations, and statistical analyses. This provides an example, for each LED lamp category, of comprehensive information about the overall accuracy that can be achieved in the general framework of large scale LF harmonic penetration studies, particularly in the assessment of voltage quality in low voltage networks and their future evolution

First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector

The directionality information of incoming neutrinos is essential to atmospheric neutrino oscillation analysis since it is directly related to the oscillation baseline length. Large homogeneous liquid scintillator detectors, while offering excellent energy resolution, are traditionally very limited in their capabilities of measuring event directionality. In this paper, we present a novel directionality reconstruction method for atmospheric neutrino events in large homogeneous liquid scintillator detectors based on waveform analysis and machine learning techniques. We demonstrate for the first time that such detectors can achieve good direction resolution and potentially play an important role in future atmospheric neutrino oscillation measurements.Comment: Prepared for submission to PR

Microbial traits determine soil C emission in response to fresh carbon inputs in forests across biomes

Soil priming is a microbial-driven process, which determines key soil–climate feedbacks in response to fresh carbon inputs. Despite its importance, the microbial traits behind this process are largely undetermined. Knowledge of the role of these traits is integral to advance our understanding of how soil microbes regulate carbon (C) emissions in forests, which support the largest soil carbon stocks globally. Using metagenomic sequencing and C-glucose, we provide unprecedented evidence that microbial traits explain a unique portion of the variation in soil priming across forest biomes from tropical to cold temperature regions. We show that microbial functional profiles associated with the degradation of labile C, especially rapid simple sugar metabolism, drive soil priming in different forests. Genes involved in the degradation of lignin and aromatic compounds were negatively associated with priming effects in temperate forests, whereas the highest level of soil priming was associated with β-glucosidase genes in tropical/subtropical forests. Moreover, we reconstructed, for the first time, 42 whole bacterial genomes associated with the soil priming effect and found that these organisms support important gene machinery involved in priming effect. Collectively, our work demonstrates the importance of microbial traits to explain soil priming across forest biomes and suggests that rapid carbon metabolism is responsible for priming effects in forests. This knowledge is important because it advances our understanding on the microbial mechanisms mediating soil–climate feedbacks at a continental scale.This work were financially supported by the National Natural Science Foundation of China (41907031), the Chinese Academy of Sciences “Light of West China” Program for Introduced Talent in the West, the National Natural Science Foundation of China (31570440, 31270484), the Key International Scientific and Technological Cooperation and Exchange Project of Shaanxi Province, China (2020KWZ-010), the 2021 First Funds for Central Government to Guide Local Science and Technology Development in Qinghai Province (2021ZY002), the i-LINK +2018 (LINKA20069) from CSIC, and a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I