Changes from 2017 ATB to 2018 ATB

The ATB provides a transparent set of technology cost and performance data for electric sector analysis. The update of the 2017 version of ATB to this 2018 version includes:

Updated Financial Assumptions

Several new features related to financial assumptions are included in the 2018 ATB:

  • Cost recovery period: The period over which initial capital investment is recovered-a cost recovery period-of 30 years is assumed for all technologies throughout this website, and it can be varied in the ATB data spreadsheet. This change from a default cost recovery period of 20 years in the 2017 version reduces the levelized cost of energy, as shown in the sensitivity to cost recovery period. The implication of this assumption is that residual value beyond 30 years is not included.
  • Financial sensitivity cases
    • R&D Only Financial Assumptions: The development of this financial sensitivity case reflects that we are trying to differentiate risk across technologies through differentiated financing terms. Although we recognize that in practice these risks may be reflected in CAPEX, in soft costs, or elsewhere (e.g., through a contingency fund), we decided that reflecting risk in the financing terms is clearest and most consistent. The ATB assumptions are reflective of technology risk within the U.S. market; an individual technology may receive more favorable financing terms outside the United States, due to less technology and project risk as a result of more project development experience (e.g., offshore wind in Europe), or more government or market guarantees. This sensitivity case allows technology-specific changes to debt interest rates, return on equity rates, and debt fraction to reflect effects of R&D on technological risk perception, but it holds background rates constant at 2016 values from AEO 2018 and excludes effects of tax reform, tax credits, and technology specific tariffs.
    • R&D Only + Market Financial Assumptions: This sensitivity case retains the technology-specific changes to debt interest, return on equity rates, and debt fraction from the R&D Only case and adds in the variation over time consistent with AEO 2018, as well as effects of tax reform, tax credits, and tariffs.
    • ReEDS Financial Assumptions: ReEDS uses the R&D Only + Market Financial Assumptions for the "Mid" technology cost scenario.

    A constant cost recovery period-over which the initial capital investment is recovered-of 30 years is assumed for all technologies throughout this website, and it can be varied in the ATB data spreadsheet.

The 2018 ATB is the first edition of the ATB to include technology-specific financial assumptions to capture more granularity of current and future energy markets, as well as to show cost reduction potential through improvements in financing rates attributable to technological risk reduction from R&D. Multiple financing options are currently available in the marketplace, with varying associated costs; these options will likely grow as technologies become more mature. Additionally, many macroeconomic factors may influence financing rates in the future, independent of the method by which a project is financed or the underlying changes in characteristics of a technology; these factors include the underlying inflation rate as well as the cost of the Federal Reserve interest rate. For these reasons, we assume the same inflation rate, tax rate (changing in 2018 to reflect the new federal corporate tax rate in the R&D + Market case), and in many instances, interest rate across technologies.

To create a consistent financial landscape across technologies, we have also binned technologies by their general maturity within the U.S. marketplace, as well as any unique characteristics that they may have in raising funds. We have given some technologies the same financing assumptions despite certain differences in marketplace today that are caused by resource variability, capacity factor, credit quality and ease of funding, as well as market size. However, given the variety of reported financing costs currently available in the marketplace, we feel it is justified, as their reported financing costs overlap quite a bit.

For the reasons described above, land-based wind and distributed and utility PV are viewed as relatively mature technologies and have the same return on equity and debt interest as the financing costs for other mature technologies as outlined in AEO 2018. We still view the possibility for improved financing terms for wind and PV technologies; to capture this, we increase the assumed debt fraction from 60% in 2016 (under the " R&D Only Financial Assumptions") to 65% and 70% in 2030 in the Mid and Low cost scenarios, which is when we assume these technologies will be viewed as fully mature. (The ATB assumes that the debt fraction is reduced to 40% in years in which technologies receive tax credits due to tax credits' impact on financial transactions.)

These debt fractions are consistent with land-based wind and PV values calculated in Bolinger (2014) and Feldman and Bolinger (2016). We assume higher financing rates for concentrating solar power (CSP), offshore wind, and geothermal generation plants. U.S. CSP currently has lower levels of deployment, historically has relied on governmental involvement with financing, and recently has had project start-up challenges.

While offshore wind deployment is mature in Europe, only one plant has been installed in the United States to date. Several projects are currently under development; however, because of previous project cancellations and delays, there is still more project risk in offshore wind than in land-based wind.

Geothermal plants incur higher financing costs that are due to exploration and well field development risk in early project stages. For these reasons, we assume that in 2016 CSP, offshore wind, and geothermal projects have equity rate premiums relative to PV and wind, of 4%, 3%. and 10% above AEO 2018 values respectively, as well as a lower debt fraction of 50% (under the " R&D Only Financial Assumptions"). These values are similar to equity premiums and leverage as calculated by DOE technology program analysts. However, in the Low and Mid costs scenarios for CSP and offshore wind, and in the Low cost scenario for geothermal plants, these technologies become financially mature by 2030 and have financing costs comparable to land-based wind and PV generating assets.

We assume hydropower plants are financed through 100% bond financing, similarly to how they have been financed in the past. For consistency, the interest rate of these bonds is the same as the interest rates for other mature technologies.

Financing assumptions for natural gas, nuclear, and biopower plants are all based on the assumptions from AEO 2018; the weighted-average cost of capital (WACC) for coal plants is three percentage points higher.

The impact of these project finance assumptions is explored here.

The following sections list updates to the 2017 ATB in the 2018 ATB.

General Updates to All Technologies

  • The Base Year was updated from 2015 to 2016 using new market data or analysis where applicable.
  • The dollar year was updated from 2015 to 2016 with 1.3% inflation.
  • Historical data were updated to include data reported through year end 2016.

Natural Gas, Coal, Nuclear, and Biopower

  • Cost and performance estimates were updated to match AEO 2018.
  • Natural gas and coal fuel costs were updated to match AEO 2018.
  • Information about current costs in published literature was updated.

Land-Based Wind

  • Base Year: The base year was updated to reflect 2016 wind plant cost and performance characteristics (Moné et al. (2017); Stehly et al. 2017).
  • Projections: The low cost case was updated to reflect recent bottom-up analysis regarding the potential of R&D to enable significant cost reductions. More specifically, a study conducted by NREL (Dykes et al. (2017)) assessed a variety of intelligent and novel technologies that comprise of next-generation wind plant technologies in order to estimate the LCOE of a SMART wind plant in 2030. Considering the technology advantages of the SMART wind plant, the potential for LCOE reduction is estimated to be approximately 50% by 2050 and 60% by 2050 as compared to the 2016 ATB Low case, which targeted LCOE reductions of approximately 44% by 2030 and 53% by 2050.

Offshore Wind

  • Base Year: The base case represents the 2017 value from the 2016 ATB, adjusted for inflation (i.e., from $2015 to $2016).
  • Projections: The percentage reduction in LCOE was derived from an expert study (Wiser et al. (2016)) to reflect 50% and 10% probability levels. CAPEX, O&M, and CF were assigned to each TRG based on Wiser et al. (2016) and technology pathway assumptions across the wind resource. The 2016 ATB implemented a literature survey such that ATB Mid equaled the median of the literature and ATB Low equaled the low bound of the literature.

PV (Utility-scale, Commercial, Residential)

  • Resources Representation: The number of resource locations represented was expanded from three to five to increase resolution. ILR was updated from 1.1 to 1.3 to reflect marketplace trends.
  • Base Year: CAPEX for 2016 were updated based on new data from Barbose and Dargouth (2017) and Bolinger et al. (2017); CAPEX from 2017 were updated based on Fu et al. (2017). Capacity factors for the five resource areas in the United States were calculated using the SAM model with PV system characteristics from Fu et al. (2017). FOM is based on the average ratio of O&M costs to CAPEX costs, as reported in Lazard (2017).
  • Projections: The literature survey was updated with seven new publications, and six outdated publications were removed. Because the projections of CAPEX were made before the Section 201 proclamation implementing a tariff on imported PV modules and cells, we adjusted projections to incorporate Section 201 tariff per pricing from internal NREL analysis in the R&D + Market sensitivity case. FOM was calculated using the current average ratio of O&M costs to CAPEX costs, as reported in Lazard (2017), with projected CAPEX values. Future capacity factor projections for Mid and Low cost scenarios reduce degradation rates by 2050, using a straight-line basis, from 0.75% to 0.5%/year and 0.3%/year respectively.

Concentrating Solar Power (CSP)

  • Base Year: A new survey of component cost establishes CAPEX for plants that will become operational in 2018 (which become the basis for Constant technology cost scenario).
  • Projections: A Mid case based on median of literature projections to 2030 was implemented in terms of major component improvements (i.e., solar field, turbine, and storage); A Low case was implemented based on component pathway analysis for On the Path to SunShot and learning rates for post-2025 cost reductions, similarly to the 2016 ATB; O&M projections were based on SunShot (Low) and fraction of SunShot (Mid), similarly to the 2016 ATB.

Geothermal

  • Base Year: updated to $2016 based on Consumer Price Index
  • Projections: Projection of future geothermal plant CAPEX for the Low cost case is based on the Improved Technology (IT) scenario from the Geothermal Vision Study. The Mid cost case is based on minimum learning rates as implemented in AEO (EIA (2015)): 10% by 2035. The Constant technology cost scenario retains all cost and performance assumptions equivalent to the Base Year through 2050.

Hydropower

  • Additional charts from HydroVision are included.

Storage

  • A simplistic representation of the CAPEX scenarios used in ReEDS modeling for lithium-ion battery storage is included. A more detailed and comprehensive update to include a broader array of storage technologies is planned for a future ATB.

References

Lazard's Levelized Cost of Energy Analysis: Version 11.0. November 2017. New York: Lazard. https://www.lazard.com/perspective/levelized-cost-of-energy-2017.

Barbose, Galen, and Naïm Dargouth. 2017. Tracking the Sun X: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-2001062. September 2017. http://eta-publications.lbl.gov/sites/default/files/tracking_the_sun_10_report.pdf.

Bolinger, Mark, Joachim Seel, and Kristina Hamachi LaCommare. 2017. Utility-Scale Solar 2016: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL- 2001055. September 2017. http://eta-publications.lbl.gov/sites/default/files/utility-scale_solar_2016_report.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). 2015. Annual Energy Outlook with Projections to 2040. Washington, D.C.: U.S. Department of Energy. DOE/EIA-0383(2015). April 2015. http://www.eia.gov/outlooks/aeo/pdf/0383(2015).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, David Feldman, Robert Margolis, Mike Woodhouse, and Kristen Ardani. 2017. U.S. Solar Photovoltaic System Cost Benchmark: Q1 2017. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-68925. https://www.nrel.gov/docs/fy17osti/68925.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.

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.