EDT.

Title: Topics in Power Systems and Sustainability

Date: July 18th, 2025

Time: 9:00 a.m. - 11:00 a.m. EDT

Meeting Link: Zoom Meeting

Location: Groseclose 404

Nicole Redder

Ph.D. Candidate in Industrial Engineering

School of Industrial and Systems Engineering

Georgia Institute of Technology

Committee:

Dr. Santanu Dey (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Valerie Thomas (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Constance Crozier, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Dawn Strickland, Trinity College of Arts & Sciences, Duke University

Dr. Burak Kocuk, Sabancı University

Abstract:

The electric grid faces increased need to improve efficiency and flexibility while accommodating new applications and demands. Much of current energy use, including transportation and industry, is expected to be electrified in coming decades, and integration of distributed intermittent renewable generation increases the challenges for grid management. The developments in electricity management and electrification of transportation infrastructure present a rich set of optimization problems in operations research. This thesis addresses the optimal power flow problem and the problem of electric vehicle charging station placement in urban areas.

Chapter 2 develops an approach to the AC optimal power flow problem. A polyhedral study related to a certain node-based relaxation of the Optimal Transmission Switching Problem is performed. An integral extended formulation of this substructure is constructed, and a practical approach to obtain valid inequalities is proposed. These valid inequalities are used in a branch-and-bound approach improving on both default and feasibility-oriented CPLEX.

In Chapter 3, a mixed integer programming model for the Electric Vehicle (EV) charger location planning problem for commuters within cities is proposed. The model is solved with varying objectives over real-world commuter origin-destination data. Model variants solved include maximizing commuters served, minimizing charging stations needed to serve all commuters, and adding constraints to ensure equitable model solutions. The model and methodology for data collection can be used for solving the EV location charger placement problem for any city in the USA, and globally if similar data are available. Although data for the city of Atlanta is used, the solutions given by the models yield several general insights. When maximizing commuters served for a limited number of chargers, the optimal solution puts most chargers at work locations, serving commuters with short commutes and thus low charging needs. As the number of chargers increases, more are placed at larger distances from the city center, providing more commuters with charging near home. Assuming that those with home charging capability will charge at home, equity goals can be met with only minimal effect on the number of commuters served and the distribution of chargers.

In Chapter 4, public bus and rail transit options are incorporated into the model to provide a more realistic evaluation of commuting options. The model continues to place fast-charging stations for driving commuters’ use in each tract. Additionally, commuters can use bus or rail transit in up to two additional travel steps. We present an additional variation to our model which minimizes total energy consumption required for commuters. The improvements to Chapter 3’s model solutions are considered when allowing first only rail transit use. With that addition, there is some improvement to each objective, but such improvement is limited by the relatively small coverage of Atlanta’s rail transit system. With the addition of both bus and rail transit to the original model,more commuters can be served using public transit options, and the environmental impact of commutes is improved.