Engineering Commons^™

Modeling Portfolios Of Low Carbon Energy Generation Under Deep Uncertainty, Franklyn Kanyako

Doctoral Dissertations

In the 2015 Paris Agreement, nearly every country pledge through the Nationally Determined Contributions (NDCs) increased adoption of low carbon energy technologies in their energy system. However, allocating investments to different low carbon energy technologies under rising demand for energy and budget constraints, uncertain technical change in these technologies involves maneuvering significant uncertainties among experts, models, and decision-makers. We examine the interactions of low carbon energy sources (LCES) under the condition of deep uncertainty. Deep uncertainty directly impacts the understanding of the role of low carbon energy technologies in climate change mitigation and how much R&D investment should be allocated …

Next Generation Energy Storage: An Examination Of Lignin-Based Carbon Composite Anodes For Sodium Ion Batteries Through Modeling And Simulation, Dayton G. Kizzire

Doctoral Dissertations

The current energy market relies heavily on fossil fuel sources; however, we are amidst a momentous shift towards wind, solar, and water based renewable energies. Large-scale energy storage allows renewable energy to be stored and supply the grid with consistent energy despite changing weather conditions. Improvements to large-scale energy storage in terms of cost, safety, and sustainability are crucial to wide-scale adoption. A promising candidate for large-scale energy storage are sodium-ion batteries using hard carbon anodes. Sodium is globally available, cheaper, and more sustainable than lithium, but requires a different anode structure. A sustainable hard carbon anode with excellent Li-ion …

Infrastructure Systems Modeling Using Data Visualization And Trend Extraction, Jacob Marshal Hale

Doctoral Dissertations

“Current infrastructure systems modeling literature lacks frameworks that integrate data visualization and trend extraction needed for complex systems decision making and planning. Critical infrastructures such as transportation and energy systems contain interdependencies that cannot be properly characterized without considering data visualization and trend extraction.

This dissertation presents two case analyses to showcase the effectiveness and improvements that can be made using these techniques. Case one examines flood management and mitigation of disruption impacts using geospatial characteristics as part of data visualization. Case two incorporates trend analysis and sustainability assessment into energy portfolio transitions.

Four distinct contributions are made in this …