Distribution power markets: detailed modeling and tractable algorithms

The increasing integration of renewable generation presents power systems with economic and reliability challenges, mostly due to renewables' volatility, which cannot be effectively addressed with business-as-usual practices. Fortunately, this is concurrent with rising levels of Distributed Energy Resources (DERs), including photovoltaics, microgeneration and flexible loads like HVAC loads and electric vehicles. DERs are capable of attractive time-shiftable behavior and of transacting reactive power and reserves in addition to real power. If DER capacity is optimally allocated among these three products, distribution network and economic benefits can be realized and renewable-related challenges can be mitigated, enabling increased renewable integration safety limits. In order to achieve optimal DER scheduling, this thesis proposes the formulation of a spatiotemporal marginal-cost based distribution power market and develops and implements tractable clearing algorithms. First, we formulate a centralized market clearing algorithm whose result is the optimal DER real power, reactive power and reserves schedules and the optimal nodal marginal costs. Our market formulation develops for the first time detailed and realistic models of the salient distribution network variable costs (transformer degradation, voltage sensitive loads) together with distribution network constraints (voltage bound constraints, that reflect distribution network congestion and AC load flow), and intertemporal DER dynamics and capabilities. However, the centralized algorithm does not scale, motivating the use of distributed algorithms. We propose two distributed algorithms: • A fully distributed algorithm that relies on massively parallel DER and distribution line specific sub-problem solutions, iteratively coordinated by nodal price estimates which promote and eventually enforce nodal balances. Upon convergence, nodal balances hold and optimal marginal costs are discovered. We further existing practices by using local penalty updates and stopping criteria that significantly reduce communication requirements. • A novel, partially distributed formulation in which DERs self-schedule in parallel based on centrally calculated price estimates, resulting from a load flow calculation. Nodal balances hold during all iterations. Finally, we are, to the best of our knowledge, the first to study voltage-constrained distribution market instances cleared with distributed methods. We decrease the deviation of marginal costs from their optimal values using first order optimality conditions and use voltage barrier functions for speedier convergence.2020-03-31T00:00:00

Evaluation of internal damage in reinforced concrete elements using ultrasonic tomography

Quality control and quality assurance (QC/QA) of the concrete infrastructure has become an important national issue, especially because construction inaccuracies and invisible internal defects can result in unexpected structural response and failure. In order to evaluate the condition of an existing concrete structure, non-destructive testing (NDT) has been widely used as an assessment tool. Ultrasonic pulse velocity (UPV) is an efficient method to characterize the condition of concrete elements, and tomographic imaging is a powerful tool for visually identifying internal damage. However, the implementation of UPV data within a tomographic imaging scheme for application to full-scale concrete (RC) structures has not been realized to date because of practical and technological restrictions. In this dissertation, some of those barriers are overcome by using contactless air-coupled ultrasonic sensors in a scanning test configuration to acquire large amounts of ultrasonic data to create ultrasonic tomograms of large-scale concrete structures. The development of the testing system is described. The measurements are carried out using an automated robotic scanning frame using new sensing technology. Image reconstruction algorithms, including synthetic aperture focusing technique (SAFT) and algebraic reconstruction technique (ART), are reviewed and evaluated for application to imaging of full-scale RC columns. The performance of the data collection system and selected optimal imaging approach are verified through tests on a RC column test sample containing embedded artificial defects. The obtained tomographic images are compared with those from a commercially available ultrasonic imaging device. A comprehensive visualization scheme to characterize the column test sample, based on fusion of integrated ultrasonic tomography and 3-D computer vision, is presented. Such integrated visualization provides holistic characterization of the test sample. Next, the utility of attenuation tomography for enhanced damage detection is evaluated, both through numerical simulation and experimental studies. Finally, the developed ultrasonic tomographic testing system is applied to full-scale RC columns and slab-beam-column sub-assemblages subjected to simulated earthquake loads. Different concrete types, including normal reinforced concrete and high performance fiber-reinforced concrete, and seismic different loading schemes are considered. Comparisons of ultrasonic tomograms and strain gauge data illustrate the potential for velocity and attenuation tomography to monitor internal damage progression of structural RC elements both at global and local levels

Control Design for Dengue Fever Model with Disturbance

A mathematical model has become a useful tool to predict and control dengue fever dynamics. In reality, the dynamic of dengue fever transmission can be disturbed by uncertainty measurements, so it is needed to consider the disturbance in the model. Then, dengue fever model with disturbance is constructed by using a gain matrix consisting a covariance matrix and random vector. As dengue vaccine has been challenging to reduce the pandemic, a dengue model with vaccination as control is constructed. The aim is to propose a feedback controller that can reduces the infected human (H2 control problem) and the uncertainty measurements (H∞ control problem). The control u denotes the proportion of susceptible humans that one decides to vaccinate at time t. A random mass vaccination with wanning immunity is chosen because vaccine still on development process. A Design of mixed H2 - H∞ control with State-dependent Riccati Equation (SDRE) approach is applied. The SDRE has been an effective method to solve for synthesizing nonlinear feedback controller by transforming the system to an State-dependent coefficient (SDC) form. By comparing the mixed scheme with basic H∞, numerical simulation shows that the control application effectively decreases the number of infected humans and reduces the disturbance

Efficient Techniques for Wave-based Sound Propagation in Interactive Applications

Sound propagation techniques model the effect of the environment on sound waves and predict their behavior from point of emission at the source to the final point of arrival at the listener. Sound is a pressure wave produced by mechanical vibration of a surface that propagates through a medium such as air or water, and the problem of sound propagation can be formulated mathematically as a second-order partial differential equation called the wave equation. Accurate techniques based on solving the wave equation, also called the wave-based techniques, are too expensive computationally and memory-wise. Therefore, these techniques face many challenges in terms of their applicability in interactive applications including sound propagation in large environments, time-varying source and listener directivity, and high simulation cost for mid-frequencies. In this dissertation, we propose a set of efficient wave-based sound propagation techniques that solve these three challenges and enable the use of wave-based sound propagation in interactive applications. Firstly, we propose a novel equivalent source technique for interactive wave-based sound propagation in large scenes spanning hundreds of meters. It is based on the equivalent source theory used for solving radiation and scattering problems in acoustics and electromagnetics. Instead of using a volumetric or surface-based approach, this technique takes an object-centric approach to sound propagation. The proposed equivalent source technique generates realistic acoustic effects and takes orders of magnitude less runtime memory compared to prior wave-based techniques. Secondly, we present an efficient framework for handling time-varying source and listener directivity for interactive wave-based sound propagation. The source directivity is represented as a linear combination of elementary spherical harmonic sources. This spherical harmonic-based representation of source directivity can support analytical, data-driven, rotating or time-varying directivity function at runtime. Unlike previous approaches, the listener directivity approach can be used to compute spatial audio (3D audio) for a moving, rotating listener at interactive rates. Lastly, we propose an efficient GPU-based time-domain solver for the wave equation that enables wave simulation up to the mid-frequency range in tens of minutes on a desktop computer. It is demonstrated that by carefully mapping all the components of the wave simulator to match the parallel processing capabilities of the graphics processors, significant improvement in performance can be achieved compared to the CPU-based simulators, while maintaining numerical accuracy. We validate these techniques with offline numerical simulations and measured data recorded in an outdoor scene. We present results of preliminary user evaluations conducted to study the impact of these techniques on user's immersion in virtual environment. We have integrated these techniques with the Half-Life 2 game engine, Oculus Rift head-mounted display, and Xbox game controller to enable users to experience high-quality acoustics effects and spatial audio in the virtual environment.Doctor of Philosoph

Adaptive Augmentation of Non-Minimum Phase Flexible Aerospace Systems

This work demonstrates the efficacy of direct adaptive augmentation on a robotic flexible system as an analogue of a large flexible aerospace structure such as a launch vehicle or aircraft. To that end, a robot was constructed as a control system testbed. This robot, named “Penny,” contains the command and data acquisition capabilities necessary to influence and record system state data, including the flex states of its flexible structures. This robot was tested in two configurations, one with a vertically cantilevered flexible beam, and one with a flexible inverted pendulum (a flexible cart-pole system). The physical system was then characterized so that linear analysis and control design could be performed. These characterizations resulted in linear and nonlinear models developed for each testing configuration. The linear models were used to design linear controllers to regulate the nominal plant’s dynamical states. These controllers were then augmented with direct adaptive output regulation and disturbance accommodation. To accomplish this, sensor blending was used to shape the output such that the nonminimum phase open loop plant appears to be minimum phase to the controller. It was subsequently shown that augmenting linear controllers with direct adaptive output regulation and disturbance accommodation was effective in enhancing system performance and mitigating oscillation in the flexible structures through the system’s own actuation effort

Multi-disciplinary Green IT Archival Analysis: A Pathway for Future Studies

With the growth of information technology (IT), there is a growing global concern about the environmental impact of such technologies. As such, academics in several research disciplines consider research on green IT a vibrant theme. While the disparate knowledge in each discipline is gaining substantial momentum, we need a consolidated multi-disciplinary view of the salient findings of each research discipline for green IT research to reach its full potential. We reviewed 390 papers published on green IT from 2007 to 2015 in three disciplines: computer science, information systems and management. The prevailing literature demonstrates the value of this consolidated approach for advancing our understanding on this complex global issue of environmental sustainability. We provide an overarching theoretical perspective to consolidate multi-disciplinary findings and to encourage information systems researchers to develop an effective cumulative tradition of research

Quantifying the value of viral genomics when inferring who infected whom in the 2014–16 Ebola virus outbreak in Guinea

Transmission trees can be established through detailed contact histories, statistical or phylogenetic inference, or a combination of methods. Each approach has its limitations, and the extent to which they succeed in revealing a 'true' transmission history remains unclear. In this study, we compared the transmission trees obtained through contact tracing investigations and various inference methods to identify the contribution and value of each approach. We studied eighty-six sequenced cases reported in Guinea between March and November 2015. Contact tracing investigations classified these cases into eight independent transmission chains. We inferred the transmission history from the genetic sequences of the cases (phylogenetic approach), their onset date (epidemiological approach), and a combination of both (combined approach). The inferred transmission trees were then compared to those from the contact tracing investigations. Inference methods using individual data sources (i.e. the phylogenetic analysis and the epidemiological approach) were insufficiently informative to accurately reconstruct the transmission trees and the direction of transmission. The combined approach was able to identify a reduced pool of infectors for each case and highlight likely connections among chains classified as independent by the contact tracing investigations. Overall, the transmissions identified by the contact tracing investigations agreed with the evolutionary history of the viral genomes, even though some cases appeared to be misclassified. Therefore, collecting genetic sequences during outbreak is key to supplement the information contained in contact tracing investigations. Although none of the methods we used could identify one unique infector per case, the combined approach highlighted the added value of mixing epidemiological and genetic information to reconstruct who infected whom

Wireless industrial intelligent controller for a non-linear system

Modern neural network (NN) based control schemes have surmounted many of the limitations found in the traditional control approaches. Nevertheless, these modern control techniques have only recently been introduced for use on high-specification Programmable Logic Controllers (PLCs) and usually at a very high cost in terms of the required software and hardware. This ‗intelligent‘ control in the sector of industrial automation, specifically on standard PLCs thus remains an area of study that is open to further research and development. The research documented in this thesis examined the effectiveness of linear traditional control schemes such as Proportional Integral Derivative (PID), Lead and Lead-Lag control, in comparison to non-linear NN based control schemes when applied on a strongly non-linear platform. To this end, a mechatronic-type balancing system, namely, the Ball-on-Wheel (BOW) system was designed, constructed and modelled. Thereafter various traditional and intelligent controllers were implemented in order to control the system. The BOW platform may be taken to represent any single-input, single-output (SISO) non-linear system in use in the real world. The system makes use of current industrial technology including a standard PLC as the digital computational platform, a servo drive and wireless access for remote control. The results gathered from the research revealed that NN based control schemes (i.e. Pure NN and NN-PID), although comparatively slower in response, have greater advantages over traditional controllers in that they are able to adapt to external system changes as well as system non-linearity through a process of learning. These controllers also reduce the guess work that is usually involved with the traditional control approaches where cumbersome modelling, linearization or manual tuning is required. Furthermore, the research showed that online-learning adaptive traditional controllers such as the NN-PID controller which maintains the best of both the intelligent and traditional controllers may be implemented easily and with minimum expense on standard PLCs

Joint University Program for Air Transportation Research, 1982

A summary of the research on air transportation is addressed including navigation; guidance, control and display concepts; and hardware, with special emphasis on applications to general aviation aircraft. Completed works and status reports are presented also included are annotated bibliographies of all published research sponsored on these grants since 1972