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    Small-Scale Smart Grid Construction and Analysis

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    Surface_ku_0099M_13454_DATA_1.pdf (1.611Mb)
    Issue Date
    2014-05-31
    Author
    Surface, Nicholas James
    Publisher
    University of Kansas
    Format
    69 pages
    Type
    Thesis
    Degree Level
    M.E.
    Discipline
    Mechanical Engineering
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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    Abstract
    The smart grid (SG) is a commonly used catch-phrase in the energy industry yet there is no universally accepted definition. The objectives and most useful concepts have been investigated extensively in economic, environmental and engineering research by applying statistical knowledge and established theories to develop simulations without constructing physical models. In this study, a small-scale version (SSSG) is constructed to physically represent these ideas so they can be evaluated. Results of construction show data acquisition three times more expensive than the grid itself although mainly due to the incapability to downsize 70% of data acquisition costs to small-scale. Experimentation on the fully assembled grid exposes the limitations of low cost modified sine wave power, significant enough to recommend pure sine wave investment in future SSSG iterations. Findings can be projected to full-size SG at a ratio of 1:10, based on the appliance representing average US household peak daily load. However this exposes disproportionalities in the SSSG compared with previous SG investigations and recommended changes for future iterations are established to remedy this issue. Also discussed are other ideas investigated in the literature and their suitability for SSSG incorporation. It is highly recommended to develop a user-friendly bidirectional charger to more accurately represent vehicle-to-grid (V2G) infrastructure. Smart homes, BEV swap stations and pumped hydroelectric storage can also be researched on future iterations of the SSSG.
    URI
    http://hdl.handle.net/1808/15776
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3828]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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