2019: 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 2017, 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 Technology Cost Scenario: 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 AEO2019 (EIA, 2019)
- Relies on EIA scenarios for fuel price projections through 2050 AEO2019 (EIA, 2019); future work may include national laboratory projections for these technologies.
For biopower plants, the ATB:
- Relies on EIA representation of current future plant cost estimates through 2050 from AEO2019 (EIA, 2019)
- Represents the average biopower feedstock price based on the Billion Ton Update study (DOE et al., 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 the modeling.
Base Year (2017) Costs in the ATB
Base year (2017) costs in the ATB are from the following sources:
| Technology | Source |
| Land-based wind power plants | Bottom-up modeling (Stehly, Beiter, Heimiller, & Scott, 2018), compared to wind market data reports (Wiser & Bolinger, 2018), methodology updated from Wind Vision (DOE & NREL, 2015) |
| Offshore wind power plants | Bottom-up modeling (Beiter et al., 2016), methodology and data updated to the latest cost and technology trends observed in the U.S. and European offshore wind markets ((Beiter, Spitsen, Musial, & Lantz, 2019), (Musial, Beiter, Spitsen, & Nunemaker, forthcoming)) |
| Utility, residential, and commercial PV plants | Market data reports (Bolinger & Seel, 2018) supplemented with bottom-up cost modeling (Fu, Feldman, & Margolis, 2018) |
| Concentrating solar power plants | Bottom-up cost modeling from Turchi et al. (2019) and an NREL survey of projects under construction for operation in 2018 |
| Geothermal plants | Bottom-up cost modeling using GETEM and inputs from the GeoVision BAU scenario (DOE, 2019) |
| Hydropower plants | Hydropower Vision (DOE, 2016), bottom-up cost modeling from Hydropower Baseline Cost Modeling (O'Connor, DeNeale, Chalise, Centurion, & Maloof, 2015) |
| Fossil, nuclear, and biopower plants | Annual Energy Outlook (EIA, 2019) 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 on (EIA, 2016) and contribute 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 that 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 |
| Land-Based Wind | Bottom-up analysis learning | (Stehly et al. forthcoming) (Dykes et al., 2017) | Bottom-up analysis of median wind R&D opportunities | Bottom-up analysis of next generation wind R&D opportunities | Mid and Low reflect relative LCOE decomposed to CAPEX, CF, O&M; learning rates (Wiser, Jenni, et al., 2016) |
| Offshore Wind | Expert elicitation | Valpy et al. (2017), Hundleby et al. (2017) | Reduction from base year from expert elicitation 50% probability | Reduction relative to Mid case informed by bottom-up and cost modeling | Low scenario has twice the cost reduction rates of Mid scenario |
| 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) | Pathway analysis, learning, literature survey | NREL analysis (Kurup) and On the Path to SunShot | Based on median of literature sample and expert assessment | Based on lower bound of literature sample, and Power to Change Report (IRENA, 2016) | Low projection informed by pathway analysis combined with learning rates Mid projection based on literature sample and expert assessment |
| 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 | Pathway Analysis Minimum learning | (DOE, 2019); AEO2015 (EIA, 2015) | GeoVision Business-as-Usual (BAU) scenario plus-5% CAPEX by 2035 | GeoVision Technology Improvement (TI) scenario | GeoVision study contains details of BAU and TI scenario assumptions |
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
The following references are specific to this page; for all references in this ATB, see References.Beiter, P., Musial, W., Smith, A., Kilcher, L., Damiani, R., Maness, M., … Scott, G. (2016). A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030 (Technical Report No. NREL/TP-6A20-66579). https://doi.org/10.2172/1324526
Beiter, P., Spitsen, P., Musial, W., & Lantz, E. (2019). The Vineyard Wind Power Purchase Agreement: Insights for Estimating Costs of U.S. Offshore Wind Projects (Technical Report No. NREL/TP-5000-72981). Retrieved from National Renewable Energy Laboratory website: https://www.nrel.gov/docs/fy19osti/72981.pdf
Bolinger, M., & Seel, J. (2018). Utility-Scale Solar: An Empirical Trends in Project Technology, Cost, Performance, and PPA Pricing in the United States (2018 Edition). Retrieved from Lawrence Berkeley National Laboratory website: https://emp.lbl.gov/sites/default/files/lbnl_utility_scale_solar_2018_edition_report.pdf
DOE, & NREL. (2015). Wind Vision: A New Era for Wind Power in the United States (Technical Report No. DOE/GO-102015-4557). Retrieved from U.S. Department of Energy website: https://www.nrel.gov/docs/fy15osti/63197-2.pdf
DOE. (2011). U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry (No. ORNL/TM-2011/224). https://doi.org/10.2172/1023318
DOE. (2016). Hydropower Vision: A New Chapter for America's Renewable Electricity Source (No. DOE/GO-102016-4869). Retrieved from U.S. Department of Energy website: https://www.energy.gov/sites/prod/files/2018/02/f49/Hydropower-Vision-021518.pdf
DOE. (2019). GeoVision: Harnessing the Heat Beneath Our Feet (No. DOE/EE-1306). Retrieved from U.S. Department of Energy website: https://www.energy.gov/eere/geothermal/geovision
Dykes, K., Hand, M., Stehly, T., Veers, P., Robinson, M., Lantz, E., & Tusing, R. (2017). Enabling the SMART Wind Power Plant of the Future Through Science-Based Innovation (No. NREL/TP-5000-68123). https://doi.org/10.2172/1378902
EIA. (2015). Annual Energy Outlook 2015 with Projections to 2040 (No. AEO2015). Retrieved from U.S. Energy Information Administration website: https://www.eia.gov/outlooks/aeo/pdf/0383(2015).pdf
EIA. (2016b). Capital Cost Estimates for Utility Scale Electricity Generating Plants. Retrieved from U.S. Energy Information Administration website: https://www.eia.gov/analysis/studies/powerplants/capitalcost/pdf/capcost_assumption.pdf
EIA. (2019a). Annual Energy Outlook 2019 with Projections to 2050. Retrieved from U.S. Energy Information Administration website: https://www.eia.gov/outlooks/aeo/pdf/AEO2019.pdf
Fu, R., Feldman, D., & Margolis, R. (2018). U.S. Solar Photovoltaic System Cost Benchmark: Q1 2018. https://doi.org/10.2172/1484344
Hundleby, G., Freeman, K., Logan, A., & Frost, C. (2017). Floating Offshore: 55 Technology Innovations that will have greater impact on reducing the cost of electricity from European floating offshore wind farms. Retrieved from KiC InnoEnergy and BVG Associates website.
IRENA. (2016b). The Power to Change: Solar and Wind Cost Reduction Potential to 2025. Retrieved from International Renewable Energy Agency website: https://www.irena.org/DocumentDownloads/Publications/IRENA_Power_to_Change_2016.pdf
Musial, Walter, Beiter, P., Spitsen, P., & Nunemaker, J. (2018). 2018 Offshore Wind Technologies Market Report. https://www.energy.gov/sites/prod/files/2019/08/f65/2018%20Offshore%20Wind%20Market%20Report.pdf
O'Connor, P. W., DeNeale, S. T., Chalise, D. R., Centurion, E., & Maloof, A. (2015). Hydropower Baseline Cost Modeling, Version 2 (No. ORNL/TM-2015/471). https://doi.org/10.2172/1244193
Stehly, T., Beiter, P., Heimiller, D., & Scott, G. (2018). 2017 Cost of Wind Energy Review (Technical Report No. NREL/TP-6A20-72167). Retrieved from National Renewable Energy Laboratory website: www.nrel.gov/docs/fy18osti/72167.pdf
Turchi, Craig, Boyd, M., Kesseli, D., Kurup, P., Mehos, M., Neises, T., … Wendelin, T. (2019). CSP Systems Analysis - Final Project Report (No. NREL/TP-5500-72856). Retrieved from National Renewable Energy Laboratory website: https://www.nrel.gov/docs/fy19osti/72856.pdf
Valpy, B., Hundleby, G., Freeman, K., Roberts, A., & Logan, A. (2017). Future renewable energy costs: Offshore wind. Retrieved from KiC InnoEnergy and BVG Associates website: http://www.innoenergy.com/wp-content/uploads/2014/09/InnoEnergy-Offshore-Wind-anticipated-innovations-impact-2017_A4.pdf
Wiser, R., & Bolinger, M. (2018). 2017 Wind Technologies Market Report (No. DOE/EE-1798). https://doi.org/10.2172/1471044
Wiser, R., Jenni, K., Seel, J., Baker, E., Hand, M., Lantz, E., & Smith, A. (2016). Forecasting Wind Energy Costs and Cost Drivers: The Views of the World's Leading Experts (No. LBNL-1005717; p. 87 pp.). Retrieved from Lawrence Berkeley National Laboratory website: https://emp.lbl.gov/publications/forecasting-wind-energy-costs-and