Content displaying: Projections

Annual Technology Baseline 2018

National Renewable Energy Laboratory


Recommended Citation:
NREL (National Renewable Energy Laboratory). 2018. 2018 Annual Technology Baseline. Golden, CO: National Renewable Energy Laboratory. http://atb.nrel.gov/.


Please consult Guidelines for Using ATB Data:
https://atb.nrel.gov/electricity/user-guidance.html

Utility-Scale PV

Plant Cost and Performance Projections Methodology

Currently, CAPEX-not LCOE-is the most common metric for PV cost. Due to differing assumptions in long-term incentives, system location and production characteristics, and cost of capital, LCOE can be confusing and often incomparable between differing estimates. While CAPEX also has many assumptions and interpretations, it involves fewer variables to manage. Therefore, PV projections in the ATB are driven primarily by CAPEX cost improvements, along with minor improvements in operational cost and cost of capital.

The Constant, Mid, and Low technology cost cases explore the range of possible outcomes of future PV cost improvements:

  • Constant: no improvements made beyond today
  • Mid: current expectations of price reductions in a " business-as-usual" scenario
  • Low: expectations of potential cost reductions given improved R&D funding, favorable financing, and more aggressive global deployment targets.

While CAPEX is one of the drivers to lower costs, R&D efforts continue to focus on other areas to lower the cost of energy from utility-scale PV, such as longer system lifetime and improved performance.

Projections of future utility-scale PV plant CAPEX are based on 15 system price projections from 9 separate institutions. Projections include short-term U.S. price forecasts (BNEF (2017a); GTM Research (2016); EIA 2017; IEA (2016); IHS (2017)) made in the past year and long-term global and U.S. price forecasts (ABB (2017); BNEF (2017b); Carlsson et al. (2014); Fraunhofer ISE (2015); Teske et al. (2015)) made in the past four years. The short-term forecasts were primarily provided by market analysis firms with expertise in the PV industry, through a subscription service with NREL. The long-term forecasts primarily represent the collection of publicly available, unique forecasts with either a long-term perspective of solar trends or through capacity expansion models with assumed learning by doing.

To adjust all projections to the ATB's assumption of single-axis tracking systems, $0.08/WDC was added to all price projections that assumed fix-tilt tracking technology, and $0.04/WDC was added for all price projections that did not list whether the technology was fixed-tilt or single-axis tracking. All price projections quoted in $/WAC were converted to $/WDC using a 1.3 ILR. In addition, because the projections 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. In instances in which literature projections did not include all years, a straight-line change in price was assumed between any two projected values. To generate Constant, Mid, and Low scenarios, we took the " min," " median," and " max" of the data sets; however, we only included short-term U.S. forecasts until 2030 as they focus on near-term pricing trends within the industry. Starting in 2030, we include long-term global and U.S. forecasts in the data set, as they focus more on long-term trends within the industry. It is also assumed that after 2025, U.S. prices will be on par with global averages; the federal tax credit for solar assets reverts down to 10% for all projects placed in service after 2023, which has the potential to lower upfront financing costs and remove any distortions in reported pricing, compared to other global markets. Additionally, a larger portion of the United States will have a more mature PV market, which should result in a narrower price range. Changes in price for the Constant, Mid and Low scenarios between 2020 and 2030 are interpolated on a straight-line basis.

We adjusted the " min," " median," and " max" projections in a few different ways. All 2015 pricing is based on the capacity-weighted average reported utility-scale system price as reported in Utility-Scale Solar 2016 (Bolinger et al. (2017)) and adjusted by the ReEDS state-level capital cost multipliers to remove geographic price distortions from 2016 reported pricing. All 2017 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2017 (adjusted for inflation and accounting for $0.1/W higher than expected module prices due to tariff concerns in the R&D + Market sensitivity case) (Fu et al. (2017)). These figures are in line with other estimated system prices reported in Feldman et al. (2017).

We adjusted the Mid and Low projections for 2018-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. In addition, because the projections 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. The Constant technology cost scenario is kept constant at the 2017 CAPEX value, assuming no improvements beyond2017.

All prices quoted in euros are converted to USD (1 € = $1.25).
All prices quoted in WAC are converted to WDC (1 WAC=1.2 WDC).

We derive future FOM based on the same 0.8% ratio of O&M to CAPEX that we used to estimate Base Year O&M costs. Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2011 to 2016 fleetwide average O&M and CAPEX costs fell 43% and 33% respectively, as reported in Bolinger et al. (2017).

O&M cost reductions are likely to be achieved over the next decade by a transition from manual and reactive O&M to semi-automated and fully automated O&M where possible. While many of these tasks are very site and region specific, emerging technologies have the potential to reduce the total O&M costs across all systems. For example, automated processes of sensors, monitors, remote-controlled resets, and drones to perform operations have the potential to allow O&M on PV systems to operate more efficiently at lower cost. Not all tasks have a clear path of automation due to complexity, safety, and some policy. This is one reason some level of manual interventions will likely exist for quite some time. Also, as systems age, O&M tasks that rely strictly on manpower are likely to increase in cost over the system lifetime.

Projections of capacity factors for plants installed in future years are unchanged from the Base Year for the Constant cost scenario. Capacity factors for Mid and Low cost scenarios are projected to increase over time, caused by a straight-line reduction in PV plant capacity degradation rates, reaching 0.5%/year and 0.3%/year by 2050 for the Mid and Low cost scenarios respectively.

References

Annual Energy Outlook 2017 with Projections to 2050. Washington, D.C.: U.S. Department of Energy. January 5, 2017. http://www.eia.gov/outlooks/aeo/pdf/0383(2017).pdf.

H2 2017 US PV Market Outlook. December 13, 2017. New York: BNEF.

PV Market Outlook, Q4 2017. November 17, 2017. New York: BNEF.

ABB. 2017. Spring 2017 Power Reference Case: Preview of key changes & potential impacts. ABB Enterprise Software. April 5, 2017.

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.

Carlsson, J., M. del Mar Perez Fortes, G. de Marco, J. Giuntoli, M. Jakubcionis, A. Jäger-Waldau, R. Lacal-Arantegui, S. Lazarou, D. Magagna, C. Moles, B. Sigfusson, A. Spisto, M. Vallei, and E. Weidner. 2014. ETRI 2014: Energy Technology Reference Indicator, Projections for 2010-2050. European Commission: JRC Science and Policy Reports. http://publications.jrc.ec.europa.eu/repository/bitstream/JRC92496/ldna26950enn.pdf.

Feldman, David, Jack Hoskins, and Robert Margolis. 2017. Q2/Q3 2017 Solar Industry Update. U.S. Department of Energy. NREL/PR-6A42-70406. November 13, 2017. https://www.nrel.gov/docs/fy18osti/70406.pdf.

Fraunhofer ISE. 2015. Current and Future Cost of Photovoltaics: Long-term Scenarios for Market Development, System Prices and LCOE of Utility-Scale PV Systems. Prepared for Agora Energiewende. Freiburg, Germany: Fraunhofer-Institute for Solar Energy Systems (ISE). 059/01-S-2015/EN. February 2015. https://www.agora-energiewende.de/fileadmin/Projekte/2014/Kosten-Photovoltaik-2050/AgoraEnergiewende_Current_and_Future_Cost_of_PV_Feb2015_web.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.

GTM Research. 2016. U.S. PV System Pricing H1 2017: System Pricing, Breakdowns and Forecasts. Boston, MA: GTM Research. June 2017.

IEA (International Energy Agency). 2016. World Energy Outlook 2016. Paris: International Energy Agency. December 2016.

IHS. 2017. PV Demand Market Tracker. Q4 2017. IHS. December 8, 2017. https://technology.ihs.com/572649/pv-demand-market-tracker-q4-2017.

Teske, Sven, Steve Sawyer, and Oliver Schäfer, Thomas Pregger, Sonja Simon, and Tobias Naegler. 2015. Energy [r]evolution: A Sustainable World Energy Outlook 2015. Global Wind Energy Council, Solar Power Europe & Greenpeace. September 2015.