This dissertation explores the application and examination of the Food Energy Water (FEW) nexus within the context of urban farms, with the goal of contributing fresh food supply to the local community. Every urban farm is connected to a community-scale microgrid (MG) that utilizes renewable energy sources to generate electricity for the local community. The microgrid (MG) aims to achieve environmental sustainability objectives by significantly reducing carbon emissions in comparison to conventional energy sources such as coal, natural gas, and crude oil. An Agent Based Model (ABM) is formulated to investigate the impacts of the different constituents through an analysis of societal, economic, and environmental sustainability measures. The model is employed to analyze the interplay among various agents, enabling a comprehensive comprehension of the synergistic effects and tradeoffs inherent in a Food Energy Water (FEW) nexus. Machine learning models that can provide input data such as crop prediction and electricity data are prepared and their potential integration within the ABM is discussed. The application of the framework on different case studies provided a qualitative connection between water, food, and energy in the community, quantified these connections, and identified critical links in the system. These findings enhanced our understanding of how the incorporation of renewable energy can influence food and water in communities. A mathematical programming model to optimize the FEW Nexus incorporating renewable energy has also been developed. The model addressed the supply-demand balance with existing and future FEW infrastructures. Depending on water-energy-food demand trend obtained from the ABM, the optimal way of matching FEW supply and demand over time is identified using indicators such as cost and carbon footprint.
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Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Elkamel, Marwen, "Agent-Based Simulation, Machine Learning, Micro Supply Chain, and Optimization of the Food-Energy-Water (FEW) Nexus and Incorporating Renewable Energy" (2023). Electronic Theses and Dissertations, 2020-. 1834.
Restricted to the UCF community until August 2024; it will then be open access.