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Charge Park Vehicle-to-Grid Service: Modeling, Control, and Optimization

Chen Peng, University of Nebraska - Lincoln


This thesis focuses on modeling the charge park vehicle-to-grid (V2G) service, as well as evaluating and maximizing its profitability, from both the aspects of individual charge park and the V2G service network. The first part of this thesis deals with modeling, control and profitability evaluation of the V2G service of individual charge parks. Starting from modeling the energy storage formed by multiple EV batteries, the V2G energy distribution over equivalent prices (V2G energy costs) is derived. With a case study on the future New York City workplace and recreational environment charge parks, the V2G energy distribution analysis reveals facts on the time and places where V2G energy is abundant. Next, the charge park V2G service is evaluated by simulating the EV activities and charge park scheduling using the 2017 National Household Travel Survey and a model predictive controller. The purpose of the evaluation is to characterize the V2G output capability of a charge park and reveal insights for the profit-seeking decision-making process of the V2G service provider. Furthermore, methods are proposed to compute the best V2G output power for maximizing profit. The second part of this thesis focuses on optimizing the V2G service network formed by multiple V2G charge parks and V2G energy customers. The optimization of the V2G service network is studied in two directions, i.e., structural optimization and practical large-scale real-valued optimization. Considering the current stage of development of the EV and relevant auxiliary service industries, the V2G service provider is assumed to use a local supply network for V2G energy delivery. Therefore, a distributed decomposition algorithm is developed to optimize the supply network topology, from the aspect of network theory. On the other hand, optimization problems in V2G service network have the following characteristics: i) large number of variables and constraints, and ii) uncertainty and nonlinearity. For the optimization of such large-scale constrained problems with high nonlinearity, hybrid evolutionary algorithms are developed, which combined the advantage of the state-of-the-art large-scale unconstrained algorithms and small-scale constrained algorithms.

Subject Area

Electrical engineering|Energy|Sustainability

Recommended Citation

Peng, Chen, "Charge Park Vehicle-to-Grid Service: Modeling, Control, and Optimization" (2019). ETD collection for University of Nebraska - Lincoln. AAI13902940.