Learning curves for emerging energy technologies

Abstract

The transition to sustainable energy systems requires the accelerated development and deployment of emerging low-carbon energy supply technologies. Future sources of energy are less economically competitive than conventional technologies, but have potential to reduce costs through innovation and learning. Tools for modelling technological change are therefore important for assessing the potential of early-stage technologies. This study focuses on combining learning curves with engineering assessment and parametric models for improved methods of assessing and managing innovation for emerging energy technologies. Integrating engineering methods and parametric modelling into learning curve analysis involves bringing together potentially complementary approaches so as to build a more complete representation of learning effects for emerging energy technologies and thereby provide improved data for energy systems modelling and policy support mechanisms. The development of the integrated approach discussed here has been applied first to wind turbines, with scope for more emergent technologies such as marine energy to be considered in further work. Satisfactorily representing and forecasting technical change for early-stage technologies is a formidable challenge. Using integrated approaches, as suggested in this paper, can offer more complete and robust representations of the drivers and barriers involved.