Linear optimization methods for vehicle energy and communication networks
- This thesis considers energy and communication systems among vehicles that utilize two developing technologies: battery-powered drive trains and accurate sensors. We use linear optimization to construct separate algorithms for networks of Plug-in Electric Vehicles (PEVs) and networks of Sensor-equipped Vehicles (SVs). For a fleet of PEVs, we construct an automated mechanism that efficiently organizes distributed energy trading between consumers and the electric utilities in order to benefit both entities. A linear programming model of the fleet provides a composite valuation, which can be used in an online environment managed by a fleet aggregator to allocate feasible energy exchange schedules that decrease the peak electricity demand and reduce the cost to consumers. We give empirical results based on real data. For a network of SVs, we consider the problem of determining the locations of vehicles on the road given some of the pair-wise distances between them. We present two results on a semidefinite programming (SDP) relaxation that can be applied to localizing SVs. First, we provide the first non-asymptotic bound on the required sensor communication range to ensure a unique localization of the sensors. Second, we show that networks with a certain framework will admit positive semidefinite stress matrix with maximal rank, and hence the SDP relaxation will produce a correct localization of the points.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Taheri, Nicole Anahita
|Stanford University, Institute for Computational and Mathematical Engineering.
|Statement of responsibility
|Nicole Anahita Taheri.
|Submitted to the Institute for Computational and Mathematical Engineering.
|Thesis (Ph.D.)--Stanford University, 2012.
- © 2012 by Nicole Anahita Taheri
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