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Author ORCID Identifier
Open Access Dissertation
Doctor of Philosophy (PhD)
Industrial Engineering & Operations Research
Year Degree Awarded
Month Degree Awarded
Energy Systems | Industrial Engineering | Operational Research | Risk Analysis
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 to each technology. We complete three projects that advance the understanding of energy transition under deep uncertainty that include (1) conduct uncertainty analysis on the impacts of the future cost of wind energy on global electricity generation and the value of wind energy to climate change mitigation. (2) We apply a new, rigorous, analytical framework to select portfolios of low carbon energy sources (LCES) of R&D investments that are robust across beliefs and models, and finally, investigate the benefit of regional cooperation for electric power capacity expansion, cross border electricity trade across the West Africa Power Pool (WAPP).
Kanyako, Franklyn, "MODELING PORTFOLIOS OF LOW CARBON ENERGY GENERATION UNDER DEEP UNCERTAINTY" (2021). Doctoral Dissertations. 2346.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Available for download on Thursday, September 01, 2022