The cyber-physical systems (CPS), such as smart grid and intelligent transportation system, permeate into our modern societies recently. The infrastructures in such systems are closely interconnected and related, e.g., the intelligent transportation system is based on the reliable communication system, which requires the stable electricity provided by power grid for the proper function. We call such mutually related systems interdependent networks. This thesis addresses the cascading failure issue in interdependent cyber physical system. We consider CPS as a system that consists of physical-resource and computational-resource networks. The failure in physical-resource network might cause the failures in computational-resource network, and vice versa. This failure may recursively occur and cause a sequence of failures in both networks. In this thesis, we propose two novel interdependence models that better capture the interdependent networks. Then, we study the effect of cascading failures using percolation theory and present the detailed mathematical analysis on failure propagation in the system. By calculating the size of functioning parts in both networks, we analyze the robustness of our models against the random attacks and failures. The cascading failures in smart grid is also investigated, where two types of cascading failures are mixed. We estimate how the node tolerance parameter T (ratio of capacity to initial workload) affect the system performance. This thesis also explores the small clusters. We give insightful views on small cluster in interdependent networks, under different interdependence models and network topologies.

Smart grid is an emerging technology that aims to empower the current power grid with the integration of two-way communication and computer technology. This thesis deals with energy management in smart grid with focus on the smart home and the Intelligent Transportation System (ITS). The smart home contains a network that connects home elements like smart appliances, HVAC (heating, ventilation, and air conditioning), thermostat, smart meter, sensors, solar panels and energy storage. ITS integrates computer and communication technologies for advanced traffic management and communication among road infrastructure, vehicles and users. A web service describes a collection of operations that are accessible via the Internet. Web services can also provide security and interoperability. Due to the rising cost of energy, more and more residential consumers are interested in controlling temperature or appliances in order to reduce energy consumption. In this thesis, we propose an approach that uses Web services to remotely and efficiently interact with smart home devices in order to manage energy consumption, in a smart grid environment. Consequently, the user is able to adjust the temperature, control appliances or read energy consumption values quickly, remotely and securely. A smart home with a wireless network based on ZigBee and XMPP (eXtensible Messaging and Presence Protocol) is simulated. The advantage of XMPP is that it provides near real-time communication and security. There is a central computer that can communicate with all home elements. Business Process Execution Language (BPEL) is used to implement the Web service on a central computer. Furthermore, quality of service is offered. Therefore, different levels of security and an access control mechanism are provided. An algorithm is proposed that can sell stored energy back to the grid from smart home. Another algorithm is proposed that can facilitate demand response. Moreover, dynamic programming is used to minimize energy consumption. Also, a broadcasting algorithm is presented that can be used by an electric vehicle to find the most suitable charging station in ITS. Simulation and analytical study is undertaken to demonstrate the performance and advantage of the proposed approach and algorithms.

Power demands will increase day-by-day because of widely adopting of Plug-in Electric Vehicles (PEVs) in the world and growing population. Finding and managing additional power resources for upcoming demands is a challenge. Renewable power is one of the alternatives. However, to manage and control renewable resources, we need suitable Energy Storage System (ESS). PEVs have a large battery pack that is used mainly to supply electric motor. Moreover, PEV battery could be used as an ESS to store power at a certain time and use it at another time. Nevertheless, it can play the same role with electric power grids, so it can store power at a time and return it at another time. This role might help the grid to meet the growing demands. In this thesis, we propose a charging and discharging coordination algorithm that effectively addresses the problem of power demand on peak time using the PEV’s batteries as a backup power storage, namely, Flexible Charging and Discharging (FCD) algorithm. The FCD algorithm aims to manage high power demands at peak times using Vehicle to Home (V2H) technologies in Smart Grid and PEV’s batteries. Intensive computer simulation is used to test FCD algorithm. The FCD algorithm shows a significant reduction in power demands and total cost, in proportion to two other algorithms, without affecting the performance of the PEV or the flexibility of PEV owner’s trip schedule.

To achieve a high level of reliability, efficiency, and robustness in electric systems, the concept of smart grid has been proposed. It is an update of the traditional electric grid designed to meet current and future customers' requirements. With the smart grid, demand management has been adopted in order to shape the load pattern of the consumers, maintain supply-demand balance, and reduce the total energy cost. In this thesis, we focus mainly on energy savings by critically investigating the problem of load management in the smart grid. We first propose a user aware demand management approach that manages residential loads while taking into consideration users' comfort. This latter is modeled in a simple yet effective way that considers waiting time, type of appliance, as well as a weight factor to prioritize comfort or savings. The proposed approach is based on game theory using a modified regret matching procedure. It provides users with high incentives to participate actively in load management and borrows advantages of both centralized and decentralized schemes. Then, we investigate the issue of fairness within demand response programs. The fair division of the system bill stemming from the use of shared microgrid resources with different costs is examined. The Shapley value provides one of the core solutions to fairness problems; however, it has been known to be computationally expensive for systems such as microgrids. Therefore, we incorporate an approximation of the Shapley value into a demand response algorithm to propose a fair billing mechanism based on the contribution of each user towards attaining the aggregated system cost. Finally, we study energy trading in the smart grid as an alternative way to reduce the load on the grid by efficiently using renewable energy resources. We propose a solution that takes into account the smart grid physical infrastructure, in addition to the distribution of its users. Different constraints stemming from the nature of the smart grid have been considered towards a realistic solution. We show through simulation results that all of the proposed schemes reduce the load on the grid, the energy bills, and the total system energy cost while maintaining the users' comfort as well as fairness.

Voltage source converter high voltage direct current (VSC-HVDC) technology has become increasingly cost-effective and technically feasible in recent years. It is likely to play a vital role in integrating remotely-located renewable generation and reinforcing existing power systems. Multi-terminal VSC-HVDC (MTDC) systems, with superior reliability, redundancy and flexibility over the conventional point-to-point HVDC, have attracted a great deal of attention globally. MTDC however remains an area where little standardisation has taken place, and a series of challenges need to be fully understood and tackled before moving towards more complex DC grids. This thesis investigates modelling, control and stability of MTDC systems. DC voltage, which indicates power balance and stability of DC systems, is of paramount importance in MTDC control. Further investigation is required to understand the dynamic and steady-state behaviours of various DC voltage and active power control schemes in previous literature. This work provides a detailed comparative study of modelling and control methodologies of MTDC systems, with a key focus on the control of grid side converters and DC voltage coordination. A generalised algorithm is proposed to enable MTDC power flow calculations when complex DC voltage control characteristics are employed. Analysis based upon linearised power flow equations and equivalent circuit of droop control is performed to provide further intuitive understanding of the steady-state behaviours of MTDC systems. Information of key constraints on the stability and robustness of MTDC control systems has been limited. A main focus of this thesis is to examine these potential stability limitations and to increase the understanding of MTDC dynamics. In order to perform comprehensive open-loop and closed-loop stability studies, a systematic procedure is developed for mathematical modelling of MTDC systems. The resulting analytical models and frequency domain tools are employed in this thesis to assess the stability, dynamic performance and robustness of active power and DC voltage control of VSC-HVDC. Limitations imposed by weak AC systems, DC system parameters, converter operating point, controller structure, and controller bandwidth on the closed-loop MTDC stability are identified and investigated in detail. Large DC reactors, which are required by DC breaker systems, are identified in this research to have detrimental effects on the controllability, stability and robustness of MTDC voltage control. This could impose a serious challenge for existing control designs. A DC voltage damping controller is proposed to cope with the transient performance issues caused by the DC reactors. Furthermore, two active stabilising controllers are developed to enhance the controllability and robust stability of DC voltage control in a DC grid.

The unprecedented rise of mobile user demand over the years have led to an enormous growth of the energy consumption of wireless networks as well as the greenhouse gas emissions which are estimated currently to be around 70 million tons per year. This significant growth of energy consumption impels network companies to pay huge bills which represent around half of their operating expenditures. Therefore, many service providers, including mobile operators, are looking for new and modern green solutions to help reduce their expenses as well as the level of their CO2 emissions. Base stations are the most power greedy element in cellular networks: they drain around 80% of the total network energy consumption even during low traffic periods. Thus, there is a growing need to develop more energy-efficient techniques to enhance the green performance of future 4G/5G cellular networks. Due to the problem of traffic load fluctuations in cellular networks during different periods of the day and between different areas (shopping or business districts and residential areas), the base station sleeping strategy has been one of the main popular research topics in green communications. In this presentation, we present several practical green techniques that provide significant gains for mobile operators. Indeed, combined with the base station sleeping strategy, these techniques achieve not only a minimization of the fossil fuel consumption but also an enhancement of mobile operator profits. We start with an optimized cell planning method that considers varying spatial and temporal user densities. We then use the optimal transport theory in order to define the cell boundaries such that the network total transmit power is reduced. Afterwards, we exploit the features of the modern electrical grid, the smart grid, as a new tool of power management for cellular networks and we optimize the energy procurement from multiple energy retailers characterized by different prices and pollutant levels in order to achieve green goals. Finally, we introduce the notion of green mobile operator collaboration as a new aspect of the green networking where competitive cellular companies might cooperate together in order to achieve green goals.

Most problems in engineering and natural sciences involve parametric equations in which the parameters are not known exactly due to measurement errors, lack of measurement data, or even intrinsic variability. In such problems, one objective is to compute point or aggregate values, called “quantities of interest”. A rapidly growing research area that tries to tackle this problem is Uncertainty Quantification (UQ). As the name suggests, UQ aims to accurately quantify the uncertainty in quantities of interest. To that end, the approach followed in this thesis is to describe the parameters using probabilistic measures and then to employ probability theory to approximate the probabilistic information of the quantities of interest. In this approach, the parametric equations must be accurately solved for multiple values of the parameters to explore the dependence of the quantities of interest on these parameters, using various so-called “sampling methods”. In almost all cases, the parametric equations cannot be solved exactly and suitable numerical discretization methods are required. The high computational complexity of these numerical methods coupled with the fact that the parametric equations must be solved for multiple values of the parameters make UQ problems computationally intensive, particularly when the dimensionality of the underlying problem and/or the parameter space is high. This thesis is concerned with optimizing existing sampling methods and developing new ones. Starting with the Multilevel Monte Carlo (MLMC) estimator, we first prove its normality using the Lindeberg-Feller CLT theorem. We then design the Continuation Multilevel Monte Carlo (CMLMC) algorithm that efficiently approximates the parameters required to run MLMC. We also optimize the hierarchies of one-dimensional discretization parameters that are used in MLMC and analyze the tolerance splitting parameter between the statistical error and the bias constraints. An important contribution of this thesis is the novel Multi-index Monte Carlo (MIMC) method which is an extension of MLMC in high dimensional problems with significant computational savings. Under reasonable assumptions on the weak and variance convergence, which are related to the mixed regularity of the underlying problem and the discretization method, the order of the computational complexity of MIMC is, at worst up to a logarithmic factor, independent of the dimensionality of the underlying parametric equation. We also apply the same multi-index methodology to another sampling method, namely the Stochastic Collocation method. Hence, the novel Multi-index Stochastic Collocation method is proposed and is shown to be more efficient in problems with sufficient mixed regularity than our novel MIMC method and other standard methods. Finally, MIMC is applied to approximate quantities of interest of stochastic particle systems in the mean-field when the number of particles tends to infinity. To approximate these quantities of interest up to an error tolerance, TOL, MIMC has a computational complexity of O(TOL-2log(TOL)2). This complexity is achieved by building a hierarchy based on two discretization parameters: the number of time steps in an Milstein scheme and the number of particles in the particle system. Moreover, we use a partitioning estimator to increase the correlation between two stochastic particle systems with different sizes. In comparison, the optimal computational complexity of MLMC in this case is O(TOL-3) and the computational complexity of Monte Carlo is O(TOL-4).

We investigate experimentally the turbulent flow through a two-dimensional contraction. Using a water tunnel with an active grid we generate turbulence at Taylor microscale Reynolds number Reλ ~ 250 which is advected through a 2.5:1 contraction. Volumetric and time-resolved Tomo-PIV and Shake-The-Box velocity measurements are used to characterize the evolution of coherent vortical structures at three streamwise locations upstream of, and within the contraction. We confirm the conceptual picture of coherent large-scale vortices being stretched and aligned with the mean rate of strain. This alignment of the vortices with the tunnel centerline is stronger compared to the alignment of vorticity with the large-scale strain observed in numerical simulations of homogeneous turbulence. We judge this by the peak probability magnitudes of these alignments. This result is robust and independent of the grid-rotation protocols. On the other hand, while the point-wise vorticity vector also, to a lesser extent, aligns with the mean strain, it principally remains aligned with the intermediate eigen-vector of the local instantaneous strain-rate tensor, as is known in other turbulent flows. These results persist when the distance from the grid to the entrance of the contraction is doubled, showing that modest transverse inhomogeneities do not significantly affect these vortical-orientation results.

When the pandemic hit, and grocery stores and other necessities started to shut down and create havoc amongst the general public, it became clear that having the ability to rely on a self-sufficient homestead might be the only way to survive and thrive. As a graduate student who has studied architecture and sustainability, this idea seems possible. As an average human with no prior architectural or homesteading knowledge, this idea is daunting. This thesis is asking, is there a systematic way to develop a tool to evaluate, and aid in the design of, a self-sustaining, off-grid homestead? Can this tool make homesteading and living a self-sufficient, off-grid lifestyle more attainable to any person who wishes to try it out? With these questions in mind, the Homestead Helper Handbook: A Guide to Help Start a Self-Sufficient, Off-Grid Homestead in New England from the Ground Up has been developed to offer a cohesive approach, detailing the components that could go into the makeup of the homestead. Suggestions regarding the site, livestock, crops, and built structures will be made based off of specific input values of the future homesteader, leaving the reader with a well-rounded, precise breakdown and understanding of what might go into the homestead, allowing it to successfully function off-grid and self-sufficiently. Thus, it makes the idea of living a self-sufficient and off-grid life in New England more attainable to any human who wishes to do so.

In this thesis, we describe an approach that won a psychiatric symptom severity prediction challenge. The challenge was to correctly predict the severity of psychiatric symptoms on a 4-point scale. Our winning submission uses a novel stacked machine learning architecture in which (i) a base data ingestion/cleaning step was followed by the (ii) derivation of a base set of features defined using text analytics, after which (iii) association rule learning was used in a novel way to generate new features, followed by a (iv) feature selection step to eliminate irrelevant features, followed by a (v) classifier training algorithm in which a total of 22 classifiers including new classifier variants of AdaBoost and RandomForest were trained on seven different data views, and (vi) finally an ensemble learning step, in which ensembles of best learners were used to improve on the accuracy of individual learners. All of this was tested via standard 10-fold cross-validation on training data provided by the N-GRID challenge organizers, of which the three best ensembles were selected for submission to N-GRID's blind testing. The best of our submitted solutions garnered an overall final score of 0.863 according to the organizer's measure. All 3 of our submissions placed within the top 10 out of the 65 total submissions. The challenge constituted Track 2 of the 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-Scale and RDOC Individualized Domains (N-GRID) Shared Task in Clinical Natural Language Processing.