Doctoral Dissertations

Date of Award

5-2025

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Industrial Engineering

Major Professor

James Ostrowski

Committee Members

Michael Starke, Rebekah Herrman, Hugh Medal

Abstract

We consider different approaches in mathematical optimization to solve mixed integer programs for fields related in quantum computing and network infrastructure planning and scheduling for electric vehicle charging microgrid. The intersection of mathematical optimization within these two fields is discussed in Chapter 1. This work uses tools in this field in novel methods. The approaches are separated by chapters.

In Chapter 2, we propose a classical decomposition algorithm applied on combinatorial optimization problems with variational quantum algorithms. Using a particular structure seen in a class of graphs, we can exploit it and reduce the number of qubits needed to solve for the optimal objective value for a graph.

To ensure energy independence, electrification of vehicles is impertinent. Utilizing renewables and a MW-scale microgrid, we propose a mixed integer linear programming method in Chapter 3 for the planning of a MW-scale microgrid electric vehicle charging station for medium and heavy duty vehicles. Utilizing a optimal power flow and optimal transmission switching framework, a microgrid can be designed for the optimal layout of lines while maintaining some reliability critera.

Once a medium and heavy duty electric vehicle charging station with a microgrid architecture is built. It is important to schedules these vehicles in a manner to balance fulfilling the needs of the truckers to get them in and out to abide by their schedule while minimizing the effects this microgrid on the public grid. In Chapter 4, we propose two scheduling approaches, a day-ahead formulation and a heuristic model.

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