Essays on uncertainty analysis in energy modeling : capacity planning, R&D portfolio management, and fat-tailed uncertainty
Abstract/Contents
- Abstract
- The characterization and analysis of uncertainty are central components of decision-making, especially in the energy sector; however, there is currently a gap in the energy modeling community between the recognition of uncertainty's importance and its incorporation in large-scale models. This dissertation explores how the explicit inclusion of uncertainty through sequential decision-making approaches like stochastic programming can provide insights to energy planners in different domains. The dissertation first investigates the dynamics of capacity planning and dispatch in the electric power sector under technological, economic, and policy-related uncertainties. Metrics like the expected value of perfect information and the value of the stochastic solution quantify the benefits of reducing uncertainty and of incorporating uncertainty explicitly in modeling efforts. Model results highlight risks associated with shale gas and climate policy, offer policy guidance in these areas, and indicate that planners are likely underestimating the impacts of uncertainty. Hedging and strategic delay are explained in terms of the optionality of energy investments, leading to insights about uncertainty, learning, and irreversibility. A second application presents a framework for allocating investments across a portfolio of energy technology research and development (R& D) programs, which incorporates uncertainties in the effectiveness of investments and in diffusion markets. This work analyzes how R& D valuations vary in different decision-making settings and shows how wait-and-see valuation approaches, by not explicitly accounting for exogenous market uncertainties, may undervalue the hedging potential of technologies. The results indicate that R& D is more valuable in suboptimal planning and policy environments. The final section discusses policy and modeling questions about low-probability, high-impact risks in climate change economics. This analysis examines the impacts of fat-tailed uncertainty about the climate sensitivity parameter on near-term abatement using a sequential decision-making framework. The results demonstrate how policy prescriptions from integrated assessment models are highly sensitive to the specifications of uncertainty, learning, and damages. Fat tails alone do not merit stringent mitigation immediately, which also requires strongly convex damages and slow learning. The analysis illustrates the potential value of midcourse corrections on reducing consumption risks imposed by uncertain damages and focuses attention on the dynamics of learning.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2013 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Bistline, John Erik |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. |
| Primary advisor | Weyant, John P. (John Peter) |
| Thesis advisor | Weyant, John P. (John Peter) |
| Thesis advisor | Infanger, Gerd |
| Thesis advisor | Sweeney, James L |
| Advisor | Infanger, Gerd |
| Advisor | Sweeney, James L |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | John Erik Bistline. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2013. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2013 by John Erik Bistline
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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