Electric companies require reliable, technology-specific cost information to make sound investments and achieve strategic goals. Many organizations produce technology and/or macroeconomic outlooks to help various actors better understand potential future states of the world. However, these outlooks may not provide actionable insights and/or outputs. This research develops cost projections and annual cost projection factors – covering a wide range of potential trajectories – that electric company planners can directly utilize for modeling and analysis. The approach leverages empirically observed cost-deployment relationships (i.e., learning curves) to provide a structured framework for projecting scenario-based future costs.
The following technologies are studied: natural gas combustion turbines (NGCT), natural gas combined cycles (NGCC), solar photovoltaic (PV), onshore wind, light water-cooled small modular reactors (lwSMR), lithium ion battery energy storage systems (Li Ion BESS), 100% hydrogen-fired combustion turbines (H2-CT) and combined cycles (H2-CTCC), NGCC with CO2 capture, and electrolyzers.
Baseline cost projections for NGCT, NGCC, solar PV, onshore wind, and Li Ion BESS were developed using an endogenous technology learning (experience curve) model. Literature-based learning rates vetted by EPRI experts and informed by statistical fit were applied to deployment outlooks from the U.S. Energy Information Administration’s 2025 Annual Energy Outlook Reference, Low Economic Growth, High Economic Growth, and High Oil/Gas Supply cases, as well as EPRI’s U.S. Regional Economy, Greenhouse Gas, and Energy Model (REGEN) economy-wide Net-zero by 2050 case. For emerging technologies with no domestic utility-scale deployment – lwSMRs, H2-CT, H2-CTCC, and NGCC with CO2 capture – literature-obtained learning rates and assumptions were used to project a plausible cost range. These projections begin in the 2030s (assumed baseline years) and extend to 2050. Finally, electrolyzer cost projections from EPRI’s Low-Carbon Resources Initiative were adapted to provide coverage of key hydrogen production pathways.
The annual cost projection factors show the magnitude of cost in each future year relative to 2025. The attached Excel file enables users to copy and paste the cost factors into their own tools or apply them directly to their own data. Users may also generate cost curves within the file itself by entering a few fields – such as their own 2025 cost estimate – allowing immediate customization of the projections while retaining the underlying learning rates, capacity outlooks, and baseline model costs developed by EPRI.