Electric companies require reliable information on current and projected technology costs to make sound investments and achieve strategic sustainability goals. Many organizations produce global technology and/or macroeconomic outlooks to help various actors, including electric companies, better understand potential future states of the world. However, these outlooks may not provide actionable insights and/or outputs. This research develops cost projection factors reflecting a range of potential trajectories that resource planners can directly apply to their own cost data. The following technologies are studied: natural gas combustion turbines (NGCT), natural gas combined-cycles (NGCC), solar photovoltaic (PV), onshore wind, light water small modular reactors (lwSMR), lithium ion (Li ion) battery energy storage systems (BESS), 100% hydrogen (H2)-fired combustion turbines (CT) and combined-cycles (CTCC), NGCC with carbon (CO2) capture, and electrolyzers.
Baseline cost projections for NGCT, NGCC, solar PV, and onshore wind technologies were developed using a model of endogenous technology learning/experience. Technology learning rates identified from the literature and deemed informative by EPRI experts were applied to deployment outlooks from the Energy Information Administration’s (EIA) 2023 Annual Energy Outlook reference case, low Inflation Reduction Act (IRA) uptake, and high IRA uptake cases, as well as EPRI’s U.S. Regional Economy, Greenhouse Gas, and Energy Model (REGEN) net-zero by 2035 electric sector scenario. Cost projection factors for a given technology, scenario, learning rate, and year were developed before being converted to annual factors showing the magnitude of cost in a given future year relative to 2024 cost. Endogenous learning-based cost projections were developed for Li ion BESS and presented here along with recent projections from EPRI Program 94 on Energy Storage and Distributed Generation based on other methods. Various learning rates and assumptions obtained from the literature were utilized to identify a range of potential cost trajectories for lwSMRs, 100% H2-fired turbines, and NGCC+CO2 capture as emerging technologies with no commercial experience. Annual cost projection factors were developed from 2030 (the assumed baseline year) through 2050 for these technologies. Finally, electrolyzer cost projections from EPRI’s Low-Carbon Resources Initiative were adapted to include H2 production technologies.