Graduation Year

2020

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Tapas K. Das, Ph.D.

Co-Major Professor

Changhyun Kwon, Ph.D.

Committee Member

Lingling Fan, Ph.D.

Committee Member

Adrei Barbos, Ph.D.

Committee Member

Hadi Charkhgard, Ph.D.

Keywords

Electric Vehicles, Shared Autonomous Electric Vehicles, End-Use Consumers of Electricity, Option Contract, Demand Response

Abstract

Even though the total number of light-duty vehicles in the U.S. is expected to increase by 2030, total fuel consumption is expected to significantly decrease in the same timeframe. This contradictory behavior is in part explained by the increasing utilization of electricity as the primary source of energy in the transportation sector. Due to its potential to decrease dependency on fossil fuels, electric transportation has become a promising approach to alleviate the increasing environmental crisis. Passenger car markets are expected to experience a flood of new Electric vehicles (EVs) in the next few years. EVs are considered effective resources to support both transportation and power systems in urban areas. Such effectiveness arises from their ability to store energy for later use and their potential to reduce greenhouse gas emissions.

The overarching goal of this dissertation is to examine the integration of EVs in smart and connected communities and to understand how these vehicles can link smart power markets and transportation systems. Using a combination of optimization and data science tools, this dissertation intends to develop the methodologies and frameworks with which a large fleet of EVs can optimally be coordinated to support the operations of power system operators, end-use consumers of electricity, ride-sharing providers, and generate economic benefit to the EV-owners. If not properly coordinated, these new EVs can potentially be highly disruptive to both power and transportation networks, reducing reliability in both systems.

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