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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

Land-Based Wind

Representative Technology

Most land-based wind plants in the United States range in capacity from 50 MW to 100 MW (Wiser and Bolinger (2017)). Wind turbines installed in the United States in 2016 were, on average, 2.2-MW turbines with rotor diameters of 108 m and hub heights of 84 m (Wiser and Bolinger (2017)).

Resource Potential

Wind resource is prevalent throughout the United States but is concentrated in the central states. Total land-based wind technical potential exceeds 10,000 GW (almost tenfold current total U.S. electricity generation capacity), which would use the wind resource on 3.5 million km2 of land area but would disrupt or exclude other uses from a fraction of that area. This technical potential does not include standard exclusions-lands such as federally protected areas, urban areas, and water. Resource potential has been expanded from approximately 6,000 GW (DOE (2015)) by including locations with lower wind speeds to provide more comprehensive coverage of U.S. land areas where future technology may improve economic potential).

Renewable energy technical potential, as defined by Lopez et al. 2012, represents the achievable energy generation of a particular technology given system performance, topographic limitations and environmental and land-use constraints. The primary benefit of assessing technical potential is that it establishes an upper-boundary estimate of development potential. It is important to understand that there are multiple types of potential-resource, technical, economic, and market (Lopez et al. 2012; NREL, "Renewable Energy Technical Potential ").

The resource potential is calculated by using over 130,000 distinct areas for wind plant deployment that cover over 3.5 million km2. The potential capacity is estimated to total over 10,000 GW if a power density of 3 MW/km2 is assumed.

map of land-based wind resource in the contiguous United States
Map of the land-based wind resource in the contiguous United States
Source: NREL (2012)

Base Year and Future Year Projections Overview

For each of the 130,000 distinct areas, an LCOE is estimated taking into consideration site-specific hourly wind profiles. Representative wind turbines derived from annual installation statistics are associated with a range of average annual wind speed based on actual historical wind plant installations This method is described in Moné et al. (2017) and summarized below.

  • Capital expenditures (CAPEX) associated with wind plants installed in the interior of the country are used to characterize CAPEX for hypothetical wind plants with average annual wind speeds that correspond with the median conditions for recently installed wind facilities. A range of CAPEX across the full range of observed wind speeds at each site-specific location is developed using engineering models and assumed differences in rotor diameter. Wind turbines at lower wind speed sites have larger rotors and, therefore, higher CAPEX.
  • Capacity factor is determined for each unique location using the site-specific hourly wind profile and a power curve that corresponds with the representative wind turbine defined to represent the annual average wind speed for each site.
  • Average annual operations and maintenance (O&M) costs are assumed to be equivalent at all geographic locations.
  • LCOE is calculated for each area based on the CAPEX and capacity factor estimated for each area.

For representation in the ATB, the full resource potential, reflecting the 130,000 individual areas, was divided into 10 techno-resource groups (TRGs). The capacity-weighted average CAPEX, O&M, and capacity factor for each group is presented in the ATB.

Three different projections were developed for scenario modeling as bounding levels:

  • Constant Technology Cost Scenario: no change in CAPEX, O&M, or capacity factor from 2016 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid Technology Cost Scenario: LCOE percentage reduction from the Base Year equivalent to that corresponding to the Median Scenario (50% probability) from Wiser et al. (2016), an international expert elicitation study
  • Low Technology Cost Scenario: LCOE percentage reduction from the Base Year is derived from a bottom-up analysis of specific wind advancements enabled by additional R&D activities (Dykes et al. (2017)).

More specifically, future year projections for the Mid cost scenario are derived from the estimated cost reduction potential for land-based wind technologies as calculated from an elicitation of over 160 wind industry experts (Wiser et al. (2016)). Their study produced three different cost reduction pathways, and the median estimates for LCOE reduction are used for ATB Mid cost scenario. Future year projections for the Low cost scenario are derived from the estimated cost reduction potential considering a collection of intelligent and novel technologies that comprise next-generation wind turbine and plant technology and characterized as System Management of Atmospheric Resource through Technology, or SMART strategies (Dykes et al. (2017)). In both scenarios, the overall LCOE reductions resulting from these analyses were used as the basis for the ATB projections. Accordingly, all three cost elements - CAPEX, O&M, and capacity factor-should be considered together; individual cost element projections are derived.

References

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.

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.

Lopez, Anthony, Billy Roberts, Donna Heimiller, Nate Blair, and Gian Porro. 2012. U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis. National Renewable Energy Laboratory. NREL/TP-6A20-51946. http://www.nrel.gov/docs/fy12osti/51946.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.

NREL (National Renewable Energy Laboratory). 2012. Renewable Electricity Futures Study. Hand, M.M., S. Baldwin, E. DeMeo, J.M. Reilly, T. Mai, D. Arent, G. Porro, M. Meshek, and D. Sandor, eds. 4 vols. NREL/TP-6A20-52409. Golden, CO: National Renewable Energy Laboratory. http://www.nrel.gov/analysis/re_futures/.

Wiser, Ryan, and Mark Bolinger. 2017. 2016 Wind Technologies Market Report. https://www.energy.gov/sites/prod/files/2017/10/f37/2016_Wind_Technologies_Market_Report_101317.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.