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Offshore Wind

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.

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Map of the offshore resource in the United States

Source: (Walt Musial et al. 2016)

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

TRG		Weighted Water Depth (m)	Weighted Distance Site to Cable Landfall (km)	Weighted Average CAPEX ($/kW)	Weighted Average OPEX ($/kW/yr)	Weighted Average Net CF (%)	Potential Wind Plant Capacity of Fixed-Bottom/Floating Total (%)
Fixed-Bottom	TRG 1	18	27	3,361	105	45	2
	TRG 2	22	29	3,475	106	44	3
	TRG 3	24	33	3,660	109	44	7
	TRG 4	29	57	4,001	112	41	44
	TRG 5	31	65	4,633	107	30	44
Floating	TRG 6	144	38	4,602	77	52	1
	TRG 7	159	45	4,661	78	51	2
	TRG 8	157	46	4,710	78	49	4
	TRG 9	148	64	4,936	84	49	8
	TRG 10	107	101	5,209	91	47	15
	TRG 11	375	116	5,320	98	43	15
	TRG 12	467	116	5,331	97	36	15
	TRG 13	663	166	5,785	92	34	15
	TRG 14	432	147	6,145	87	30	15
	TRG 15	468	133	6,599	83	28	11

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.

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 spatial site parameters in the contiguous United States.

CAPEX Definition

Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.

Based on Moné at al. (2015) and Beiter et al. (2016), the System Cost Breakdown Structure of the ATB for the wind plant envelope is defined to include:

Wind turbine supply
Balance of system (BOS)

Turbine installation, substructure supply and installation
Site preparation, port and staging area support for delivery, storage, handling, and installation of underground utilities
Electrical infrastructure, such as transformers, switchgear, and electrical system connecting turbines to each other (array cable system costs) and to the cable landfall (export cable system costs)
Development and project management

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
Interest during construction estimated based on three-year duration accumulated 40%/40%/20% at half-year intervals and an 8% interest rate (ConFinFactor).

CAPEX can be determined for a plant in a specific geographic location as follows:

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

where GCC = OnSpurCost + OffSpurCost

(See the Financial Definitions tab in the ATB data spreadsheet.)

Regional cost variations are not included in the ATB (CapRegMult = 1). In the ATB, the input value is overnight capital cost (OCC) and details to calculate interest during construction (ConFinFactor). Because transmission infrastructure between an offshore wind plant and the point at which a grid connection is made onshore is a significant component of the offshore wind plant cost, an offshore spur line cost (OffSpurCost) for each TRG is included in the CAPEX estimate. The offshore spur line cost reflects a capacity-weighted average of all potential wind plant areas within a TRG, similar to OCC.

In the ATB, CAPEX represents the capacity-weighted average values of all potential wind plant areas within a TRG and varies with water depth and distance from shore. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by DOE and NREL (2015) expand the range of CAPEX. Unique land-based spur line costs for each of the 7,000 areas based on distance and transmission line costs expand the range of CAPEX even further. The following figure illustrates the ATB representative plants 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.

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R&D Only Financial Assumptions (constant background rates, no tax changes)

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 offshore wind plants installed globally are shown for comparison to the ATB Base Year estimates. The estimate for a given year represents CAPEX of a new plant that reaches commercial operation in that year.

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Historical data (global) shown in box-and-whiskers format where a bar represents the median, a box represents the 20th and 80th percentiles, and whiskers represent the minimum and maximum.

Year represents Commercial Online Date for a past or future plant. TRG is defined below.

R&D Only Financial Assumptions (constant background rates, no tax changes)

Recent Trends

Actual global offshore wind plant CAPEX is shown in box-and-whiskers format for comparison to the ATB current CAPEX estimates and future projections. CAPEX estimates for 2017 correspond well with market data for plants installed in 2017. Projections reflect a continuation of the downward trend observed in the recent past and are anticipated to continue based on preliminary data for 2017 projects.

Base Year Estimates

Base Year estimates for CAPEX were derived 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 water depth, distance from shore, distance to ports, and wave height to estimate CAPEX. CAPEX estimates were calibrated to correspond 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% on top 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.

Future Year Projections

Mid Technology Cost Scenario: CAPEX percentage reduction estimated from BVG ((Valpy et al. 2017) for fixed-bottom and (Hundleby et al. 2017)) from the Base Year, which is intended to correspond to a 50% probability of exceedance.
Low Technology Cost Scenario: CAPEX 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.

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.

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 2013).

The ReEDS model determines offshore spur line and land-based spur line (GCC) uniquely for each of the 7,000 areas based on distance and transmission line cost.

Operation and Maintenance (O&M) Costs

Operations and maintenance (O&M) costs represent the annual fixed expenditures required to operate and maintain a wind plant over its lifetime, including:

Insurance, taxes, land lease payments and other fixed costs (e.g., project management and administration, weather forecasting, and condition monitoring)
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 and floating 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. The range of Base Year O&M estimates reflect the variation in spatial parameters such as distance from shore and metocean conditions.

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Base Year Estimates

FOM costs vary by distance from shore and metocean conditions. As a result, O&M costs in the "Mid Technology Cost Scenario" vary from $87/kW-year (TRG 6) to $127/kW-year (TRG 4) in 2017. The average in the ATB "Mid Technology Cost Scenario" for fixed-bottom offshore technology (TRGs 1-5) is $121/kW-year; the corresponding value for floating offshore wind technology (TRGs 6-15) is $97/kW-year.

Future Year Projections

Future fixed-bottom offshore wind technology O&M is estimated to decline 66% by 2050 in the Mid cost case.

Future floating offshore wind technology O&M is estimated to decline 61% by 2050 in the Mid cost case.

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 the rotor swept area/generator capacity, hub height, hourly wind profile, expected downtime, and energy losses within the wind plant. It is referenced to 100-m above-water-surface, long-term average hourly wind resource data from Musial et al. (2016).

The following figure shows a range of capacity factors based on variation in the wind resource for offshore wind plants in the contiguous United States. Preconstruction estimates for offshore wind plants operating globally 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 illustrates the effect of locating an offshore wind plant in a variety of wind resource (TRGs 1-5 are fixed-bottom offshore wind plants and TRGs 6-15 are floating offshore wind plants). Future projections are shown for Mid and Low technology cost scenarios.

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Historical data shown in box-and-whiskers format where a bar represents the median, a box represents the 20^th and 80^th percentiles, and whiskers represent the minimum and maximum.

Historical data represent preconstruction capacity factor estimates for plants with Commercial Online Date specified by year.

Projection data represent expected annual average capacity factor for plants with Commercial Online Date specified by year.

Recent Trends

Preconstruction annual energy estimates from publicly available global operating wind capacity in 2017 (Walter Musial et al. forthcoming) are shown in a box-and-whiskers format for comparison with the ATB current estimates and future projections. The historical data illustrate preconstruction estimated capacity factors for projects by year of commercial online date. The figure shows the comparison between the range of capacity factors defined by the ATB TRGs and the reported capacity factors for projects installed in 2017.

Base Year Estimates

The capacity factor is determined using a representative power curve for a generic NREL-modeled 6-MW offshore wind turbine (Beiter et al. 2016) and includes geospatial estimates of gross capacity factors for the entire resource area (Walt Musial et al. 2016). The net capacity factor considers spatial variation in wake losses, electrical losses, turbine availability, and other system losses. For illustration in the ATB, all 7,000 wind plant areas are represented in 15 TRGs.

Future Year Projections

Projections of capacity factors for plants installed in future years were determined based on estimates obtained from BVG ((Valpy et al. 2017), (Hundleby et al. 2017)) for both fixed-bottom and floating offshore wind technologies. Projections for capacity factors implicitly reflect technology innovations such as larger rotors and taller towers that will increase energy capture at the same geographic location without explicitly specifying tower height and rotor diameter changes.

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

The ReEDS model output capacity factors for offshore wind can be lower than input capacity factors due to endogenously estimated curtailments determined by scenario constraints.

Plant Cost and Performance Projections Methodology

ATB projections between the Base Year and 2032 (COD) were derived from an expert elicitation conducted by BVG Associates ((Valpy et al. 2017), (Hundleby et al. 2017)). The expert elicitation assesses the impact of more than 50 technology innovations on various cost components of European offshore wind farms up to 2032 (COD). The cost impact from technology innovations was assessed by soliciting industry experts about the maximum cost impact from a technology innovation in combination with an assessment of an innovation's commercial readiness and market share in a given year.

For the ATB, three different technology cost scenarios were adapted from the expert survey results for scenario modeling as bounding levels:

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, 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.

Expert survey estimates from BVG Associates were normalized to the ATB Base Year starting point and applied to overnight capital costs (OCC), FOM, and annual energy production in 2022 (COD), 2027 (COD), and 2032 (COD). These serve as the ATB Mid projection between 2017-2032 (COD). The ATB Mid scenario is intended to represent a cluster of technology innovations that change the manufacturing, installation, operation, and performance of a wind farm consistent with historical cost reduction rates. The ATB Low scenario is intended to represent a cluster of technology innovations that fundamentally change the manufacturing, installation, operation and performance of a wind farm resulting in considerably higher cost reductions than the ATB Mid scenario. The ATB Low scenario is represented by cost reductions that are twice the rate achieved in the ATB Mid scenario. The percentage reduction in LCOE was applied equally across all TRGs. The cost reductions for ATB Mid and ATB Low scenarios between 2032 and 2050 were extrapolated (i.e., exponentially fit) based on the estimated trend between 2017 and 2032 (COD).

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 offshore wind across the range of resources present in the contiguous United States. 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 ATB representative plant characteristics that best align with those of recently installed or anticipated near-term offshore wind plants are associated with TRG 3. 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

R&D Only

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R&D + Market

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The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term offshore wind plants are associated with TRGs 3-5.

R&D Only Financial Assumptions (constant background rates, no tax changes)

The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term offshore wind plants are associated with TRGs 3-5.

R&D Only + Market Financial Assumptions (dynamic background rates, taxes)

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:

The financing conditions assumed for the ATB are applicable to commercial-scale offshore wind projects only. Commercial-scale fixed-bottom offshore wind projects are expected to be installed starting in the early 2020s (Walter Musial et al. forthcoming). Offshore wind projects using floating technology are in a pre-commercial phase currently (i.e., multi-turbine arrays between 12-50 MW in size) for which the ATB financial assumptions are likely too favorable. Once floating technology is deployed at commercial scale, the ATB financial assumptions are expected to appropriately reflect the terms of financing. The use of floating technology for commercial-scale projects is expected by the mid-2020s. Continued turbine scaling to larger-megawatt turbines with larger rotors such that swept area/megawatt capacity decreases resulting in higher capacity factors for a given location
Greater market competition in the production of primary components (e.g., turbines, support structure), and installation services
Economy-of-scale and productivity improvements in manufacturing, including mass production of substructure component and optimized installation strategies
Improved plant siting and operation to reduce plant-level energy losses, resulting in higher capacity factors
More efficient O&M procedures combined with more reliable components to reduce annual average FOM costs
Adoption of a wide range of innovative control, design, and material concepts that facilitate the high-level trends described above.

Commercial PV

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).

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Map of the mean solar resource available to PV systems in the United 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.

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.

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 hardware, 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 technology cost scenarios are represented: Constant, Mid, and Low. Historical data from commercial PV installed in the United States are shown for comparison to the ATB Base Year estimates. The estimate for a given year represents CAPEX of a new plant that reaches commercial operation in that year.

/electricity/2019/images/solar-distrib/chart-solar-dist-capex-RD-2019.png

Historical data shown in box-and-whiskers format where a bar represents the median, a box represents the 20th and 80th percentiles, and whiskers represent the minimum and maximum. Capacity-weighted average is also shown.

Year represents Commercial Online Date for a past or future plant.

CAPEX is shown as a single value because it does not vary with solar resource.

R&D Only Financial Assumptions (constant background rates, no tax changes)

Recent Trends

Reported historical commercial-scale PV installation CAPEX (Barbose and Dargouth 2018) is shown in box-and-whiskers format for comparison to historical commercial-scale PV benchmark overnight capital cost and ATB future CAPEX projections. The data in Barbose and Dargouth (2018) represent 81% of all U.S. residential and commercial PV capacity installed through 2017 and 75% of capacity installed in 2017.

The difference in each year's price between the market and benchmark data reflects differences in methodologies. There are a variety of reasons reported and benchmark prices can differ, as enumerated by Barbose and Dargouth (2018) and Bolinger and Seel (2018), including:

Timing-related issues: For instance, the time between contract completion date and project placed in service may vary.
Variations over time in the size, technology, installer margin, and design of systems installed in a given year
Which cost categories are included in CAPEX (e.g., financing costs and initial O&M expenses).

Due to the investment tax credit, projects are encouraged to include as many costs incurred in the upfront CAPEX to receive a higher tax credit, which may have otherwise been reported as operating costs. The bottom-up benchmarks are more reflective of an overnight capital cost, which is in line with the ATB methodology of inputting overnight capital cost and calculating construction financing to derive CAPEX.

PV pricing and capacities are quoted in kW_DC (i.e., module rated capacity) unlike other generation technologies, which are quoted in kW_AC. For PV, this would correspond to the combined rated capacity of all inverters. This is done because kW_DC is the unit that the majority of the PV industry uses. Although costs are reported in kW_DC, the total CAPEX includes the cost of the inverter, which has a capacity measured in kW_AC.

CAPEX estimates for 2018 reflect continued rapid decline in pricing supported by analysis of recent system pricing for projects that became operational in 2018 (Feldman and Margolis 2018).

The range in CAPEX estimates reflects the heterogeneous composition of the commercial PV market in the United States.

Base Year Estimates

For illustration in the ATB, a representative commercial-scale PV installation is shown. Although the PV technologies vary, typical installation costs are represented with a single estimate because the CAPEX does not vary with solar resource.

Although the technology market share may shift over time with new developments, the typical installation cost is represented with the projections above.

A system price of $1.83/W_DC in 2017 and $1.79/W_DC in 2018 are based on bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2018 (Fu, Feldman, and Margolis 2018), adjusted for inflation. The 2017 and 2018 bottom-up benchmarks are reflective of an overnight capital cost, which is in line with the ATB methodology of inputting overnight capital cost and calculating construction financing to derive CAPEX. These figures are in line with other estimated system prices reported by Feldman and Margolis (2018).

The Base Year CAPEX estimates should tend toward the low end of observed cost because no regional impacts are included. These effects are represented in the historical market data.

Future Year Projections

Projections of future commercial PV installation CAPEX are based on seven system price projections from five separate institutions. We adjusted the "min," "median," and "max" projections in a few different ways. All 2017 pricing is based on the capacity-weighted average historically reported commercial PV prices reported in Tracking the Sun XI (Barbose and Dargouth 2018). All 2018 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2018 (Fu, Feldman, and Margolis 2018). These figures are in line with other estimated system prices reported in Q2/Q3 2018 Solar Industry Update (Feldman and Margolis 2018).

We adjusted the Mid and Low projections for 2019-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. For the Constant technology cost scenario, the 2018 CAPEX value is held constant, assuming no improvements beyond 2018.

A detailed description of the methodology for developing future year projections is found in Projections Methodology.

Technology innovations that could impact future CAPEX costs are summarized in LCOE Projections.

CAPEX Definition

Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. For commercial PV, this is modeled for a host-owned business model only with access to debt.

For the ATB, and based on EIA (2016b) and the NREL Solar-PV Cost Model (Fu, Feldman, and Margolis 2018), the distributed solar PV plant envelope is defined to include:

Hardware
Module supply
Power electronics
Racking
Foundation
AC and DC materials and installation
Balance of system (BOS)
Site and/or roof preparation
Permitting, inspection, and interconnection costs
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, legal fees, and insurance costs
Depreciation and interest on debt (ConFinFactor).

CAPEX can be determined for a plant in a specific geographic location as follows:

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

(See the Financial Definitions tab in the ATB data spreadsheet)

Regional cost variations are not included in the ATB (CapRegMult = 1). Because distributed PV plants are located directly at the end use, there are no grid connection costs (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 distributed residential/commercial PV plant and does not vary with resource. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by EIA (2016b) 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.

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R&D Only Financial Assumptions (constant background rates, no tax changes)

Standard Scenarios Model Results

CAPEX in the ATB does not represent regional variants (CapRegMult) associated with labor rates, material costs, etc., but dSolar does include state-level cost multipliers (EIA 2016b).

Operation and Maintenance (O&M) Costs

Operations and maintenance (O&M) costs represent the annual expenditures required to operate and maintain a solar PV plant over its lifetime:

Insurance, property taxes, site security, legal and administrative fees, and other fixed costs
Present value and annualized large component replacement costs over technical life (e.g., inverters at 15 years)
Scheduled and unscheduled maintenance of solar PV plants, transformers, etc. over the technical lifetime of the plant (e.g., general maintenance, including cleaning and vegetation removal).

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 of $18/kWDC - yr is based on modeled pricing for a commercial PV system quoted in Q1 2017 as reported by Fu, Feldman, and Margolis (2018), adjusted for inflation. The values in this report (ATB 2019) are higher than those from ATB 2018 to better align with the benchmarks reported in Fu, Feldman, and Margolis (2018); the previous edition relied solely on an O&M-to-CAPEX ratio, derived from multiple reports (IEA 2016). A wide range in reported prices exists in the market, in part depending on what maintenance practices exist for a particular system. These cost categories include asset management (including compliance and reporting for incentive payments), different insurance products, site security, cleaning, vegetation removal, and failure of components. Not all these practices are performed for each system; additionally, some factors depend on the quality of the parts and construction. NREL analysts estimate O&M costs can range from $0 to $40/kWDC - yr.

Future Year Projections

FOM for 2018 is also based on pricing reported in Fu, Feldman, and Margolis (2018), adjusted for inflation. From 2019-2050, FOM is based on the historical average ratio of O&M costs ($/kW-yr) to CAPEX costs ($/kW), 1.0:100, as reported by Fu, Feldman, and Margolis (2018). Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2018 benchmark commercial PV O&M and CAPEX costs fell 47% and 66% respectively, as reported by Fu, Feldman, and Margolis (2018).

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

PV system capacity is not directly comparable to other technologies' capacity factors. Other technologies' capacity factors are represented in exclusively AC units (see Solar PV AC-DC Translation). However, because PV pricing in this ATB documentation is represented in $/W_DC, PV system capacity is a DC rating. Because each technology uses consistent capacity ratings, the LCOEs are comparable.

The capacity factor is influenced by the hourly solar profile, technology (e.g., thin-film versus crystalline silicon), axis type (e.g., none, one, or two), expected downtime, and inverter losses to transform from DC to AC power. The DC-to-AC ratio is a design choice that influences the capacity factor. For the ATB, commercial PV systems are modeled for a 300-kW_DC fixed-tilt (5°), roof-mounted system.

PV plant capacity factor incorporates an assumed degradation rate of 0.75%/year (Fu, Feldman, and Margolis 2018) in the annual average calculation. R&D could lower degradation rates of PV plant capacity factor; future projections for Mid and Low cost scenarios reduce degradation rates by 2050, using a straight-line basis, to 0.5%/year and 0.2%/year respectively.

The following figure shows a range of capacity factors based on variation in solar resource in the contiguous United States. The range of the Base Year estimates illustrate the effect of locating a utility-scale PV plant in places with lower or higher solar irradiance. These five values use specific locations as examples of high (Daggett, California), high-mid (Los Angeles, California), mid (Kansas City, Missouri), low-mid (Chicago, Illinois), and low (Seattle, Washington) resource areas in the United States as implemented in the System Advisor Model using PV system characteristics from Fu, Feldman, and Margolis (2018).

/electricity/2019/images/solar-distrib/chart-solar-dist-capacity-factor-2019.png

The data illustrated include estimated degradation over the lifetime of a PV plant, which reduces over time in the Mid and Low scenarios (resulting in a higher average capacity factor over time).

Base Year Estimates

For illustration in the ATB, a range of capacity factors is associated with solar irradiance diversity and the range of latitude for five resource locations in the contiguous United States:

Low: Seattle, Washington
Low-mid: Chicago, Illinois
Mid: Kansas City, Missouri
High-mid: Los Angeles, California
High: Daggett, California

First-year operation capacity factors as modeled range from 12.7% to 20.8%, though these depend significantly on geography and system configuration (e.g., fixed-tilt versus single-axis tracking).

Over time, PV installation output is reduced due to degradation in module quality. This degradation is accounted in ATB estimates of capacity factor over the 30-year lifetime of the plant. The adjusted average capacity factor values in the ATB Base Year are 11.9%, 14.0%, 15.2%, 17.3%, and 19.6%.

Future Year Projections

Projections of capacity factors for plants installed in future years are unchanged from the Base Year for the Constant technology cost scenario. Capacity factors for Mid and Low cost scenarios are projected to increase over time, caused by a straight-line reduction in PV plant capacity degradation rates, reaching 0.5%/year and 0.2%/year by 2050 for the Mid and Low cost scenarios respectively. The following table summarizes the difference in average capacity factor in 2050 caused by different degradation rates in the Constant, Mid, and Low cost scenarios.

2050 Commercial PV DC Capacity Factors by Resource Location and Cost Scenario

	Seattle, WA	Chicago, IL	Kansas City, MO	Los Angeles, CA	Daggett, CA
Low Cost (0.30% degradation rate)	12.3%	14.5%	15.8%	18.0%	20.3%<
Mid Cost (0.50% degradation rate)	12.1%	14.3%	15.5%	17.7%	20.0%
Constant Cost (0.75% degradation rate)	11.9%	14.0%	15.2%	17.3%	19.6%

Solar PV plants have very little downtime, inverter efficiency is already optimized, and tracking is already assumed. That said, there is potential for future increases in capacity factors through technological improvements beyond lower degradation rates, such as less panel reflectivity and improved performance in low-light conditions.

Standard Scenarios Model Results

dSolar does not endogenously consider curtailment from surplus renewable energy generation, though this is a feature of the linked ReEDS-dSolar model (Cole et al. 2016), where balancing area-level marginal curtailments can be applied to distributed PV generation as determined by scenario constraints.

Plant Cost and Performance Projections Methodology

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

We created Constant, Mid and Low CAPEX cases to explore the range of possible outcomes of future PV cost improvements. The Constant technology cost scenario represents no CAPEX improvements made beyond today, the Mid cost case represents current expectations of price reductions in a "business-as-usual" scenario, and the Low cost case represents current expectations of potential cost reductions given improved R&D funding and more aggressive global deployment targets.

While CAPEX is one of the drivers to lower costs, R&D efforts continue to focus on other areas to lower the cost of energy from residential PV. While these are not incorporated in the ATB, they include longer system lifetime, improved performance and reliability, and lower cost of capital.

Projections of future commercial PV installation CAPEX are based on seven system price projections from five separate institutions. Projections included short-term U.S. price forecasts made in the past six months and long-term global and U.S. price forecasts made in the past primarily provided by market analysis firms with expertise in the PV industry, through a subscription service with NREL. The long-term forecasts primarily represent the collection of publicly available, unique forecasts with either a long-term perspective of solar trends or through capacity expansion models with assumed learning by doing.

Short-Term Forecast Institutions: Bloomberg New Energy Finance, GTM Research, Navigant Research (Labastida and Gauntlett 2016), U.S. Energy Information Administration
Long-Term Forecast Institutions: Bloomberg New Energy Finance, CAISO, and U.S. Energy Information Administration.

In instances in which literature projections did not include all years, a straight-line change in price was assumed between any two projected values. To generate Mid and Low technology cost scenarios, we took the "median" and "min" of the data sets; however, we only included short-term U.S. forecasts until 2030 as they focus on near-term pricing trends within the industry. Starting in 2030, we include long-term global and U.S. forecasts in the data set, as they focus more on long-term trends within the industry. It is also assumed after 2025 U.S. prices will be on par with global averages; the federal tax credit for solar assets reverts down to 10% for all projects placed in service after 2023, which has the potential to lower upfront financing costs and remove any distortions in reported pricing, compared to other global markets. Additionally, a larger portion of the United States will have a more mature PV market, which should result in a narrower price range. Changes in price for the Mid and Low technology cost scenarios between 2020 and 2030 are interpolated on a straight-line basis.

We adjusted the "median" and "min" projections in a few different ways. All 2017 and 2018 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2018 (Fu, Feldman, and Margolis 2018).

We adjusted the Mid and Low cost projections for 2019-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. The Constant cost projection case is kept constant at the 2018 CAPEX value, assuming no improvements beyond 2018.

All prices quoted in W_AC are converted to W_DC (1 W_AC=1.1 W_DC).

From 2019-2050, FOM is based on the historical average ratio of O&M costs ($/kW-yr) to CAPEX costs ($/kW), 1.0:100, as reported by Fu, Feldman, and Margolis (2018). Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2018 benchmark commercial PV O&M and CAPEX costs fell 47% and 66% respectively, as reported by Fu, Feldman, and Margolis (2018).

Projections of capacity factors for plants installed in future years are unchanged from 2018 for the Constant technology cost scenario. Capacity factors for Mid and Low cost scenarios are projected to increase over time, caused by a straight-line reduction in PV plant capacity degradation rates from 0.75%, reaching 0.5%/year and 0.2%/year by 2050 for the Mid and Low cost scenarios respectively.

Levelized Cost of Energy (LCOE) Projections

The following three figures illustrate LCOE, which includes the combined impact of CAPEX, O&M, and capacity factor projections for commercial PV 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 of 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 ATB representative plant characteristics that best align with those of recently installed or anticipated near-term commercial PV plants are associated with Comm (commercial) PV: Kansas City. 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. In the R&D + Market LCOE case, there is an increase in LCOE from 2018-2020, caused by an increase WACC, and an increase from 2023-2024, caused by the reduction in tax credits.

R&D Only | R&D + Market

R&D Only

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R&D + Market

/electricity/2019/images/solar-distrib/chart-solar-dist-lcoe-market-2019.png

The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term commercial PV plants are associated with Comm PV: Kansas City

R&D Only Financial Assumptions (constant background rates, no tax changes)

The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term commercial PV plants are associated with Comm PV: Kansas City

R&D Only + Market Financial Assumptions (dynamic background rates, taxes)

Constant Technology Cost Scenario: 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.

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 2019b)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 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.

Modules

Increased module efficiencies and increased production-line throughput to decrease CAPEX; overhead costs on a per-kilowatt basis will go down if efficiency and throughput improvement are realized
Reduced wafer thickness or the thickness of thin-film semiconductor layers
Development of new semiconductor materials
Development of larger manufacturing facilities in low-cost regions

Balance of system (BOS)

Increased module efficiency, reducing the size of the installation
Development of racking systems that enhance energy production or require less robust engineering
Integration of racking or mounting components in modules
Reduction of supply chain complexity and cost

Creation of standard packaged system design
Improvement of supply chains for BOS components in modules

Improved power electronics

Improvement of inverter prices and performance, possibly by integrating microinverters

Decreased installation costs and margins

Reduction of supply chain margins (e.g., profit and overhead charged by suppliers, manufacturer, distributors, and retailers); this will likely occur naturally as the U.S. PV industry grows and matures.
Streamlining of installation practices through improved workforce development and training and developing standardized PV hardware
Expansion of access to a range of innovative financing approaches and business models
Development of best practices for permitting interconnection and PV installation such as subdivision regulations, new construction guidelines, and design requirements.

FOM cost reduction represents optimized O&M strategies, reduced component replacement costs, and lower frequency of component replacement.

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/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).

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Map of mean solar resource available to CSP systems in the United States (Roberts 2018)

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).

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R&D Only Financial Assumptions (constant background rates, no tax changes)

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:

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

(See the Financial Definitions tab in the ATB data spreadsheet)

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.

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R&D Only Financial Assumptions (constant background rates, no tax changes)

Standard Scenarios Model Results

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.

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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

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.

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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

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).

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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 Only

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R&D + Market

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R&D + Market

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.

R&D Only Financial Assumptions (constant background rates, no tax changes)

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.

R&D Only + Market Financial Assumptions (dynamic background rates, taxes)

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.

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.

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.

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.

Geothermal

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.

Map of the favorability of occurrence for geothermal resources in the western United States

Warmer colors equate with higher favorability. Identified geothermal systems are represented by black dots. Source: USGS, 2008

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.

Map of identified hydrothermal sites and favorability of deep EGS in the United States

Source: Roberts, 2009

The NF-EGS resource potential is based on data from USGS for EGS potential on the periphery of select studied and identified hydrothermal sites (Augustine et al., 2019).

The deep EGS resource potential (Augustine, 2016) is based on Southern Methodist University Geothermal Laboratory temp-at-depth maps and the methodology is from (Tester & et al., 2006).

The EGS resource is thousands of gigawatts (5,000 GW) but many locations are likely not commercially feasible.

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.

Six representative geothermal plants are shown (two are hidden by other lines). Two energy conversion processes are common: binary organic Rankine cycle and flash. Examples using each of these plant types in each of the three resource types are shown. Note that cost curves use 2015 as the reference year in keeping with the GeoVision report (DOE, 2019) used as the data source. This is why the cost curves do not converge to a single point in 2017.

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.

Geothermal Resource and Cost Characteristics

	Temp (°C)	>=200C	150-200	135-150	<135
Hydrothermal	Number of identified sites	21	23	17	59
	Total capacity (MW)	15,338	2,991	820	4,632
	Average OCC ($/kW)	3,906	7,720	8,794	16,248
	Min OCC ($/kW)	3,000	4,140	7,004	10,950
	Max OCC ($/kW)	5,491	29,135	11,027	21,349
	Example of plant OCC ($/kW)	4,229	5,455
NF-EGS	Number of sites	12	20
	Total capacity (MW)	787	596
	Average OCC ($/kW)	11,041	26,077
	Min OCC ($/kW)	8,778	18,172
	Max OCC ($/kW)	18,009	39,987
	Example of plant OCC ($/kW)	14,512	32,268
Deep EGS (3-6 km)	Number of sites	n/a	n/a
	Total capacity (MW)	100,000+
	Average OCC ($/kW)	28,418	60,170
	Min OCC ($/kW)	18,320	39,329
	Max OCC ($/kW)	54,047	77,983
	Example of plant OCC ($/kW)	14,512	32,268

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.

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 GETEM component cost calculations – the geothermal plant envelope is defined to include:

Geothermal generation plant

Exploration, confirmation drilling, well field development, reservoir stimulation (EGS), plant equipment, and plant construction
Power plant equipment, well-field equipment, and components for wells (including dry/non-commercial wells)

Balance of system (BOS)

Installation and 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 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 four-year and five-year duration for hydrothermal and EGS respectively (for the low scenario) and an eight-year duration and ten-year duration for hydrothermal and EGS respectively (for the mid- and constant-scenario) accumulated at different intervals for hydro and EGS based on scheduled at outlined by the GeoVision Study (ConFinFactor).

CAPEX can be determined for a plant in a specific geographic location as follows:

CAPEX = ConFinFactor x (OCC x CapRegMult + GCC).

(See the Financial Definitions tab in the ATB data spreadsheet.)

In the ATB, CAPEX is shown for six representative plants. Examples of CAPEX for binary organic Rankine cycle and flash energy conversion processes in each of three geothermal resource types are presented. CAPEX estimates for all hydrothermal NF-EGS potential 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 high costs for some sites. Regional cost effects and distance-based spur line costs are not estimated.

Standard Scenarios Model Results

The ReEDS model represents cost and performance for hydrothermal, NF-EGS, and EGS potential in 5 bins for each of 134 geographic regions, resulting in a greater CAPEX range in the reference supply curve than what is shown in examples in the ATB.

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.

Operations and maintenance (O&M) costs represent average annual fixed expenditures (and depend on rated capacity) required to operate and maintain a hydrothermal plant over its lifetime of 30 years (plant and reservoir), 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., downhole pumps)
Scheduled and unscheduled maintenance of geothermal plant components and well field components over the technical lifetime of the plant and reservoir.

Note that cost curves use 2015 as the reference year in keeping with the GeoVision report (DOE, 2019) used as the data source. This is why the cost curves do not converge to a single point in 2017.

Base Year Estimates

FOM is estimated for each example of a plant based on technical characteristics.

GETEM is used to estimate FOM for each of the six representative plants. FOM for NF-EGS and EGS are equivalent.

Future Year Projections

Future FOM cost reductions are based on results from the GeoVision scenario (DOE, 2019) and are described in detail in Augustine, Ho, and Blair (2019).

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 technical 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.

Geothermal plant capacity factor is influenced by diurnal and seasonal air temperature variation (for air-cooled plants), technology (e.g., binary or flash), downtime, and internal plant energy losses.

The following figure shows a range of capacity factors based on variation in the resource for plants in the contiguous United States. The range of the Base Year estimates illustrates Binary or Flash geothermal plants. Future year projections for the Constant, Mid, and Low technology cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX cost elements.

Base Year Estimate

The capacity factor estimates are developed using GETEM at typical design air temperature and based on design plant capacity net losses. An additional reduction is applied to approximate potential variability due to seasonal temperature effects.

Some geothermal plants have experienced year-on-year reductions in energy production, but this is not consistent across all plants. No approximation of long-term degradation of energy output is assumed.

Ongoing work at NREL and the Idaho National Laboratory is helping improve capacity factor estimates for geothermal plants. As this work progresses, it will be incorporated into future versions of the ATB.

Future Year Projections

Capacity factors remain unchanged from the Base Year through 2050. Technology improvements are focused on CAPEX costs. Estimates of capacity factor for geothermal plants 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 geothermal plants to manage grid services.

Plant Cost and Performance Projections Methodology

The site-specific nature of geothermal plant cost, the relative maturity of hydrothermal plant technology, and the very early stage development of EGS technologies make cost projections difficult. No thorough literature reviews have been conducted for cost reduction of hydrothermal geothermal technologies or EGS technologies. However, the GeoVision BAU scenario is based on a bottom-up analysis of costs and performance improvements. The inputs for the BAU scenario were developed by the national laboratories as part of the GeoVision effort, and it was reviewed by industry experts.

Projection of future geothermal plant CAPEX for the Low cost case is based on the GeoVision Technology Improvement scenario. It assumes that cost and technology improvements are achieved by 2030 and that costs decrease linearly from present values to the 2030 projected values. The Mid cost case is based on minimum learning rates 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. The Constant technology cost scenario retains all cost and performance assumptions equivalent to the Base Year through 2050.

Levelized Cost of Energy (LCOE) Projections

Levelized cost of energy (LCOE) is a simple metric that combines the primary technology cost and performance parameters: CAPEX, O&M, and capacity factor. It is included in the ATB for illustrative purposes. The ATB focuses on defining the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. The LCOE accounts for the energy component of electric system planning and operation. The LCOE uses an annual average capacity factor when spreading costs over the anticipated energy generation. This annual capacity factor ignores specific operating behavior such as ramping, start-up, and shutdown that could be relevant for more detailed evaluations of generator cost and value. Electricity generation technologies have different capabilities to provide such services. For example, wind and PV are primarily energy service providers, while the other electricity generation technologies such as geothermal, 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 geothermal 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, technology-specific tariffs, 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, tax credits, and tariffs. The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term geothermal plants are associated with Hydrothermal/Flash. 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

R&D Only

R&D + Market

Note that cost curves use 2015 as the reference year in keeping with the GeoVision report (DOE, 2019) used as the data source. This is why the cost curves do not converge to a single point in 2017.

The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term geothermal plants are associated with Hydrothermal/Flash.

R&D = R&D Only Financial Assumptions (constant background rates, no tax or tariff changes); R&D + Market = R&D Only + Market Financial Assumptions (dynamic background rates, taxes, and tariffs)

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.

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, tax credits, and tariffs.
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 AEO2018, as well as effects of tax reform, tax credits, and technology-specific tariffs. For a detailed discussion of these assumptions, see Changes from 2018 ATB to 2019 ATB.

Development of exploration and reservoir characterization tools that reduce well-field costs through risk reduction by locating and characterizing low- and moderate-temperature hydrothermal systems prior to drilling
High-temperature tools and electronics for geothermal subsurface operations
Development of reservoir engineering techniques and technologies that enable EGS
More efficient drilling practices and advanced drilling systems such as using flames or lasers to drill through rock; drilling steering technology; and other technologies to reduce drilling costs.

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.

Map of the hydropower resource in the United States

Source: Energy Information Administration, Environmental Systems Research Institute, Federal Energy Regulatory Commission, The National Atlas of the United States, National Hydrography Dataset, National Hydropower Asset Assessment Program, National Inventory of Dams, Natural Earth, NHDPlus, Tennessee Valley Authority, U.S. Army Corps of Engineers, U.S. Bureau of Reclamation; U.S. Geological Survey Earth Resources Observation and Science Center; Watershed Boundary Dataset. https://hydrosource.ornl.gov.

To view high resolution version of the picture, click here.

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) .

Cost = (277 × ExpansionMW^-0.3) + (2,230 × ExpansionMW^-0.19)

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.

/electricity/2019/images/hydropower/chart-hydro-hydrovision-upgrade-potential.jpg

Source: Hydropower Vision (DOE, 2016)

Standard Scenarios Model Results

Upgrade potential becomes available in the ReEDS model at the relicensing date, end of plant life (50 years), or both.

An Assessment of Energy Potential at Non-Powered Dams in the United States (Hadjerioua, Wei, & Kao, 2012)

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:

/electricity/2019/images/hydropower/chart-hydro-hydrovision-NPD.jpg

Source: Hydropower Vision (DOE, 2016) , Figure 3.2

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

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.

/electricity/2019/images/hydropower/hydro-orn-nsd-resource-assessment.jpg

Source: ORNL NSD resource assessment

/electricity/2019/images/hydropower/chart-hydro-hydrovision-NSD.jpg

Source: ORNL NSD resource assessment

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.

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

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.

/electricity/2019/images/hydropower/chart-hydro-capex-RD-2019.png

Historical data shown in box-and-whiskers format where a bar represents the median, a box represents the 20^th and 80^th percentiles, and whiskers represent the minimum and maximum. Capacity-weighted average is also shown.

CAPEX estimates represent actual and proposed projects from 1981 to 2014.

Year represents Commercial Online Date for a past or future plant.

R&D Only Financial Assumptions (constant background rates, no tax changes)

Recent Trends

Actual and proposed NPD and NSD CAPEX from 1981 to 2014 (O'Connor, DeNeale, et al., 2015) are shown in box-and-whiskers format for comparison to the ATB current CAPEX estimates and future projections.

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.

/electricity/2019/images/hydropower/chart-hydro-hydrovision-supply-curve.jpg

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

Reference plants are representative of the range of resource potential shown in the columns to the right.

30 + represents head >= 30 feet; similarly, 10 + represents capacity >= 10 MW.

ICC = Initial Capital Cost. CAPEX = ICC + Licensing Costs.

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 (O'Connor, DeNeale, et al., 2015) in order to calculate site CAPEX and O&M costs.

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 (O'Connor, DeNeale, et al., 2015) .

NPD CAPEX = (11,489,245 × P^0.976 × H^-0.24) + (310,000 × P^0.7)

and

NSD CAPEX = (9,605,710 × P^0.977 × H^-0.126) + (610,000 × P^0.7)

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

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:

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

(See the Financial Definitions tab in the ATB data spreadsheet.)

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.

/electricity/2019/images/hydropower/chart-hydro-capex-definition-RD-2019.png

Standard Scenarios Model Results

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.

/electricity/2019/images/hydropower/chart-hydro-operation-maintenance-2019.png

Year represents Commercial Online Date for a past or future plant.

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$.

Lesser of Annual O&M = (227,000 × P^0.547) or (2.5% of CAPEX)

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 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.

/electricity/2019/images/hydropower/chart-hydro-capacity-factor-2019.png

Historical data shown in box-and-whiskers format where a bar represents the median, a box represents the 20^th and 80^th percentiles, and whiskers represent the minimum and maximum.

Historical data represent energy production from about 200 run-of-river plants operating in the United States from 2003 through 2012 where Year represents calendar year.

Projection data represent expected annual average capacity factor for plants with Commercial Online Date specified by year.

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

Reference plants below are representative of the range of resource potential shown in the columns to the right.

30 represents head >= 30 ft; similarly, 10 + represents capacity >= 10 MW.

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

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

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.

/electricity/2019/images/hydropower/chart-hydro-cost-performance-methodology-2019.png

IEA-ETSAP=International Energy Agency (IEA)-Energy Technology Systems Analysis Program (ETSAP) IRENA = International Renewable Energy Agency NPD = non-powered dam, NSD = new stream-reach development

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

R&D Only

/electricity/2019/images/hydropower/chart-hydro-lcoe-RD-2019.png

R&D + Market

/electricity/2019/images/hydropower/chart-hydro-lcoe-market-2019.png

The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term hydropower plants are associated with NPD 4.

R&D Only Financial Assumptions (constant background rates, no tax changes)

The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term hydropower plants are associated with NPD 4.

R&D Only + Market Financial Assumptions (dynamic background rates, taxes)

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.

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.

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.

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.

/electricity/2019/images/coal/chart-coal-capex-RD-2019.png

Current estimates and future projections calculated from AEO2019, modified as described in the CAPEX section.

R&D Only Financial Assumptions (constant background rates, no tax changes)

Comparison with Other Sources

/electricity/2019/images/coal/chart-coal-overnight-capital-cost-2019.png

Data sources include the ATB, (Black & Veatch, 2012), (Lazard, 2016), (Zoelle et al., 2015), and (Rubin, Davison, & Herzog, 2015).

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 NO_x 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

The various coal technologies have the same construction financing factor, which is a simplification. The factor will vary based on the construction schedule, which is likely to be longer for plants with CCS than those without CCS.

CAPEX can be determined for a plant in a specific geographic location as follows:

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

See the Financial Definitions tab in the ATB data spreadsheet.

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.

/electricity/2019/images/coal/chart-coal-capex-definition-RD-2019.png

R&D Only Financial Assumptions (constant background rates, no tax changes)

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.

/electricity/2019/images/coal/chart-coal-operation-maintenance-2019.png

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.

/electricity/2019/images/coal/chart-coal-capacity-factor-2019.png

Current estimates and future projections calculated from AEO2019 (EIA, 2019a) and modified.

Levelized Cost of Energy (LCOE) Projections

Nuclear

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.

Capital Expenditures (CAPEX): Historical Trends, Current Estimates, and Future Projections

Nuclear power plant CAPEX is taken from the AEO2019 Reference Scenario (EIA, 2019a) with the adjustments discussed in the CAPEX definition section. The EIA advanced nuclear option is based on two AP1000 nuclear power plant units built on a brownfield site. The ATB includes only a single CAPEX projection for nuclear plants.

/electricity/2019/images/nuclear/chart-nuclear-capex-RD-2019.png

Current estimates and future projections calculated from AEO2019 (EIA, 2019a) and modified.

R&D Only Financial Assumptions (constant background rates, no tax changes)

Comparison with Other Sources

/electricity/2019/images/nuclear/chart-nuclear-overnight-capital-cost-2019.png

ATB cost numbers are for a brownfield nuclear facility.

Data sources include the ATB, Black & Veatch (2012), Entergy Arkansas (2015),Varro and Ha(2015), and Lazard (2016).

All sources have been normalized to the same dollar year. Costs vary due to differences in system design, methodology, and plant cost definitions.

CAPEX Definition

Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.

	Overnight Capital Cost ($/kW)	Construction Financing Factor (ConFinFactor)	CAPEX ($/kW)
Nuclear: Advanced nuclear power generation	$6,200	1.087	$6,742

CAPEX can be determined for a plant in a specific geographic location as follows:

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

See the Financial Definitions tab in the ATB data spreadsheet.

In the ATB, CAPEX represents each type of nuclear 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 (Plant × Region). 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.

/electricity/2019/images/nuclear/chart-nuclear-capex-definition-RD-2019.png

R&D Only Financial Assumptions (constant background rates, no tax changes)

Operation and Maintenance (O&M) Costs

Nuclear 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.

/electricity/2019/images/nuclear/chart-nuclear-operation-maintenance-2019.png

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 nuclear plants, the capacity factor is typically the same as (or very close to) their availability factor. For the ATB we assign the nuclear capacity factor as the fleet-wide average from 2017.

/electricity/2019/images/nuclear/chart-nuclear-capacity-factor-2019.png

Current estimates and future projections calculated from AEO2019 (EIA, 2019a) and modified.

Levelized Cost of Energy (LCOE) Projections

The following three figures illustrate LCOE, which includes the combined impact of CAPEX, O&M, and capacity factor projections for nuclear 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.

R&D Only | R&D + Market

R&D Only

/electricity/2019/images/nuclear/chart-nuclear-lcoe-RD-2019.png

R&D + Market

/electricity/2019/images/nuclear/chart-nuclear-lcoe-market-2019.png

R&D Only Financial Assumptions (constant background rates, no tax changes)

R&D Only + Market Financial Assumptions (dynamic background rates, taxes)

The LCOE of nuclear power plants is directly impacted by the cost of uranium, variations in the heat rate, and O&M costs, but the biggest factor is the capital cost (including financing costs) of the plant. The LCOE can also be impacted by the amount of downtime from refueling or maintenance. For a given year, the LCOE assumes that the fuel prices from that year continue throughout the lifetime of the plant.

Fuel prices are based on the AEO2019 (EIA, 2019a).

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.

Biopower

The ATB includes both dedicated biopower cost options and a biomass cofired with coal option. 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

Biopower plant CAPEX is taken from the AEO2019 Reference Scenario (EIA, 2019a) with the adjustments discussed in the CAPEX definition section.

/electricity/2019/images/biomass/chart-biomass-capex-RD-2019.png

Current estimates and future projections calculated from AEO2019 (EIA, 2019a) and modified.

R&D Only Financial Assumptions (constant background rates, no tax changes)

CAPEX Definition

Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.

Fuel costs are taken from the Billion Ton Update study (DOE et al., 2011).

	Overnight Capital Cost ($/kW)	Construction Financing Factor (ConFinFactor)	CAPEX ($/kW)
Dedicated: Dedicated biopower plant	$3,827	1.042	$3,990
CofireOld: Pulverized coal with sulfur dioxide (SO₂) scrubbers and biomass co-firing	$4,013	1.042	$4,184
CofireNew: Advanced supercritical coal with SO₂ and NO_x controls and biomass co-firing	$4,013	1.042	$4,184

CAPEX can be determined for a plant in a specific geographic location as follows:

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

See the Financial Definitions tab in the ATB data spreadsheet.

In the ATB, CAPEX represents each type of biopower 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.

/electricity/2019/images/biomass/chart-biomass-capex-definition-RD-2019.png

R&D Only Financial Assumptions (constant background rates, no tax changes)

Operation and Maintenance (O&M) Costs

Biopower 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 biopower plants, the capacity factors are typically lower than their availability factors. Biopower plant availability factors have a wide range depending on system design, fuel type and availability, and maintenance schedules.

Biopower plants are typically baseload plants with steady capacity factors. For the ATB, the biopower capacity factor is taken as the average capacity factor for biomass plants for 2017, as reported by EIA.

Biopower capacity factors are influenced by technology and feedstock supply, expected downtime, and energy losses.

/electricity/2019/images/biomass/chart-biomass-capacity-factor-2019.png

Current estimates and future projections are taken as the 2017 capacity factor from EIA (2019b).

Levelized Cost of Energy (LCOE) Projections

The following three figures illustrate LCOE, which includes the combined impact of CAPEX, O&M, and capacity factor projections for biomass 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. 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

R&D Only

/electricity/2019/images/biomass/chart-biomass-lcoe-RD-2019.png

R&D + Market

/electricity/2019/images/biomass/chart-biomass-lcoe-market-2019.png

R&D Only Financial Assumptions (constant background rates, no tax changes)

R&D Only + Market Financial Assumptions (dynamic background rates, taxes)

The LCOE of biopower plants is directly impacted by the differences in CAPEX (installed capacity costs) as well as by heat rate differences. For a given year, the LCOE assumes that the fuel prices from that year continue throughout the lifetime of the plant.

Regional variations will ultimately impact biomass feedstock costs, but these are not included in the ATB.

The projections do not include any cost of carbon.

Fuel costs are taken from the Billion Ton Update study (DOE et al., 2011).

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.

References

The following references are specific to this page; for all references in this ATB, see References.

Augustine, Chad, Ho, J., & Blair, N. (2019). GeoVision Analysis Supporting Task Force Report: Electric Sector Potential to Penetration (No. NREL/ TP-6A20-71833). Retrieved from National Renewable Energy Laboratory website: https://www.nrel.gov/docs/fy19osti/71833.pdf

Augustine, Chad. (2016). Updates to Enhanced Geothermal System Resource Potential Estimate. GRC Transactions, 40, 673–677. Retrieved from http://pubs.geothermal-library.org/lib/grc/1032382.pdf

Barbose, G., & Darghouth, N. (2018). Tracking the Sun XI: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States (No. LBNL-2001062). https://doi.org/10.2172/1477384

Beiter, P., Musial, W., Smith, A., Kilcher, L., Damiani, R., Maness, M., … Scott, G. (2016). A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030 (Technical Report No. NREL/TP-6A20-66579). https://doi.org/10.2172/1324526

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

Bolinger, M., & Seel, J. (2018). Utility-Scale Solar: An Empirical Trends in Project Technology, Cost, Performance, and PPA Pricing in the United States (2018 Edition). Retrieved from Lawrence Berkeley National Laboratory website: https://emp.lbl.gov/sites/default/files/lbnl_utility_scale_solar_2018_edition_report.pdf

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

Cole, W., Lewis, H., Sigrin, B., & Margolis, R. (2016). Interactions of Rooftop PV Deployment with the Capacity Expansion of the Bulk Power System. Applied Energy, 168, 473–481. https://doi.org/10.1016/j.apenergy.2016.02.004

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, & 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. (2011). U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry (No. ORNL/TM-2011/224). https://doi.org/10.2172/1023318

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. (2019). GeoVision: Harnessing the Heat Beneath Our Feet (No. DOE/EE-1306). Retrieved from U.S. Department of Energy website: https://www.energy.gov/eere/geothermal/geovision

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. (2013). Updated 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/archive/2013/pdf/updated_capcost.pdf

EIA. (2015). Annual Energy Outlook 2015 with Projections to 2040 (No. AEO2015). Retrieved from U.S. Energy Information Administration website: https://www.eia.gov/outlooks/aeo/pdf/0383(2015).pdf

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

EIA. (2019b, January). Table 6.7.B Capacity Factors for Utility Scale Generators Not Primarily Using Fossil Fuels. Retrieved April 22, 2019, from https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_6_07_b

Entergy Arkansas. (2015). 2015 Integrated Resource Plan. Retrieved from Entergy Arkansas, Inc. website: http://entergy-arkansas.com/content/transition_plan/IRP_Materials_Compiled.pdf

Feldman, D., & Margolis, R. (2018, November). Q2/Q3 2018 Solar Industry Update. Retrieved from https://www.nrel.gov/docs/fy19osti/72810.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

Fu, R., Feldman, D., & Margolis, R. (2018). U.S. Solar Photovoltaic System Cost Benchmark: Q1 2018. https://doi.org/10.2172/1484344

Gagnon, P., Margolis, R., Melius, J., Phillips, C., & Elmore, R. (2016). Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment (No. NREL/TP-6A20-65298). https://doi.org/10.2172/1236153

Hundleby, G., Freeman, K., Logan, A., & Frost, C. (2017). Floating Offshore: 55 Technology Innovations that will have greater impact on reducing the cost of electricity from European floating offshore wind farms. Retrieved from KiC InnoEnergy and BVG Associates website.

IEA. (2016). World Energy Outlook 2016 (No. WEO2016). Retrieved from International Energy Agency website: https://webstore.iea.org/world-energy-outlook-2016

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

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

Labastida, R. R., & Gauntlett, D. (2016). Next-Generation Solar PV: High Efficiency Solar PV Modules and Module-Level Power Electronics: Global Market Analysis and Forecasts [Market Report]. Chicago, IL: Navigant Research.

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

Mines, G. (2013, April). Geothermal Electricity Technology Evaluation Model (GETEM). Presented at the Geothermal Technologies Office 2013 Peer Review, Washington, D.C. Retrieved from https://energy.gov/sites/prod/files/2014/02/f7/mines_getem_peer2013.pdf

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

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

Musial, Walt, Heimiller, D., Beiter, P., Scott, G., & Draxl, C. (2016). 2016 Offshore Wind Energy Resource Assessment for the United States (No. NREL/TP-5000-66599). https://doi.org/10.2172/1324533

Musial, Walter, Beiter, P., Spitsen, P., & Nunemaker, J. (2018). 2018 Offshore Wind Technologies Market Report. https://www.energy.gov/sites/prod/files/2019/08/f65/2018%20Offshore%20Wind%20Market%20Report.pdf

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. (2009). Geothermal Resource of the United States: Locations of Identified Hydrothermal Sites and Favorability of Deep Enhanced Geothermal Systems (EGS). Retrieved from https://www.nrel.gov/gis/images/geothermal_resource2009-final.jpg

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

SolarReserve. (n.d.). Sandstone (project overview). Retrieved February 11, 2019, from SolarReserve website: https://www.solarreserve.com/en/global-projects/csp/sandstone

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

Tester, J. W., & et al. (2006). The Future of Geothermal Energy: Impact of Enhanced Geothermal Systems (EGS) on the United States in the 21st Century. Cambridge, MA: Massachusetts Institute of Technology.

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

USGS. (2008). Assessment of Moderate- and High-Temperature Geothermal Resources of the United States (No. Fact Sheet 2008-3082). Retrieved from U.S. Geological Survey website: https://pubs.usgs.gov/fs/2008/3082/pdf/fs2008-3082.pdf

Valpy, B., Hundleby, G., Freeman, K., Roberts, A., & Logan, A. (2017). Future renewable energy costs: Offshore wind. Retrieved from KiC InnoEnergy and BVG Associates website: http://www.innoenergy.com/wp-content/uploads/2014/09/InnoEnergy-Offshore-Wind-anticipated-innovations-impact-2017_A4.pdf

Varro, L., & Ha, J. (2015). Projected Costs of Generating Electricity: 2015 Edition. Retrieved from International Energy Agency website: https://www.iea.org/media/presentations/150831_ProjectedCostsOfGeneratingElectricity_Presentation.pdf

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