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Author ORCID Identifier
https://orcid.org/0000-0001-5510-0293
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Industrial Engineering & Operations Research
Year Degree Awarded
2021
Month Degree Awarded
September
First Advisor
Erin Baker
Subject Categories
Energy Systems | Industrial Engineering | Operational Research | Risk Analysis
Abstract
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).
DOI
https://doi.org/10.7275/24653714
Recommended Citation
Kanyako, Franklyn, "MODELING PORTFOLIOS OF LOW CARBON ENERGY GENERATION UNDER DEEP UNCERTAINTY" (2021). Doctoral Dissertations. 2346.
https://doi.org/10.7275/24653714
https://scholarworks.umass.edu/dissertations_2/2346
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Energy Systems Commons, Industrial Engineering Commons, Operational Research Commons, Risk Analysis Commons