Electronic Thesis/Dissertation


Essays on Investments in Electricity Generation Technologies under Multiple Uncertainties Open Access

This research agenda seeks to develop a policy analysis evaluation tool to facilitate investment decision-making considering the impact of multiple uncertainties on the future mix of energy systems. In two essays, I propose to address the following distinct but related questions:1. How does policy uncertainty influence the optimal allocation of research and development (R&D;) investments in conventional technologies such as coal and gas versus non-fossil technologies such as solar, wind, geothermal, and biomass?To answer this question, we apply optimal control theory with uncertainties in the magnitude and timing of two market-based environmental policies to model R&D; investments on fossil and non-fossil generation technologies. Modeling generation technologies implicitly, we solve the model analytically using dynamic programming. A deeper and more intuitive second attempt involves employing the Runge-Kutta algorithm.2. How do uncertainties in electricity demand, capacity factor, and environmental policy affect the decisions on the optimal allocation of generation capacity expansion investments?To address this question, we model fossil and non-fossil generation technologies explicitly and apply a two-stage stochastic optimization framework with recourse to the generation expansion problem to unpack the effects of simultaneous uncertainties across three dimensions - regulatory, capacity factor, and demand.When investigating question 1, the results show that, under environmental regulatory uncertainty, the type of policy to be enacted in the future influences the firm's R&D; investment strategy. Under a carbon tax policy, the value of the firm and the net R&D; invest will decrease as the magnitude of the tax rate increases. Under tradable permits of CO2 emissions policy, the value of the firm and the net R&D; invest will increase as the price of the permit increases. The result also shows that the e_ect of timing uncertainty dominates the investment strategy.In question 2, when uncertainties in electricity demand and capacity factor are simultaneously considered under any of the emissions policies analyzed in this study, we find that no single uncertainty dominates the other but instead they are complementary. Speci_cally, within the confines of our modeling approach, capacity factor uncertainty dictates the types of technologies to be deployed while demand uncertainty determines the amount of capacity be installed or purchased. Furthermore, when all uncertainty dimensions are considered, uncertainty in CO2 emissions policy adds generation capacity to the demand and capacity factor dimensions, dictating what types of technologies are included in the generation mix.

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