ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

Displaying 1 - 1 of 1
Filtering by

Clear all filters

158642-Thumbnail Image.png

The Impact of Energy Routers on the Planning of Transmission and Electric Vehicle Charging Stations

Description
Transmission line capacity is an obstacle for the utilities because there is a load increment annually, and new power plants are being connected, which requires an update. Energy router (ER) is a device that provides an additional degree of freedom to the utilities by controlling the reactive power. The ER…

Transmission line capacity is an obstacle for the utilities because there is a load increment annually, and new power plants are being connected, which requires an update. Energy router (ER) is a device that provides an additional degree of freedom to the utilities by controlling the reactive power. The ER reactive power injection is demonstrated by changing the line's reactance value to increase its capacity and give the utility a deferral time for the project upgrade date. Changing the reactance manually and attaching Smart Wire's device to the branches have effectively solved the overload in three locations of a local utility in Arizona (LUA) system.

Furthermore, electric vehicle charging stations (EVCSs) have been increasing to meet EV needs, which calls for an optimal planning model to maximize the profits. The model must consider both the transportation and power systems to avoid damages and costly operation. Instead of coupling the transportation and power systems, EVCS records have been analyzed to fill the gap of EV demand. For example, by accessing charging station records, the moment knowledge of EV demand, especially in the lower order, can be found. Theoretically, the obtained low-order moment knowledge of EV demand is equivalent to a second-order cone constraint, which is proved. Based on such characteristics, a chance-constrained (CC) stochastic integer program for the planning problem is formulated. For planning EV charging stations with ER, this method develops a simple ER model to investigate the interaction between the mobile placement of power flow controller and the daily pattern of EV power demand.
ContributorsAlali, Yousef (Author) / Weng, Yang (Thesis advisor) / Cui, Qiushi (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2020