Land-Based Wind
The 2019 ATB characterization for land-based wind updates the Base Year and future wind technology cost and performance estimates from years past to align with current expectations for wind energy costs over the coming decades. This year's ATB characterization for land-based wind relies on a new bottoms-up engineering approach for 2030 turbine and plant technology that is used to inform cost and performance characterizations through 2030. This new approach was developed based on persistent feedback since the release of the Wind Vision report (DOE & NREL, 2015) and the System Management of Atmospheric Resource through Technology (SMART) strategies wind plant analysis (Dykes et al., 2017) , from the wind industry original equipment manufacturer (OEM) and stakeholder community that noted the wind ATB mid case assumptions to be overly conservative. Based on this feedback and observations of substantial technology gains in recently commercialized turbine offerings an array of industry experts now anticipate wind energy LCOEs of 2-2.5 cent/kWh by the mid-2020s, depending on specific financing terms and conditions. In terms of technology gains, the most noteworthy has been the substantial and rapid scaling of wind turbines from the 2-MW to 4-MW with increases in rotor size from approximately 100 m to 150 m. These gains in scale are allowing modern technology to capture turbine level economies of scale and balance of plant efficiencies while placing the turbine in better resource regimes at greater heights above ground level.
To better align with the OEM and industry stakeholder cost reduction expectations NREL redefined the methodology used for estimating future energy costs. Specifically, for this year's ATB NREL used expert input to define one of many potential turbine technology pathways for a Mid and Low scenario in 2030. Bottom-up engineering cost and performance analysis were then executed to obtain the future cost reduction trajectories (Stehly, Beiter, Heimiller, & Scott, 2020) . Although this method has resulted in a cost reduction pathway that maintains and could even accelerate recent significant cost reduction gains, these results are believed to be more in line with wind industry analyst and OEM expectations. There is substantial focus throughout the global wind industry on driving down costs and increasing performance due to fierce competition from within as well as among several power generation technologies including solar PV and natural gas-fired generation.
Representative Technology
Representative technologies for land-based wind for the base year (2017) and 2030 assume a 50-MW to 100-MW facility, consistent with current project sizes (Wiser & Bolinger, 2018) . Our base year characterization is extracted from wind turbines installed in the United States in 2017 which were, on average, 2.3-MW turbines with rotor diameters of 113 m and hub heights of 86 m (Wiser & Bolinger, 2018) . Our 2030 representative technology assumes a 4.5-MW turbine with a rotor diameter of 167 m and a hub height of 110 m. Notably turbines that are nearly of this scale (i.e., 4-MW, 150 m rotor and 80-m to 110-m hub height are commercially available today and expected to be installed in facilities in the U.S. in the early 2020s.
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 & NREL, 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, including resource, technical, economic, and market potential (see NREL: "Renewable Energy Technical Potential").
The resource potential is calculated by using more than 130,000 distinct areas for wind plant deployment that cover more than 3.5 million km2. The potential capacity is estimated to total more than 10,000 GW if a power density of 3 MW/km2 is assumed.
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 by Moné (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.
- 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. ATB Base Year costs for land-based wind are calibrated to NREL's 2017 Cost of Wind Energy Review (Stehly, Beiter, Heimiller, & Scott, 2018).
Focusing on future costs, this year's ATB characterization represents an update relative to years past in order to realign with current expectations for costs over the next decade. 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 2017 to 2050; consistent across all renewable energy technologies in the ATB
- Mid Technology Cost Scenario: 2030 cost estimates for the Mid scenario are estimated (1) using NREL's Cost and Scaling Model (CSM) and inputs from analyst-predicted turbine and plant technology in 2030 specific to the Mid case (Stehly et al., 2020) and (2) applying technology-specific cost adjustments for factors such as construction contingencies and transportation. Beyond 2030, costs and performance were derived from an estimated change in LCOE from 2030 to 2050 based on historical land-based wind LCOE and single-factor learning rates ranging from 10.5% to 18.6%, meaning LCOE declines by this amount for each doubling of global cumulative wind capacity (Wiser et al., 2016) . NREL's Cost and Scaling applies component level scaling relationships (e.g., for the blades, hub, generator, and tower) that reflect the component-specific and often nonlinear relationship between size and cost (Christopher Moné et al., 2017) .
- Low Technology Cost Scenario: 2030 cost estimates for the Low scenario are estimated using NREL's CSM and inputs from the analyst-predicted turbine technology in 2030 specific to the Low case (Stehly et al., 2020) and applying technology-specific cost adjustments; beyond 2030, the costs were derived using the same learning rate methodology as for the Mid case but assuming an increase in global cumulative capacity.
In last year's ATB, the mid case cost projections were informed by the expert survey that reported expected LCOE changes in percentage terms relative to 2014 baseline values (Wiser et al., 2016) . Prior mid case projections were estimated using the entire sample size from the survey work (163 experts) which included strategic, system-level thought leaders with wind technology, costs, and/or market expertise. However, the survey also identified a smaller group – deemed "leading experts" through a deliberative process by a core group of International Energy Agency (IEA) Wind Task 26 members. This leading experts group (22 leading experts) generally expected more aggressive wind energy cost reductions. Recent wind industry data focused on price points more than 3-5 years into the future indicate that costs are falling more quickly than the full sample predicted and more in line with the leading Experts predictions. This year's ATB mid case is informed both by this leading group of experts' predictions as well as the expert input and bottom-up engineering pathways analysis referenced previously.
The Low case is primarily informed by the wind program's Atmosphere to Electrons (A2e) applied research initiative that advances the fundamental science necessary to drive innovation and the realization of the SMART wind power plant of the future (Dykes et al., 2017) . This research in addition to ongoing Wind Energy Technologies Office (WETO) projects including Big Adaptive Rotors (DOE, 2018) , Lightweight Drivetrains (DOE EERE, 2019) , and Tall Towers (2019 Wind Energy Technologies Office Funding Opportunity Announcement, 2019) support the wind industry's ongoing scaling activities and manufacturing improvements and were combined to assess additional potential pathways and related projected cost impacts for each LCOE component.
As done in the expert survey work (Wiser et al., 2016) historical LCOE estimates were compared to the LCOE projections of the Mid and Low scenarios. The LCOE projections were found to require continued sizable cost reductions consistent with and potentially somewhat greater than the historical LCOE trends. Possible justifications for maintaining recent rates of high cost reduction and potentially even going beyond long-term historical LCOE learning include stiff competition from around the globe as well as a highly capitalized industry with annual expenditures on the order of $100 billion. Accordingly, the revised Mid scenario is now considered representative of the reference case for land-based wind. The Low cost scenario projections are derived from the same methodology as the Mid case but applying additional cost reduction potentials from 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): Historical Trends, Current Estimates, and Future Projections
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.
The CAPEX improvements for future land-based wind projects may be realized through many technology pathways to achieve LCOE reductions. It is also important to note that CAPEX improvements are not the only pathway to LCOE reductions as LCOE is also influenced by capacity factor, financing, O&M, and project life. For the purpose of reducing the vast combinations of future pathways NREL analysts defined a single future turbine configuration in 2030 to conduct the bottom-up cost analysis for the Mid and Low scenarios. The specific 2030 turbine configuration for the Mid scenario assumes a nameplate capacity of 4.5 MW with a rotor diameter of 167 m placed on a 110 m tower. Additional turbine configurations explored that resulted in LCOEs that were within 10% (or less) of the current Mid scenario included turbines with nameplate capacity ratings of 3-MW and 4-MW, rotor diameters down to 150 m, and hub heights up to 140 m. The relatively low sensitivity of LCOE to these changes in turbine configuration is indicative of the array potential pathways and solutions to the LCOE values estimated in this year's ATB.
The defined turbine characteristics were then used to estimate the total system CAPEX of a theoretical commercial scale (e.g., 100-MW) project. Although the relatively low observed sensitivity to significantly different turbine configurations for a single reference site indicate some uncertainty need for and value of wind turbine tailoring for varied site conditions, it is generally expected that over the long-term wind turbine designs will be optimized for a specific plant's site conditions. In this year's ATB, this site-specific design optimization process which is often reflected in different CAPEX values across TRGs was not included. Instead we assumed the single turbine configuration across all 10 TRGs. This simplification was applied due to limited resources and lack of necessary data to robustly characterize CAPEX variation across the 10 TRGs. Nevertheless, the ability to assess and tailor turbine configurations and CAPEX estimates for each TRG is expected to be reimplemented in future iterations of the ATB.
CAPEX Definition
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
For the ATB, and based on a capital cost estimates report from EIA (2016) and the System Cost Breakdown Structure defined by Moné et al. (2015), the wind plant envelope is defined to include:
- Wind turbine supply
- Balance of system (BOS)
- Turbine installation, substructure supply, and installation
- Site preparation, installation of underground utilities, access roads, and buildings for operations and maintenance
- Electrical infrastructure, such as transformers, switchgear, and electrical system connecting turbines to each other and to the control center
- Project-related indirect costs, including engineering, distributable labor and materials, construction management start up and commissioning, and contractor overhead costs, fees, and profit.
- Financial costs
- Owners' costs, such as development costs, preliminary feasibility and engineering studies, environmental studies and permitting, legal fees, insurance costs, and property taxes during construction
- Onsite electrical equipment (e.g., switchyard), a nominal-distance spur line (< 1 mile), and necessary upgrades at a transmission substation; distance-based spur line cost (GCC) not included in the ATB
- Interest during construction estimated based on three-year duration accumulated 10%/10%/80% at half-year intervals and an 8% interest rate (ConFinFactor).
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).
Standard Scenarios Model Results
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, 2016).
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.
Operation and Maintenance (O&M) Costs
Operations and maintenance (O&M) costs depend on capacity and represent the annual fixed expenditures required to operate and maintain a wind plant, including:
- Insurance, taxes, land lease payments, and other fixed costs
- Present value and annualized large component replacement costs over technical life (e.g., blades, gearboxes, and generators)
- Scheduled and unscheduled maintenance of wind plant components, including turbines and transformers, over the technical lifetime of the plant.
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.
Base Year Estimates
The FOM of $44/kW-yr in the Base Year was estimated in the 2017 Cost of Wind Energy Review (Stehly, Beiter, Heimiller, & Scott, 2018); no variation of FOM with TRG (or wind speed) was assumed. The following chart shows sample historical data for reference.
Future Year Projections
Future FOM is assumed to decline by approximately 25% by 2050 in the Mid case and 45% in the Low case. These values are informed by recent work benchmarking work for wind power operating costs in the United States (Wiser, Bolinger, & Lantz, 2019).
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.
Capacity Factor: Expected Annual Average Energy Production Over Lifetime
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 wind profile, 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.
Recent Trends
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 2017, shown by year of commercial online date. As reported in the 2017 DOE Wind Technologies Market Report (Wiser & Bolinger, 2018).
Base Year Estimates
Most 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 well as very low wind resource sites associated with TRGs 8-10 are not as common in the historical data, but the range of observed data encompasses ATB estimates.
To calculate the Base Year capacity factors the 2017 turbine characteristics (Wiser & Bolinger, 2018) are input into the System Advisor Model, or SAM and run for each of the weighted average wind speeds in each TRG.
The capacity factor is referenced to an 80-m, above-ground-level, long-term average hourly wind resource data from AWS Truepower (2012).
Future Year Projections
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. In practice future turbine designs will be optimized for a specific site with attempts to maximize capacity factor. This optimization is not captured in this analysis since one turbine configuration is defined for each of the TRGs. Analysts hope to enhance the ability to define site-specific turbine configurations for each TRG in future iteration of the ATB.
- Mid: The projected capacity factors in 2030 are calculated in SAM using the inputs of the predicted turbine technology in 2030 that are specific to the Mid scenario (Stehly et al., 2020) for each of the TRGs; additional wind plant performance and availability are also applied through technology innovations assessed in the SMART wind power plant of the future work (Dykes et al., 2017); beyond 2030, analysts predict generally modest improvements in wind plant performance through 2050 for the resource-rich site (i.e., TRGs 1 and 2) and increasing slightly for the less favorable resource sites (i.e., TRGs 8-10).
- Low: The projected capacity factors in 2030 are calculated in SAM using the inputs of the predicted turbine technology in 2030 that are specific to the Low scenario (Stehly et al., 2020) for each of the TRGs; additional wind plant performance and availability are also applied through technology innovations assessed in the SMART wind power plant of the future work (Dykes et al., 2017), but they assume further reduction in losses than in the Mid case; beyond 2030, analysts predict slightly higher improvements in wind plant performance than in the Mid case through 2050 for the resource-rich site (i.e., TRGs 1 and 2) and increasing slightly for the less favorable resource sites (i.e., TRGs 8-10).
Increased energy capture through turbine scaling and wind plant optimization are the two primary factors influencing wind plant capacity factor. The introduction of novel control mechanisms will continue to increase energy capture with more-precise control of the flow through the entire wind plant. These technology advancements are expected to increase capacity factor for all TRGs, with a more rapid rate of increases in capacity factor through 2030 and a slower rate of increase through 2050. This analysis is illustrative of one of many capacity factor improvement pathways for LCOE reduction. Of course, as was the case for CAPEX there are many different pathways to a given capacity factor. Turbine rotor diameter, specific power, and hub height can each be traded off to achieve a given capacity factor, depending on site conditions and relative costs for pursuing one approach or the other; plant layout and operational strategies that impact losses are additional levers that may be used to achieve a given capacity factor.
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.
Standard Scenarios Model Results
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.
Plant Cost and Performance Projections Methodology
ATB projections were derived from two different sources for the Mid and Low cases:
- Mid Technology Cost Scenario: 2030 cost estimates for the Mid scenario are estimated (1) using CSM and inputs from analyst-predicted turbine technology in 2030 that are specific to the Mid case (Stehly et al., 2020) and (2) applying technology-specific cost adjustments; beyond 2030, the costs are derived from estimated change in LCOE from 2030 to 2050 based on historical land-based wind LCOE and single-factor learning rates ranging from 10.5% to 18.6%, meaning LCOE declines by this amount for each doubling of global cumulative wind capacity (Wiser et al., 2016). Future FOM is assumed to decline by approximately 25% by 2050 in the Mid case. These values are informed by recent work benchmarking work for wind power operating costs in the United States (Wiser et al., 2019). The projected capacity factors in 2030 are calculated in SAM using the inputs of the predicted turbine technology in 2030 that are specific to the Mid case (Stehly et al., 2020) for each of the TRGs; additional wind plant performance and availability are also applied through technology innovations assessed in the SMART wind power plant of the future work (Dykes et al., 2017); beyond 2030, analysts predict generally modest improvements in wind plant performance through 2050 for the resource-rich site (i.e., TRGs 1 and 2) and increasing slightly for the less favorable resource sites (i.e., TRGs 8-10).
- Low Technology Cost Scenario: 2030 cost estimates for the Low scenario are estimated (1) using NREL's CSM and inputs from the analyst-predicted turbine technology in 2030 that are specific to the Low case (Stehly et al., 2020) and (2) applying technology-specific cost adjustments; beyond 2030, the costs are derived using the same learning rate methodology as for the Mid case but assuming an increase in global cumulative capacity. Future FOM is assumed to decline by approximately 45% in the Low case. These values are informed by recent work benchmarking work for wind power operating costs in the United States (Wiser et al., forthcoming). The projected capacity factors in 2030 are calculated in SAM using the inputs of the predicted turbine technology in 2030 that are specific to the Low case (Stehly et al., 2020) for each of the TRGs; additional wind plant performance and availability are also applied through technology innovations assessed in the SMART wind power plant of the future work (Dykes et al., 2017), but they assume further reduction in losses than in the Mid case; beyond 2030, analysts predict slightly higher improvements in wind plant performance than in the Mid case through 2050 for the resource-rich site (i.e., TRGs 1 and 2) and increasing slightly for the less favorable resource sites (i.e., TRGs 8-10).
Projections of the cost of wind energy from the literature provide context for the ATB Constant, Mid, and Low technology cost projections. The ATB Mid cost projection results in LCOE reductions that are higher than other scenarios that are in median range of the literature ((Shreve, 2018), (BNEF, 2018)), and lower than ((Kost, Shammugam, Julch, Huyen-Tran, & Schlegl, 2018), (Wiser et al., 2016)). The ATB Low cost projection, which corresponds to the NREL bottom-up cost analysis, is relatively in line with the leading experts prediction (Wiser et al., 2016).
- Mid case projection institutions: Bloomberg New Energy Finance, Wood Mackenzie, Global Wind Energy Council.
Levelized Cost of Energy (LCOE) Projections
Levelized cost of energy (LCOE) is a summary 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 but not in the upfront capital costs (e.g., developer fees and contingencies). An individual technology may receive more favorable financing terms outside the United States, 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 and tax credits, and changing interest rates over time. The R&D + Market LCOE case adds to these financial assumptions: (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform and tax credits. 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; for simplicity, not all resource categories are shown in the figures.
R&D Only | R&D + Market
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:
- Constant Technology: Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
- 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.
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. Two project finance structures are used within the ATB:
- R&D Only Financial Assumptions: 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 2017 values from AEO2019 (EIA, 2019) and excludes effects of tax reform and tax credits.
- 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 AEO2019 (EIA, 2019) as well as effects of tax reform and tax credits. For a detailed discussion of these assumptions, see Project Finance Impact on LCOE.
A constant cost recovery period – or 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 equations and variables used to estimate LCOE are defined on the Equations and Variables page. For illustration of the impact of changing financial structures such as WACC, see Project Finance Impact on LCOE. For LCOE estimates for the Constant, Mid, and Low technology cost scenarios for all technologies, see 2019 ATB Cost and Performance Summary.
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.
- Continued turbine scaling to larger-megawatt turbines with larger rotors such that the swept area/megawatt capacity decreases, resulting in higher capacity factors for a given location
- Continued diversification of turbine technology whereby the largest rotor diameter turbines tend to be located in lower wind speed sites, but the number of turbine options for higher wind speed sites increases
- Taller towers that result in higher capacity factors for a given site due to the wind speed increase with elevation above ground level
- Improved plant siting and operation to reduce plant-level energy losses, resulting in higher capacity factors
- Wind turbine technology and plants that are increasingly tailored to and optimized for local site-specific conditions
- More efficient O&M procedures combined with more reliable components to reduce annual average FOM costs
- Continued manufacturing and design efficiencies such that capital cost/kilowatt decreases with larger turbine components
- Adoption of a wide range of innovative control, design, and material concepts that facilitate the above high-level trends.
Concentrating Solar Power
Representative Technology
Concentrating solar power (CSP) technology is currently assumed to be molten-salt power towers. Thermal energy storage (TES) is accomplished by storing hot molten salt in a two-tank system, which includes a hot-salt tank and a cold-salt tank. Stored hot salt can be dispatched to the power block as needed, regardless of solar conditions, to continue power generation and allow for electricity generation after sunlight hours. In the ATB, CSP plants with 10 hours of TES are illustrated. Ten hours is the amount of storage at the Crescent Dunes CSP plant in Nevada, which is representative of most new molten-salt power tower projects.
Molten-salt power towers (with 10 hours of storage) were selected as the representative technology over parabolic trough with synthetic oil-heat transfer fluid for two main reasons. First, most new global capacity of CSP plants in development or under construction are molten-salt power towers; in 2015, 3.7 GWe of molten-salt power towers were in development or under construction ((IRENA 2018), (SolarReserve n.d.)) – compared to 1.3 GWe of parabolic trough (IRENA 2018). Second, current indications are that molten-salt power towers have the greatest cost reduction potential, in terms of both CAPEX and LCOE ((IRENA 2016), (Mehos et al. 2017)). And they are part of the DOE Generation 3 (Gen3) road map for the next generation of commercial CSP plants (Mehos et al. 2017).
Crescent Dunes (110 MWe with 10 hours of storage) was the first large molten-salt power tower plant in the United States. It was commissioned in 2015 with a reported installed CAPEX of $8.96/WAC ((Danko 2015), (Taylor 2016)). No new molten salt storage CSP plants were commissioned in the United States in 2018 or 2019. Molten-salt power tower plants are being bid and built in Chile and Dubai (NREL and SolarPaces n.d.).
Resource Potential
Solar resource is prevalent throughout the United States, but the Southwest is particularly suited to CSP plants. The direct normal irradiance (DNI) resource across the Southwest, which is some of the best in the world, ranges from 6.0 to more than 7.5 kWh/m2/day (Roberts 2018). The raw resource technical potential of seven western states (Arizona, California, Colorado, Nevada, New Mexico, Utah, and Texas) exceeds 11,000 GWe, which is almost tenfold current total U.S. electricity generation capacity-considering regions in these states with an annual average resource > 6.0 kWh/m2/day. After accounting for exclusions such as land slope (> 1%), urban areas, water features, and parks, preserves, and wilderness areas (Mehos, Kabel, and Smithers 2009).
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 (see NREL: "Renewable Energy Technical Potential").
The Solar Programmatic Environmental Impact Statement identified 17 solar energy zones for priority development of utility-scale solar facilities in six western states. These zones total 285,000 acres and are estimated to accommodate up to 24 GWe of solar potential. The program also allows development, subject to a more rigorous review, on an additional 19 million acres of public land. Development is prohibited on approximately 79 million acres.
According to NREL's Concentrating Solar Power Projects website and the CSP Today Global Projects Tracker ("CSP Today Global Tracker" n.d.), 12 of the 14 currently operational CSP plants greater than 5 MWe in the United States use parabolic trough technology, and two are power tower facilities – Ivanpah (392 MWe, utilizing direct steam generation in the towers) and Crescent Dunes (110 MWe, as highlighted, utilizing molten-salt in the tower).
Base Year and Future Year Projections Overview
For the ATB, various factors are used to demonstrate the range of LCOE and performance across the United States. These include:
- CAPEX is determined using manufacturing cost models and is benchmarked with industry data. CSP performance and cost are based on the molten-salt power tower technology with dry-cooling to reduce water consumption.
- O&M cost is benchmarked against industry data.
- Capacity factor varies with inclusion of TES and solar irradiance. The listed resource classes assume power towers with 10 hours of TES at three types of locations:
- Fair resource (e.g., Abilene Regional Airport, Texas 6.16 kWh/m2/day based on the National Solar Radiation Database [NSRDB] site 2017 Physical Solar Model [PSM] TMY file)
- Good resource (e.g., Phoenix, Arizona 7.26 kWh/m2/day based on the site TMY3 file)
- Excellent resource (e.g., Daggett, California 7.65 kWh/m2/day based on the site TMY3 file)
- Representative CSP plant size is net 100 MWe.
The CSP costs originated from: (1) a NREL survey leading to updated cost estimates in the System Advisor Model (SAM) 2017.09.05; and (2) further cost estimates for CSP components which are now present in SAM 2018.11.11 (Craig Turchi et al. 2019). These SAM 2018 CSP costs were deflated to the ATB Base Year of 2017 via the consumer price index. The SAM 2018 costs translate to ATB costs in 2021 due to the three-year construction period. (SAM costs are based on the project announcement year, while the ATB is based on the plant commissioning year).
Future year projections are informed by a variety of published literature, NREL expertise and technology pathway assessments for CAPEX and O&M cost reductions. 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 current estimates (2021 for CSP) to 2050; consistent across all renewable energy technologies in the ATB
- The Mid Technology Cost Scenario is based on recently published literature projections and NREL judgment of U.S. costs for future CAPEX at 2025, 2030, 2040 and 2050 ((IRENA 2016), (Breyer et al. 2017), (Feldman et al. 2016), and (World Bank 2014)). It is anticipated that CSP costs could fall by approximately 25% from the ATB CSP 2021 costs of $6,450/kWe to approximately $4,800/kWe by 2030. From 2030 to 2050, CSP CAPEX is projected to fall to approximately $3,380/kWe.
- The Low Technology Cost Scenario is based on the lower bound of the literature sample, and on the Power to Change report (IRENA 2016).
Capital Expenditures (CAPEX): Historical Trends, Current Estimates, and Future Projections
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. These expenditures include the generation plant, the balance of system (e.g., site preparation, installation, and electrical infrastructure), and financial costs (e.g., development costs, 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 resource in the contiguous United States.
The following figure shows the Base Year estimate and future year projections for CAPEX costs. Three cost scenarios are represented: Constant, Mid, and Low. The estimate for a given year represents CAPEX of a new plant that reaches commercial operation in that year (i.e., SAM 2018 CSP costs are reflected in the ATB in 2021).
Base Year Estimates
CAPEX is unchanged for resource class because the same plant is assumed to be built in each location. The capacity factor will change with resource.
TES increases plant CAPEX but also increases capacity factor and annual efficiency. TES generally lowers LCOE for power towers.
The CAPEX estimate (with a base year of 2017) is approximately $7,730/kWe in 2017$. It is for a representative power tower with 10 hours of storage and a solar multiple of 2.4. Based on recent assessment of the industry in 2017 and updated CSP systems costs reflected in SAM 2018.11.11 (Craig Turchi et al. 2019), the CAPEX estimate for 2021 is $6,450/kWe in 2017$.
Future Year Projections
Three cost projections are developed for CSP technologies:
- Constant Technology Cost Scenario: no change in CAPEX, O&M, or capacity factor from current estimates (2021 for CSP) to 2050; consistent across all renewable energy technologies in the ATB
- The Mid Technology Cost Scenario is based on recently published literature projections and NREL judgment of U.S. costs for future CAPEX at 2025, 2030, 2040 and 2050 ((IRENA 2016), (Breyer et al. 2017), (Murphy et al. 2019), (Feldman et al. 2016), and (World Bank 2014)). It is anticipated that CSP costs could fall by approximately 25% from the ATB CSP 2021 costs of $6,450/kWe to approximately $4,800/kWe by 2030. From 2030 to 2050, CSP CAPEX is projected to fall to approximately $3,380/kWe.
- The Low Technology Cost Scenariois based on the lower bound of the literature sample, and on the Power to Change report (IRENA 2016).
Relative to today's reported CAPEX for plants either announced or in construction, $6,450/kWe in the ATB in 2021 and $4,800/kWe in 2030 is possible. For example, Noor III (150 MWe molten salt power tower with 7.5 hours of storage and operational in 2018), had an estimated CAPEX of $6,500/kWe in 2017$ (Kistner 2016). The Dubai Electricity and Water Authority (DEWA) 700-MWe CSP portion (600-MWe parabolic trough and 100-MWe molten salt tower, each with 12-15 hours of storage), which is in construction had an estimated bundled CAPEX of $5,500/kWe ( (Shemer 2018), (Craig Turchi et al. 2019)).
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.
CAPEX Definition
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
The ATB represents the year in which a plant starts commercial operation. Accordingly, for plants whose construction duration exceeds one year, CAPEX costs will represent technology costs that are lagging current-year estimates by at least one year. For CSP plants, the construction period is typically three years.
For the ATB – and based on key sources ((EIA 2016), (C. Turchi 2010), and (Craig Turchi and Heath 2013)) – the CSP generation plant envelope is defined to include:
- CSP generation plant
- Solar collectors
- Solar receiver
- Piping and heat-transfer fluid system
- Power block (heat exchangers, power turbine, generator, and cooling system)
- Thermal energy storage system
- Installation
- Balance of system, including installation, land acquisition, electrical infrastructure, and project indirect costs
- Land acquisition, site preparation, and installation of underground utilities, access roads, fencing, and buildings for operations and maintenance
- Electrical infrastructure, such as transformers, switchgear, and electrical system connecting modules to each other and to control the center; the generator voltage is 13.8 kV, the step-up transformer is 13.8/230kV, and the transmission tie line is 230 kV
- Project indirect costs, including costs related to engineering, distributable labor and materials, construction management start up and commissioning, and contractor overhead costs, fees, and profit
- Financial costs
- Owners' costs, such as development costs, preliminary feasibility and engineering studies, environmental studies and permitting, legal fees, insurance costs, and property taxes during construction
- Onsite electrical equipment (e.g., switchyard), a nominal-distance spur line (< 1 mile), and necessary upgrades at a transmission substation; distance-based spur line cost (GCC) not included in the ATB
- Interest during construction estimated based on three-year duration accumulated 80%/10%/10% at half-year intervals and a nominal interest rate (ConFinFactor).
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 solar-CSP plant with 10 hours of thermal storage and does not vary with resource. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by the U.S. Energy Information Administration (EIA 2016) expand the range of CAPEX. Unique land-based spur line costs based on distance and transmission line costs expand the range of CAPEX even further. The following figure illustrates the ATB representative plant relative to the range of CAPEX including regional costs across the contiguous United States. The ATB representative plants are associated with a regional multiplier of 1.0.
Standard Scenarios Model Results
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 2016).
The ReEDS model determines the land-based spur line (GCC) uniquely for each potential CSP plant based on distance and transmission line cost.
Operation and Maintenance (O&M) Costs
Operations and maintenance (O&M) costs represent the annual expenditures required to operate and maintain a solar CSP plant over its lifetime, including:
- Operating and administrative labor, insurance, legal and administrative fees, and other fixed costs
- Utilities (water, power, and natural gas if any) and mirror washing
- Scheduled and unscheduled maintenance, including replacement parts for solar field and power block components over the technical lifetime of the plant.
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.
Base Year Estimates
FOM is assumed to be $66/kW-yr until 2021. Variable O&M is approximately $4.1/MWh until 2021 and $3.50/MWh after that (Kurup and Turchi 2015).
Future Year Projections
Future FOM is assumed to decline to $51/kW-yr by 2030 in the Mid cost case (i.e., approximately a 25% drop) and approximately $40/kW-yr by 2030 in the Low cost case based on DOE investments likely to help to lower costs (DOE 2012).
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.
Capacity Factor: Expected Annual Average Energy Production Over Lifetime
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.
Capacity factors are influenced by power block technology, storage technology and capacity, the solar resource, expected downtime, and energy losses. The solar multiple is a design choice that influences the capacity factor.
The following figure shows a range of capacity factors based on variation in the resource, for locations where currently CSP plants could be built in the contiguous United States. The range of the Base Year estimates illustrates the effect of locating a CSP plant at a site with Fair, Good, or Excellent solar resource. The future projections for the Constant, Mid, and Low technology cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX and O&M cost elements.
Base Year Estimates
For illustration in the ATB, a range of capacity factors calculated in SAM 2018.11.11 (NREL n.d.) is associated with three resource locations in the contiguous United States, as represented in the ReEDS model for three classes of insolation:
- Fair resource: Abilene, Texas: 6.16 kWh/m2/day based on the site TMY3 file, leads to a 50% capacity factor; the 2017 PSM TMY file for a site adjacent to the airport is used, and it was downloaded from the NSRDB (NREL n.d.).
- Good resource: Phoenix, Arizona: 7.26 kWh/m2/day based on the site TMY3 leads to a 61% capacity factor; the TMY file used is available in SAM 2018.11.11(NREL n.d.), titled "phoenix_az_33.450495_-111.983688_psmv3_60_tmy"..
- Excellent resource: Daggett, California: 7.65 kWh/m2/day based on the site TMY3 file, leads to a 64% capacity factor; the TMY file used is available in SAM 2018.11.11 (NREL n.d.), titled "daggett_ca_34.865371_-116.783023_psmv3_60_tmy".
A key finding from a recent report is that if future costs of CSP decrease sufficiently, it could be deployed across a greater range of the United States and DNI resources. For example, with aggressive cost decreases and due to the regional market constraints, southeastern states with lower DNI resources (e.g., Florida and South Carolina) could see increased CSP capacity deployments of up to 5 GWe (Murphy et al. 2019).
Future Year Projections
The CSP technologies highlighted in the ATB are assumed to be power towers but with different power cycles and operating conditions as time passes:
- 2017: a molten-salt (sodium nitrate/potassium nitrate; aka, solar salt) power tower with direct two-tank TES combined with a steam-Rankine power cycle running at 574° C and 41.2% gross efficiency
- 2021: a similar design to that of 2017 with identified near-term reductions in heliostat and power system costs
- 2030 Mid Technology Cost Scenario: longer-term cost reductions (e.g., in the heliostats and power system)
- 2030 Low Technology Cost Scenario: In the ATB, the Low projection is based on molten-salt power tower with direct two-tank TES combined with a power cycle running at 700° C and 55% gross efficiency.
While an advanced molten salt projection is used for the ATB Low Cost scenario, lower costs for baseload CSP are being investigated via different technology options (i.e. Solid Particle and Gas phase towers) and as defined by the CSP Gen3 program ((EERE n.d.), (Mehos et al. 2017)).
Over time, CSP plant output may decline. Capacity factor degradation due to mirror and other component degradation is not accounted for in ATB estimates of capacity factor or LCOE.
The ATB capacity factors are generated from Constant, Mid and Low technology cost plant simulations in SAM 2018.11.11 (NREL n.d.).
Estimates of capacity factors for CSP in the ATB represent typical operation. The dispatch characteristics of these systems are valuable to the electric system to manage changes in net electricity demand. Actual capacity factors will be influenced by the degree to which system operators call on CSP plants to manage grid services.
Standard Scenarios Model Results
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.
CSP plants with TES can be dispatched by grid operators to accommodate diurnal and seasonal load variations and output from variable generation sources (wind and solar PV). Because of this, their annual energy production and the value of that generation are determined by the electric system needs and capacity and ancillary services markets.
Plant Cost and Performance Projections Methodology
A range of literature projections is shown to illustrate the comparison with the ATB. When comparing the ATB projections with other projections, note that there are major differences in technology assumptions, radiation conditions, field sizes, storage configurations, and other factors. As shown in the chart, the ATB 2019 CSP Mid projection is in line with other recently analyzed projections from other organizations. The Low cost ATB projection is based on the lower bound of the literature sample, and on the Power to Change report (IRENA 2016).
Projections of future utility-scale CSP plant CAPEX and O&M are based on three different technology cost scenarios that were developed for scenario modeling as bounding levels:
- Constant Technology Cost Scenario
- Modeled as molten-salt (sodium nitrate/potassium nitrate, aka, solar salt) power tower with direct two-tank TES combined with a steam-Rankine power cycle running at 574°C and 41.2% gross efficiency in 2017
- Costs stay the same from the 2021 estimate through 2050, consistent with ATB renewable energy technologies
- Mid Technology Cost Scenario:
- Based on recently published literature projections and NREL judgment of U.S. costs for future CAPEX in 2025, 2030, 2040 and 2050 ((IRENA 2016), (Breyer et al. 2017), (Murphy et al. 2019), (Feldman et al. 2016), and (World Bank 2014)).
- It is anticipated that CSP costs could fall by approximately 25% from the ATB CSP 2021 costs of $6,450/kWe to approximately $4,800/kWe by 2030.
- CSP CAPEX is projected to fall from 2030 to 2050 to approximately $3,380/kWe.
- The cost reductions are anticipated due to gradual reductions in heliostat and power system cost due to greater deployment volume and learning assumed for 2021 and onwards based on current state of industry ((IRENA 2016), (Lilliestam et al. 2017)).
- CAPEX and O&M both drop by approximately 25% by 2030, relative to 2021 costs
- A further 30% overall CAPEX decrease is estimated from 2030 to 2050. All three components of the CSP CAPEX (the turbine, storage, and the solar field), decrease proportionately by approximately 30% from 2030 to 2050.
- Low Technology Cost Scenario:
- Significant reductions in heliostat and power system cost due to greater deployment volume and R&D are used for 2021 onwards; the plant was modeled as an advanced molten-salt power tower with direct two-tank TES combined with a power cycle running at 700°C and 55% gross efficiency in 2030 (Mehos et al. 2017).
- CAPEX and O&M decrease from likely cost reductions including new high-efficiency power cycles and low-cost heliostats.
- A further 20% overall CAPEX decrease is assumed from 2030 to 2050 based on potential deployment in the United States. The 20% decrease in overall CSP CAPEX is split over the three components: the turbine, storage, and the solar field. It can be expected that greater cost reductions could be achieved for the power block/turbine and the solar field than for the storage.
Levelized Cost of Energy (LCOE) Projections
Levelized cost of energy (LCOE) is a summary 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 such as CSP can 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 power-tower CSP 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 but not in the upfront capital costs (e.g., developer fees and contingencies). An individual technology may receive more favorable financing terms outside the United States, 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, and changing interest rates over time. The R&D + Market LCOE case adds to these financial assumptions: (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform and tax credits. For example, the projected LCOE for most utility scale renewable energy generation technologies could potentially increase from the end of 2020 due to the decreasing levels of the ITC. The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term CSP plants are associated with Tower-Excellent Resource. Data for all the resource categories can be found in the ATB Data spreadsheet; for simplicity, not all resource categories are shown in the figures.
Note: the future projection of the "Good resource" (i.e., for a CSP plant built in Phoenix, Arizona) is not shown in the figures to simplify the figures and because the projection lies between the Excellent and the Fair Resource projections.
R&D Only | R&D + Market
R&D + MarketThe 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:
- Constant Technology: Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
- 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.
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. Two project finance structures are used within the ATB:
- R&D Only Financial Assumptions: 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 2017 values from AEO2019 (EIA 2019) and excludes effects of tax reform and tax credits.
- 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 AEO2019 (EIA 2019) as well as effects of tax reform and tax credits. For a detailed discussion of these assumptions, see Project Finance Impact on LCOE.
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 can be varied in the ATB data spreadsheet.
The equations and variables used to estimate LCOE are defined on the Equations and Variables page. For illustration of the impact of changing financial structures such as WACC, see Project Finance Impact on LCOE. For LCOE estimates for the Constant, Mid, and Low technology cost scenarios for all technologies, see 2019 ATB Cost and Performance Summary.
For illustration of the impact of changing financial structures such as WACC, see Project Finance Impact on LCOE. For LCOE estimates for the Constant, Mid, and Low technology cost scenarios for all technologies, see 2019 ATB Cost and Performance Summary.
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.
- Power tower improvements
- Better and longer-lasting selective surface coatings improve receiver efficiency and reduce O&M costs.
- New salts allow for higher operating temperatures and lower-cost TES.
- Development of the power cycle running at approximately 700°C and 55% gross efficiency improves cycle efficiency, reduces powerblock cost, and reduces O&M costs.
- Lower-cost heliostats developed due to design changes and automated and high-volume manufacturing.
- General and "soft" costs improvements
- Expansion of world market leads to greater and more efficient supply chains, and reduction of supply chain margins (e.g., profit and overhead charged by suppliers, manufacturer, distributors, and retailers).
- Expansion of access to a range of innovative financing approaches and business models
- The LCOE range shown is based on locations with Fair (Abilene, Texas), Good (Phoenix, Arizona), and Excellent (Daggett, California) resources. The CAPEX is the same for each resource as the same plant is used. Future-year LCOE projections for the Good case are not shown to simplify the figure. Using the R&D Only scenario and financials, and with the capacity factor of 64% for Daggett, California (NREL n.d.), in the Mid case by 2030, the LCOE could reach approximately 55$/MWh, and it could potentially reach $47/MWh by 2050.
Hydropower
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 (see NREL: "Renewable Energy Technical Potential").
Note: Pumped-storage hydropower is considered a storage technology in the ATB and will be addressed in future years. It and other storage technologies are represented in Standard Scenarios Model Results from the ReEDS model, and battery storage technologies are shown in the associated portion of the ATB.
Representative Technology
Hydropower technologies have produced electricity in the United States for over a century. Many of these infrastructure investments have potential to continue providing electricity in the future through upgrades of existing facilities (DOE, 2016) . At individual facilities, investments can be made to improve the efficiency of existing generating units through overhauls, generator rewinds, or turbine replacements. Such investments are known collectively as "upgrades," and they are reflected as increases to plant capacity. As plants reach a license renewal period, upgrades to existing facilities to increase capacity or energy output are typically considered. While the smallest projects in the United States can be as small as 10-100 kW, the bulk of upgrade potential is from large, multi-megawatt facilities.
Resource Potential
The estimated total upgrade potential of 6.9 GW/24 terawattt-hours (TWh) (at about 1,800 facilities) is based on generalizable information drawn from a series of case studies or owner-specific assessments (DOE, 2016) . Information available to inform the representation of improvements to the existing fleet includes:
- A systematic, full-fleet assessment of expansion potential at Reclamation projects performed under the Reclamation Hydropower Modernization Initiative (see U.S. Bureau of Reclamation, "Hydropower Resource Assessment at Existing Reclamation Facilities")
- Case study reports from the U.S. Army Corps of Engineers (Corps) performed under its Hydropower Modernization Initiative (MWH, Watson, & Harza, 2009)
- Case study reports combining assessments of upgrade and unit and plant optimization potential from the U.S. Department of Energy/Oak Ridge National Laboratory Hydropower Advancement Project.
Base Year and Future Year Projections Overview
Upgrades are often among the lowest-cost new capacity resource, with the modeled costs for individual projects ranging from $800/kW to nearly $20,000/kW. This differential results from significant economies of scale from project size, wherein larger capacity plants are less expensive to upgrade on a dollar-per-kilowatt basis than smaller projects are. The average cost of the upgrade resource is approximately $1,500/kW.
CAPEX for each existing facility is based on direct estimates
The capacity factor is based on actual 10-year average energy production reported in EIA 923 forms. Some hydropower facilities lack flexibility and only produce electricity when river flows are adequate. Others with storage capabilities are operated to meet a balance between electric system, reservoir management, and environmental needs using their dispatch capability.
No future cost and performance projections for hydropower upgrades are assumed.
Upgrade cost and performance are not illustrated in this documentation of the ATB for the sake of simplicity.
Standard Scenarios Model Results
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.
Upgrade potential becomes available in the ReEDS model at the relicensing date, end of plant life (50 years), or both.
Representative Technology
Non-powered dams (NPD) are classified by energy potential in terms of head. Low head facilities have design heads below 20 m and typically exhibit the following characteristics (DOE, 2016) :
- 1 MW to 10 MW
- New/rehabilitated intake structure
- Little, if any, new penstock
- Axial-flow or Kaplan turbines (2-4 units)
- New powerhouse (indoor)
- New/rehabilitated tailrace
- Minimal new transmission (< 5 miles, if required)
- Capacity factor of 35% to 60%.
High head facilities have design heads above 20 m and typically exhibit the following characteristics (DOE, 2016) :
- 5 MW to 30 MW
- New/rehabilitated intake structure
- New penstock (typically < 500 feet of steel penstock, if required)
- Francis turbines (1-3 units)
- New powerhouse (indoor)
- New/rehabilitated tailrace
- Minimal new transmission line (up to 15 MW, if required)
- Capacity factor of 35% to 60%.
Resource Potential
Up to 12 GW of technical potential exists to add power to U.S. NPD. However, based on financial decisions in recent development activity, the economic potential of NPD may be approximately 5.6 GW at more than 54,000 dams in the contiguous United States. Most of this potential (5 GW or 90% of resource capacity) is associated with less than 700 dams. These resource considerations are discussed below:
According to the National
Inventory of Dams, more than 80,000 dams exist that do not produce
power. This data set was filtered to remove dams with erroneous flow
and geographic data and dams whose data could not be resolved to a
satisfactory level of detail
A new methodology for sizing potential hydropower facilities that was developed for the new-stream reach development resource (Kao et al., 2014) was applied to non-powered dams. This resource potential was estimated to be 5.6 GW at more than 54,000 dams. In the development of the Hydropower Vision, the NPD resource available to the ReEDS model was adjusted based on recent development activity and limited to only those projects with power potential of 500 kW or more. As the ATB uses the Hydropower Vision supply curves, this results in a final resource potential of 5 GW/29 TWh from 671 dams.
For each facility, a design capacity, average monthly flow rate over a 30-year period, and a design flow rate exceedance level of 30% are assumed. The exceedance level represents the fraction of time that the design flow is exceeded. This parameter can be varied and results in different capacity and energy generation for a given site. The value of 30% was chosen based on industry rules of thumb. The capacity factor for a given facility is determined by these design criteria.
Design capacity and flow rate dictate capacity and energy generation potential. All facilities are assumed sized for 30% exceedance of flow rate based on long-term, average monthly flow rates.
Base Year and Future Year Projections Overview
Site-specific CAPEX, O&M, and capacity factor estimates are made
for each site in the available resource potential. CAPEX and O&M
estimates are made based on statistical analysis of historical plant
data from 1980 to 2015
Projections developed for the Hydropower Vision study (DOE, 2016) using technological learning assumptions and bottom-up analysis of process and/or technology improvements provide a range of future cost outcomes. 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 2017 to 2050; consistent across all renewable energy technologies in the ATB
- Mid Technology Cost Scenario: incremental technology learning, consistent with Reference in Hydropower Vision (DOE, 2016); CAPEX reductions for new stream-reach development (NSD) only
- Low cost: gains that are achievable when pushing to the limits of potential new technologies, such as modularity (in both civil structures and power train design), advanced manufacturing techniques, and materials, consistent with Advanced Technology in Hydropower Vision (DOE, 2016); both CAPEX and O&M cost reductions implemented.
Standard Scenarios Model Results
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 includes a sensitivity scenario that restricts the resource potential to sites greater than 500 kW, consistent with the Hydropower Vision, which results in 5 GW/29 TWh at 671 dams.
Representative Technology
Greenfield or new stream-reach development (NSD) sites are defined as new hydropower developments along previously undeveloped waterways and typically exhibit the following characteristics (DOE, 2016) :
- 1 MW to 100 MW
- New diversion/intake structure
- New penstock
- Steel with length being head/terrain dependent
- Various turbine selections
- Impulse/Francis are common for recently completed projects
- New powerhouse (indoor)
- New tailrace
- New transmission line (up to 15 miles for new projects)
- 30% to 80% capacity factor.
Resource Potential
The resource potential is estimated to be 53.2 GW/301 TWh at nearly 230,000 individual sites (Kao et al., 2014) after accounting for locations statutorily excluded from hydropower development such as national parks, wild and scenic rivers, and wilderness areas.
About 8,500 stream reaches were evaluated to assess resource potential (i.e., capacity) and energy generation potential (i.e., capacity factor). For each stream reach, a design capacity, average monthly flow rate over a 30-year period, and design flow rate exceedance level of 30% are assumed. The exceedance level represents the fraction of time that the design flow is exceeded. This parameter can be varied and results in different capacity and energy generation for a given site. The value of 30% was chosen based on industry rules of thumb. The capacity factor for a given facility is determined by these design criteria. Plant sizes range from kilowatt-scale to multi-megawatt scale (Kao et al., 2014) .
The resource assessment approach is designed to minimize the footprint of a hydropower facility by restricting inundation area to the Federal Emergency Management Agency (FEMA) 100-year floodplain.
New hydropower facilities are assumed to apply run-of-river operation strategies. Run-of-river operation means the flow rate into a reservoir is equal to the flow rate out of the facility. These facilities do not have dispatch capability.
Design capacity and flow rate dictate capacity and energy generation potential. All facilities are assumed sized for 30% exceedance of flow rate based on long-term, average monthly flow rates.
Base Year and Future Year Projections Overview
Site-specific CAPEX, O&M, and capacity factor estimates are made
for each site in the available resource potential. CAPEX and O&M
estimates are made based on statistical analysis of historical plant
data from 1980 to 2015
Projections developed for the Hydropower Vision study (DOE, 2016) using technological learning assumptions and bottom-up analysis of process and/or technology improvements provide a range of future cost outcomes. 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 2017 to 2050; consistent across all renewable energy technologies in the ATB
- Mid Technology Cost Scenario: incremental technology learning, consistent with Reference in Hydropower Vision (DOE, 2016); CAPEX reductions for NSD only
- Low Technology Cost Scenario: gains that are achievable
when pushing to the limits of potential new technologies, such as
modularity (in both civil structures and power train design), advanced
manufacturing techniques, and materials, consistent with Advanced
Technology in Hydropower Vision
(DOE, 2016) ; both CAPEX and O&M cost reductions implemented.
Standard Scenarios Model Results
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 includes a sensitivity scenario that restricts the resource potential to sites greater than 1 MW, which results in 30.1 GW/176 TWh on nearly 8,000 reaches.
Capital Expenditures (CAPEX): Historical Trends, Current Estimates, and Future Projections
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. These expenditures include the hydropower generation plant, 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 resource in the contiguous United States.
The following figure shows the Base Year estimate and future year projections for CAPEX costs. Mid and Low technology cost scenarios are shown. Historical data from actual and proposed non-powered dam (NPD) and new stream-reach development (NSD) plants installed in the United States from 1981 to 2014 are shown for comparison to the ATB Base Year. The estimate for a given year represents CAPEX of a new plant that reaches commercial operation in that year.
Recent Trends
Actual and proposed NPD and NSD CAPEX from 1981 to 2014
The higher-cost ATB sites generally reflect small-capacity, low head sites that are not comparable to the historical data sample's generally larger-capacity and higher head facilities. These characteristics lead to higher ATB Base Year CAPEX estimates than past data suggest. For example, the NSD projects that became commercially operational in this period are dominated by a few high head projects in the mountains of the Pacific Northwest or Alaska.
The Base Year estimates of CAPEX for NPDs in the ATB range from $3,800/kW to $6,000/kW. These estimates reflect facilities with 3 feet of head to more than 60 feet of head and from 0.5 MW to more than 30 MW of capacity. In general, the higher-cost sites reflect much smaller-capacity (< 10 MW), lower head (< 30 ft.) sites that have fewer analogues in the historical data, but these characteristics result in higher CAPEX.
The Base Year estimates of CAPEX for NSD range from $5,500/kW to $7,900/kW. The estimates reflect potential sites with 3 feet of head to more than 60 feet head and from 1 MW to more than 30 MW of capacity. In general, NSD potential represents smaller-capacity facilities with lower head than most historical data represent. These characteristics lead to higher CAPEX estimates than past data suggest, as many of the larger, higher head sites in the United States have been previously developed.
Base Year Estimates
For illustration in the ATB, all potential NPD and NSD sites were first binned by both head and capacity. Analysis of these bins provided groupings that represent the most realistic conditions for future hydropower deployment. The design values of these four reference NPD and four reference NSD plants are shown below. The full range of resource and design characteristics is summarized in the ATB data spreadsheet.
Representative Hydropower Plants
| Resource Characteristics Ranges | Weighted Average Values | Calculated Plant Values | |||||
| Plants | Head (feet) | Capacity (MW) | Head (feet) | Capacity (MW) | Capacity Factor | ICC (2014$/kW) | O&M (2014$/kW) |
| NPD 1 | 3-30 | 0.5-10 | 15.4 | 4.8 | 0.62 | 5,969.33 | 111.73 |
| NPD 2 | 3-30 | 10+ | 15.9 | 82.2 | 0.64 | 5,433.24 | 30.74 |
| NPD 3 | 30+ | 0.5-10 | 89.6 | 4.2 | 0.60 | 3,997.79 | 118.67 |
| NPD 4 | 30+ | 10+ | 81.3 | 44.7 | 0.60 | 3,769.22 | 40.54 |
| NSD 1 | 3-30 | 1-10 | 15.7 | 3.7 | 0.66 | 7,034.81 | 125.01 |
| NSD 2 | 3-30 | 10+ | 19.6 | 44.1 | 0.66 | 6,280.15 | 40.76 |
| NSD 3 | 30+ | 1-10 | 46.8 | 4.3 | 0.62 | 6,151.05 | 117.61 |
| NSD 4 | 30+ | 10+ | 45.3 | 94.0 | 0.66 | 5,537.34 | 28.93 |
The reference plants shown above were developed using the average characteristics (weighted by capacity) of the resource plants within each set of ranges. For example, NPD 1 is constructed from the capacity-weighted average values of NPD sites with 3-30 feet of head and 0.5-10 MW of capacity.
The weighted-average values were used as input to the cost formulas
CAPEX for each plant is based on statistical analysis of historical
plant data from 1980 to 2015 as a function of key design parameters,
plant capacity, and hydraulic head
and
Where P is capacity in megawatts, and H is head in feet. The first term represents the initial capital costs, while the second represents licensing.
Future Projections
Projections developed for the Hydropower Vision study (DOE, 2016) using technological learning assumptions and bottom-up analysis of process and/or technology improvements provide a range of future cost outcomes. Three different CAPEX projections were developed for scenario modeling as bounding levels:
- Constant Technology Cost Scenario:
- NPD and NSD CAPEX unchanged from the Base Year; consistent across all renewable energy technologies in the ATB
- Mid Technology Cost Scenario: consistent with the Reference case in Hydropower Vision:
- NSD CAPEX reduced 5% in 2035 and 8.6% in 2050
- NPD CAPEX unchanged from the Base Year
- Low Technology Cost Scenario: consistent with the Advanced Technology case in Hydropower Vision:
- Low head NPD/All NSD CAPEX reduced 30% in 2035 and 35.3% in 2050. Low Head NPD is NPD-1 and NPD-2
- High head NPD CAPEX reduced 25% in 2035 and 32.7% in 2050; High Head NPD is NPD-3 and NPD-4
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.
Standard Scenarios Model Results
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 uses resource/cost supply curves representing estimates at each individual facility (~700 NPD and ~8,000 NSD).
The ReEDS model represents cost and performance for NPD and NSD potential in 5 bins for each of 134 geographic regions, which results in CAPEX ranges of $2,750/kW-$9,000/kW for NPD resource and $5,200/kW-$15,600/kW for NSD.
CAPEX Definition
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
For the ATB, and based on (EIA, 2016) and the System Cost Breakdown Structure described by (O'Connor, Zhang, DeNeale, Chalise, & Centurion, 2015), the hydropower plant envelope is defined to include:
- Hydropower generation plant
- Civil works, such as site preparation, dams and reservoirs, water conveyances, and powerhouse structures
- Equipment, such as the powertrain and ancillary plant electrical and mechanical systems
- Balance of system (BOS)
- Installation and O&M infrastructure
- Electrical infrastructure, such as transformers, switchgear, and electrical system connecting turbines to each other and to the control center
- Project indirect costs, including costs related to environmental mitigation and regulatory compliance, engineering, distributable labor and materials, construction management start up and commissioning, and contractor overhead costs, fees, and profit
- Financial costs
- Owners' costs, such as development costs, preliminary feasibility and engineering studies, environmental studies and permitting, legal fees, insurance costs, and property taxes during construction
- Electrical interconnection and onsite electrical equipment (e.g., switchyard), a nominal-distance spur line (< 1 mile), and necessary upgrades at a transmission substation; distance-based spur line cost (GCC) not included in the ATB
- Interest during construction estimated based on three-year duration accumulated 10%/10%/80% at half-year intervals and an 8% interest rate (ConFinFactor).
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 is shown for four representative non-powered dam plants and four representative new stream-reach development plants. CAPEX estimates for all identified hydropower potential (~700 NPD and ~8,000 NSD) results in a CAPEX range that is much broader than that shown in the ATB. It is unlikely that all the resource potential will be developed due to the very high costs for some sites. Regional cost effects and distance-based spur line costs are not estimated.
Standard Scenarios Model Results
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., and neither does the ReEDS model.
CAPEX in the ATB does not include geographically determined spur line (GCC) from plant to transmission grid, and neither does the ReEDS model.
Operation and Maintenance (O&M) Costs
Operations and maintenance (O&M) costs represent average annual fixed expenditures (and depend on rated capacity) required to operate and maintain a hydropower plant over its lifetime, including:
- Insurance, taxes, land lease payments, and other fixed costs
- Present value and annualized large component overhaul or replacement costs over technical life (e.g., rewind stator, patch cavitation damage, and replace bearings)
- Scheduled and unscheduled maintenance of hydropower plant components, including turbines, generators, etc. over the technical lifetime of the plant.
The following figure shows the Base Year estimate and future year projections for fixed O&M (FOM) costs. Mid and Low technology cost scenarios are shown. 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.
Base Year Estimates
A statistical analysis of long-term plant operation costs from Federal Energy Regulatory Commission Form-1 resulted in a relationship between annual, FOM costs, and plant capacity. Values were updated to 2017$.
Future Year Projections
Projections developed for the Hydropower Vision study (DOE, 2016) using technological learning assumptions and bottom-up analysis of process and/or technology improvements provide a range of future cost outcomes. Three different O&M projections were developed for scenario modeling as bounding levels:
- Constant Technology Cost Scenario: FOM costs unchanged from the Base Year to 2050; consistent with all ATB technologies
- Mid Technology Cost Scenario: FOM costs for both NPD and NSD plants are unchanged from 2017 to 2050; consistent with the Reference case in Hydropower Vision
- Low Technology Cost Scenario: FOM costs for both NPD and NSD plants are reduced by 50% in 2035 and 54% in 2050, consistent with the Advanced Technology case in Hydropower Vision.
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.
Capacity Factor: Expected Annual Average Energy Production Over Lifetime
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 site hydrology, design factors (e.g., exceedance level), and operation characteristics (e.g., dispatch or run-of-river). Capacity factors for all potential NPD sites and NSDs are estimated based on design criteria, long-term monthly flow rate records, and run-of-river operation.
The following figure shows a range of capacity factors based on variation in the resource for hydropower plants in the contiguous United States. Historical data from run-of-river hydropower plants operating in the United States from 2003 through 2012 are shown for comparison with the Base Year estimates. The range of the Base Year estimates illustrates the effect of resource variation. Future projections for the Constant, Mid, and Low technology cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX and O&M cost elements.
Recent Trends
Actual energy production from about 200 run-of-river plants operating in the United States from 2003 to 2012 (EIA, 2016) is shown in box-and-whiskers format for comparison with current estimates and future projections. This sample includes some very old plants that may have lower availability and efficiency. It also includes plants that have been relicensed and may no longer be optimally designed for current operating regime (e.g., a peaking unit now operating as run-of-river). This contributes to the broad range, particularly on the low end.
Interannual variation of hydropower plant output for run-of-river plants may be significant due to hydrological changes such as drought. This impact may be exacerbated by climate change over the long term.
Current and future estimates for new hydropower plants are within the range of observed plant performance. These potential hydropower plants would be designed for specific site conditions, which would indicate operation toward the high end of the range.
Base Year Estimates
For illustration in the ATB, all potential NPD and NSD sites are represented with four reference plants, each as described below.
Representative Hydropower Plants
| Resource Characteristics Ranges | Weighted Average Values | Calculated Plant Values | |||||
| Plants | Head (feet) | Capacity (MW) | Head (feet) | Capacity (MW) | Capacity Factor | ICC (2014$/kW) | O&M (2014$/kW) |
| NPD 1 | 3-30 | 0.5-10 | 15.4 | 4.8 | 0.62 | 5,969.33 | 111.73 |
| NPD 2 | 3-30 | 10 + | 15.9 | 82.2 | 0.64 | 5,433.24 | 30.74 |
| NPD 3 | 30 + | 0.5-10 | 89.6 | 4.2 | 0.60 | 3,997.79 | 118.67 |
| NPD 4 | 30 + | 10 + | 81.3 | 44.7 | 0.60 | 3,769.22 | 40.54 |
| NSD 1 | 3-30 | 1-10 | 15.7 | 3.7 | 0.66 | 7,034.81 | 125.01 |
| NSD 2 | 3-30 | 10 + | 19.6 | 44.1 | 0.66 | 6,280.15 | 40.76 |
| NSD 3 | 30 + | 1-10 | 46.8 | 4.3 | 0.62 | 6,151.05 | 117.61 |
| NSD 4 | 30 + | 10 + | 45.3 | 94.0 | 0.66 | 5,537.34 | 28.93 |
Future Year Projections
The capacity factor remains unchanged from the Base Year through 2050. Technology improvements are focused on CAPEX and O&M costs.
Standard Scenarios Model Results
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 uses resource/cost supply curves representing estimates at each individual facility (~700 NPD and ~8,000 NSD).
The ReEDS model represents cost and performance for NPD and NSD potential in 5 bins for each of 134 geographic regions, which results in capacity factor ranges of 38%-80% for the NPD resources and 53%-81% for NSD.
Existing hydropower facilities in the ReEDS model provide dispatch capability such that their annual energy production is determined by the electric system needs by dispatching generators to accommodate diurnal and seasonal load variations and output from variable generation sources (e.g., wind and solar PV).
Plant Cost and Performance Projections Methodology
Projections developed for the Hydropower Vision study (DOE, 2016) using technological learning assumptions and bottom-up analysis of process and/or technology improvements provide a range of future cost outcomes. Three different projections were developed for scenario modeling as bounding levels:
- Constant Technology Cost Scenario: no change in CAPEX or OPEX from 2017 to 2050; consistent across all renewable energy technologies in the ATB
- Mid Technology Cost Scenario: incremental learning, consistent with Reference in the Hydropower Vision study (DOE, 2016); CAPEX reductions for NSD only
- Low Technology Cost Scenario: gains that are achievable when pushing to the limits of potential new technologies, such as modularity (in both civil structures and power train design), advanced manufacturing techniques, and materials, consistent with Advanced Technology in Hydropower Vision (DOE, 2016); both CAPEX and O&M cost reductions implemented.
A range of literature projections is shown for comparison. The Mid and Low cost cases use a mix of inputs based on EIA technological learning assumptions, input from a technical team of Oak Ridge National Laboratory researchers, and the experience of expert hydropower consultants. Estimated 2035 cost levels are intended to provide order-of-magnitude cost reductions deemed to be at least conceptually possible, and they are meant to stimulate a broader discussion with the hydropower industry and its stakeholders that will be necessary to the future of cost reduction in the industry. Cost projections were derived independently for NPD and NSD technologies.
For context, ATB cost projections are compared to the literature, which represents 7 independent published studies and 11 cost projection scenarios within these studies. Cost reduction literature for hydropower is limited with several studies projecting no change through 2050. It is unclear whether (1) this represents a deliberate estimate of no future change in cost or (2) no estimate has been made.
Hydropower investment costs are very site specific and vary with type of technology. Literature was reviewed to attempt to isolate perceived CAPEX reduction for resources of similar characteristics over time (e.g., estimated cost to develop the same site in 2015, 2030, and 2050 based on different technology, installation, and other technical aspects). Some studies reflect increasing CAPEX over time. These studies were excluded from the ATB based on the interpretation that rising costs reflect a transition to less attractive sites as the better sites are used earlier.
Literature estimates generally reflect hydropower facilities of sizes similar to those represented in U.S. resource potential (i.e., they exclude estimates for very large facilities). Due to limited sample size, all projections are analyzed together without distinction between types of technology. Note that although declines are shown on a percentage basis, the reduction is likely to vary with initial capital cost. Large reductions for moderately expensive sites may not scale to more expensive sites or to less expensive sites. Projections derived for the Hydropower Vision study for different technologies (Low Head NPD, High Head NPD, and NSD) address this simplification somewhat.
Levelized Cost of Energy (LCOE) Projections
Levelized cost of energy (LCOE) is a summary 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 such as hydropower can 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 hydropower 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 but not in the upfront capital costs (e.g., developer fees and contingencies). An individual technology may receive more favorable financing terms outside the United States, 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, and changing interest rates over time. The R&D + Market LCOE case adds to these financial assumptions: (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform and tax credits. The representative plant characteristics in the ATB that best align with those of recently installed or anticipated near-term hydropower plants are associated with NPD 4. Data for all the resource categories can be found in the ATB Data spreadsheet; for simplicity, not all resource categories are shown in the figures.
R&D Only | R&D + Market
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:
- Constant Technology: Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
- 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.
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. Two project finance structures are used within the ATB:
- R&D Only Financial Assumptions: 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 2017 values from AEO2019 (EIA, 2019) and excludes effects of tax reform and tax credits.
- 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 AEO2019 (EIA, 2019), as well as effects of tax reform and tax credits. For a detailed discussion of these assumptions, see Project Finance Impact on LCOE.
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 can be varied in the ATB data spreadsheet.
The equations and variables used to estimate LCOE are defined on the Equations and Variables page. For illustration of the impact of changing financial structures such as WACC, see Project Finance Impact on LCOE. For LCOE estimates for the Constant, Mid, and Low technology cost scenarios for all technologies, see 2019 ATB Cost and Performance Summary.
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.
- Widespread implementation of value engineering and design/construction best practices
- Modular "drop-in" systems that minimize civil works and maximize ease of manufacture reduce both capital investment and O&M expenditures
- Use of alternative materials in place of steel for water diversion (e.g., penstocks)
- Implementation of standardized "smart" automation and remote monitoring systems to optimize scheduling of maintenance
- Research and development on environmentally enhanced turbines to improve performance of the existing hydropower fleet
- Efficient, certain, permitting, licensing, and approval procedures.
The Hydropower Vision study (DOE, 2016) includes road map actions that result in lower-cost technology.
Natural Gas Plants
The ATB includes three natural gas power plant types: a natural gas combustion turbine (gas-CT) and a natural gas combined cycle system (gas-CC) and a natural gas combined cycled system with carbon capture and storage (gas-CC-CCS). The cost and performance characteristics of these plants are adapted from EIA data rather than derived from original analysis.
Natural gas plant CAPEX is taken from the AEO2019 (EIA, 2019a) with the adjustments discussed in the CAPEX definition section. The ATB includes only a single CAPEX projection for each type of natural gas plant.
Comparison with Other Sources
Costs vary due to differences in configuration (e.g., 2x1 versus 1x1), turbine class, and methodology. All costs were converted to the same dollar year.
CAPEX Definition
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
Overnight capital costs are modified from Table 123 of the AEO2019 Reference scenario (EIA, 2019a).
EIA reports two types of gas-CT and gas-CC technologies in EIA's Annual Energy Outlook: advanced (H-class for gas-CC, F-class for gas-CT) and conventional (F-class for gas-CC, LM-6000 for gas-CT). Because we represent a single gas-CT and gas-CC technology in the ATB, the characteristics for the ATB plants are taken to be the average of the advanced and conventional systems as reported by EIA. For example, the overnight capital cost for the gas-CC technology in the ATB is the average of the capital cost of the advanced and conventional combined cycle technologies from the Annual Energy Outlook. The EIA only has a single advanced technology for gas-CC-CCS, which we use as the basis for that plant type in the ATB. The CCS plant configuration includes only the cost of capturing and compressing the CO2. It does not include CO2 delivery and storage.
The EIA projections were further adjusted by removing the material price index. The material price index accounts for projected changes in the price index for metals and metals products, and it is independent of the learning-based cost reductions applied in the EIA projections.
| Overnight Capital Cost ($/kW) | Construction Financing Factor (ConFinFactor) | CAPEX ($/kW) | |
| Gas-CT: National-gas-fired combustion turbine | $899 | 1.022 | $919 |
| Gas-CC: National-gas-fired combined cycle | $906 | 1.022 | $927 |
| Gas-CC-CCS: Combined cycle with carbon capture sequestration | $2,242 | 1.022 | $2,292 |
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 each type of gas plant with a unique value. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by (EIA, 2016) expand the range of CAPEX. Unique land-based spur line costs based on distance and transmission line costs are not estimated. The following figure illustrates the ATB representative plant relative to the range of CAPEX including regional costs across the contiguous United States. The ATB representative plants are associated with a regional multiplier of 1.0.
Operation and Maintenance (O&M) Costs
Natural gas plant fixed and variable O&M costs are taken from table 8.2 of the AEO2019, and they are assumed to be constant over time.
Capacity Factor: Expected Annual Average Energy Production Over Lifetime
The capacity factor represents the assumed annual energy production divided by the total possible annual energy production, assuming the plant operates at rated capacity for every hour of the year. For natural gas plants, the capacity factor is typically lower (and, in the case of combustion turbines, much lower) than their availability factor. Natural gas plants have availability factors approaching 100%.
The capacity factors of dispatchable units is typically a function of the unit's marginal costs and local grid needs (e.g., need for voltage support or limits due to transmission congestion). The average capacity factor is the average fleet-wide capacity factor for these plant types in 2017. The high capacity factor is taken from Table 1a of (EIA, 2019a) for a new power plant and represents a high bound of operation for a plant of this type.
Gas-CT power plants are less efficient than gas-CC power plants, and they tend to run as intermediate or peaker plants.
Gas-CC with CCS has not yet been built, but when built it is expected to operate as a baseload unit.
Levelized Cost of Energy (LCOE) Projections
Levelized cost of energy (LCOE) is a summary 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, fuel prices, and capacity factor projections for natural gas 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 but not in the upfront capital costs (e.g., developer fees and contingencies). An individual technology may receive more favorable financing terms outside the United States, 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, and changing interest rates over time. The R&D + Market LCOE case adds to these financial assumptions: (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform and tax credits (the 45Q tax credits are not included in this year's ATB). The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term natural gas plants are associated with Gas-CC-HighCF. Data for all the resource categories can be found in the ATB Data spreadsheet; for simplicity, not all resource categories are shown in the figures; for simplicity, not all resource categories are shown in the figures. Variations in LCOE among the low, mid, and high projections for natural gas plants are driven by fuel price differences only.
R&D Only | R&D + Market
The LCOE of natural gas plants is directly impacted by the price of the natural gas fuel, so we include low, mid, and high natural gas price trajectories. The LCOE is also impacted by variations in the heat rate and O&M costs. Because the reference and high natural gas price projections from AEO2019 (EIA, 2019a) are rising over time, the LCOE of new natural gas plants can increase over time if the gas prices rise faster than the capital costs decline. For a given year, the LCOE assumes that the fuel prices from that year continue throughout the lifetime of the plant.
These projections do not include any cost of carbon, which would influence the LCOE of fossil units. Also, for CCS plants, the potential revenue from selling the captured carbon is not included (e.g., enhanced oil recovery operations may purchase CO2 from a CCS plant).
Fuel prices are based on the AEO2019.
LCOE is sensitive to assumptions about the financing of electricity generation projects. Two project finance structures are used within the ATB:
- R&D Only Financial Assumptions: 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 2017 values from AEO2019 (EIA, 2019a) and excludes effects of tax reform and tax credits. A constant cost recovery period-or period over which the initial capital investment is recovered-of 30 years is assumed for all technologies.
- 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 AEO2019 (EIA, 2019a) as well as effects of tax reform and tax credits. As in the R&D Only case, a constant cost recovery period-or period over which the initial capital investment is recovered-of 30 years is assumed for all technologies. For a detailed discussion of these assumptions, see Project Finance Impact on LCOE.
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.
The equations and variables used to estimate LCOE are defined on the Equations and Variables page. For illustration of the impact of changing financial structures such as WACC, see Project Finance Impact on LCOE. For LCOE estimates for the Constant, Mid, and Low technology cost scenarios for all technologies, see 2019 ATB Cost and Performance Summary.
Coal
The ATB includes three coal power plant types: coal-new, coal-IGCC, and coal-CCS. The cost and performance characteristics of these plants are adapted from EIA data rather than derived from original analysis.
Capital Expenditures (CAPEX): Historical Trends, Current Estimates, and Future Projections
Coal power plant CAPEX is taken from the AEO2019 Reference Scenario (EIA, 2019a) with the adjustments discussed in the CAPEX definition section. The ATB includes only a single CAPEX projection for each type of coal plant.
Comparison with Other Sources
Lazard (2016) does not explicitly define its ranges with and without CCS; thus, the high end of their pulverized coal and IGCC ranges and the low end of their IGCC-CCS range are assumed to be the middle of the full reported range. All sources have been normalized to the same dollar year. Costs vary due to differences in system design (e.g., coal rank), methodology, and plant cost definitions. The coal capital costs include environmental controls to meet current federal regulations.
CAPEX Definition
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
For coal power plants, CAPEX equals interest during construction (ConFinFactor) times the overnight capital cost (OCC).
Overnight capital costs are modified from AEO2019 (EIA, 2019a). The EIA projections were adjusted by removing the material price index. The material price index accounts for projected changes in the price index for metals and metals products, and it is independent of the learning-based cost reductions applied in the EIA projections.
For the ATB, coal-CCS technology is ultra-supercritical pulverized coal technology fitted with CCS. Both 30% capture and 90% capture options are included for the coal-CCS technology. The CCS plant configuration includes only the cost of capturing and compressing the CO2. It does not include CO2 delivery and storage.
| Overnight Capital Cost ($/kW) | Construction Financing Factor (ConFinFactor) | CAPEX ($/kW) | |
| Coal-new: Ultra-supercritical pulverized coal with SO2 and NOx controls | $3,711 | 1.087 | $4,036 |
| Coal-IGCC: Integrated gasification combined cycle (IGCC) | $4,055 | 1.087 | $4,409 |
| Coal-CCS: Ultra-supercritical pulverized coal with carbon capture and sequestration (CCS) options (30% / 90% capture) | $5,180 / $5,728 | 1.087 | $5,633 / $6,229 |
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 each type of coal plant with a unique value. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by (EIA, 2016) expand the range of CAPEX. Unique land-based spur line costs based on distance and transmission line costs are not estimated. The following figure illustrates the ATB representative plant relative to the range of CAPEX including regional costs across the contiguous United States. The ATB representative plants are associated with a regional multiplier of 1.0.
Operation and Maintenance (O&M) Costs
Coal power plant fixed and variable O&M costs are taken from table 8.2 of the AEO2019, and they are assumed to be constant over time.
Capacity Factor: Expected Annual Average Energy Production Over Lifetime
The capacity factor represents the assumed annual energy production divided by the total possible annual energy production, assuming the plant operates at rated capacity for every hour of the year. For coal plants, the capacity factors are typically lower than their availability factors. Coal plant availability factors have a wide range depending on system design and maintenance schedules.
The capacity factor of dispatchable units is typically a function of the unit's marginal costs and local grid needs (e.g., need for voltage support or limits due to transmission congestion).
Coal power plants have typically been operated as baseload units, although that has changed in many locations due to low natural gas prices and increased penetration of variable renewable technologies. The average capacity factor used in the ATB is the fleet-wide average reported by EIA for 2017. The high capacity factor represents a new plant that would operate as a baseload unit. New coal plants would likely be more efficient than existing coal plants, and therefore would be more likely to be dispatched more often, resulting in capacity factors closer to the "high" level than the "average" level, but actual capacity factors will vary based on local grid conditions and needs.
Even though IGCC and coal with CCS have experienced limited deployment in the United States, it is expected that their performance characteristics would be similar to new coal power plants.
Levelized Cost of Energy (LCOE) Projections
Levelized cost of energy (LCOE) is a summary 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.
References
The following references are specific to this page; for all references in this ATB, see References.2019 Wind Energy Technologies Office Funding Opportunity Announcement. (2019, April). U.S. Department of Energy, EERE.
AWS. (2012). Wind Resource of the United States: Mean Annual Wind Speed at 200m Resolution. Retrieved from https://aws-dewi.ul.com/assets/Wind-Resource-Map-UNITED-STATES-11x171.pdf
Black & Veatch. (2012). Cost and Performance Data for Power Generation Technologies. Retrieved from Black & Veatch Corporation website: https://www.bv.com/docs/reports-studies/nrel-cost-report.pdf
BNEF. (2018b). BloombergNEF - EPVAL Wind Inputs 2H 2018 - US - Onshore. BNEF.
Breyer, C., Afanasyeva, S., Brakemeier, D., Engelhard, M., Giuliano, S., Puppe, M., … Moser, M. (2017). Assessment of Mid-Term Growth Assumptions and Learning Rates for Comparative Studies of CSP and Hybrid PV-Battery Power Plants. AIP Conference Proceedings, 1850, 160001-1-160001–160009. https://doi.org/10.1063/1.4984535
CSP Today Global Tracker. (n.d.). Retrieved February 11, 2019, from New Energy Update website: http://tracker.newenergyupdate.com/tracker/projects
Danko, P. (2015, February 10). SolarReserve: Crescent Dunes Solar Tower Will Power Up in March, Without Ivanpah's Woes. Retrieved February 5, 2019, from Breaking Energy website: https://breakingenergy.com/2015/02/10/solarreserve-crescent-dunes-solar-tower-will-power-up-in-march-without-ivanpahs-woes/
DOE EERE. (2019, February). Advanced Next-Generation High Efficiency Lightweight Wind Turbine Generator. U.S. Department of Energy, EERE.
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. (2012). SunShot Vision Study. Retrieved from https://www1.eere.energy.gov/solar/pdfs/47927.pdf
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. (2018, May 8). Pathways to Success for Next-Generation Supersized Wind Turbine Blades. U.S. Department of Energy, EERE.
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
EERE. (n.d.). Goals of the Solar Energy Technologies Office. Retrieved March 16, 2019, from DOE Office of Energy Efficiency and Renewable Energy website: https://www.energy.gov/eere/solar/goals-solar-energy-technologies-office
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
Feldman, D., Margolis, R., Denholm, P., & Stekli, J. (2016). Exploring the Potential Competitiveness of Utility-Scale Photovoltaics plus Batteries with Concentrating Solar Power, 2015-2030 (No. NREL/TP-6A20-66592). https://doi.org/10.2172/1321487
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
IRENA. (2018). Renewable Power Generation Costs in 2017. Retrieved from International Renewable Energy Agency website: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdf
Kao, S.-C., McManamay, R. A., Stewart, K. M., Samu, N. M., Hadjerioua, B., DeNeale, S. T., … Smith, B. T. (2014). New Stream-Reach Development: A Comprehensive Assessment of Hydropower Energy Potential in the United States (No. ORNL/TM-2013/514). https://doi.org/10.2172/1130425
Kistner, R. (2016). Update on recent developments in the CSP technology. Retrieved from https://www.giz.de/de/downloads/giz2016_en_CSP%20Update_Abu%20Dhabi.pdf
Kost, C., Shammugam, S., Julch, V., Huyen-Tran, N., & Schlegl, T. (2018). Levelized Cost of Electricity Renewable Energy Technologies. Retrieved from Fraunhofer website: https://www.ise.fraunhofer.de/content/dam/ise/en/documents/publications/studies/EN2018_Fraunhofer-ISE_LCOE_Renewable_Energy_Technologies.pdf
Kurup, P., & Turchi, C. (2015). Parabolic Trough Collector Cost Update for the System Advisor Model (SAM) (No. NREL/TP-6A20-65228). https://doi.org/10.2172/1227713
Lazard. (2016). Lazard's Levelized Cost of Energy Analysis: Version 10.0. Retrieved from Lazard website: https://www.lazard.com/media/438038/levelized-cost-of-energy-v100.pdf
Lilliestam, J., Labordena, M., Patt, A., & Pfenninger, S. (2017). Empirically Observed Learning Rates for Concentrating Solar Power and their Responses to Regime Change. Nature Energy, 2(7), 17094. https://doi.org/10.1038/nenergy.2017.94
Lopez, A., Roberts, B., Heimiller, D., Blair, N., & Porro, G. (2012). U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis (Technical Report No. NREL/TP-6A20-51946). https://doi.org/10.2172/1219777
Mehos, M., Kabel, D., & Smithers, P. (2009). Planting the Seed: Greening the Grid with Concentrating Solar Power. IEEE Power and Energy Magazine, 7(3), 55–62. https://doi.org/10.1109/MPE.2009.932308
Mehos, M., Turchi, C., Vidal, J., Wagner, M., & Ma, Z. (2017). Concentrating Solar Power Gen3 Demonstration Roadmap (No. NREL/TP-5500-67464). https://doi.org/10.2172/1338899
Moné, C., Smith, A., Maples, B., & Hand, M. (2015). 2013 Cost of Wind Energy Review (No. NREL/TP-5000-63267). https://doi.org/10.2172/1172936
Moné, Christopher, Hand, M., Bolinger, M., Rand, J., Heimiller, D., & Ho, J. (2017). 2015 Cost of Wind Energy Review (No. NREL/TP-6A20-66861). https://doi.org/10.2172/1351062
Murphy, C., Sun, Y., Cole, W., Maclaurin, G., Turchi, C., & Mehos, M. (2019). The Potential Role of Concentrating Solar Power Within the Context of DOE's 2030 Solar Cost Targets (No. NREL/TP-6A20-71912). https://doi.org/10.2172/1491726
MWH. (2009). Hydropower Modernization Initiative, Phase I, Needs and Opportunities Evaluation and Ranking (No. Contract No. W9127N-08-D-0003. Task Order 001.). Montgomery Watson Harza.
NREL, & SolarPaces. (n.d.). Concentrating Solar Power Projects. Retrieved February 11, 2019, from https://solarpaces.nrel.gov/
NREL. (n.d.a). National Solar Radiation Database (NSRDB) Data Viewer. Retrieved March 17, 2019, from NREL Geospatial Data Science Data and Tools website: https://maps.nrel.gov/nsrdb-viewer/?aL=f69KzE%255Bv%255D%3Dt&bL=H7Qphn&cE=0&lR=f69KzE.0%255Ba%255D%3Df%26f69KzE.1%255Ba%255D%3Df%26f69KzE.2%255Ba%255D%3Df%26f69KzE.3%255Ba%255D%3Df%26f69KzE.4%255Ba%255D%3Df%26f69KzE.5%255Ba%255D%3Df%26f69KzE.7%255Ba%255D%3Df%26f69KzE.8%255Ba%255D%3Df&mC=33.92626920481366%2C-110.75248718261719&zL=12
NREL. (n.d.b). System Advisor Model (SAM). Retrieved January 26, 2018, from National Renewable Energy Laboratory website: https://sam.nrel.gov/
O'Connor, P. W., Zhang, Q. F. (Katherine), DeNeale, S. T., Chalise, D. R., & Centurion, E. E. (2015). Hydropower Baseline Cost Modeling (No. ORNL/TM-2015/14). https://doi.org/10.2172/1185882
Roberts, B. J. (2018). Map of solar resource in contiguous United States. Retrieved from https://www.nrel.gov/gis/assets/pdfs/solar_dni_2018_01.pdf
Rubin, E. S., Davison, J. E., & Herzog, H. J. (2015). The Cost of CO2 Capture and Storage. International Journal of Greenhouse Gas Control, 40, 378–400. https://doi.org/10.1016/j.ijggc.2015.05.018
Shemer, N. (2018, April 16). CSP capex costs fall by almost half as developers shift towards China and Middle East. Retrieved April 24, 2019, from http://newenergyupdate.com/csp-today/csp-capex-costs-fall-almost-half-developers-shift-towards-china-and-middle-east
Shreve, D. (2018). United States Wind Energy Market Outlook. Wood Mackenzie.
SolarReserve. (n.d.). Sandstone (project overview). Retrieved February 11, 2019, from SolarReserve website: https://www.solarreserve.com/en/global-projects/csp/sandstone
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
Stehly, T., Beiter, P., Heimiller, D., & Scott, G. (2020). 2018 Cost of Wind Energy Review (Technical Report No. NREL/TP-5000-74598). Retrieved from National Renewable Energy Laboratory website: https://www.nrel.gov/docs/fy20osti/74598.pdf
Taylor, P. (2016, March 29). Nev. Plant Solves Quandary of How to Store Sunshine. E&E Greenwire. Retrieved from https://www.eenews.net/stories/1060034748
Turchi, C. (2010). Parabolic Trough Reference Plant for Cost Modeling with the Solar Advisor Model (SAM) (No. NREL/TP-550-47605). https://doi.org/10.2172/983729
Turchi, Craig, & Heath, G. (2013). Molten Salt Power Tower Cost Model for the System Advisor Model (SAM) (No. NREL/TP-5500-57625). https://doi.org/10.2172/1067902
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
Wiser, R., & Bolinger, M. (2018). 2017 Wind Technologies Market Report (No. DOE/EE-1798). https://doi.org/10.2172/1471044
Wiser, R., Bolinger, M., & Lantz, E. (2019). Assessing Wind Power Operating Costs in the United States: Results from a Survey of Wind Industry Experts. Renewable Energy Focus.
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
World Bank. (2014). Project Appraisal Document on a Proposed Loan in the Amount of EUR234.50 Million and US$80 Million (US$400 Million Equivalent) and a Proposed Loan from the Clean Technology Fund in the Amount of US$119 Million to the Moroccan Agency for Solar Energy with Guarantee from the Kingdom of Morocco for the Noor-Ouarzazate Concentrated Solar Power Plant Project (No. PAD1007). Retrieved from The World Bank website: http://documents.worldbank.org/curated/en/748641468279941398/pdf/PAD10070PAD0P100disclosed0120220140.pdf
Zoelle, A., Keairns, D., Pinkerton, L. L., Turner, M. J., Woods, M., Kuehn, N., … Chou, V. (2015). Cost and Performance Baseline for Fossil Energy Plants Volume 1a: Bituminous Coal (PC) and Natural Gas to Electricity Revision 3 (No. DOE/NETL-2015/1723). https://doi.org/10.2172/1480987