Transformers and storage systems in the electrical grid must be provisioned or sized just as routers and buffers must be sized in the Internet. We prove the formal equivalence between these two systems and use this insight to apply teletraffic theory to sizing the electrical grid, obtaining the capacity region corresponding to a given transformer and storage size. We conduct a fine-grained measurement study of household electrical load. These measurements are essential for two reasons. First, we use them to construct reference models for home loads; these models are used to find the capacity region using the teletraffic theory. Second, these measurements are used in numerical simulations that are done to validate our analysis. More specifically, we compare results of numerical simulations with the results from teletraffic theory. We show not only that teletraffic theory agrees well with numerical simulations but also that it closely matches with the heuristics used in current practice. Moreover, our analysis permits us to develop sizing rules for battery storage electrical grid, advancing the state of the art.

Recently, there have been many initiatives to incorporate advanced controls, two way communications, digital technologies and advanced power system components in the operation and control of power distribution systems. These initiatives are aiming to realize what becomes known as the “Smart grid”. It is expected that a Smart Grid will lead to enhancement in the reliability and efficiency of the power system. The movement towards the Smart Grid is motivated by many factors; the need to integrate more renewable power to mitigate the global warming, the increasing interest in connecting more distributed generation (DG) as a way to postpone large investment in transmission and bulk generation, and the need to increase the reliability of the power system overall to minimize disturbance costs. It is the overall goal of this research to introduce novel distribution system operation techniques to assist in the effort of realizing the “Smart Distribution System” in both normal and system restoration modes. In particular, three main operation functions are dealt with in this research work; Voltage Control, Reactive Power Control and Distribution System Restoration. First for Voltage Control, a reliable and efficient method is proposed to control voltage regulators in order to enable the regulation of multiple feeders with diversified loads using only one regulator provided that no DG is connected to the feeders. Regulator’s tap is selected based on the solution of an integer linear optimization problem. The method has a closed form solution for the optimal tap; that is valuable for real time operation. In addition, necessary condition for feasible solutions is examined. Next, a novel coordinated voltage control scheme is proposed to enable the voltage regulator to efficiently regulate the voltage of multiple feeders in the presence of DGs. The proposed technique is based on placing a Remote Terminal Unit (RTUs) at each DG and each line capacitor. These RTUs coordinate together, through communication, and form a multi-agent system. An important contribution of this research is that the proposed scheme provides the minimum hardware requirement to efficiently estimate the voltage profile of a feeder with DGs. The proposed scheme enables the integration of more DGs into the system by, efficiently, coordinating the operation of voltage regulators and DGs to mitigate voltage rise problem caused by the connection of DGs to the system. Second, for Reactive Power Control, a decentralized reactive power control scheme is proposed to optimally control switched shunt capacitors of the system in order to minimize system losses and maintain acceptable voltage profile. The proposed algorithm provides capacitors with “Advanced Voltage Sensing” capability to enable capacitors to switch in and out according to the global minimum and maximum voltage of the feeder. The proposed technique utilizes the same RTU used for voltage control and relies on the voltage profile estimation technique proposed in this research for the coordinated voltage control. In addition, novel decentralized algorithm is proposed to estimate the feeder voltage profile change as a result of injecting reactive power at the capacitor bus. The proposed reactive power control scheme can be used to coordinate the operation of any number of capacitors connected to the distribution system. Combining voltage control and reactive power control schemes, generalized coordinated voltage control is proposed to coordinate between DGs, shunt capacitors and voltage regulators in order to achieve optimal voltage control for the distribution system and solve the steady state voltage rise problem caused by the connection of DGs, hence, allowing more DGs to be connected to the system. Over and above, the proposed generalized coordinated voltage control enables the realization of a new operation-time DG connection impact assessment concept. Based on this concept, the system will carry out a real-time assessment and decide, based on the available control actions, the maximum DG power that can be allowed to connect to the system at particular operating conditions. This new concept will allow great flexibility to the connection of DGs, most notably, when, due to a change in system configuration, the DG is needed to be connected to a feeder other than the one it was planned for during the planning stage. The last operation function dealt with in this research work is the distribution system restoration. Novel decentralized distribution system restoration scheme is proposed. The proposed scheme is based on dividing the distribution system into zones based on the availability of disconnecting switches. Each zone is controlled by an Agent. The restoration is done based on the coordination between these Agents. Proposed communication protocols between Agents are discussed in details. The goal of the proposed restoration scheme is to maximize the restored power while preserving the radial structure of the distribution system and without exceeding the thermal limit of any equipment in the system. As the proposed technique does not assume any supervision from any central point, this technique will enable the realization of a self-healing distribution system restoration.

Although modern society is critically reliant on power grids, even modern power grids are subject to unavoidable outages due to storms, lightning strikes, and equipment failures. The situation in developing countries is even worse, with frequent load shedding lasting several hours a day due to unreliable generation. We study the use of battery storage to allow a set of homes in a single residential neighbour- hood to avoid power outages. Due to the high cost of storage, our goal is to choose the smallest battery size such that, with high target probability, there is no loss of power despite a grid out- age. Recognizing that the most common approach today for mitigating outages is to use a diesel generator (genset), we study the related problem of minimizing the carbon footprint of genset operation. Drawing on recent results, we model both problems as buffer sizing problems that can be ad- dressed using stochastic network calculus. We show that this approach greatly improves battery sizing in contrast to prior approaches. Specifically, a numerical study shows that, for a neigh- bourhood of 100 homes, our approach computes a battery size, which is less than 10% more than the minimum possible size necessary to satisfy a one day in ten years loss probability (2.7 ∗ 10^4 ). Moreover, we are able to estimate the carbon footprint reduction, compared to an exact numerical analysis, within a factor of 1.7. We also study the genset scheduling problem when the rate of genset fuel consumption is given by an affine function instead of a linear function of the current power. We give alternate scheduling, an online scheduling strategy that has a competitive ratio of (k1 G/C +k2)/(k1+k2) , where G is the genset capacity, C is the battery charging rate, and k1, k2 are the affine function constants. Numerically, we show that for a real industrial load alternate scheduling is very close to the offline optimal strategy.

Many important traits in plants, animals and humans are quantitative, and most such traits are generally believed to be affected by multiple genetic loci. Standard computational tools for mapping of quantitative traits (i.e. for finding Quantitative Trait Loci, QTL, in the genome) use linear regression models for relating the observed phenotypes to the genetic composition of individuals in an experimental population. Using these tools to simultaneously search for multiple QTL is computationally demanding. The main reason for this is the complex optimization landscape for the multidimensional global optimization problems that must be solved. This thesis describes parallel algorithms, implementations and tools for simultaneous mapping of several QTL. These new computational tools enable genetic analysis exploiting new classes of multidimensional statistical models, potentially resulting in interesting results in genetics. We first describe how the standard, brute-force algorithm for global optimization in QTL analysis is parallelized and implemented on a grid system. Then, we also present a parallelized version of the more elaborate global optimization algorithm DIRECT and show how this can be efficiently deployed and used on grid systems and other loosely-coupled architectures. The parallel DIRECT scheme is further developed to exploit both coarse-grained parallelism in grid systems or clusters as well as fine-grained, tightly-coupled parallelism in multi-core nodes. The results show that excellent speedup and performance can be archived on grid systems and clusters, even when using a tightly-coupled algorithm such as DIRECT. Finally, we provide two distinctly different front-ends for our code. One is a grid portal providing a graphical front-end suitable for novice users and standard forms of QTL analysis. The other is a prototype of an R-based grid-enabled problem solving environment. Both of these front-ends can, after some further refinement, be utilized by geneticists for performing multidimensional genetic analysis of quantitative traits on a regular basis. / eSSENCE

Real-Time Kinematic (RTK) positioning is a technique used to provide precise positioning services at centimetre accuracy level in the context of Global Navigation Satellite Systems (GNSS). While a Network-based RTK (N-RTK) system involves multiple continuously operating reference stations (CORS), the simplest form of a NRTK system is a single-base RTK. In Australia there are several NRTK services operating in different states and over 1000 single-base RTK systems to support precise positioning applications for surveying, mining, agriculture, and civil construction in regional areas. Additionally, future generation GNSS constellations, including modernised GPS, Galileo, GLONASS, and Compass, with multiple frequencies have been either developed or will become fully operational in the next decade. A trend of future development of RTK systems is to make use of various isolated operating network and single-base RTK systems and multiple GNSS constellations for extended service coverage and improved performance. Several computational challenges have been identified for future NRTK services including: • Multiple GNSS constellations and multiple frequencies • Large scale, wide area NRTK services with a network of networks • Complex computation algorithms and processes • Greater part of positioning processes shifting from user end to network centre with the ability to cope with hundreds of simultaneous users’ requests (reverse RTK) There are two major requirements for NRTK data processing based on the four challenges faced by future NRTK systems, expandable computing power and scalable data sharing/transferring capability. This research explores new approaches to address these future NRTK challenges and requirements using the Grid Computing facility, in particular for large data processing burdens and complex computation algorithms. A Grid Computing based NRTK framework is proposed in this research, which is a layered framework consisting of: 1) Client layer with the form of Grid portal; 2) Service layer; 3) Execution layer. The user’s request is passed through these layers, and scheduled to different Grid nodes in the network infrastructure. A proof-of-concept demonstration for the proposed framework is performed in a five-node Grid environment at QUT and also Grid Australia. The Networked Transport of RTCM via Internet Protocol (Ntrip) open source software is adopted to download real-time RTCM data from multiple reference stations through the Internet, followed by job scheduling and simplified RTK computing. The system performance has been analysed and the results have preliminarily demonstrated the concepts and functionality of the new NRTK framework based on Grid Computing, whilst some aspects of the performance of the system are yet to be improved in future work.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaves 59-61). Also available via the Internet.

Zugl.: Stuttgart, Univ., Diss., 2008.

Thesis (M.S.)--State University of New York at Binghamton, Watson School of Engineering and Applied Science (Mechanical Engineering), 2007. / Includes bibliographical references.

Stuttgart, Univ., Diplomarb., 2003.

Thesis (M.S.)--Brigham Young University. Dept. of Civil and Environmental Engineering, 2006. / Includes bibliographical references (p. 67-68).