For each electricity generation technology in the ATB, this website provides:

• Capital expenditures (CAPEX): the definition of CAPEX used in the ATB and the historical trends, current estimates, and future projections of CAPEX used in the ATB
• Operations and maintenance (O&M) costs: the definition of O&M and the current estimates and future projections of O&M used in the ATB
• Capacity factor (CF): the definition of CF and the historical trends, current estimates, and future projections of CF used in the ATB
• Future cost and performance methods: an outline of the methodology used to make the projections of future cost and performance in the ATB for Constant, Mid, and Low technology cost cases
• Levelized cost of energy (LCOE): metric that combines CAPEX, O&M, CF, and projections for Constant, Mid, and Low technology cost cases for illustration of the combined effect of the primary cost and performance components and discussion of technology advances that yield future projections
• Financing assumptions: development of technology-specific interest rate on debt, return on equity, and debt-to-equity ratios and their impact on LCOE are documented in each technology section, where applicable, and summarized here.

Electricity generation technologies are selected on the left side of the screen, and the topics highlighted above can be selected using the drop-down menu at the top right of the screen.

Guidelines for using and interpreting ATB content and comparisons to other literature are provided. LCOE accounts for many variables important to determining the competitiveness of building and operating a specific technology (e.g., upfront capital costs, capacity factor, and cost of financing); however, it does not necessarily demonstrate which technology in a given place and time would provide the lowest cost option for the electricity grid. Such analysis is performed using electric sector models such as the Regional Energy Deployment Systems (ReEDS) model and corresponding analysis results such as the NREL Standard Scenarios.

The NREL Standard Scenarios, a companion product to the ATB, provides a suite of electric sector scenarios and associated assumptions, including technology cost and performance assumptions from the ATB.

ATB data sources and references are also provided for each technology. All dollar values are presented in 2017 U.S. dollars, unless noted otherwise.

Additional information is available here: About the 2019 ATB.

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.

Representative Technology

In 2016, the first offshore wind plant (30 MW) commenced commercial operation in the United States near Block Island (Rhode Island). In 2018, Vineyard Wind LLC and Massachusetts electric distribution companies submitted a 20-year power purchase agreement (PPA) for 800 MW of offshore wind generation and renewable energy certificates to the Massachusetts Department of Public Utilities for review and approval. In the ATB, cost and performance estimates are made for commercial-scale projects with 600 MW in capacity. The ATB Base Year offshore wind plant technology reflects a machine rating of 6 MW with a rotor diameter of 155 m and hub height of 100 m, which is typical of European projects installed in 2016 and 2017 and the turbine rating installed at the Block Island Wind Farm (GE Haliade 150-6MW).

Resource Potential

Wind resource is prevalent throughout major U.S. coastal areas, including the Great Lakes. The resource potential exceeds 2,000 GW, excluding Alaska, based on domain boundaries from 50 nautical miles (nm) to 200 nm, turbine hub heights of 100 m (previously 90 m), and a capacity array power density of 3 MW/km²(Walt Musial et al. 2016). A range of technology exclusions were applied based on maximum water depth for deployment, minimum wind speed, and limits to floating technology in freshwater surface ice. Resource potential was represented by more than 7,000 areas for offshore wind plant deployment after accounting for competing use and environmental exclusions, such as marine protected areas, shipping lanes, pipelines, and others.

Base Year and Future Year Projections Overview

Based on a resource assessment (Walt Musial et al. 2016), LCOE was estimated at more than 7,000 areas (with a total capacity of approximately 2,000 GW). Using an updated version of NREL's Offshore Regional Cost Analyzer (ORCA) (Beiter et al. 2016), a variety of spatial parameters were considered, such as wind speeds, water depth, distance from shore, distance to ports, and wave height. CAPEX, O&M, and capacity factor are calculated for each geographic location using engineering models, hourly wind resource profiles, and representative sea states. ORCA was calibrated to the latest cost and technology trends observed in the U.S. and European offshore wind markets, including:

• Turbine CAPEX: $1,300/kW were assumed for the Base Year to account for decreases in Turbine CAPEX that were observed in global offshore wind markets.
• Turbine Rating: A turbine technology trend of 6 MW (Base Year), 8 MW (2022 commercial operation date [COD]), 10 MW (2027 COD), and 10 MW (2032 COD) was assumed to correspond to recent technology trends. While higher turbine ratings may be commercially available during this period (e.g., up to 15-MW turbine ratings are expected to be commercially available by the early 2030s), the selected turbine ratings are intended to reflect the average turbine rating of the operating U.S. offshore wind fleet.
• Export System Cable Costs: A reduction of 25% in comparison to an earlier version of ORCA (Beiter et al. 2016) was assumed for the Base Year to account for increased competition within the supply chain in recent years and for changes in material use and commodity prices.
• Cost Reduction Trajectory: Recent literature was surveyed to identify the most up-to-date cost reduction trends expected for U.S. and European offshore wind projects; the version of ORCA used to estimate cost reduction trajectories are derived from Valpy et al. (2017)(fixed bottom) and Hundleby et al. (2017)(floating).
• Contingency Levels: A markup of 25% above of European contingency levels was assumed to account for the higher risk of installing and operating early offshore wind farms in the United States.
• Lease Area Price: This cost to projects was included to account for the recent increase in the tendered U.S. lease area prices.

The spatial LCOE assessment served as the basis for estimating the ATB baseline LCOE in the Base Year 2017, weighted by the available capacity, for fixed-bottom and floating offshore wind technology. Only sites that exceed a distance to cable landfall of 20 kilometers (km) and a water depth of 10 meters (m) were included in the spatial assessment for the ATB to represent those sites only that are likely to be developed in the near-to-medium term. Long-term average hourly wind profiles are assumed and estimated LCOE reflects the least-cost choice among four substructure types (Beiter et al. 2016):

• Monopile (fixed-bottom)
• Jacket (fixed-bottom)
• Semi-submersible (floating)
• Spar (floating).

The representative offshore wind plant size is assumed to be 600 MW (Beiter et al. 2016). For illustration in the ATB, the full resource potential, represented by 7,000 areas, was divided into 15 techno-resource groups (TRGs), of which TRGs 1-5 are representative of fixed-bottom wind technology and TRGs 6-15 are representative of floating offshore wind technology. The capacity-weighted average CAPEX, O&M, grid connection costs, and capacity factor for each group are presented in the ATB.

For illustration in the ATB, all potential offshore wind plant areas were represented in 15 TRGs with TRGs 1-5 representing fixed-bottom offshore technology and TRGs 6-15 representing floating offshore wind technology. These were defined by resource potential (GW) and have higher resolution on the least-cost TRGs, as these are the most likely sites to be deployed, based on their economics. The following table summarizes the capacity-weighted average water depth, distance to cable landfall, and cost and performance parameters for each TRG. The table includes the relative resource potential. Typical offshore wind installations in 2017 that have spatial and economic conditions that may lend themselves well to near-term deployment are associated with TRG 3.

TRG Definitions for Offshore Wind

Future year projections for CAPEX, O&M, and capacity factor in the "Mid Technology Cost" Scenario are derived from the estimated cost reduction potential for offshore wind technologies based on assessments by BVG Associates ((Valpy et al. 2017), (Hundleby et al. 2017)) for 2017-2032. Based on the modeled data between 2017-2032, an (exponential) trend fit is derived for CAPEX, O&M in the "Mid Technology Cost" Scenario, and capacity factor to extrapolate ATB data for years 2033-2050. The specific scenarios are:

• Constant Technology Cost Scenario: no change in CAPEX, O&M, or capacity factor from 2015 to 2050; consistent across all renewable energy technologies in the ATB.
• Mid Technology Cost Scenario: LCOE percentage reduction estimated from BVG ((Valpy et al. 2017), (Hundleby et al. 2017)) from the Base Year, which is intended to correspond to a 50% probability of exceedance.
• Low Technology Cost Scenario: LCOE deviation from the Mid Technology Cost Scenario in CAPEX, O&M, or capacity factor from 2017 to 2050 informed by analysts' bottom-up technology and cost modeling. This scenario is intended to correspond to a 10%-30% probability of exceedance.

Representative Technology

Utility-scale PV systems in the ATB are representative of one-axis tracking systems with performance and pricing characteristics in line with a 1.3 DC-to-AC ratio-or inverter loading ratio (ILR) (Fu, Feldman, and Margolis 2018). PV system performance characteristics in previous ATB versions were designed in the ReEDS model at a time when PV system ILRs were lower than they are in current system designs; performance and pricing in the 2019 ATB incorporates more up-to-date system designs and therefore assumes a higher ILR.

Resource Potential

Solar resources across the United States are mostly good to excellent at about 1,000-2,500 kWh/m²/year. The Southwest is at the top of this range, while Alaska and part of Washington are at the low end. The range for the contiguous United States is about 1,350-2,500 kWh/m²/year. Nationwide, solar resource levels vary by about a factor of two.

The total U.S. land area suitable for PV is significant and will not limit PV deployment. One estimate (Denholm and Margolis 2008) suggests the land area required to supply all end-use electricity in the United States using PV is about 5,500,000 hectares (ha) (13,600,000 acres), which is equivalent to 0.6% of the country's land area or about 22% of the "urban area" footprint (this calculation is based on deployment/land in all 50 states).

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").

Base Year and Future Year Projections Overview

The Base Year estimates rely on modeled CAPEX and O&M estimates benchmarked with industry and historical data. Capacity factor is estimated based on hours of sunlight at latitude for five representative geographic locations in the United States. The ATB presents capacity factor estimates that encompass a range associated with Low, Mid, and Constant technology cost scenarios across the United States.

Future year projections are derived from analysis of published projections of PV CAPEX and bottom-up engineering analysis of O&M costs. 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: based on the median of literature projections of future CAPEX; O&M technology pathway analysis
• Low Technology Cost Scenario: based on the low bound of literature projections of future CAPEX and O&M technology pathway analysis.

Representative Technology

For the ATB, commercial PV systems are modeled for a 300-kW_DC fixed-tilt (5°), roof-mounted system. Flat-plate PV can take advantage of direct and indirect insolation, so PV modules need not directly face and track incident radiation. This gives PV systems a broad geographical application, especially for commercial PV systems.

Resource Potential

Solar resources across the United States are mostly good to excellent at about 1,000-2,500 kWh)/m²/year. The Southwest is at the top of this range, while only Alaska and part of Washington are at the low end. The range for the contiguous United States is about 1,350-2,500 kWh/m²/year. Nationwide, solar resource levels vary by about a factor of two.

Distributed-scale PV is assumed to be configured as a fixed-tilt, roof-mounted system. Compared to utility-scale PV, this reduces both the potential capacity factor and amount of land (roof space) that is available for development. A recent study of rooftop PV technical potential (Gagnon et al. 2016) estimated that as much as 731 GW (926 TWh/yr) of potential exists for small buildings (< 5,000 m² footprint) and 386 GW (506 TWh/yr) for medium (5,000-25,000 m²) and large buildings (> 25,000 m²).

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").

Base Year and Future Year Projections Overview

The Base Year estimates rely on modeled CAPEX and O&M estimates benchmarked with industry and historical data. Capacity factor is estimated based on hours of sunlight at latitude for five representative geographic locations in the United States.

Future year projections are derived from analysis of published projections of PV CAPEX and bottom-up engineering analysis of O&M costs. 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: based on the median of literature projections of future CAPEX and O&M technology pathway analysis
• Low Technology Cost Scenario: based on the low bound of literature projections of future CAPEX and O&M technology pathway analysis.

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/W_AC ((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).

Geothermal technology cost and performance projections have been updated with analysis and results from the GeoVision: Harnessing the Heat Beneath our Feet report (DOE, 2019). The GeoVision report is a collaborative multiyear effort with contributors from industry, academia, national laboratories, and federal agencies. The analysis in the report updates resource potential estimates as well as current and projected capital and O&M costs based on rigorous, bottom-up modeling.

Representative Technology

Hydrothermal geothermal technologies encompass technologies for exploring for the resource, drilling to access the resource, and building power plants to convert geothermal energy to electricity. Technology costs depend heavily on the hydrothermal resource temperature and well productivity and depth, so much so that project costs are site-specific and applying a "typical" cost to any given site would be inaccurate. The 2019 ATB uses scenarios developed by the DOE Geothermal Technology Office (Mines, 2013) for representative binary and flash hydrothermal power plant technologies. The first scenario assumes a 175°C resource at a depth of 1.5 km with wells producing an average of 110 kg/s of geothermal brine supplied to a 30-MW_e binary (organic Rankine cycle) power plant. The second scenario assumes a 225°C resource at a depth of 2.5 km with wells producing 80 kg/s of geothermal brine supplied to a 40-MW_e dual-flash plant. These are mid-grade or "typical" temperatures and depths for binary and flash hydrothermal projects. The ReEDS model uses the full hydrothermal supply curve. The 2019 ATB representative technologies fall in the middle or near the end of the hydrothermal resources typically deployed in ReEDS model runs.

Resource Potential

The hydrothermal geothermal resource is concentrated in the western United States. The total mean potential is 39,090 MW: 9,057 MW identified and 30,033 MW undiscovered (USGS, 2008). The resource potential identified by the U.S. Geological Survey (USGS, 2008) at each site is based on available reservoir thermal energy information from studies conducted at the site. The undiscovered hydrothermal technical potential estimate is based on a series of GIS statistical models for the spatial correlation of geological factors that facilitate the formation of geothermal systems.

The U.S. Geological Survey resource potential estimates for hydrothermal were used with the following modifications:

• Installed capacity of about 3 GW in 2016 is excluded from the resource potential.
• Resources on federally protected and U.S. Department of Defense (DOD) lands, where development is highly restricted are excluded from the resource potential, as are resources on lands where significant barriers that prevent or inhibit development of geothermal projects were identified by Augustine, Ho, and Blair (2019).

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").

Base Year and Future Year Projections Overview

The Base Year cost and performance estimates are calculated using Geothermal Electricity Technology Evaluation Model (GETEM), a bottom-up cost analysis tool that accounts for each phase of development of a geothermal plant (DOE "Geothermal Electricity Technology Evaluation Model").

• Cost and performance data for hydrothermal generation plants are estimated for each potential site using GETEM. Model results are based on resource attributes (e.g., estimated reservoir temperature, depth, and potential) of each site. GETEM inputs are derived from the Business-as-Usual (BAU) scenario from GeoVision ( (DOE, 2019), (Augustine, Ho, & Blair, 2019)).
• Site attribute values are from (USGS, 2008) for identified resource potential and from capacity-weighted averages of site attribute values of nearby identified resources for undiscovered resource potential.
• GETEM is used to estimate CAPEX, O&M, and parasitic plant losses that affect net energy production.

Capacity factor and O&M costs for plants installed in future years are unchanged from the Base Year. Projections for hydrothermal and EGS technologies are equivalent.

• Constant Technology Cost Scenario: no change in CAPEX, O&M, or capacity factor from 2015 to 2050; consistent across all renewable energy technologies in the ATB
• Mid Technology Cost Scenario: CAPEX cost reduction based on assumed minimum learning as implemented in AEO (EIA, 2015): 10% by 2035; this corresponds to a 0.5% annual improvement in CAPEX, which is assumed to continue on through 2050
• Low Technology Cost Scenario: CAPEX based on the Technology Improvement scenario from GeoVision Study ( (DOE, 2019); (Augustine, Ho, & Blair, 2019)); cost and technology improvements change linearly from present values and are fully achieved by 2030.

Representative Technology

As with cost for projects that use hydrothermal resources, EGS resource project costs depend so heavily on the hydrothermal resource temperature and well productivity and depth that project costs are site-specific. The 2019 ATB uses scenarios developed by the DOE Geothermal Technology Office (Mines, 2013) for representative binary and flash EGS power plants assuming current (immature) EGS technology performance metrics. The first scenario assumes a 175°C resource at a depth of 3 km with wells producing an average of 40 kg/s of geothermal brine supplied to a 25-MW_e binary (organic Rankine cycle) power plant. The second scenario assumes a 250°C resource at a depth of 3.5 km with wells producing 40 kg/s of geothermal brine supplied to a 30-MW_e dual-flash plant. These temperatures and depths are at the low-cost end of the EGS supply curve and would be some of the first developed. The ReEDS model uses the full EGS supply curve. Neither of these technologies is typically used.

Resource Potential

The enhanced geothermal system (EGS) resource is concentrated in the western United States. The total potential is greater than 100,000 MW: 1,493 MW of near-hydrothermal field EGS (NF-EGS) and the remaining potential comes from deep EGS.

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, 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 there are multiple types of potential-resource, technical, economic, and market (see NREL: "Renewable Energy Technical Potential").

Base Year and Future Year Projections Overview

The Base Year cost and performance estimates are calculated using the Geothermal Electricity Technology Evaluation Model (GETEM), a bottom-up cost analysis tool that accounts for each phase of development of a geothermal plant (DOE "Geothermal Electricity Technology Evaluation Model").

• Cost and performance data for EGS generation plants are estimated for each potential site using GETEM. Model results based on resource attributes (e.g., estimated reservoir temperature, depth, and potential) of each site. GETEM inputs are derived from the GeoVision BAU scenario ( (DOE, 2019), (Augustine, Ho, & Blair, 2019)).
• Approaches to restrict resource potential to about 500 GW based on USGS analysis may be implemented in the future.
• GETEM is used to estimate CAPEX and O&M and parasitic plant losses that affect net energy production.

Capacity factor and O&M costs for plants installed in future years are unchanged from the Base Year. Projections for hydrothermal and enhanced geothermal system technologies are equivalent.

• Constant Technology Cost Scenario: no change in CAPEX, O&M, or capacity factor from 2015 to 2050, consistent across all renewable energy technologies in the ATB
• Mid Technology Cost Scenario: CAPEX cost reduction based on assumed minimum learning as implemented in AEO (EIA, 2015): 10% by 2035; this corresponds to a 0.5% annual improvement in CAPEX, which is assumed to continue on through 2050.
• Low Technology Cost Scenario: CAPEX based on the GeoVision Technology Improvement scenario ( (DOE, 2019), (Augustine, Ho, & Blair, 2019)). Cost and technology improvements decrease linearly from present values and are fully achieved by 2030.

Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. These expenditures include the geothermal 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. Three cost scenarios are represented: Constant, Mid, and Low technology cost. The estimate for a given year represents CAPEX of a new plant that reaches commercial operation in that year.

Base Year Estimates

For illustration in the ATB, six representative geothermal plants are shown. Two energy conversion processes are common: binary organic Rankine cycle and flash.

• Binary plants use a heat exchanger to transfer geothermal energy to an organic Rankine cycle. This technology generally applies to lower-temperature systems. These systems have higher CAPEX than flash systems because of the increased number of components, their lower-temperature operation, and a general requirement that a number of wells be drilled for a given power output.
• Flash plants create steam directly from the thermal fluid through a pressure change. This technology generally applies to higher-temperature systems. Due to the reduced number of components and higher-temperature operation, these systems generally produce more power per well, thus reducing drilling costs. These systems generally have lower CAPEX than binary systems.

Examples using each of these plant types in each of the three resource types (hydrothermal, NF-EGS, and deep EGS) are shown in the ATB.

Costs are for new or greenfield hydrothermal projects, not for re-drilling or additional development/capacity additions at an existing site.

Characteristics for the six examples of plants representing current technology were developed based on discussion with industry stakeholders. The CAPEX estimates were generated using GETEM. CAPEX for NF-EGS and EGS are equivalent.

The following table shows the range of OCC associated with the resource characteristics for potential sites throughout the United States.

Future Year Projections

Projection of future geothermal plant CAPEX for the Low case is based on the GeoVision Technology Improvement scenario (DOE, 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.

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 (DOI & Bureau of Reclamation, 2010) where available. Costs at non-reclamation plants were developed using (Hall, Hunt, Reeves, & Carroll, 2003) .

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 (Hadjerioua, Wei, & Kao, 2012) . This initial assessment of 54,391 dams resulted in 12 GW of capacity.

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 (O'Connor, DeNeale, Chalise, Centurion, & Maloof, 2015) . Capacity factors are estimated based on historical flow rates. For presentation in the ATB, a subset of resource potential is aggregated into four representative NPD plants that span a range of realistic conditions for future hydropower deployment.

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 (O'Connor, DeNeale, et al., 2015) . Capacity factors are estimated based on historical flow rates. For presentation in the ATB, a subset of resource potential is aggregated into four representative NSD plants that span a range of realistic conditions for future hydropower deployment.

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.

Energy storage technologies are important to document in the ATB because of their potential role in enhancing grid flexibility, especially under scenarios of high penetration of variable renewable technologies. CSP with TES and Hydropower both include storage capabilities, and a variety of other storage technologies could enhance the flexibility of the electrical grid. This section documents assumptions about only one of them: 4-hour, utility-scale, lithium-ion battery storage. NREL has completed recent analysis on ranges of costs related to other battery sizes (Fu, Remo, & Margolis, 2018) with relative costs represented in Figure ES-1 of the report (included below) which looked at 4-hour to 0.5 hour battery duration of utility scale plants.

The ATB does not currently have costs for distributed battery storage-either for residential nor commercial applications behind the meter nor for a micro-grid or off-grid application. NREL has completed prior work on residential battery plus solar PV system analysis (Ardani et al., 2017) resulting in a range of costs of PV+battery systems as shown in the figure below. Note these costs are for 2016 and published in 2017, so we anticipate battery costs to be significantly lower currently.

Base Year and Future Year Projections Overview

Battery cost and performance projections are based on a literature review of 25 sources published between 2016 and 2019, as described by Cole and Frazier (2019) . Three different projections from 2017 to 2050 were developed for scenario modeling based on this literature:

• High Technology Cost Scenario: generally based on the maximum of literature projections of future CAPEX and O&M technology pathway analysis; distinct from the Constant technology cost scenarios used among renewable energy technologies in the ATB
• Mid Technology Cost Scenario: generally based on the median of literature projections of future CAPEX and O&M technology pathway analysis
• Low Technology Cost Scenario: generally based on the low bound of literature projections of future CAPEX and O&M technology pathway analysis.

Standard Scenarios Model Results

ATB CAPEX, O&M, and round-trip efficiency assumptions for the Base Year and future projections through 2050 for High, Mid, and Low technology cost scenarios are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

Representative Technology

The representative technology was a utility-scale lithium-ion battery storage system with a 15-year life and a 4-hour rating, meaning it could discharge at its rated capacity for four hours as described by Cole and Frazier (2019) . Within the ATB spreadsheet, the costs are separated into energy and power cost estimates, which allow capital costs to be constructed for durations other than 4 hours according to the following equation:

For more information on the power vs. energy cost breakdown, see Cole and Frazier (2019) .

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.

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.

The ATB includes a single nuclear power plant type. The cost and performance of this plant type is adapted from EIA data rather than derived from original analysis.