*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Probabilistic Analysis Methods For Electric Vehicle Charging
Committee:
Dr. Samuel Coogan, ECE, Chair, Advisor
Dr. Yorai Wardi, ECE
Dr. Emily Grubert, CEE
Dr. Daniel Molzahn, ECE
Dr. Fumin Zhang, EECE
Abstract: The objective of this thesis is to study the design of tools for the analysis and operation of electric vehicle charging facilities. We develop a set of operational pricing models that allow for the management of charging facilities and their limited resources. Furthermore, we study the sensitivity of such operational models to mischaracterizations of user assumptions. Lastly, we detail how to design the respective model parameters. First, we formulate two operational pricing models with which a charging facility can operate. The first model we present is a service level model where users choose a service level, associated with a charging rate and a price, that minimizes the cost to themselves. The second model is a deadline-based model where arriving users choose a charging deadline to fulfill their energy demand. In both models, the user decides what service level or deadline to select based on their demands upon arrival to the charging facility. The implementation of the introduced models relies on having access to accurate information about the distribution of a user's demand profile. Hence, we study the sensitivity of the presented service level model to mischaracterizations in the user profiles. In particular, we derive several results that quantify how specific mischaracterizations affect the estimates of resource consumption at the charging facility. Finally, we detail how to design the parameters for each of the two operational pricing models. We present a collection of optimization programs that are utilized to set the pricing parameters with considerations for the resource constraints and the financial objectives of the charging facility. Moreover, we derive a high-confidence bound on revenue projections for charging facility operating under the service level model.
