Electric companies require reliable technology cost information to make sound investments to consistently deliver electricity to customers and achieve strategic sustainability goals. The importance of reliable technology cost information will likely only increase as major shifts in the electric supply mix continue with the global energy transition. Many organizations produce global technology and/or macroeconomic outlooks to help various actors, including electric companies, better understand potential future states of the world. This research evaluates how different levels of capacity (demand) indicated in selected scenarios could impact future costs of natural gas combined-cycle (NGCC), solar photovoltaic (PV), and onshore wind generation technologies in an endogenous learning model.
Technology learning rates evaluated and deemed informative in Endogenous Learning for Projecting Future Capital Costs – Evaluation and Implications for Electric Power Generation Technologies (3002019786) are applied to demand outlooks from the Energy Information Administration’s (EIA) reference, low economic growth, and high economic growth scenarios, as well as EPRI’s U.S. Regional Economy, Greenhouse Gas, and Energy Model (REGEN) carbon tax and 100% renewable portfolio standard (RPS) scenarios. In addition to cost projections to 2050, demand and cost differentials between the reference case and all other scenarios are presented to quantitively consider how differences in demand are related to differences in cost. The relatively small and steady rates of increase in forecasted demand across scenarios for NGCC result in minor cost differentials, ranging from $0-15/kW. Larger and highly variable rates of increase for solar PV and onshore wind produce larger ranges of cost differentials ranging from $0-462/kW and $0-338/kW, respectively.