The human-induced increase in greenhouse gas (GHG) concentrations is the primary driver of recent global warming, leading to a series of climate changes, including sea level rise, ocean acidification, and more frequent and severe weather extremes. These changes have profound impacts on global ecosystems and human societies.
The world has recognized the necessity for countries to cooperate in combating climate change, resulting in international treaties such as the Kyoto Protocol and the Paris Agreement. The success of international cooperation depends on a portfolio of various approaches to eliminate, reduce, substitute, and compensate for GHG emissions.
The transition from fossil fuels to renewable energy can effectively mitigate GHG emissions. Wind and solar energy have experienced substantial growth over the past decade, yet the development of geothermal energy remains stagnant despite its immense potential. Considering its additional advantages, such as the dual benefits of generating both electricity and heat, the stability of energy generation, and the synthesis with carbon capture and storage, more focus should be placed on geothermal energy. Meanwhile, most efforts to address climate change have focused on mitigating carbon dioxide (CO2) emissions and removing their accumulation from the atmosphere. While there is ~210x more CO2 than methane (CH4) in the atmosphere, the atmospheric concentration of CH4 has increased faster and alone contributes an amount of radiative forcing that is about 30% of the contribution from CO2. With positive temperature-driven feedbacks that release CH4 to the atmosphere as temperatures rise, a shorter atmospheric lifetime than CO2, and continued reliance on natural gas, a portfolio approach is urgently needed to slow, stop, and reverse the accumulation of CH4 in the atmosphere. To provide insights to address the above issues, this dissertation focuses on optimizing strategies for geothermal heat mining and CH4 control.
Chapter 2 explored the optimal geothermal heat mining (OGHM) problem at the facility level, which aims at maximizing profit within a time horizon for a site. The problem is formulated as an optimal control model, which is solved by a proposed analytical algorithm. Solutions to the OGHM problem can be categorized into four situations: 1) the mass flow rate keeps the maximum; 2) the mass flow rate keeps as 0; 3) the mass flow rate starts as the maximum, decreases to a constant value, and finally recovers to the maximum; and 4) the mass flow rate starts as 0, and changes to the maximum. Further based on analysis from an economic view, for the cases that have positive optimal profits, the solutions of finite-time OGHM problems can be considered as a combination of the solution of the infinite-time problem and one final stage with a maximum mass flow rate. Results show that surrounding media temperature, efficiency, and compression cost have significant influences on the optimal profit, and CO2 geothermal systems perform better for shallow, low-grade heat sources when compared to water geothermal systems.
Chapter 3 investigates pathways for CH4 control in a top-down system view, including mitigation to avoid CH4 emissions and removal of CH4 that is in the atmosphere. We develop and implement the Model for Optimization of Methane Emissions and removal with Negative Technologies Under climate Mitigation (MOMENTUM) that determines cost-effective pathways for CH4 emissions mitigation in energy, agriculture, and waste sectors, and atmospheric CH4 removal amid trajectories for mitigation and removal of CO2. Results indicate that relying solely on mitigating CH4 emissions is not feasible to meet climate goals, and it is imperative that CH4 removal technologies are developed and deployed at a substantial scale. Initial CH4 removal cost and CH4 removal learning rate have more impact on the total cost of CH4 control than the maximum CH4 removal potential or the maximum CH4 removal growth rate, and when CH4 removal needs to begin is influenced by scale-related parameters much more than by cost-related parameters. In addition, if societal influences are considered, the avoided social cost always outweighs the optimal CH4 control cost, which indicates a net benefit to controlling CH4 emissions.
Chapter 4 establishes an agent-based model in a bottom-up system view to simulate the interactions among the government, suppliers, and consumers, which considers the introduction of a CH4 emission market to initial commodity/service markets. Three sectors are analyzed, including agriculture, energy, and waste sectors, accounting for ~90% of CH4 emissions in the US. The suppliers and consumers in each sector are modeled with heterogeneity, local interactions, and adaptations. Case studies on Ohio, US indicate that the emission cap is the main factor influencing CH4 control, which should be established in the most efficient way (reduced by 3% per year). The emission market penalty price has a minimal effect on the amount of CH4 reduction. Moreover, CH4 control always leads to a net benefit, and the government should implement more incentives to encourage earlier deployments of negative-cost CH4 mitigation techniques. Meanwhile, the government should pay more attention to the waste sector, especially the landfill source, which is faced with the most difficulties in mitigating CH4 emissions.