This report presents an overview of the engineering considerations involved in the design of grid-scale battery energy storage systems. Grid-scale is defined here as systems over 1 MW in rated power, typically operated by a utility, independent power producer, or Independent System Operator (ISO). The physical components of a BESS are presented and explained, including power electronics, an introduction to various commercially available battery technologies, necessary control systems, and balance of plant hardware. Also presented are a variety of real-world applications of battery energy storage systems, showing how the specific application determines what mix of technology will be selected when designing the system, as well as explaining the foundation for the control algorithms. / text

In the context of the development of renewable energy sources in the U.K., and of the increase in anthropogenic atmospheric CO2, it is important to develop alternative ways of providing energy to the community. The shift to renewable sources of electricity comes to a cost: variable generation. At present, an important part of the renewable electricity capacity is being curtailed during low demand periods. One way to ensure that electricity supply matches demand is to store excess energy when it is available and deliver it when demand cannot be met by primary generation alone. Compressed Air Energy Storage (CAES) allows this storage. The aim of this project is to build upon existing knowledge on CAES using porous rocks (PM-CAES) to assess the technical feasibility for this storage technology to be developed offshore of the UK. The focus is on inter-seasonal storage. This assessment is undertaken by developing geological and power plant models to calculate the storage potential of offshore UK formations. Modelling of a conceptual aquifer air store enables approximations of the subsurface pressure response to CAES operations. These pressure changes are coupled with surface facilities models to provide estimates of both load/generation capacity and roundtrip efficiencies. Algebraic predictive models can be developed from the results of a sensitivity analysis of the store and plant idealised models. Screening of the CO2 Stored database, containing data on geological formations offshore of the UK (initially developed for CO2 storage), was then performed to estimate PM-CAES potential using the predictive models. The results suggest that there is substantial PM-CAES potential in the UK. Results indicate an energy storage potential in the range of 77-96 TWh, which can be released over 60 days. A geographic information system (GIS) study was then performed to identify the portion of the identified storage potential colocated with offshore windfarm. 19 TWh of the storage potential identified is colocated with windfarm and would be achievable at an average levelised cost of electricity of 0.70 £/kWh.

>Magister Scientiae - MSc / Transition metals are a group of metals which are light in weight and have high hydrogen solubility. Their interaction with hydrogen is exorthermic and this phenomenon makes them “ideal” candidates for various applications of hydrogen storage systems. This explains why the phenomenon of hydrogen storage in Pd is used as a model for hydrogen storage systems because of the nature of absorption associated with it (like a sponge even at low temperatures). The hydrogenation process can be conducted at either room or high temperatures in a furnace under low pressure-low hydrogen gas concentration-short hydrogenation time (LP-LC-ST) and in intelligent gravimetric analyser under high pressurehigh hydrogen gas concentration-long hydrogenation time conditions. Most of the research on hydrogen storage sytems is based on gravimetric analysis of absorbed and desorbed hydrogen concentration. In this work, a comparison study of the hydrogen content in pure Pd, Pd-Pt coated systems, Pd-Pt alloys, commercially pure Ti and Ti-6Al-4V alloy determined by gravimetric methods and elastic recoil detection analysis (which is based on the detection of recoiled hydrogen after interaction with He+ ions) technique was investigated. The changes in the structural properties and the hydrogen content of the materials when exposed to a hydrogen gas environment for different durations at various system temperatures and pressures will be reported. These changes have an effect on the microstructure of CP-Ti and Ti-6Al-4V alloy and structural properties of all the hydrogenated materials. The results obtained from optical microscopy, scanning electron microscopy, x-ray diffraction, intelligent gravimetric analyser, digital balance, elastic recoil detection analysis and Vickers hardness test, show the following: it is found that hydrogenation of Pd at elevated temperatures (550 ˚C and 650 ˚C) does not yield hydrides under LP-LC-ST conditions. However, at room temperature the absorption of hydrogen occurred faster at the beginning of the process. Furthermore, the absorption of hydrogen increased with pressure where optimum absorption (0.67 wt. % hydrogen concentration) occurred under a system pressure of 2000 mbar. After pressure release, the remaining hydrogen content in the Pd sample was 0.6 wt. %. The Pd-Pt coated system provide hydrogen mobility at 550 and 650 ˚C where hydrides were formed under LP-LC-ST conditions. In addition to the decrease of hydrogen solubility in Pd-Pt alloys with an increase in Pt content, the probability of the alloys to achieve full saturation also decreases with an increase in Pt content under HP-HC-LT conditions. CP-Ti and Ti-6Al-4V alloy absorb substantial amount of hydrogen in the first hour of room temperature hydrogenation under LP-LC-ST conditions but hydrides were not formed. Therefore, under LP-LC-ST conditions at room temperature, Pd is able to store hydrogen in the form of hydrides whereas Ti and Ti-6Al-4V alloy could not. The 550 ˚C is the optimum temperature for hydrogenation of CP-Ti under LP-LC-ST conditions. The Ti- 6Al-4V alloy absorb optimum hydrogen at 650 ˚C under LP-LC-ST conditions. Consequently, the change of microhardness of CP-Ti and Ti-6Al-4V alloy was found to depend on hydrogenation temperature.

Computer data storage is growing at an astonishing rate. With cloud computing and the growth of the Internet enterprise storage has been predicted to grow at rates as high as 300\% per year. To fulfill this need technologies such as Redundant Array of Independent Disks or RAID are being used in industry today. Not only does RAID increase I/O performance but also provides redundancy measures to protect against hardware failure. Even though RAID has existed for some time now and is well understood, proprietary optimizations such as command scheduling and cache strategies that are employed by current RAID controllers are not well known. This thesis presents a model for RAID 5 that incorporates these features and describes the overall function of hardware RAID controllers. Also a python implementation of this model, Accurate Hardware RAID Simulator (AHRS) is presented and validated against a current hardware RAID controller. It is shown that AHRS can reproduce the behavior of a hardware RAID system with an accuracy of 97.92\% on average compared to a LSI hardware RAID controller.

In this dissertation, I first incorporate declustered redundant array of independent disks (RAID) technology in the existing system by maximizing the aggregated recovery I/O and accelerating post-failure remediation. Our analytical model affirms the accelerated data recovery stage significantly improves storage reliability. Then I present a proactive data protection framework that augments storage availability and reliability. It utilizes the failure prediction methods to efficiently rescue data on drives before failures occur, which significantly reduces the storage downtime and lowers the risk of nested failures. Finally, I investigate how an active storage system enables energy-efficient computing. I explore an emerging storage device named Ethernet drive to offload data-intensive workloads from the host to drives and process the data on drives. It not only minimizes data movement and power usage, but also enhances data availability and storage scalability. In summary, my dissertation research provides intelligence at the drive, storage node, and system levels to tackle the rising reliability challenge in modern HPC datacenters. The results indicate that this novel storage paradigm cost-effectively improves storage scalability, availability, and reliability.

Nowadays increasing multimedia applications e.g., video and voice over IP, social networks and emails poses higher demands for sever storages and bandwidth in the networks. There is a concern that existing resource may not able to support higher demands and reliability. Network coding was introduced to improve distributed storage system. This thesis proposes the way to improve distributed storage system such as increase a chance to recover data in case there is a fail storage node or link fail in a network. In this thesis, we study the concept of network coding in distributed storage systems. We start our description from easy code which is replication coding then follow with higher complex code such as erasure coding. After that we implement these concepts in our test bed and measure performance by the probability of success in download and repair criteria. Moreover we compare success probability for reconstruction of original data between minimum storage regenerating (MSR) and minimum bandwidth regenerating (MBR) method. We also increase field size to increase probability of success. Finally, link failure was added in the test bed for measure reliability in a network. The results are analyzed and it shows that using maximum distance separable and increasing field size can improve the performance of a network. Moreover it also improves reliability of network in case there is a link failure in the repair process.

Today’s economy and infrastructure is dependent on raw natural resources, like crude oil and natural gases, that are optimally transported through a net- work of hundreds of thousands of miles of pipelines throughout America[28]. A damaged pipe can negatively a↵ect thousands of homes and businesses so it is vital that they are monitored and quickly repaired[1]. Ideally, pipeline operators are able to detect damages before they occur, but ensuring the in- tegrity of the vast amount of pipes is unrealistic and would take an impractical amount of time and manpower[1]. Natural disasters, like earthquakes, as well as construction are just two of the events that could potentially threaten the integrity of pipelines. Due to the diverse collection of data sources, the necessary geospatial data is scat- tered across di↵erent physical locations, stored in di↵erent formats, and owned by di↵erent organizations. Pipeline companies do not have the resources to manually gather all input factors to make a meaningful analysis of the land surrounding a pipe. Our solution to this problem involves creating a single, centralized system that can be queried to get all necessary geospatial data and related informa- tion in a standardized and desirable format. The service simplifies client-side computation time by allowing our system to find, ingest, parse, and store the data from potentially hundreds of repositories in varying formats. An online web service fulfills all of the requirements and allows for easy remote access to do critical analysis of the data through computer based decision support systems (DSS). Our system, REST API for Pipeline Integrity Data (RAPID), is a multi- tenant REST API that utilizes HTTP protocol to provide a online and intuitive set of functions for DSS. RAPID’s API allows DSS to access and manage data stored in a geospatial database with a supported Django web framework. Full documentation of the design and implementation of RAPID’s API are detailed in this thesis document, supplemented with some background and validation of the completed system.

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The DataFrame is a powerful table-like data structure used frequently in Data Science, the in-demand and innovative field focused on the extraction of valuable insights from data. Typically, datasets are not perfect upon collection and need to be prepared so that the resulting dataset is useful for statistical analysis. A DataFrame API supports optimized methods such as, selecting, aggregating and filtering rows, columns, and cells as well as renaming row and column labels. It also supports methods for normalizing data, merging data, adding new columns and labelling missing data among numerous other features. An API to work with tabular data would be useful in any general purpose language, so DataFrames have been incorporated into libraries like Pandas for Python and provided as native libraries in the languages R and Scala. Due to their wide-ranging use it is not uncommon to find implementations in many other languages like Java and Julia \cite{BigData}. In this work, we introduce RacketFrames, a Racket V8.0+ DataFrame implementation. We show the benefits an implementation can have on existing and future Racket projects. To quantify the performance of major DataFrame operations, we measure speed against Python Pandas and compare functional and object oriented paradigms. We hope to continue the trend for further Data Science tool development for Racket and other programming languages.