Doctoral Dissertations
Date of Award
5-2025
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Industrial Engineering
Major Professor
Anahita Khojandi
Committee Members
James Ostrowski, Zana Cranmer, John Kobza
Abstract
In the United States, many households, especially those in lower- and middle-income brackets, struggle with the 'heat or eat' dilemma, where essential energy needs compete with basic necessities. This dissertation investigates strategies to minimize energy burdens for disadvantaged communities using a multi-scale optimization framework.
The first chapter introduces a linear optimization model to determine optimal investments in weatherization, rooftop solar, community solar, and community wind interventions. A case study of 32,660 households in Tennessee identifies key parameters influencing energy burden reduction and highlights how targeted measures can improve quality of life.
In the second chapter, the study expands to include Florida, incorporating temporal granularity by comparing annual, seasonal, and monthly intervention strategies. The analysis explores varying budget constraints and examines fluctuations in energy burdens before and after adopting interventions. This comparative approach offers insights into how local conditions and granular consumption patterns affect the effectiveness of these strategies.
The third chapter introduces stochastic parameters to address uncertainties inherent in real-world applications. By incorporating fluctuating electricity prices, renewable energy variability, demographic changes, and increased electric vehicle adoption, the study assesses intervention robustness through 2030. This approach ensures adaptability and sustainability, capturing the complexity of future energy demands.
The findings demonstrate that a portfolio of tailored interventions can significantly reduce energy burdens, providing policymakers and practitioners with a scalable framework for promoting energy equity. This research advances the understanding of energy burden mitigation by integrating deterministic and stochastic models, offering practical solutions for disadvantaged communities in diverse regions and conditions.
Recommended Citation
Dey, Rupam Kumar, "Optimizing Energy Solutions for Equity in Disadvantaged Communities: A Multi-Scale Approach from Deterministic to Stochastic Models. " PhD diss., University of Tennessee, 2025.
https://trace.tennessee.edu/utk_graddiss/12311