Abstract

Electric vehicles (EVs) are one of the eminent alternatives to decarbonize the transportation sector. However, large-scale EV adoption brings new challenges and opportunities to both transportation and power systems (TPSs). The challenges include the lack of understanding of EV driving behaviors and the associated charging demand (CD) distribution, the complex interaction of the decentralized decision-makers from TPSs, and the insufficient infrastructure from TPSs to accommodate the growing CD of EVs. On the other hand, the opportunities include benefiting the power systems by leveraging vehicle-to-grid (V2G) technologies and improving transportation mobility by incorporating strategic infrastructure planning. The goal of this dissertation is to address the challenges and leverage opportunities associated with large-scale EV adoption from planning and operational perspectives in TPSs. We have the following objectives: 1- Better understanding the impacts of driving patterns on the spatio-temporal distribution of EV CD. 2- Investigate the value of EVs on the coupled TPSs. 3- Plan the supporting power and transportation infrastructure for the growing CD of EVs. More specifically, we first utilized machine learning approaches to model and forecast CD of EVs based on their driving behavior. Secondly, we propose a multi-agent model that captures the decentralized interactions between key stakeholders in TPSs to investigate the value of EVs in distribution system support. Thirdly, we modeled infrastructure planning for EV adoption from two perspectives: 1) We study the multi-stage DG and CS planning problem considering decentralized investors in a multi-agent optimization framework to understand the system evolvement. 2) We study the centralized CS planning problem in a bi-level programming framework to optimize transportation mobility by strategically placing CSs. To overcome the computational difficulties, we have proposed effective computational algorithms based on exact convex reformulation and value-decomposition algorithms. Our numerical examples demonstrate that the proposed models can identify the equilibrium investment patterns of DGs and CSs, as well as determine the optimal locations of CSs from a centralized entity's perspective. Additionally, our operational framework shows how EVs can provide system support for load pickup with endogenously determined incentives and energy prices. These modeling and computational strategies can provide foundations for future investigation, planning, and market design with large-scale EVs in coupled TPSs.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Guo, Zhaomiao

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Identifier

CFE0009693; DP0027800

URL

https://purls.library.ucf.edu/go/DP0027800

Language

English

Release Date

August 2023

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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