Smart electric vehicle charging strategy in direct current microgrid

This thesis proposes novel electric vehicle (EV) charging strategies in DC microgrid (DCMG) for integrating network loads, EV charging/discharging and dispatchable generators (DGs) using droop control within DCMG. A novel two-stage optimization framework is deployed, which optimizes power flow in the network using droop control within DCMG and solves charging tasks with a modified Djistra algorithm. Charging tasks here are modeled as the shortest path problem considering system losses and battery degradation from the distribution system operator (DSO) and electric vehicles aggregator (EVA) respectively. Furthermore, a probabilistic distribution model is proposed to investigate the EV stochastic behaviours for a charging station including time-of-arrival (TOA), time-of-departure(TOD) and energy-to-be-charged (ETC) as well as the coupling characteristic between these parameters. Markov Chain Monte Carlo (MCMC) method is employed to establish a multi-dimension probability distribution for those load profiles and further tests show the scheme is suitable for decentralized computing of its low burn-in request, fast convergent and good parallel acceleration performance. Following this, a three-stage stochastic EV charging strategy is designed to plug the probabilistic distribution model into the optimization framework, which becomes the first stage of the framework. Subsequently, an optimal power flow (OPF) model in the DCMG is deployed where the previous deterministic model is deployed in the second stage which stage one and stage two are combined as a chance-constrained problem in stage three and solved as a random walk problem. Finally, this thesis investigates the value of EV integration in the DCMG. The results obtained show that with smart control of EV charging/discharging, not only EV charging requests can be satisfied, but also network performance like peak valley difference can be improved by ancillary services. Meanwhile, both system loss and battery degradation from DSO and EVA can be minimized.Open Acces

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Evaluating and developing parameter optimization and uncertainty analysis methods for a computationally intensive distributed hydrological model

This study focuses on developing and evaluating efficient and effective parameter calibration and uncertainty methods for hydrologic modeling. Five single objective optimization algorithms and six multi-objective optimization algorithms were tested for automatic parameter calibration of the SWAT model. A new multi-objective optimization method (Multi-objective Particle Swarm and Optimization & Genetic Algorithms) that combines the strengths of different optimization algorithms was proposed. Based on the evaluation of the performances of different algorithms on three test cases, the new method consistently performed better than or close to the other algorithms. In order to save efforts of running the computationally intensive SWAT model, support vector machine (SVM) was used as a surrogate to approximate the behavior of SWAT. It was illustrated that combining SVM with Particle Swarm and Optimization can save efforts for parameter calibration of SWAT. Further, SVM was used as a surrogate to implement parameter uncertainty analysis fo SWAT. The results show that SVM helped save more than 50% of runs of the computationally intensive SWAT model The effect of model structure on the uncertainty estimation of streamflow simulation was examined through applying SWAT and Neural Network models. The 95% uncertainty intervals estimated by SWAT only include 20% of the observed data, while Neural Networks include more than 70%. This indicates the model structure is an important source of uncertainty of hydrologic modeling and needs to be evaluated carefully. Further exploitation of the effect of different treatments of the uncertainties of model structures on hydrologic modeling was conducted through applying four types of Bayesian Neural Networks. By considering uncertainty associated with model structure, the Bayesian Neural Networks can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation

Optimization of Potable Water Distribution and Wastewater Collection Networks: A Systematic Review and Future Research Directions

Potable water distribution networks (WDNs) and wastewater collection networks (WWCNs) are the two fundamental constituents of the complex urban water infrastructure. Such water networks require adapted design interventions as part of retrofitting, extension, and maintenance activities. Consequently, proper optimization methodologies are required to reduce the associated capital cost while also meeting the demands of acquiring clean water and releasing wastewater by consumers. In this paper, a systematic review of the optimization of both WDNs and WWCNs, from the preliminary stages of development through to the state-of-the-art, is jointly presented. First, both WDNs and WWCNs are conceptually and functionally described along with illustrative benchmarks. The optimization of water networks across both clean and waste domains is then systematically reviewed and organized, covering all levels of complexity from the formulation of cost functions and constraints, through to traditional and advanced optimization methodologies. The rationales behind employing these methodologies as well as their advantages and disadvantages are investigated. This paper then critically discusses current trends and identifies directions for future research by comparing the existing optimization paradigms within WDNs and WWCNs and proposing common research directions for optimizing water networks. Optimization of urban water networks is a multidisciplinary domain, within which this paper is anticipated to be of great benefit to researchers and practitioners

Improving the convergence rate of seismic history matching with a proxy derived method to aid stochastic sampling

History matching is a very important activity during the continued development and management of petroleum reservoirs. Time-lapse (4D) seismic data provide information on the dynamics of fluids in reservoirs, relating variations of seismic signal to saturation and pressure changes. This information can be integrated with history matching to improve convergence towards a simulation model that predicts available data. The main aim of this thesis is to develop a method to speed up the convergence rate of assisted seismic history matching using proxy derived gradient method. Stochastic inversion algorithms often rely on simple assumptions for selecting new models by random processes. In this work, we improve the way that such approaches learn about the system they are searching and thus operate more efficiently. To this end, a new method has been developed called NA with Proxy derived Gradients (NAPG). To improve convergence, we use a proxy model to understand how parameters control the misfit and then use a global stochastic method with these sensitivities to optimise the search of the parameter space. This leads to an improved set of final reservoir models. These in turn can be used more effectively in reservoir management decisions. To validate the proposed approach, we applied the new approach on a number of analytical functions and synthetic cases. In addition, we demonstrate the proposed method by applying it to the UKCS Schiehallion field. The results show that the new method speeds up the rate of convergence by a factor of two to three generally. The performance of NAPG is much improved by updating the regression equation coefficients instead of keeping it fixed. In addition, we found that the initial number of models to start NAPG or NA could be reduced by using Experimental Design instead of using random initialization. Ultimately, with all of these approaches combined, the number of models required to find a good match reduced by an order of magnitude. We have investigated the criteria for stopping the SHM loop, particularly the use of a proxy model to help. More research is needed to complete this work but the approach is promising. Quantifying parameter uncertainty using NA and NAPG was studied using the NA-Bayes approach (NAB). We found that NAB is very sensitive to misfit magnitude but otherwise NA and NAPG produce similar uncertainty measures

Reverse Engineering of Biological Systems

Gene regulatory network (GRN) consists of a set of genes and regulatory relationships between the genes. As outputs of the GRN, gene expression data contain important information that can be used to reconstruct the GRN to a certain degree. However, the reverse engineer of GRNs from gene expression data is a challenging problem in systems biology. Conventional methods fail in inferring GRNs from gene expression data because of the relative less number of observations compared with the large number of the genes. The inherent noises in the data make the inference accuracy relatively low and the combinatorial explosion nature of the problem makes the inference task extremely difficult. This study aims at reconstructing the GRNs from time-course gene expression data based on GRN models using system identification and parameter estimation methods. The main content consists of three parts: (1) a review of the methods for reverse engineering of GRNs, (2) reverse engineering of GRNs based on linear models and (3) reverse engineering of GRNs based on a nonlinear model, specifically S-systems. In the first part, after the necessary background and challenges of the problem are introduced, various methods for the inference of GRNs are comprehensively reviewed from two aspects: models and inference algorithms. The advantages and disadvantages of each method are discussed. The second part focus on inferring GRNs from time-course gene expression data based on linear models. First, the statistical properties of two sparse penalties, adaptive LASSO and SCAD, with an autoregressive model are studied. It shows that the proposed methods using these two penalties can asymptotically reconstruct the underlying networks. This provides a solid foundation for these methods and their extensions. Second, the integration of multiple datasets should be able to improve the accuracy of the GRN inference. A novel method, Huber group LASSO, is developed to infer GRNs from multiple time-course data, which is also robust to large noises and outliers that the data may contain. An efficient algorithm is also developed and its convergence analysis is provided. The third part can be further divided into two phases: estimating the parameters of S-systems with system structure known and inferring the S-systems without knowing the system structure. Two methods, alternating weighted least squares (AWLS) and auxiliary function guided coordinate descent (AFGCD), have been developed to estimate the parameters of S-systems from time-course data. AWLS takes advantage of the special structure of S-systems and significantly outperforms one existing method, alternating regression (AR). AFGCD uses the auxiliary function and coordinate descent techniques to get the smart and efficient iteration formula and its convergence is theoretically guaranteed. Without knowing the system structure, taking advantage of the special structure of the S-system model, a novel method, pruning separable parameter estimation algorithm (PSPEA) is developed to locally infer the S-systems. PSPEA is then combined with continuous genetic algorithm (CGA) to form a hybrid algorithm which can globally reconstruct the S-systems

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

APPLICATION OF MACHINE LEARNING TO CHF MODELLING

Accurate prediction of CHF is still a challenging issue in the study of boiling heat transfer. Many factors contribute to the occurrence of CHF and the various trigger mechanisms are proposed to unravel physical phenomena behind CHF. However, those mechanisms cannot cover the multiple primary factors simultaneously and even some of them still remain controversially unresolved. In light of the complexity and difficulty of CHF modelling, hereby an ensemble-learning based framework is proposed to model and predict CHF based on the databank of CHF. Some prior trials have been done for three primary aspects of dominant factors, that is, surface morphology, geometrical dimension and operation condition. These three primary constituents are respectively analyzed though three different sub-models of the ensemble framework in Chapter 3, 4 and 5. In Chapter Three, relevant experiments about micro-pillar enhanced CHF are reviewed and the corresponding databank of microstructure enhanced CHF is compiled based on those CHF experiments from published papers. Although the impacts of micro-pillars on CHF are still not clear, through qualitative analyses, the parametrical trends of CHF with respect to geometrical parameters of pillar array can be roughly foreseen. Meanwhile, this study also evaluates performance of prediction accuracy among four current physical models of microstructure-enhanced CHF. Comparative results show that two capillary wicking models have higher prediction accuracy. Particularly, a special terminology, zero-infinity convergence, is introduced to discuss the parametrical trends of CHF and qualitatively assess veracity of two capillary wicking models. Given the drawbacks of current physical models, the DBN is proposed to more accurately predict CHF and study parametric trends of CHF based on the microstructure enhanced CHF databank. Different from the training process of other regression modelling problems, constrained CHF points, which are artificially derived from the training data datasets, are required to be coupled with the raw training datasets for achieving the zero-infinity convergence of the DBN based CHF model, exhibiting accurate parametric trends of CHF and improving the prediction accuracy. This new training technique provides a new reliable solution to the similar constrained machine learning problems. Numerical results demonstrate that DBN can achieve the best performance of CHF prediction in terms of prediction accuracy. Through studying parametrical trends of CHF reveals that micro pillar arrays with the same parameters on heat transfer substrates with different dimensional sizes presents different CHF enhancement profiles. The presented methodology provides new insights for CHF modelling in pool boiling enhanced by other surface modification techniques, including porous layer coating, nanoparticle deposition, textured roughen, and nanowire fabrication. The effects of dimensions and materials of boiling surfaces on CHF are correlated and studied through the GRNN modelling in Chapter Four. Instead of inputting all parameters that indicate the thermal properties of materials into the trained model, the aggregated parameters from the primitive parameters of thermal properties, thermal activity and thermal diffusivity, are utilized as the input parameters of the trained model. This technique not only could capture the effects of thermal properties of materials on CHF effectively but also helps reduce the computational loads. The trained model shows the similar parametric trends of CHF to that of the traditional empirical correlation with respect to the thermal activity. If the thermal activity of heat transfer substrate is beyond a certain value, the corresponding effect of thermal activity will be absent, which somehow implies that the thickness of heat transfer substrate will not impact CHF after the asymptomatic thickness is reached. On the other hand, thermal diffusivity still affects CHF occurrence even if the effect of thermal activity is negligible. When coming to the effect of dimension size on CHF, it was found that when the side length of square heat transfer substrate is 5 times greater than the capillary length of working fluid, the CHF will be independent on the side length. Otherwise, CHF will be affected by the side length, and the influence of side length on CHF reaches ultimate if the side length of square boiling surface is exactly equal to the Raleigh-Plateau instability wavelength. This instability wavelength is only dependent on the thermal properties of working fluids, meaning that the optimal side length for CHF optimization is only related to the thermal properties of working fluid, namely, the surface tension, and the liquid and vapor densities of working fluid. In Chapter Five of this study, n-support vector machine is adopted to explore and study experimental strategies for the data-driven approaches of CHF look-up table construction, on the basis of sparingly-distributed experimental CHF data points. In the virtue of the CHF look-up table of Groeneveld et al (2007), those CHF data was used as the reference data of this research. In this data collection, CHF data of the subcooled flow boiling (Xe \u3c 0) is chosen to concentrate on the PWR steady-state condition because the in the normal operation of PWR, the system is under the subcooled flow boiling. The numerical results have demonstrated that ν-SVM trained by well sparsely-distributed training data in the parameter region of interest (pressure and mass flux) can yield a fairly acceptable degree of CHF prediction accuracy. Procuring training data points that can imply the parametric behaviors of CHF with respect to pressure and mass flux for support vector machine is the essential key of machine learning to achieving a high level of CHF prediction accuracy. For capturing the pressure-variant CHF behavior, training data that are in the proximity of the CHF inflection point significantly contribute to the improvement of prediction accuracy. Hence, training data preparation physics-informed with knowledge of CHF inflection points definitely augments the prediction accuracy of CHF. How the parametrical trends of CHF with respect to pressure and mass flux are close to the linear trends determines the level of prediction accuracy when lacking of a good spread of training data points. Besides, it is found that CHF extrapolation to a higher pressure with many data points collected at different low pressures can be effectively achieved by SVM if a few CHF data points are available under the high pressure, especially for PWR pressure of 15.5 MPa. This announces a possibility of strategic integration experiments between high pressure and low pressure, reducing experimental costs associated with the high pressure testing in terms of efforts and money. The proposed methodologies provides engineers and experimentalists with useful strategies to construct the look-up table tabulation of advanced cladding materials of ATFs. It is found out that there are multiple sub-problems that could be divided for CHF prediction and each sub-problem has its individual suitable machine learning model. Those prior work done by this study proves that the data-driven CHF modelling by sub-models can provide accurate CHF prediction under various scenarios and correct parametrical trends with respect to separate variables. Last but not least, another contribution of this thesis to the field of boiling heat transfer is that two databanks of experimental CHF data are compiled for the CHF enhancement by microstructures. The compiled databanks provide useful information and guidelines to the future design of surface structures that will possibly be applied to heat exchanger and nuclear fuel rod