Program on Technology Innovation: Endogenous Learning for Projecting Future Capital Costs – Evaluation and Implications for Electric Power Generation Technologies

As the electric power sector continues to transition in terms of the mix of power generation technologies supplying electricity, it is important to understand both the observed costs of those technologies and how they change over time. Having accurate technology cost information allows electric company planners and modelers to more effectively analyze potential future scenarios, which in turn impact investment decisions and ultimately how a company plans to meet their goals of delivering electricity to customers while achieving long term sustainability goals. This research demonstrates how endogenous learning models, when utilized and interpreted in consideration of key external factors (i.e., not directly related to innovation), can provide various stakeholders better insights to inform strategic decision making related to costs.

Utilizing ranges of learning rates rather than a single learning rate may be more informative. The evaluation of literature-recommended learning rates highlighted that a 5%-10%-15% learning rate range is informative for better understanding potential future costs for natural gas combustion turbines, 1%-5%-9% for natural gas combined cycle projects, 18%-20%-22% for solar PV projects, and 10%-12%-14% for onshore wind projects. Market dynamics; evolving technology characteristics; research, development and demonstration (RD&D), input costs; project financing; regulatory requirements; government intervention; and sustainability ambitions are highlighted as external factors that have and may continue to influence cost and price developments, and/or how changes in production costs translate into changes in market prices of select power generation technologies.