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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 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.
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