Browsing by Subject "Grid"
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Item A federated grid environment with replication services(2005-12) Khurana, Vivek; Sobolewski, Michael; López-Benitez, NoéIn general, grids are classified as computational grids, data grids and access grids. Computational grids address applications that deal with complex and time intensive computational problems usually on relatively small data-sets; whereas data grids address the needs of data intensive applications that deal with the evaluation and mining of large amounts of data in the terabyte and petabyte range. While SORCER is basically a computation grid environment, a complementing data grid service called Replica Provider is introduced. To have an increased functionality, the newly developed data grid service is used to leverage the already existing SORCER compute grid. SORCER Service Oriented Programs along with replication services will now have a capability of running data grid applications. Advances in sequencing technology have created a tremendous amount of data to be analyzed. Therefore, there is an increased need to have distributed BLAST (Basic Local Alignment Search Tool) capabilities that will support easy deployment and enable large batch BLAST processes over heterogeneous platforms. Data Grid will help in maintaining and updating such databases in a distributed computing environment easily and efficiently. It will optimize access to such databases and increase reliability by replicating these at multiple locations. A federated grid environment for BLAST (S-BLAST) developed in a federated distributed environment is presented. SBLAST enables processing of large sequence files distributed over diverse system architectures and computing resources. It also enables large number of files to be replicated on multiple nodes over different heterogeneous computation platforms simultaneously for providing generic service providers fast, up-to-date, reliable and secure access to file storage.Item A dynamic model-based estimate of the potential value of a vanadium redox flow battery for energy arbitrage and frequency regulation in Texas(2012-08) Fares, Robert Leo; Webber, Michael E., 1971-; Meyers, Jeremy P.Large-scale electrochemical energy storage is a technology that is uniquely suited to integrate intermittent renewable energy sources with the electric grid on a large scale. Grid-based energy storage also has the potential to reduce costs associated with periods of peak electric demand. For these reasons, this work describes the potential applications for grid-based energy storage, and then reviews large-scale energy storage technology innovations since the development of the lead-acid battery. The potential value of grid-based battery energy storage is discussed in the context of restructured electricity markets; then, a dynamic model-based economic optimization routine is developed to gauge the potential value of a vanadium redox flow battery (VRFB) operating for wholesale energy arbitrage and frequency regulation in Texas. Based on this analysis, the relative value of a VRFB in various regions of Texas for energy arbitrage and frequency regulation is examined. It is shown that frequency regulation is an appealing application for a grid-based VRFB, with a VRFB utilized for frequency regulation service in Texas potentially worth approximately $1500/kW. Finally, the effect of a VRFB’s characteristics on its value for frequency regulation and energy arbitrage are compared, and the operational insight developed in this work is used to glean how policies to integrate a large-scale energy storage with the electricity market might be crafted.Item Monte Carlo localization for robots using dynamically expanding occupancy grids(2005-05) Gupta, Karan M.; Pyeatt, Larry D.; Watson, RichardThe past few years have seen tremendous growth in the research areas of Mobile Robotics. While growth has been fast and several problems have been very splendidly solved most mobile roboticists are faced with two primary challenges: how will the robot gather information about its environment and how will it know where it is? These two problems are referred to as: (i). Mapping and (ii). Localization. Mapping is the process whereby a robot can extract relevant information from its environment allowing it to "remember" it. Localization is using this stored map to move about in the environment with a clear sense of direction because the robot knows where it is, by referring to the map. Localization is the problem of estimating a robot’s pose relative to a map of its environment. However, both these problems are computationally intensive to solve and furthermore, limitations on a robot’s on board computational abilities and inaccuracies in sensor hardware and motor effectors make it even harder. Most mapping techniques are limited by memory and hence a robot has a limitation on the area that it can directly map. Also, if the mapped area is extended, most mapping implementations require that the mapping parameters be changed and the entire mapping algorithm be executed again. However, in recent times a new mapping technique was explored which is that of using Dynamically Expanding Occupancy Grids (Ellore 2002), and of using a Centralized Storage System (Barnes, Quasny, Garcia, and Pyeatt 2004). By using this approach, the robot has virtually unlimited storage space and a small initial map which grows as the robots explores its environment. Localization has not yet been attempted using Dynamically Expanding Occupancy Grids and a Centralised Storage System. This research is geared towards implementing Monte-Carlo Localization methods (Fox, Burgard, Dellaert, and Thrun 1999; Dellaert, Fox,Burgard, and Thrun ; Thrun, Fox, Burgard, and Dellaert 2001; Fox, Thrun, Burgard, and Dellaert 2001) to robots using Dynamically Expanding Occupancy Grids. By using this approach this research aims to provide a complete mapping and localization implementation for robots using dynamically expanding occupancy grids and a centralized storage system.