Achieving a global warming limit of 2°C is likely only possible if humanity ceases to emit greenhouse gases (GHG) well before the end of this century. This can only be accomplished through, among other things, a massive transformation of a deeply unpredictable global energy system on which billions of people depend. This thesis aims to illustrate three methodologically distinct approaches that could be integrated into a framework for energy decision-making capable of guiding thoughtful and equitable planning for robust reductions in GHG emissions in the face of deep, largely irreducible uncertainty. Although the primary object of study is the US energy system, all three analyses aim to draw generalizable conclusions that are useful in other contexts.
Chapter 2 attempts to characterize the predictability and volatility of the US energy system by analyzing errors in past US government projections and historical fluctuations in the price, production, and consumption of key energy quantities. This work finds that the period from 2005-2014 contained a disproportionate number of the largest projection errors and inter-year fluctuations in almost all of the 17 quantities examined. This indicates that the US energy system itself was more volatile and harder to predict in this period than in previous decades.
Chapter 3 uses observational residential electricity consumption data to estimate the effect of a low-income electric subsidy on electricity demand, and the externality costs associated with increased electricity generation and higher peak demand. This work finds that the externality costs are on the order of 11% of total subsidy disbursements, with no significant change in this number if intra-day estimates are used instead of time-invariant estimates. Decarbonization of the electric power system will likely eliminate most emissions from power plants, leaving only capacity costs of roughly 5% of subsidy disbursements. Thus, policy makers considering low-income subsidies as a means of ensuring that low-income households do not disproportionately bear the burden of an energy transition can use such estimates of price responsiveness to estimate any adjustments in peak capacity requirements that may result from increased demand.
Chapter 4 uses an optimization-based techno-economic model to characterize the decision space for deep decarbonization of liquid-dependent sectors such as aviation and long-distance road transportation. With today’s technology electrofuels, synthetic hydrocarbons produced using CO2 captured from the atmosphere and hydrogen from electrolysis of water, are likely a more expensive mitigation strategy than continuing to use petroleum-based fuels and offsetting the resulting emissions with direct air capture (DAC) of CO2 with sequestration (DACS). However, if DAC and electrolyzer manufacturers are able to meet near-term cost targets, electrofuels may be competitive with DACS if the cost of petroleum fuels rises substantially or if sequestration costs are higher than anticipated. Several decades into the future, electrofuel costs may fall as low as $2.70 per gallon of gasoline equivalent, potentially achieving cost parity with petroleum fuels. Electrofuel cost is most sensitive to the capital cost the DAC, electrolyzer, and renewable electricity systems, confirming their importance as priorities for research, development, and deployment (RD&D). However, without the operational flexibility afforded by storage or supplementary natural gas or grid electricity interconnections, costs could rise by more than 80%. This points to some less intuitive RD&D priorities, such as metallic phase change materials capable of storing heat above 900°C and low-cost, seasonal CO2 storage.
As a whole, this work aims to characterize the depth the uncertainties posed by the task of energy transition while synthesizing insights from analysis of historical data and modeling based on engineering knowledge and expert judgment to gain policy-relevant insights into pathways toward deep decarbonization of the energy system. I hope this represents a small step toward a decision-making paradigm capable of addressing the deep uncertainties we face while using the wealth of data and insight at our disposal to chart a thoughtful course ahead.
|Advisor:||Azevedo, Ines M. L.|
|Commitee:||Gillingham, Kenneth, Henrion, Max, Kolter, Jeremy Z., Meyer, Russell M., Morgan, Millett G.|
|School:||Carnegie Mellon University|
|Department:||Engineering and Public Policy|
|School Location:||United States -- Pennsylvania|
|Source:||DAI-B 80/09(E), Dissertation Abstracts International|
|Subjects:||Economics, Industrial engineering, Energy|
|Keywords:||Decarbonization, Econometrics, Electrofuel, Energy, Industrial optimization, Techno-economic analysis|
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