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    Price-driven charging of plug-in electric vehicles in the smart grid

    Author
    Ghavami Pakdehi, Abouzar
    View/Open
    172733_GhavamiPakdehi_rpi_0185E_10366.pdf (902.0Kb)
    Other Contributors
    Kar, Koushik; Chow, J. H. (Joe H.), 1951-; Gupta, Aparna; Sikdar, Biplab;
    Date Issued
    2014-05
    Subject
    Electrical engineering
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/1130
    Abstract
    We first develop two convex optimization algorithms for PEV charging that minimize the aggregator's convex cost function subject to transmission feeder overload constraints. The two algorithms are amenable to decentralized implementation, in which the PEVs react to the load signals on their supply paths and the distribution grid on the whole (by adjusting their charging schedules).; We then consider the day-ahead price-setting question from the perspective of the utility (or aggregator) that is interested in minimizing the average energy supply costs given the uncertainty in the charging preferences of the PEV owners. Modeling the uncertainty in the PEV charging constraints in a Bayesian framework, we propose a day-ahead pricing policy that can minimize the overall energy supply cost in expectation, subject to transmission feeder capacity constraints. The same pricing policy can be extended to maximize economic surplus, computed as the total valuation of the energy provided to all PEVs minus the total energy supply cost. A simple extension of our approach to real-time pricing of PEV demand is also discussed, and evaluated through simulations.; We next analyze the equilibrium properties of a natural price-driven charging control game in the distribution grid, between the utility (that sets the time-dependent energy usage price) and selfish PEVs (that choose their own charging schedules to minimize individual cost). We demonstrate, through analysis and simulations, that individual best-response strategies converge to socially optimal charging profiles (also equilibrium solutions) under fairly weak assumptions about the (asynchronous) charging profile update processes. We also discuss how the framework can be extended to consider the topology of the distribution tree and associated transmission feeder capacity constraints.; As the number of Plug-in Electric Vehicles (PEVs) increases, it is essential to control their charging schedules and spread the PEV load over time to reduce the energy generation and distribution costs due to this additional demand. Furthermore, due to the limited power capacity of the transmission feeders and the sensitivity of the mid-way distribution transformers to excessive load, it is crucial to control the amount of power through each specific feeder in the distribution network to avoid system overloads that may lead to breakdowns. In this thesis we develop, analyze and evaluate price-driven charging algorithms for PEVs in a smart grid environment. The algorithms we propose minimize the cost incurred on the power distribution system (or the supply cost of the electric utility or aggregator) due to the PEV load, and at the same time prevents overloading of the transmission feeders.;
    Description
    May 2014; School of Engineering
    Department
    Dept. of Electrical, Computer, and Systems Engineering;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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    • RPI Theses Online (Complete)

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