Progress in Energy Storage Technologies: Models and Methods for Policy Analysis.

摘要

Climate change and other sustainability challenges have led to the development of new technologies that increase energy efficiency and reduce the utilization of finite resources. To promote the adoption of technologies with social benefits, governments often enact policies that provide financial incentives at the point of purchase. In their current form, these subsidies have the potential to increase the diffusion of emerging technologies; however, accounting for technological progress can improve program success while decreasing net public investment.;This research develops novel methods using experience curves for the development of more efficient subsidy policies. By providing case studies in the field of automotive energy storage technologies, this dissertation also applies the methods to show the impacts of incorporating technological progress into energy policies. Specific findings include learning-dependent tapering subsidies for electric vehicles based on the lithium-ion battery experience curve, the effects of residual learning rates in lead-acid batteries on emerging technology cost competitiveness, and a cascading diffusion assessment of plug-in hybrid electric vehicle subsidy programs. Notably, the results show that considering learning rates in policy development can save billions of dollars in public funds, while also lending insight into the decision of whether or not to subsidize a given technology.