2018: ATB Approach and Methodology
The ATB represents the cost and performance of typical electricity generation plants in the United States. The ATB represents renewable electricity generation plants either by (1) reflecting the entire geographic range of resource with a few points averaging similar characteristics or (2) providing examples to demonstrate a range associated with resource potential.
Foundational to this averaging approach, NREL uses high-resolution, location-specific resource data to represent site-specific capital investment and estimated annual energy production for all potential renewable energy plants in the United States.
For each renewable technology, the ATB includes:
- Base Year estimates for a base year of 2016, the year that sufficient historical data are available
- Three future scenarios (Constant, Mid, and Low technology cost) through 2050 to reflect a range of perspectives based on published literature:
- Constant = Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences and assumes no further advancement in R&D.
- Mid Technology Cost Scenario = technology advances through continued industry growth, public and private R&D investments, and market conditions relative to current levels that may be characterized as " likely," or " not surprising"
- Low Technology Cost Scenario = Technology advances that may occur with breakthroughs, increased public and private R&D investments, and/or other market conditions that lead to cost and performance levels that may be characterized as the " limit of surprise," but not necessarily the absolute low bound.
- Descriptions of the resource, cost and performance estimation methodology, and data sources as well as a comparison with published data.
For fossil and nuclear generation plants, the ATB:
- Relies on EIA representation of current year plant cost estimates and for plant cost projections through 2050 (AEO 2018)
- Relies on EIA scenarios for fuel price projections through 2050 (AEO 2018)
- Future work may include national laboratory projections for these technologies.
For biopower plants, the ATB:
- Relies on EIA representation current year plant cost estimates
- Relies on EIA representation of future plant cost estimates through 2050 (AEO 2018)
- Represents the average biopower feedstock price based on the Billion Ton Update study (DOE (2011)) through 2030
- Holds the biopower feedstock price at 2030 levels through 2050.
Note: Capacity expansion models (including the ReEDS model used by NREL) calculate the optimized capacity factor for each conventionally fueled plant. The default capacity factors listed in the ATB data spreadsheet are meant to be representative-not to reflect exactly what values were used in themodeling.
Base Year (2016) Costs in the ATB
Base year (2016) costs in the ATB are from the following sources:
| Technology | Source |
|---|---|
| Land-based wind power plants | Bottom-up modeling (Moné et al. (2017); Stehly et al. 2017), compared to wind market data reports, methodology updated from Wind Vision (DOE (2015)) |
| Offshore wind power plants | Bottom-up modeling (Beiter et al. (2016)), compared to wind market data reports |
| Utility, residential, and commercial PV plants | Market data reports (2016) supplemented with bottom-up cost modeling from Fu et al. (2015) for 2016 estimate |
| Concentrating solar power plants | Bottom-up cost modeling from Kurup and Turchi (2015), supplemented with industry input regarding projects under construction for operation in 2018 |
| Geothermal plants | Bottom-up cost modeling using GETEM |
| Hydropower plants | Hydropower Vision (DOE (2016)), bottom-up cost modeling from Hydropower Baseline Cost Modeling (O'Connor et al. (2015b)) |
| Fossil, nuclear, and biopower plants | Annual Energy Outlook (AEO 2018) reported costs |
Future Cost Projections for Renewables
The ATB relies heavily on future cost projections developed for previous studies. This framework provides comparisons of cost projections with published literature to illustrate potential differences in perspective. In general, ATB projections are within the bounds of perspectives represented in the literature.
In the ATB, projections are developed independently for each technology using different methods, but the initial starting point for each is compared with market data (where it is available) to provide a consistent baseline methodology. Common plant envelope definitions are based onEIA 2016aandcontribute to the consistent baseline.
Developing cost and performance projections for electricity generation technologies is very difficult. Methods that rely on engineering-based models are likely to provide insight into potential technology innovations that yield a lower cost of energy. Methods that rely on learning curves in combination with high-level macroeconomic assumptions are likely to provide insight into potential rates of adoption of technology innovations. Methods that include expert elicitation may result in associated probability levels for different future cost outcomes. All methods have strengths and weaknesses in serving the varied interests that seek these types of projections. Approaches take multiple methods into account may be able to leverage strengths and mitigate weaknesses. However, high levels of uncertainty are associated with each method. Providing a range of projections (e.g., Constant, Mid and Low technology cost scenarios) produces scenario modeling results that represent a range of possible outcomes.
The following table lists the method behind the ATB cost projections for each renewable energy technology.
| Technology | Methods | Source | ATB Mid | ATB Low | Notes |
|---|---|---|---|---|---|
| Wind (land and offshore) |
Expert elicitation, bottom-up modeling | Wiser et al. (2016); Dykes et al. (2017) | 50% probability scenario from expert elicitation | Bottom-up analysis of next generation wind R&D opportunities | Mid and Low reflects relative LCOE broken down component projections (e.g., CAPEX and capacity factor). |
| Solar PV (utility and distributed) | Literature survey (CAPEX), single pathway (O&M) | Internal NREL analysis (Feldman) | Based on median of literature sample | Based on lower bound of literature sample | Long term: forecasts published in last three years Short term: forecasts published in last six months |
| CSP (10 hours thermal storage) |
Single pathway, learning, literature survey | Internal NREL analysis (Kurup) and On the Path to SunShot | Based on median of literature sample | SunShot target achieved in 2035 | Low projection informed by bottom-up analysis combined with learning rates; Mid projection based on literature sample |
| Hydropower (NPD, NSD) |
Multiple pathway, expert input, learning | Hydropower Vision (DOE (2016)) | Hydropower Vision (DOE (2016)) Reference scenario | Hydropower Vision (DOE (2016)) Advanced Technology scenario | Projections informed by industry expertise, identifiable potential future technology and process advancements, EIA minimum learning |
| Geothermal | Minimum learning | EIA NEMS | -5% CAPEX by 2035 | -10% CAPEX by 2035 | Geothermal Vision Study |
The methods identified in the table above are defined as follows:
- Expert Elicitation: formal, structured information gathering associated with probability level for multiple scenarios
- Literature Survey: assessment of statistics (e.g., median) associated with sample of published literature
- Pathway Analysis: use of engineering models, often with expert input about specific assumptions, to explore single or multiple technology advance pathways associated with future outcomes
- Expert Input: information gathering to define and support assumptions for technology pathway analysis
- Learning: application of published learning rates and assumptions of future global or national capacity additions.
References
Beiter, Philipp, Walter Musial, Aaron Smith, Levi Kilcher, Rick Damiani, Michael Maness, Senu Sirnivas, Tyler Stehly, Vahan Gevorgian, Meghan Mooney, and George Scott. 2016. A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-66579. September 2016. http://www.nrel.gov/docs/fy16osti/66579.pdf.
DOE (U.S. Department of Energy). 2011. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. Perlack, R.D., and B.J. Stokes, eds. Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/TM-2011/224. August 2011. https://www.osti.gov/scitech/biblio/1023318.
DOE (U.S. Department of Energy). 2015. Wind Vision: A New Era for Wind Power in the United States. U.S. Department of Energy. DOE/GO-102015-4557. March 2015. http://energy.gov/sites/prod/files/2015/03/f20/wv_full_report.pdf.
DOE (U.S. Department of Energy). 2016. Hydropower Vision: A New Chapter for America's Renewable Electricity Source. Washington, D.C.: U.S. Department of Energy. DOE/GO-102016-4869. July 2016. https://energy.gov/sites/prod/files/2016/10/f33/Hydropower-Vision-10262016_0.pdf.
Dykes, K., M. Hand, T. Stehly, P. Veers, M. Robinson, E. Lantz. 2017. Enabling the SMART Wind Power Plant of the Future Through Science-Based Innovation (Technical Report), NREL/TP-5000-68123. National Renewable Energy Laboratory (NREL). Golden, CO (US). https://www.nrel.gov/docs/fy17osti/68123.pdf.
EIA (U.S. Energy Information Administration). 2016a. Capital Cost Estimates for Utility Scale Electricity Generating Plants. Washington, D.C.: U.S. Department of Energy. November 2016. https://www.eia.gov/analysis/studies/powerplants/capitalcost/pdf/capcost_assumption.pdf.
EIA (U.S. Energy Information Administration). 2018. Annual Energy Outlook 2018 with Projections to 2050. Washington, D.C.: U.S. Department of Energy. February 6, 2018. https://www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf.
Fu, Ran, Donald Chung, Travis Lowder, David Feldman, Kristen Ardani, and Robert Margolis. 2016. U.S. Photovoltaic (PV) Prices and Cost Breakdowns: Q1 2016 Benchmarks for Residential, Commercial, and Utility-Scale Systems. Golden, CO: National Renewable Energy Laboratory. NREL/PR-6A20-67142. http://www.nrel.gov/docs/fy16osti/67142.pdf.
Kurup, Parthiv, and Craig S. Turchi. 2015. Parabolic Trough Collector Cost Update for the System Advisor Model (SAM). Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-65228. November 2015. http://www.nrel.gov/docs/fy16osti/65228.pdf.
Moné, Christopher, Maureen Hand, Mark Bolinger, Joseph Rand, Donna Heimiller, and Jonathan Ho. 2017. 2015 Cost of Wind Energy Review. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-66861. http://www.nrel.gov/docs/fy17osti/66861.pdf.
O'Connor, Patrick W., Qin Fen (Katherine) Zhang, Scott T. DeNeale, Dol Raj Chalise, and Emma Centurion. 2015. Hydropower Baseline Cost Modeling. Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/TM-2015/14. January 2015. http://info.ornl.gov/sites/publications/files/Pub53978.pdf.
Stehly, Tyler, Donna Heimiller, and George Scott. 2017. 2016 Cost of Wind Energy Review. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-70363. https://www.nrel.gov/docs/fy18osti/70363.pdf.
Wiser, Ryan, Karen Jenni, Joachim Seel, Erin Baker, Maureen Hand, Eric Lantz, and Aaron Smith. 2016. Forecasting Wind Energy Costs and Cost Drivers: The Views of the World's Leading Experts. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-1005717. June 2016. https://emp.lbl.gov/publications/forecasting-wind-energy-costs-and.