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
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)).
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.
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.
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:
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.
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. These expenditures include the wind turbine, the balance of system (e.g., site preparation, installation, and electrical infrastructure), and financial costs (e.g., development costs, onsite electrical equipment, and interest during construction) and are detailed in CAPEX Definition. In the ATB, CAPEX reflects typical plants and does not include differences in regional costs associated with labor, materials, taxes, or system requirements. The related Standard Scenarios product uses regional CAPEX adjustments. The range of CAPEX demonstrates variation with wind resource in the contiguous United States.
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
For the ATB-and based on EIA (2016a) and the System Cost Breakdown Structure defined by Moné et al. 2015 - the wind plant envelope is defined to include:
CAPEX can be determined for a plant in a specific geographic location as follows:
Regional cost variations and geographically specific grid connection costs are not included in the ATB (CapRegMult = 1; GCC = 0). In the ATB, the input value is overnight capital cost (OCC) and details to calculate interest during construction (ConFinFactor).
In the ATB, CAPEX represents a typical land-based wind plant and varies with annual average wind speed. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by IEA 2016a, DOE 2015 expand the range of CAPEX. Unique land-based spur line costs for each of the 130,000 areas based on distance and transmission line costs expand the range of CAPEX even further. The figure below illustrates the ATB representative plants relative to the range of CAPEX including regional costs across the contiguous United States. Note that the ATB Base Year estimate for TRG 4 is equivalent to the market data observed capacity-weighted average wind plant CAPEX in the same year. The ATB representative plants are associated with a regional multiplier of 1.0.
ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for Constant, Mid, and Low technology cost scenarios are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.
CAPEX in the ATB does not represent regional variants (CapRegMult) associated with labor rates, material costs, etc., but the ReEDS model does include 134 regional multipliers (EIA 2016a).
The ReEDS model determines the land-based spur line (GCC) uniquely for each of the 130,000 areas based on distance and transmission line cost.
Operations and maintenance (O&M) costs depend on capacity and represent the annual fixed expenditures required to operate and maintain a wind plant, including:
The following figure shows the Base Year estimate and future year projections for fixed O&M (FOM) costs. Three cost scenarios are represented. The estimate for a given year represents annual average FOM costs expected over the technical lifetime of a new plant that reaches commercial operation in that year.
Due to limited available robust market data, an assumption of FOM of $51/kW-yr was determined to be representative of the range of available data; no variation of FOM with TRG (or wind speed) was assumed (DOE (2015)). The following chart shows sample historical data for reference.
Future FOM is assumed to decline by approximately 25% by 2050 in Mid cost case and 35% in Low cost windcases. These values are the result of linear curves fit to the results of the expert survey documented in Wiser et al. (2016).
A detailed description of the methodology for developing future year projections is found in Projections Methodology.
Technology innovations that could impact future O&M costs are summarized in LCOE Projections.
The capacity factor represents the expected annual average energy production divided by the annual energy production, assuming the plant operates at rated capacity for every hour of the year. It is intended to represent a long-term average over the lifetime of the plant. It does not represent interannual variation in energy production. Future year estimates represent the estimated annual average capacity factor over the technical lifetime of a new plant installed in a given year.
The capacity factor is influenced by hourly windprofile, expected downtime, and energy losses within the wind plant. The specific power (ratio of machine rating to rotor swept area) and hub height are design choices that influence the capacity factor.
The following figure shows a range of capacity factors based on variation in the resource for wind plants in the contiguous United States. Historical data from wind plants operating in the United States in 2015, according to the year in which plants were installed, is shown for comparison to the ATB Base Year estimates. The range of Base Year estimates illustrate the effect of locating a wind plant in sites with high wind speeds (TRG 1) or low wind speeds (TRG 10). Future projections are shown for Constant, Mid, and Low technology cost scenarios.
Actual energy production from about 90% of wind plants operating in the United States since 2007 () is shown in box-and-whiskers format for comparison with the ATB current estimates and future projections. The historical data illustrate capacity factor for projects operating in 2016, shown by year of commercial online date. As reported in the 2016 DOE Wind Technologies Market Report (Wiser and Bolinger (2017)), NextEra Energy Resources, in their quarterly earnings reports, estimates that the "wind resource index" for the United States as a whole was 99% in 2016. The generation-weighted average 2016 capacity factors are also shown adjusted upward for a typical wind resource year by 1/0.99.
For illustration in the ATB, all potential land-based wind plant areas were represented in 10 TRGs. The capacity-weighted average CAPEX, capacity factor, and resource potential are shown in the table below.
The majority of installed U.S. wind plants generally align with ATB estimates for performance in TRGs 5-7. High wind resource sites associated with TRGs 1 and 2 as wellas very low wind resource sites associated with TRGs 8-10 are not as common in thehistorical data, but the range of observed data encompasses ATB estimates.
The capacity factor is referenced to an 80-m, above-ground-level, long-term average hourly wind resource data fromAWS Truepower (2012).
Projections for capacity factors implicitly reflect technology innovations such as larger rotors and taller towers that will increase energy capture at the same location without specifying precise tower height or rotor diameter changes. Improvements in plant performance through lower losses and increased availability are also included implicitly.
A detailed description of the methodology for developing future year projections is found in Projections Methodology.
Technology innovations that could impact future O&M costs are summarized in LCOE Projections.
ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for Constant, Mid, and Low technology cost scenarios are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.
The ReEDS model output capacity factors for wind and solar PV can be lower than input capacity factors due to endogenously estimated curtailments determined by scenario constraints.
ATB projections were derived from two different sources for the Mid and Low cases.
A broad sample of cost of wind energy projections is shown to provide context for the ATB Constant, Mid, and Low technology cost projections. The ATB Mid cost projection, which corresponds to the Median scenario from the expert survey, results in LCOE reductions that are slightly lower than other median scenarios in the literature (ARUP (2011); BNEF (2015); E3 (2014); EIA (2014); EPA (2015); GWEC (2014); IEA (2015c); IRENA (2016a); Teske et al. (2015)). The ATB Low cost projection, which corresponds to the NREL bottom-up cost analysis, is similar to the lower bound of the sample of literature projections (BNEF (2016); IEA (2015c); MAKE (2015)).
Levelized cost of energy (LCOE) is a simple metric that combines the primary technology cost and performance parameters: CAPEX, O&M, and capacity factor. It is included in the ATB for illustrative purposes. The ATB focuses on defining the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. The LCOE accounts for the energy component of electric system planning and operation. The LCOE uses an annual average capacity factor when spreading costs over the anticipated energy generation. This annual capacity factor ignores specific operating behavior such as ramping, start-up, and shutdown that could be relevant for more detailed evaluations of generator cost and value. Electricity generation technologies have different capabilities to provide such services. For example, wind and PV are primarily energy service providers, while the other electricity generation technologies provide capacity and flexibility services in addition to energy. These capacity and flexibility services are difficult to value and depend strongly on the system in which a new generation plant is introduced. These services are represented in electric sector models such as the ReEDS model and corresponding analysis results such as the Standard Scenarios.
The following three figures illustrate LCOE, which includes the combined impact of CAPEX, O&M, and capacity factor projections for land-based wind across the range of resources present in the contiguous United States. For the purposes of the ATB, the costs associated with technology and project risk in the U.S. market are represented in the financing costs, not in the upfront capital costs (e.g. developer fees, contingencies). An individual technology may receive more favorable financing terms outside of the U.S., due to less technology and project risk, caused by more project development experience (e.g. offshore wind in Europe), or more government or market guarantees. The R&D Only LCOE sensitivity cases present the range of LCOE based on financial conditions that are held constant over time unless R&D affects them, and they reflect different levels of technology risk. This case excludes effects of tax reform, tax credits, technology-specific tariffs, and changing interest rates over time. The R&D + Market LCOE case adds to these the financial assumptions (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform, tax credits, and tariffs. The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term land-based wind plants are associated with TRG 4. Data for all the resource categories can be found in the ATB data spreadsheet.
The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. In general, the degree of adoption of technology innovation distinguishes the Constant, Mid, and Low technology cost scenarios. These projections represent trends that reduce CAPEX and improve performance. Development of these scenarios involves technology-specific application of the following general definitions:
To estimate LCOE, assumptions about the cost of capital to finance electricity generation projects are required, and the LCOE calculations are sensitive to these financial assumptions. Three project finance structures are used within the ATB:
A constant cost recovery period -over which the initial capital investment is recovered-is assumed for all technologies throughout this website, and can be varied in the ATB data spreadsheet.
In general, differences among the technology cost cases reflect different levels of adoption of innovations. Reductions in technology costs reflect the cost reduction opportunities that are listed below.
Energy Technology Perspectives 2015.
The Power to Change: Solar and Wind Cost Reduction Potential to 2025. June 2016. Paris: International Renewable Energy Agency. http://www.irena.org/DocumentDownloads/Publications/IRENA_Power_to_Change_2016.pdf.
AWS Truepower. 2012. Wind Resource of the United States: Mean Annual Wind Speed at 200m Resolution. https://www.awstruepower.com/assets/Wind-Resource-Map-UNITED-STATES-11x171.pdf.
BNEF (Bloomberg New Energy Finance). 2015. Future Cost of Wind.
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.
E3 (Energy and Environmental Economics). 2014. Capital Cost Review of Power Generation Technologies: Recommendations for WECC's 10- and 20-Year Studies. Prepared for the Western Electric Coordinating Council. https://www.wecc.biz/Reliability/2014_TEPPC_Generation_CapCost_Report_E3.pdf.
EIA (U.S. Energy Information Administration). 2014. Annual Energy Outlook 2014 with Projections to 2040. Washington, D.C.: U.S. Department of Energy. DOE/EIA-0383(2014). April 2014. http://www.eia.gov/forecasts/aeo/pdf/0383(2014).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.
EPA (United States Environmental Protection Agency). Clean Power Plan.
Global Wind Energy Council (GWEC). 2014. Global Wind Energy Outlook 2014. October 2014.
IEA (International Energy Agency). 2016. World Energy Outlook 2016. Paris: International Energy Agency. December 2016.
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.
MAKE Consulting (MAKE). 2015. Global Wind Power Supply Chain. Market Report. December 2015.
Moné, C., A. Smith, M. Hand, and B. Maples. 2015. 2013 Cost of Wind Energy Review. Golden, CO: National Renewable Energy Laboratory. http://www.nrel.gov/docs/fy15osti/63267.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. https://www.nrel.gov/analysis/re-futures.html.
Ove Arup & Partners Ltd. (ARUP). 2011. Review of the Generation Costs and Deployment Potential of Renewable Electricity Technologies in the UK. Department of Energy and Climate REP001, Prepared by Ove Arup & Partners Ltd. London, UK.
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.
Wiser, Ryan, and Mark Bolinger. 2014. 2014 Wind Technologies Market Report. U.S. Department of Energy. DOE/GO-102015-4702. https://energy.gov/sites/prod/files/2015/08/f25/2014-Wind-Technologies-Market-Report-8.7.pdf.
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.