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Annual Technology Baseline 2017

National Renewable Energy Laboratory


Recommended Citation:
NREL (National Renewable Energy Laboratory). 2017. 2017 Annual Technology Baseline. Golden, CO: National Renewable Energy Laboratory. http://atb.nrel.gov/.


Please consult Guidelines for Using ATB Data:
https://atb.nrel.gov/electricity/user-guidance.html

Offshore Wind Power Plants

Representative Technology

In 2016, the first offshore wind plant commenced commercial operation in the United States near Block Island (Rhode Island). This demonstration project is 30 MW in capacity; in the ATB, cost and performance estimates are made for commercial-scale projects 600 MW in capacity. The ATB Base Year offshore wind plant technology reflects a machine rating of 3.4 MW with a rotor diameter of 115 m and hub height of 85 m, which is typical of European projects installed in 2015.

Resource Potential

Wind resource is prevalent throughout major U.S. coastal areas, including the Great Lakes. The resource potential exceeds 2,000 GW (Musial et al. 2016), excluding Alaska. Prior estimates of offshore wind resource potential (Schwartz et al. 2010) were updated in 2016 to extend domain boundaries from 50 nautical miles (nm) to 200 nm, consider turbine hub heights of 100 m (previously 90 m), and assume a capacity array power density of 3 MW/km2 (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 over 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.

Offshore wind resource data (100 m) used for 2016 offshore wind resource assessment
Map of the offshore resource in the United States
Source: Musial et al. 2016

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

Base Year and Future Year Projections Overview

Based on the Musial et al. (2016) resource assessment, LCOE was estimated at more than 7,000 areas (with a total capacity of approximately 2,000 GW) in Beiter et al. (2016), taking into consideration a variety of spatial parameters, 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. The spatial LCOE assessment served as the basis for estimating the ATB baseline LCOE in the Base Year 2015, weighted by the available capacity, for fixed-bottom and floating offshore wind technology.

The Base Year LCOE assumes a 3.4-MW turbine size and long-term average hourly wind profiles and it reflects the least-cost choice among three sub-structure types (Beiter et al. 2016):

  • Monopile (fixed-bottom)
  • Jacket (fixed-bottom)
  • Semi-submersible (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). The capacity-weighted average CAPEX, O&M, and capacity factor for each group is presented in the ATB.

Future year projections are derived from estimated cost reduction potential for offshore wind technologies based on elicitation of over 160 wind industry experts (Wiser et al. 2016). This study produced three different cost reduction pathways, and the median and low estimates are used for ATB Mid and ATB Low cost scenarios. Three different projections were developed for scenario modeling as bounding levels:

  • High cost: no change in CAPEX, O&M, or capacity factor from 2015 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: LCOE percent reduction from the Base Year equivalent to that corresponding to the Median Scenario (50% probability) in expert survey (Wiser et al. 2016)
  • Low Cost: LCOE percent reduction from the Base Year equivalent to that corresponding to the Low Scenario (10% probability) in expert survey (Wiser et al. 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 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 or materials. The range of CAPEX demonstrates variation with water depth and distance from shore in the contiguous United States.

The following figure shows the Base Year estimate and future year projections for CAPEX costs. Three cost reduction scenarios are represented: High, Mid, and Low. Historical data from offshore wind plants installed globally are shown for comparison to the ATB Base Year estimates (TRG 1–5 reflects fixed-bottom offshore wind plants and TRG 6–15 reflect floating offshore wind plants). The estimate for a given year represents CAPEX of a new plant that reaches commercial operation in that year.

chart: CAPEX historical trends, current estimates, and future projections for offshore wind in the 2017 ATB
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.
Year represents Commercial Online Date for a past or future plant.

Recent Trends

Actual wind plant CAPEX for European projects installed through 2015 are shown for comparison to the ATB Base Year CAPEX estimates and future projections. NREL's internal offshore wind database provides statistical representation of CAPEX for about 91% of offshore wind plants >100 MW commissioned in Europe and Asia from 2001 to 2015, based on installed capacity. All commercial-scale offshore wind plants installed to date have fixed-bottom substructures.

CAPEX estimates for the Base Year 2015 for TRGs 1–5, fixed-bottom technologies, tend to be lower than CAPEX for projects installed in 2015. The recent project installations represent characteristics in terms of water depth and distance from shore that generally align with those of TRG5. Floating technologies (TRGs 6–15) are not yet commercially deployed and are estimated to be higher cost than today's fixed-bottom project installations.

Base Year Estimates

For illustration in the ATB, offshore wind capacity was represented in 15 techno-resource groups (TRGs). Based on the share of capacity between fixed-bottom and floating technology estimated in Beiter et al. (2016), 5 TRGs were allocated to fixed-bottom technology (TRGs 1–5) (total capacity of 727 GW) and 10 TRGs to floating technology (TRGs 6–15) (total capacity of 1,330 GW). Available capacity for fixed-bottom and floating offshore wind technology was determined based on the least-cost choice between fixed-bottom and floating substructure types at more than 7,000 U.S. coastal areas in Beiter et al. (2016). Offshore wind locations were ranked by the LCOE estimated in Beiter et al. (2016) and binned into TRGs. The table below shows the capacity that was allocated by TRG. TRGs 1–3 (fixed-bottom) and 6–8 (floating) include less capacity than TRGs 4 and 5 (fixed bottom) and TRGs 9–15 to provide higher resolution at low levels of LCOE. The table also includes capacity-weighted average wind speed, water depth, distance from shore, cost and performance parameters, and resource potential in terms of capacity and energy for each TRG. Spatial conditions typically found in existing Bureau of Ocean Energy Management lease areas in the Northeast range from 10 m to 95 m in water depth (average of 32 m) and 3 km to 90 km in distance from shore (average of 22 km), corresponding to the average conditions in TRGs 3–5. Wind speeds found across existing Bureau of Ocean Energy Management lease areas in the Northeast generally tend to be more aligned with TRGs 1 and 2.

Offshore Wind TRG Definition
TRG LCOE Range ($/MWh) Wind Speed Range (m/s) Weighted Average Wind Speed (m/s) Weighted Water Depth (m) Weighted Distance Site to Cable Landfall (km) Weighted Average CAPEX ($/kW) Weighted Average OPEX ($/kW/yr) Weighted Average Net CF (%) Potential Wind Plant Capacity (GW) Potential Wind Plant Energy (TWh)
Fixed-Bottom
TRG 1 LCOE <= 141 8.5–9.0 8.6 13 6 3,891 136 45% 12 49
TRG 2 LCOE <= 149 8.0–8.5 8.4 16 9 3,982 141 43% 25 94
TRG 3 LCOE <= 157 8.0–8.5 8.3 19 15 4,121 143 42% 50 182
TRG 4 LCOE <= 192 8.0–8.5 8.3 26 36 4,657 150 40% 320 1,131
TRG 5 LCOE <= 306 7.5–8.0 7.9 36 72 5,442 157 37% 320 1,023
Floating
TRG 6 LCOE <= 166 9.5–10 9.7 130 24 6,078 105 50% 12 55
TRG 7 LCOE <= 175 9.5–10 9.7 145 40 6,338 106 50% 25 108
TRG 8 LCOE <= 188 9.5–10 9.5 139 50 6,501 110 48% 50 212
TRG 9 LCOE <= 206 9.0–9.5 9.4 136 70 6,816 121 47% 100 414
TRG 10 LCOE <= 229 9.0–9.5 9.1 140 94 7,066 128 45% 200 781
TRG 11 LCOE <= 252 8.5–9.0 8.7 323 118 7,345 132 42% 200 727
TRG 12 LCOE <= 274 8.0–8.5 8.1 404 123 7,351 134 37% 200 651
TRG 13 LCOE <= 299 7.5–8.0 7.8 474 138 7,538 135 35% 200 615
TRG 14 LCOE <= 341 7.0–7.5 7.4 615 130 7,728 130 32% 200 566
TRG 15 LCOE <= 438 7.5–8.0 7.5 797 199 8,331 137 31% 143 390
Total 2,058 6,997

Future Year Projections

Projections of future LCOE were derived from a survey of wind industry experts (Wiser et al. 2016) for scenarios that are associated with 50% and 10% probability levels in 2030 and 2050. Projections of future offshore wind plant CAPEX was determined based on adjustments to CAPEX, FOM, and capacity factor in each year to result in a predetermined LCOE value based on an expert survey conducted by Wiser et al. (2016).

In order to achieve the overall LCOE reduction associated with the median and low projections from the expert survey, CAPEX was used to accommodate all improvement aspects other than O&M and capacity factor survey results. Future fixed-bottom offshore wind technology CAPEX is assumed to decline 54% by 2050 in the Mid cost case and 62% in the Low cost wind case. Future floating offshore wind technology CAPEX is assumed to decline 49% by 2050 in the Mid cost case and 58% in the Low cost wind case.

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.

Based on EIA (2013), Moné et al. (2013 ), 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
    • Turbine installation, substructure supply and installation
    • Site preparation, port and staging area support for delivery, storage, handling, 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
    • Owner's 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 EIA (2013) 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.

chart: CAPEX definition for land-based wind in the 2017 ATB

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections 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 technical lifetime of 25 years (the distinction between economic life and technical life is described here), 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 O&M (FOM) costs. Three cost reduction 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. The range of Base Year O&M estimates reflects distance from shore and metocean conditions.

chart: base year estimate and future year projections for fixed O&M costs for offshore wind in the 2017 ATB

Base Year Estimates

FOM costs vary by distance from shore and metocean conditions. As a result, O&M costs vary from $105/kW-year (TRG 6) to $157/kW-year (TRG 5) in 2015. The capacity-weighted average in the ATB for fixed-bottom offshore technology (TRGs 1-5) is $146/kW-year; the corresponding value for floating offshore wind technology (TRGs 6-15) is $125/kW-year.

Future Year Projections

Future fixed-bottom offshore wind technology O&M is assumed to decline 7.5% by 2050 in the Mid cost case and 16% in the Low cost wind case, based on the expert survey conducted by Wiser et al. (2016).

Future floating offshore wind technology O&M is assumed to decline 7.5% by 2050 in the Mid cost case and 16% in the Low cost wind case, based on the expert survey conducted by Wiser et al. (2016).

A detailed description of the methodology for developing Future Year Projections is found in Projections Methodology.

Technology innovations that could impact future O&M costs are summarized in LCOE Projections.

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 (the distinction between economic life and technical life is described here). 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, water depth, and distance from shore for offshore wind plants in the contiguous United States. Pre-construction 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 illustrate the effect of locating an offshore wind plant in a variety of wind resource, water depth, and distance from shore conditions (TRGs 1-5 are fixed-bottom offshore wind plants and TRGs 6-15 are floating offshore wind plants). Future projections are shown for High, Mid, and Low cost scenarios.

chart: capacity factor (annual average energy production over plant lifetime) for offshore wind in the 2017 ATB
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.
Historical data represent pre-construction capacity factor estimates for plants with Commercial Online Date specified by year.
Projection data represent expected annual average CF for plants with Commercial Online Date specified by year.

Recent Trends

Pre-construction annual energy estimates from 93% of global operating wind capacity in 2015 (NREL's internal offshore wind database) is shown in a box-and-whiskers format for comparison with the ATB current estimates and future projections. The historical data illustrate pre-construction estimated capacity factors for projects by year of commercial online date. The range of capacity factors defined by the ATB TRGs compared well with the estimated capacity factors for projects installed in 2015.

Base Year Estimates

The capacity factor is determined using a representative power curve for a generic NREL-modeled offshore wind turbine (Beiter et al. 2016) and includes geospatial estimates of gross capacity factors for the entire resource area (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 (see table).

Future Year Projections

Projections of capacity factors for plants installed in future years were determined based on estimates obtained through an expert survey conducted by Wiser et al. (2016) 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

ATB CAPEX, O&M, and CF assumptions for Base Year and future projections through 2050 for Low, Mid, and High projections are used to develop Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

ReEDS 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 were derived from the results of a survey of 163 of the world's wind energy experts (Wiser et al. 2016). The survey was conducted to gain insight into the possible future cost reductions, the source of those reductions, and the conditions needed to enable continued innovation and lower costs (Wiser et al. 2016). The expert survey produced three cost reduction scenarios associated with probability levels of 10%, 50%, and 90% of achieving LCOE reductions by 2030 and 2050. In addition, the scenario results include estimated changes to CAPEX, O&M, capacity factor, project life, and weighted average cost of capital (WACC) by 2030.

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

  • High cost: no change in CAPEX, O&M, or capacity factor from 2015 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: LCOE percent reduction from the Base Year equivalent to that corresponding to the Median Scenario (50% probability) in the expert survey (Wiser et al. 2016)
  • Low Cost: LCOE percent reduction from the Base Year equivalent to that corresponding to the Low Scenario (10% probability) in the expert survey (Wiser et al. 2016).

Expert survey estimates were normalized to the ATB Base Year starting point in order to focus on projected cost reduction instead of absolute reported costs. The percent reduction in LCOE by 2020, 2030, and 2050 from the expert survey's Median and Low scenarios are implemented as the ATB Mid and Low cost scenarios. This is accomplished by utilizing survey estimates for changes to capacity factor and O&M costs by 2030 and 2050. The corresponding CAPEX value to achieve the overall LCOE reduction is computed. The percent reduction in LCOE by 2030 and by 2050 was applied equally across all TRGs. The overall reduction in LCOE by 2050 for the Mid cost scenario is 39% and for the Low cost scenario is 51%.

A broad sample of cost of wind energy projections are shown to provide context for the ATB High, Mid, and Low cost projections. In general, the ATB Mid cost projection reflects median values of the full population of literature; the ATB Low cost projection is similar to the low bound of the literature in the later years. While some published studies as well as recent project announcements for European projects to be installed by 2020 suggest significant near-term cost reduction, it is likely that the United States will lag due to a lack of industry infrastructure. Because the expert survey provided LCOE Projections that are related to each other in terms of probability, these scenarios are used in the ATB to represent two distinct levels of technology improvement pathways.

chart: plant cost and performance projections methodology for offshore wind in the 2017 ATB

The relative costs of mid-depth water plants and deep water, or floating, offshore wind plants are maintained constant throughout the scenarios for simplicity. Some hypothesize that unique aspects of floating technologies, such as the ability to assemble and commission turbines at the port, could reduce the cost of floating technologies relative to fixed-bottom technologies.

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 focus of the ATB is to define the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. LCOE captures the energy component of electric system planning and operation, but the electric system also requires capacity and flexibility services to operate reliably. 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 the combined impact of CAPEX, O&M, and capacity factor projections across the range of resources present in the contiguous United States. The Current Market Conditions LCOE demonstrates the range of LCOE based on macroeconomic conditions similar to the present. The Historical Market Conditions LCOE presents the range of LCOE based on macroeconomic conditions consistent with prior ATB editions and Standard Scenarios model results. The Normalized LCOE (all LCOE estimates are normalized with the lowest Base Year LCOE value) emphasizes the effect of resource quality and the relative differences in the three future pathways independent of project finance assumptions. The ATB representative plant characteristics that best align with recently installed or anticipated near-term offshore wind plants are associated with TRGs 3-5. Data for all the resource categories can be found in the ATB data spreadsheet.

Current Market Conditions
Historical Market Conditions
Normalized

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

The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. The three pathways are generally defined as:

  • High = Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
  • Mid = 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 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. For comparison in the ATB, two project finance structures are represented.

  • Current Market Conditions: The values of the production tax credit (PTC) and investment tax credit (ITC) are ramping down by 2020, at which time wind and solar projects may be financed with debt fractions similar to other technologies. This scenario reflects debt interest (4.4% nominal, 1.9% real) and return on equity rates (9.5% nominal, 6.8% real) to represent 2017 market conditions (AEO 2017) and a debt fraction of 60% for all electricity generation technologies. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.
  • Long-Term Historical Market Conditions: Historically, debt interest and return on equity were represented with higher values. This scenario reflects debt interest (8% nominal, 5.4% real) and return on equity rates (13% nominal, 10.2% real) implemented in the ReEDS model and reflected in prior versions of the ATB and Standard Scenarios model results. A debt fraction of 60% for all electricity generation technologies is assumed. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.

These parameters are held constant for estimates representing the Base Year through 2050. No incentives such as the PTC or ITC are included. 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 and economic life, see Project Finance Impact on LCOE. For LCOE estimates for High, Mid, and Low scenarios for all technologies, see 2017 ATB Cost and Performance Summary.

In general, the degree of adoption of a range of technology innovations distinguishes the High, Mid and Low cost cases. These projections represent the following trends to reduce CAPEX and FOM, and increase O&M.

  • Continued turbine scaling to larger-megawatt turbines with larger rotors such that the swept area/megawatt capacity decreases, resulting in higher capacity factors for a given location
  • Continued diversity of turbine technology whereby the largest rotor diameter turbines tend to be located in lower wind speed sites, but the number of turbine options for higher wind speed sites increases
  • Taller towers that result in higher capacity factors for a given site due to the wind speed increase with elevation above ground level
  • Improved plant siting and operation to reduce plant-level energy losses, resulting in higher capacity factors
  • More efficient O&M procedures combined with more reliable components to reduce annual average FOM costs
  • Continued manufacturing and design efficiencies such that capital cost/kilowatt decreases with larger turbine components
  • Adoption of a wide range of innovative control, design, and material concepts that facilitate the above high-level trends.

Commercial PV

Representative Technology

For the ATB, commercial PV systems are modeled for a 300-kWDC 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-axis, 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).

map: mean U.S. solar resource available to PV systems
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 (Lopez et al. 2012; NREL, "Renewable Energy Technical Potential").

Base Year and 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 all geographic locations in the United States. Capacity factor is estimated based on hours of sunlight at latitude for three 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:

  • High cost: no change in CAPEX, O&M, or capacity factor from 2016 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: based on the median of literature projections of future CAPEX; O&M technology pathway analysis
  • Low cost: based on low bound of literature projections of future CAPEX; 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 or materials. 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 reduction scenarios are represented: High, 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.

chart: CAPEX historical trends, current estimates, and future projections for commercial-scale PV in the 2017 ATB
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.
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.

Recent Trends

Reported historical commercial-scale PV installation CAPEX (Barbose and Dargouth 2016) is shown in box-and-whiskers format for comparison to the ATB current CAPEX estimates and future projections. The data in Barbose and Dargouth (2016) represent 85% of all U.S. residential and commercial PV capacity installed through 2015 and 82% of capacity installed in 2015.

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

CAPEX estimates for 2015 reflect continued rapid decline in pricing supported by analysis of recent system pricing for projects that became operational in 2015 (Feldman et al. 2016).

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 $2.47/WDC in 2015 represents the median reported price of a utility-scale PV system installed in 2015 reported in Tracking the Sun IX (Barbose and Dargouth 2016) and adjusted to remove regional cost multipliers based on geographic location of projects installed in 2015. The $2.18/WDC in 2016 is based on bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2016 (adjusted for inflation) (Fu et al. 2016). These figures are in line with other estimated system prices reported in Feldman et al. (2016).

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 10 system price projections from 5 separate institutions. We adjusted the "min," "median," and "max" analyst forecasts in a few different ways. All 2015 pricing is based on the 20th percentile, median, and 80th percentile historically reported commercial PV prices reported in Tracking the Sun IX (Barbose and Dargouth 2016). All 2016 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2016 (adjusted for inflation) (Fu et al. 2016). These figures are in line with other estimated system prices reported in Feldman et al. (2016).

We adjusted the Mid and Low projections for 2017–2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. The high projection case is kept constant at the 2016 CAPEX value, assuming no improvements beyond 2016.

A detailed description of the methodology for developing Future Year Projections is found in Projections Methodology.

Technology innovations that could impact future O&M costs are summarized in LCOE Projections.

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 (2016a) and the NREL Solar PV Cost Model (Fu et al. 2016) - 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
    • Owner's costs, such as development costs, legal fees, 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 (2016a) 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.

chart: CAPEX definition for commercial-scale PV in the 2017 ATB

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections 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 dGen does include state-level cost multipliers (EIA 2016a).

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 technical lifetime of 30 years (the distinction between economic life and technical life is described here), including:

  • Insurance, property taxes, site security, legal and administrative fees, and other fixed costs
  • Present value, 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 reduction 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.

chart: base year estimate and future year projections for fixed O&M costs for commercial-scale PV in the 2017 ATB

Base Year Estimates

FOM is assumed to be $18/kWDC-yr based on Woodhouse et al. (2016). This number is reasonably consistent with the 2013 "empirical O&M costs" reported in Bolinger and Weaver (2014), which indicates O&M costs ranging from $15/kWAC/yr to $25/kWAC/yr for fixed-tilt PV systems (this range would be lower if reported in $kWDC/yr). 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 are dependent on the quality of the parts and construction. NREL analysts estimate O&M costs can range between $0 and $40/kWDC-yr.

Future Year Projections

Future FOM is assumed to decline to $7.5/kWDC-yr by 2020 in the Low cost case and by 2025 in the Mid cost case.

There is currently great market variation in what individual companies perform for O&M.

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.

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 (the distinction between economic life and technical life is described here). 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.

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 $/WDC, 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-AC ratio is a design choice that influences the capacity factor. For the ATB, commercial PV systems are modeled for a 300-kWDC fixed-tilt (5°), roof-mounted system. PV plant capacity factor incorporates an assumed degradation rate of 0.5%/year (Jordan and Kurtz 2013) in the annual average calculation.

The following figure shows a range of capacity factors based on variation in the solar resource in the contiguous United States. The range of the Base Year estimates illustrate the effect of locating a residential PV plant in locations with solar irradiance similar to Seattle, Washington, Kansas City, Missouri, or Daggett, California (estimated first-year operation capacity factors of 11.4%, 14.5%, and 18.7% respectively). Future projections for High, Mid, and Low cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX and O&M cost elements.

chart: capacity factor (annual average energy production over plant lifetime) for commercial-scale solar PV in the ATB
The legend labels refer to the first-year operation capacity factors. The data illustrated modifies the first-year capacity factor by including estimated degradation over the economic life of a PV plant.

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 three resource locations in the contiguous United States:

  • Low: Seattle, Washington
  • Mid: Kansas City, Missouri
  • High: Daggett, California

First-year operation capacity factors as modeled range from 11.4% to 18.7%, 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 20-year economic life of the plant (the distinction between economic life and technical life is described here). The adjusted average capacity factor values in the ATB are 11%, 14%, and 18%.

Future Year Projections

Projections of capacity factor for installations installed in future years are unchanged from the Base Year. Solar PV installations have very little downtime, inverter efficiency is already optimized, and improvements in panel density are expected to result in smaller footprints or lower CAPEX, not necessarily increased capacity factor. That said, there is potential for future increases in capacity factor through technological improvements such as less panel reflectivity, lower degradations rates and improved performance in low-light conditions.

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

dGen does not endogenously consider curtailment from surplus renewable energy generation, though this is a feature of the linked ReEDS-dGen model (Cole et al. 2016), where balancing area-level marginal curtailments can be applied to DPV 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 High, Mid and Low CAPEX cases to explore the range of possible outcomes of future PV cost improvements. The High cost case 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 10 system price projections from 5 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.

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 a High, Mid, and Low cost forecast we took the "min," "median," and "max" 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 U.S. 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 High, Mid, and Low cost forecast between 2020 and 2030 are interpolated on a straight-line basis.

We adjusted the "min," "median," and "max" analyst forecasts in a few different ways. All 2015 pricing is based on the 20th percentile, median, and 80th percentile historically reported commercial PV price reported in Tracking the Sun IX (Barbose and Dargouth 2016). All 2016 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2016 (adjusted for inflation) (Fu et al. 2016). These figures are in line with other estimated system prices reported in Feldman et al. (2016).

We adjusted the Mid and Low cost projections for 2017-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. The High cost projection case is kept constant at the 2016 CAPEX value, assuming no improvements beyond 2016.

chart: system prices, 2015-2050, for commercial-scale solar PV in the ATB
chart: system prices, 2015-2050, for commercial-scale solar PV in the ATB
All prices quoted in euros are converted to USD (1€ = $1.25).
All prices quoted in WAC are converted to WDC (1 WAC=1.2 WDC).

Future FOM is assumed to decline to $7.5/kWDC-yr by 2020 in the Low case and by 2025 in the Mid case through improvements in system operation and more durable, better performing capital equipment (Woodhouse et al. 2016).

Capacity factors are assumed to not increase over time. All PV system efficiency improvements are assumed to result in capital cost reductions rather than capacity factor improvements.

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 focus of the ATB is to define the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. LCOE captures the energy component of electric system planning and operation, but the electric system also requires capacity and flexibility services to operate reliably. 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 the combined impact of CAPEX, O&M, and capacity factor projections across the range of resources present in the contiguous United States. The Current Market Conditions LCOE demonstrates the range of LCOE based on macroeconomic conditions similar to the present. The Historical Market Conditions LCOE presents the range of LCOE based on macroeconomic conditions consistent with prior ATB editions and Standard Scenarios model results. The Normalized LCOE (all LCOE estimates are normalized with the lowest Base Year LCOE value) emphasizes the effect of resource quality and the relative differences in the three future pathways independent of project finance assumptions. The ATB representative plant characteristics that best align with recently installed or anticipated near-term commercial PV plants are associated with Comm PV: 14.5%. Data for all the resource categories can be found in the ATB data spreadsheet.

Current Market Conditions
Historical Market Conditions
Normalized
The ATB representative plant characteristics that best align with recently installed or anticipated near-term commercial PV plants are associated with Comm PV: CF 14.5%.

The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. The three pathways are generally defined as:

  • High = Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
  • Mid = 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 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. For comparison in the ATB, two project finance structures are represented.

  • Current Market Conditions: The values of the production tax credit (PTC) and investment tax credit (ITC) are ramping down by 2020, at which time wind and solar projects may be financed with debt fractions similar to other technologies. This scenario reflects debt interest (4.4% nominal, 1.9% real) and return on equity rates (9.5% nominal, 6.8% real) to represent 2017 market conditions (AEO 2017) and a debt fraction of 60% for all electricity generation technologies. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.
  • Long-Term Historical Market Conditions: Historically, debt interest and return on equity were represented with higher values. This scenario reflects debt interest (8% nominal, 5.4% real) and return on equity rates (13% nominal, 10.2% real) implemented in the ReEDS model and reflected in prior versions of the ATB and Standard Scenarios model results. A debt fraction of 60% for all electricity generation technologies is assumed. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.

These parameters are held constant for estimates representing the Base Year through 2050. No incentives such as the PTC or ITC are included. 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 and economic life, see Project Finance Impact on LCOE. For LCOE estimates for High, Mid, and Low scenarios for all technologies, see 2017 ATB Cost and Performance Summary.

The LCOE for commercial PV systems is calculated using the same financing parameters as the utility systems. Although we recognize that commercial systems have a wide range of financing options available to them, we represent LCOEs using these utility-based financing calculations in order to allow better comparison against the utility system LCOEs.

In general, the degree of adoption of a range of technology innovations distinguishes the High, Mid and Low cost cases. These projections represent the following trends to reduce CAPEX and FOM.

  • Modules
    • Increased module efficiencies and increased production-line throughput to decrease CAPEX; overhead costs on a per-kilowatt 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 packages system design
      • Improvement of supply chains for BOS components in modules
    • Improved power electronics
      • Improvement of inverter prices and performance, possibly by integrating micro-inverters
    • 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.

Residential PV Systems

Representative Technology

For the ATB, residential PV systems are modeled for a 5.0-kWDC fixed tilt (25°), 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 residential 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-axis, 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) and 386 GW (506 TWh/yr) of potential exists for medium (5,000-25,000 m2) and large buildings (>25,000 m2).

map: mean U.S. solar resource available to PV systems
Map of 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 (Lopez et al. 2012; 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 three 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:

  • High cost: no change in CAPEX, O&M, or capacity factor from 2016 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: based on the median of literature projections of future CAPEX; O&M technology pathway analysis
  • Low cost: based on low bound of literature projections of future CAPEX; 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 or materials. 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 reduction scenarios are represented: High, Mid, and Low. Historical data from residential 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.

chart: CAPEX historical trends, current estimates, and future projections for residential solar in the ATB
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.
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.

Recent Trends

Reported residential PV installation CAPEX (Barbose and Dargouth 2016) is shown in box-and-whiskers format for comparison to the ATB current CAPEX estimates and future projections. The data in Barbose et al. (2016) represent 85% of all U.S. residential and commercial PV capacity installed through 2015 and 82% of capacity installed in 2015. The weighted-average market report numbers are expected to be higher than the national cost number projected here, as many of the historical installations are in states (e.g., California) where installation costs are higher than the national cost number.

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

CAPEX estimates for 2015 reflect a continued rapid decline in pricing supported by analysis of recent system cost and pricing for projects that became operational in 2015 (Feldman et al. 2016).

Base Year Estimates

For illustration in the ATB, a representative residential-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 $4.03/WDC in 2015 represents the median reported price of a residential-scale PV system installed in 2015 reported in Tracking the Sun IX (Barbose and Dargouth 2016) and adjusted to remove regional cost multipliers based on geographic location of projects installed in 2015. The $2.93/WDC in 2016 is based on bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2016 (adjusted for inflation) (Fu et al. 2016). These figures are in line with other estimated system prices reported in Feldman et al. (2016).

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 residential PV installation CAPEX are based on 11 system price projections from 7 separate institutions with short-term projections made in the past six months and long-term projections made in the last three years. We adjusted the "min," "median," and "max" analyst forecasts in a few different ways. All 2015 pricing is based on the median historically reported residential PV system price reported in Tracking the Sun IX (Barbose and Dargouth 2016). All 2016 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2016 (adjusted for inflation) (Fu et al. 2016). These figures are in line with other estimated system prices reported in Feldman et al. (2016).

We adjusted the Mid and Low cost projections for 2017-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. The high projection case is kept constant at the 2016 CAPEX value, assuming no improvements beyond 2016.

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 residential PV, this is modeled for a host-owned business model only.

For the ATB - and based on EIA (2016a) and the NREL Solar-PV Cost Model (Fu et al. 2016) - the distributed residential solar PV plant envelope is defined to include:

  • Hardware
    • Module supply
    • Power electronics, including inverters
    • Racking
    • Foundation
    • AC and DC wiring 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
    • Owner's costs, such as development costs, legal fees, insurance costs.

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 (2016a) 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.

chart: CAPEX definition for residential solar in the ATB

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections 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 dGen does include 134 regional multipliers (EIA 2016a).

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 technical lifetime of 30 years (the distinction between economic life and technical life is described here), including:

  • Insurance, property taxes, site security, legal and administrative fees, and other fixed costs
  • Present value, 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 reduction 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.

chart: base year estimate and future year projections for fixed O&M costs for residential PV in the 2017 ATB

Base Year Estimates

FOM is assumed to be $24/kWDC-yr based on Woodhouse et al. (2016). This number is reasonably consistent with the 2013 "empirical O&M costs" reported in Bolinger and Weaver (2014), which indicates O&M costs range from $15/kWAC/yr to $25/kWAC/yr for fixed-tilt PV systems; this range would be lower if reported in $kWDC/yr. A wide range in reported prices exists in the market, and in part, it depends 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 between $0 and $40/kWDC-yr.

Future Year Projections

Future FOM is assumed to decline to $10/kWDC-yr by 2020 in the Low cost case and by 2025 in the Mid cost case.

There is currently great market variation in what individual companies perform for O&M.

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.

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 (the distinction between economic life and technical life is described here). 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.

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 $/WDC, 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-AC ratio is a design choice that influences the capacity factor. PV plant capacity factor incorporates an assumed degradation rate of 0.5%/year (Jordan and Kurtz 2013) in the annual average calculation.

The following figure shows a range of capacity factors based on variation in the solar resource in the contiguous United States. The range of the Base Year estimates illustrate the effect of locating a residential PV plant in locations with solar irradiance similar to Seattle, Washington; Kansas City, Missouri; or Daggett, California (estimated first-year operation capacity factors of 12.5%, 16.1%, and 20.7% respectively). Future projections for High, Mid, and Low cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX and O&M cost elements.

chart: capacity factor (annual average energy production over plant lifetime) for residentialsolar PV in the ATB
The legend labels refer to the first-year operation capacity factors. The data illustrated modify the first-year capacity factor by including estimated degradation over the economic life of a PV plant.

Base Year Estimates

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

  • Low: Seattle, Washington
  • Mid: Kansas City, Missouri
  • High: Daggett, California.

First-year operation capacity factors as modeled range from 12.5% to 20.7%, 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 20-year economic life of the plant (the distinction between economic life and technical life is described here). The adjusted average capacity factor values in the ATB are 12%, 15.5%, and 19.9%.

Future Year Projections

Projections of capacity factors for installations installed in future years are unchanged from the current year. Solar PV installations have very little downtime, inverter efficiency is already optimized, and improvements in panel density are expected to result in smaller footprints or lower CAPEX - not necessarily increased capacity factor. That said, there is potential for future increases in capacity factors through technological improvements such as less panel reflectivity, lower degradations rates, and improved performance in low-light conditions.

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

dGen does not endogenously consider curtailment from surplus renewable energy generation, though this is a feature of the linked ReEDS-dGen model (Cole et al. 2016), where balancing area-level marginal curtailments can be applied to DPV 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 High, Mid, and Low CAPEX cases to explore the range of possible outcomes of future PV cost improvements. The High case represents no CAPEX improvements made beyond today, the Mid case represents current expectations of price reductions in a "business-as-usual" scenario, and the Low 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 residential PV installation CAPEX are based on 11 system price projections from 7 separate institutions. Projections include 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 three years. The short-term forecasts were 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.

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 a High, Mid, and Low cost forecast we took the "min," "median," and "max" 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. Many of the global projections are weighted heavily toward western countries (e.g., European countries, Japan, and the United States), and in the long-term, the United States should follow global trends. The U.S. 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. Many institutions used one system price for all countries. Changes in price for the High, Mid, and Low cost forecast between 2020 and 2030 are interpolated on a straight-line basis.

We adjusted the "min," "median," and "max" analyst forecasts in a few different ways. All 2015 pricing is based on the median reported residential system price reported in Tracking the Sun IX (Barbose and Dargouth 2016). All 2016 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2016 (adjusted for inflation) (Fu et al. 2016). These figures are in line with other estimated system prices reported in Feldman et al. (2016).

We adjusted the Mid and Low cost projections for 2017-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. The High projection case is kept constant at the 2016 CAPEX value, assuming no improvements beyond 2016.

chart: system prices, 2015-2050, for residential solar PV in the ATB chart: system prices, 2015-2050, for residential solar PV in the ATB
All prices quoted in euros are converted to USD (1€ = $1.25).
All prices quoted in WAC are converted to WDC (1 WAC=1.2 WDC).

Future FOM is assumed to decline to $10/kWDC-yr by 2020 in the Low case and by 2025 in the Mid case through improvements in system operation and more durable, better performing capital equipment, as per Woodhouse et al. 2016.

Capacity factors are assumed to not increase over time. All PV system efficiency improvements are assumed to result in capital cost reductions rather than capacity factor improvements.

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 focus of the ATB is to define the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. LCOE captures the energy component of electric system planning and operation, but the electric system also requires capacity and flexibility services to operate reliably. 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 the combined impact of CAPEX, O&M, and capacity factor projections across the range of resources present in the contiguous United States. The Current Market Conditions LCOE demonstrates the range of LCOE based on macroeconomic conditions similar to the present. The Historical Market Conditions LCOE presents the range of LCOE based on macroeconomic conditions consistent with prior ATB editions and Standard Scenarios model results. The Normalized LCOE (all LCOE estimates are normalized with the lowest Base Year LCOE value) emphasizes the effect of resource quality and the relative differences in the three future pathways independent of project finance assumptions. The ATB representative plant characteristics that best align with recently installed or anticipated near-term residential PV plants are associated with Res PV: CF 16.0%. Data for all the resource categories can be found in the ATB data spreadsheet.

Current Market Conditions
Historical Market Conditions
Normalized
The ATB representative plant characteristics that best align with recently installed or anticipated near-term residential PV plants are associated with Res PV: CF 16.0%.

The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. The three pathways are generally defined as:

  • High = Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
  • Mid = 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 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. For comparison in the ATB, two project finance structures are represented.

  • Current Market Conditions: The values of the production tax credit (PTC) and investment tax credit (ITC) are ramping down by 2020, at which time wind and solar projects may be financed with debt fractions similar to other technologies. This scenario reflects debt interest (4.4% nominal, 1.9% real) and return on equity rates (9.5% nominal, 6.8% real) to represent 2017 market conditions (AEO 2017) and a debt fraction of 60% for all electricity generation technologies. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.
  • Long-Term Historical Market Conditions: Historically, debt interest and return on equity were represented with higher values. This scenario reflects debt interest (8% nominal, 5.4% real) and return on equity rates (13% nominal, 10.2% real) implemented in the ReEDS model and reflected in prior versions of the ATB and Standard Scenarios model results. A debt fraction of 60% for all electricity generation technologies is assumed. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.

These parameters are held constant for estimates representing the Base Year through 2050. No incentives such as the PTC or ITC are included. 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 and economic life, see Project Finance Impact on LCOE. For LCOE estimates for High, Mid, and Low scenarios for all technologies, see 2017 ATB Cost and Performance Summary.

The LCOE for residential PV systems is calculated using the same financing parameters as the utility systems. Although we recognize that residential systems have a wide range of financing options available to them (e.g., cash payment, loan, and lease), we represent LCOEs using these utility-based financing calculations in order to allow better comparison against the utility system LCOEs.

In general, the degree of adoption of a range of technology innovations distinguishes the High, Mid, and Low cost cases. These projections represent the following trends to reduce CAPEX and FOM.

  • Modules
    • Increased module efficiencies and increased production-line throughput to decrease CAPEX; overhead costs on a per-kilowatt 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 packages system design
      • Improvement of supply chains for BOS components in modules
    • Improved power electronics
      • Improvement of inverter prices and performance, possibly by integrating micro-inverters
    • 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.

Geothermal

Hydrothermal Geothermal

Representative Technology

The typical geothermal plant size for hydrothermal resource sites is represented by a range of 30–40 MW, depending on the technology type (e.g., binary or flash) (Mines 2013).

Resource Potential

The hydrothermal geothermal resource is concentrated in the western United States. The total potential is 45,370 MW: 7,833 MW identified and 37,537 MW undiscovered (Williams et al. 2008). The U.S. Geological Survey (Williams et al. 2008) identified resource potential 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 hydrothermal resource in the western United States
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: Williams et al. 2008

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

  • Installed capacity of about 3 GW in 2014 is excluded from the resource potential
  • Technical potential estimates increased 20%–30% to reflect impact of in-field enhanced geothermal system (EGS) technologies to increase (1) productivity of dry wells and (2) recovery of heat in place from hydrothermal reservoirs.

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

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.
  • Site attribute values are from Williams et al. (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.

Projections of CAPEX for plants installed in future years are derived from minimum learning estimates (IEA 2017). 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.

  • High cost: no change in CAPEX, O&M, or capacity factor from 2015 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: CAPEX cost reduction based on half of assumed minimum learning
  • Low cost: CAPEX cost reduction based on assumed minimum learning.

Enhanced Geothermal System (EGS) Technology

Representative Technology

The typical geothermal plant size for EGS plants is represented by a range of 20-25 MW for binary or flash technologies (Mines 2013).

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 geothermal resource in the United States showing favorability of deep EGS
Locations of identified hydrothermal sites and favorability of deep enhanced geothermal systems (EGS)
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.
  • The deep EGS resource potential (Augustine 2011) is based on Southern Methodist University Geothermal Laboratory temp-at-depth maps and the methodology is from MIT (2006).
  • The EGS resource is thousands of GW (16,000 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 that there are multiple types of potential-resource, technical, economic, and market (Lopez et al. 2012; NREL, "Renewable Energy Technical Potential").

Base Year and Future 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.
  • 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.

Projections of CAPEX for plants installed in future years are derived from minimum learning estimates (IEA 2017). 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.

  • High Cost: no change in CAPEX, O&M, or capacity factor from 2015 to 2050, consistent across all renewable energy technologies in the ATB
  • Mid Cost: CAPEX cost reduction based on half of assumed minimum learning
  • Low cost: CAPEX cost reduction based on assumed minimum learning.

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 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 or materials. 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 reduction scenarios are represented: High, Mid, and Low. The estimate for a given year represents CAPEX of a new plant that reaches commercial operation in that year.

chart: CAPEX historical trends, current estimates, and future projections for geothermal in the 2017 ATB
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.

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 example 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 table below 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) 22,718 5,560 1,173 9,697
Avgerage OCC ($/kW) 4,047 6,801 8,611 15,367
Min. OCC ($/kW) 3,000 3,909 6,786 10,596
Max. OCC ($/kW) 5,906 15,314 11,885 20,612
Example plant OCC ($/kW) 4,567 5,465
NF-EGS Number of sites 12 20
Total capacity (MW) 787 707
Avgerage OCC ($/kW) 5,928 8,820
Min. OCC ($/kW) 4,871 6,757
Max. OCC ($/kW) 7,216 11,486
Example plant OCC ($/kW) 8,100 12,179
Deep EGS (3–6 km) Number of sites n/a n/a
Total capacity (MW) 100,000+
Average OCC ($/kW) 10,061 20,840
Min.OCC ($/kW) 4,782 15,951
Max. OCC ($/kW) 18,292 25,933
Example plant OCC ($/kW)8,10012,179

Future Year Projections

Projection of future geothermal plant CAPEX for the Low 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 Mid case is also considered with a 0.25% annual improvement in CAPEX through 2050.

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 the ATB - and based on EIA (2016a) 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
    • Owner's 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 duration accumulated 10%/20%/30%/40% 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.)

Regional cost variations and geographically specific grid connection costs are not included in the ATB (CapRegMult = 1; GCC = 0). In the ATB, the input value is overnight capital cost (OCC) and details to calculate interest during construction (ConFinFactor).

In the ATB, CAPEX is shown for six representative plants. Example 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 of 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.

chart: CAPEX definition for geothermal in the 2017 ATB

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

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.

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 hydrothermal plant over its technical lifetime of 30 years (plant and reservoir) (the distinction between economic life and technical life is described here), 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.

The following figure shows the Base Year estimate and future year projections for fixed O&M (FOM) costs. Three cost reduction 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.

chart: base year estimate and future year projections for fixed O&M costs for geothermal in the 2017 ATB

Base Year Estimates

FOM is estimated for each example 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

No future FOM cost reduction is assumed in this edition of the ATB.

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 (the distinction between economic life and technical life is described here). 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 High, Mid, and Low cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX cost elements.

chart: capacity factor (annual average energy production over plant lifetime) for geothermal in the 2017 ATB

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 Geothermal Vision Study, which is sponsored by the DOE Geothermal Technologies Office, is currently underway and is likely to lead to industry-developed cost reduction estimates that could be included in a future ATB..

Projection of future geothermal plant CAPEX for the Low 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 Mid cost case assumes a 0.25% annual improvement in CAPEX through 2050. The High cost case 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 focus of the ATB is to define the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. LCOE captures the energy component of electric system planning and operation, but the electric system also requires capacity and flexibility services to operate reliably. 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 the combined impact of CAPEX, O&M, and capacity factor projections across the range of resources present in the contiguous United States. The Current Market Conditions LCOE demonstrates the range of LCOE based on macroeconomic conditions similar to the present. The Historical Market Conditions LCOE presents the range of LCOE based on macroeconomic conditions consistent with prior ATB editions and Standard Scenarios model results. The Normalized LCOE (all LCOE estimates are normalized with the lowest Base Year LCOE value) emphasizes the effect of resource quality and the relative differences in the three future pathways independent of project finance assumptions. The ATB representative plant characteristics that best align with 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.

Current Market Conditions
Historical Market Conditions
Normalized
The ATB representative plant characteristics that best align with recently installed or anticipated near-term geothermal plants are associated with Hydrothermal/Flash.

The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. The three pathways are generally defined as:

  • High = Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
  • Mid = 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 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. For comparison in the ATB, two project finance structures are represented.

  • Current Market Conditions: The values of the production tax credit (PTC) and investment tax credit (ITC) are ramping down by 2020, at which time wind and solar projects may be financed with debt fractions similar to other technologies. This scenario reflects debt interest (4.4% nominal, 1.9% real) and return on equity rates (9.5% nominal, 6.8% real) to represent 2017 market conditions (AEO 2017) and a debt fraction of 60% for all electricity generation technologies. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.
  • Long-Term Historical Market Conditions: Historically, debt interest and return on equity were represented with higher values. This scenario reflects debt interest (8% nominal, 5.4% real) and return on equity rates (13% nominal, 10.2% real) implemented in the ReEDS model and reflected in prior versions of the ATB and Standard Scenarios model results. A debt fraction of 60% for all electricity generation technologies is assumed. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.

These parameters are held constant for estimates representing the Base Year through 2050. No incentives such as the PTC or ITC are included. 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 and economic life, see Project Finance Impact on LCOE. For LCOE estimates for High, Mid, and Low scenarios for all technologies, see 2017 ATB Cost and Performance Summary.

Areas identified as having potential cost reduction opportunities include:

  • 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
  • Novel or mixed working fluids in binary power plant designed to increase plant efficiency
  • Advanced drilling systems such as using flames or lasers to drill through rock; drilling steering technology; and other technologies to reduce drilling costs.

Hydropower

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.

Upgrades to Existing Facilities

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 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 (Montgomery, Watson, and 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 total upgrade potential for hydropower facilities in the United States
Map of the hydropower resource 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 (Lopez et al. 2012; NREL, "Renewable Energy Technical Potential").

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 2010) where available. Costs at non-reclamation plants were developed using Hall et al. (2003).

Cost= (277 × ExpansionMW-0.3) + (2230 × 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.

Standard Scenarios Model Results

The ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

The ReEDS model times upgrade potential availability with the relicensing date, plant age (50 years), or both.

Powering Non-Powered Dams

report cover: An Assessment of Energy Potential at Non-Powered Dams in the United States

An Assessment of Energy Potential at Non-Powered Dams in the United States

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):

  • 1MW 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, when economic decision-making approximating seen in recent development activity is taken into account, the economic potential of NPD may be approximately 5.6 GW at over 54,000 dams in the contiguous United States. The majority of this potential (5 GW or 90% of resource capacity) is associated with less than 700 dams (DOE 2016). These resource considerations are discussed below:

  • According to the National Inventory of Dams, more than 80,000 dams exist that do not produce power. This data set was filtered to remove dams with erroneous flow and geographic data and dams whose data could not be resolved to a satisfactory level of detail (Hadjerioua, Wei, and 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 over 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 20-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.

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

Base Year and Future 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 et al. 2015a). 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:

  • High cost: no change in CAPEX, O&M, or capacity factor from 2015 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: incremental technology learning, consistent with Reference in Hydropower Vision (DOE 2016); CAPEX reductions for new stream-reach development (NSD) only
  • Low cost: gains that are achievable when pushing to the limits of potential new technologies, such as modularity (in both civil structures and power train design), advanced manufacturing techniques, and materials, consistent with Advanced Technology in Hydropower Vision (DOE 2016); both CAPEX and O&M cost reductions implemented.

Standard Scenarios Model Results

ATB CAPEX, O&M and capacity factor assumptions for Base Year and future projections through 2050 for Low, Mid, and High projections are used to develop Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

ReEDS Version 2017.1 standard scenario model results restrict the resource potential to sites greater than 500 kW consistent with the Hydropower Vision, which results in 5 GW/29 TWh at 671 dams.

New Stream-Reach Development

map of new stream-reach development potential in the United States
Source: ORNL NSD resource assessment

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)
  • Capacity factor of 30% to 80%.

Resource Potential

The resource potential is estimated to be 53.2 GW/301 TWh at nearly 230,000 individual sites (Kao et al. 2014) after accounting for locations statutorily excluded from hydropower development such as national parks, wild and scenic rivers, and wilderness areas.

  • About 8,500 stream reaches were evaluated to assess resource potential (i.e., capacity) and energy generation potential (i.e., capacity factor). For each stream reach, a design capacity, average monthly flow rate over a 20-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 FEMA 100-year floodplain.
  • New hydropower facilities are assumed to apply run-of-river operation strategies. Run-of-river operation means that 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.

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

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

Projections developed for the Hydropower Vision study (DOE 2016) using technological learning assumptions and bottom-up analysis of process and/or technology improvements provide a range of future cost outcomes. Three different projections were developed for scenario modeling as bounding levels:

  • High cost: no change in CAPEX, O&M, or capacity factor from 2015 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: incremental technology learning, consistent with Reference in Hydropower Vision (DOE 2016); CAPEX reductions for NSD only
  • Low cost: gains that are achievable when pushing to the limits of potential new technologies, such as modularity (in both civil structures and power train design), advanced manufacturing techniques, and materials, consistent with Advanced Technology in Hydropower Vision (DOE 2016); both CAPEX and O&M cost reductions implemented.

Standard Scenarios Model Results

ATB CAPEX, O&M and capacity factor assumptions for Base Year and future projections through 2050 for Low, Mid, and High projections are used to develop Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

ReEDS Version 2017.1 standard scenario model results restrict 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 or materials. 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 reduction scenarios are represented: High, Mid, and Low. 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.

chart: CAPEX historical trends, current estimates, and future projections for hydropower in the 2017 ATB
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.
Historical CAPEX estimates represent actual and proposed projects from 1981 to 2014.
Year represents Commercial Online Date for a past or future plant.

Recent Trends

Actual and proposed NPD and NSD CAPEX from 1981 to 2014 (from O'Connor et al. 2015a) 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.

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 over 60 feet 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), low-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 over 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 represents. These characteristics lead to higher CAPEX estimates than past data suggests 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
Plants Resource Characteristics Ranges Weighted Average Values Calculated Plant Values
Plants Head (feet) Capacity (MW) Head (feet) Capacity (MW) Capacity Factor ICC (2015$/kW) O&M (2015$/kW)
NPD 1 3-30 0.5-10 15.4 4.8 0.62 $6,169 $112
NPD 2 3-30 10+ 15.9 82.2 0.64 $5,615 $31
NPD 3 30+ 0.5-10 89.6 4.2 0.60 $4,131 $119
NPD 4 30+ 10+ 81.3 44.7 0.60 $3,895 $41
NSD 1 3-30 1-10 15.7 3.7 0.66 $7,270 $125
NSD 2 3-30 10+ 19.6 44.1 0.66 $6,490 $41
NSD 3 30+ 1-10 46.8 4.3 0.62 $6,357 $118
NSD 4 30+ 10+ 45.3 94.0 0.66 $5,722 $29
Reference plants 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.

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 3-330 feet of head and 0.5-30 MW of capacity.

The weighted-average values were used as input to the cost formulas (O'Connor et al. 2015a) 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 et al. 2015a).

NPD CAPEX = (11,489,245 × P0.976 × H-0.24) + (310,000 × P0.7)
NSD CAPEX = (9,605,710 × P0.977 × H-0.126) + (610,000 × P0.7)

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:

  • High cost:
    • NPD and NSD CAPEX unchanged from the Base Year; consistent across all renewable energy technologies in the ATB
  • Mid cost: consistent with Reference in Hydropower Vision:
    • NSD CAPEX reduced 5% in 2035 and 8.6% in 2050
    • NPD CAPEX unchanged from the Base Year
  • Low cost: consistent with Advanced Technology 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.

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.

Standard Scenarios Model Results

ATB CAPEX, O&M and capacity factor assumptions for Base Year and future projections through 2050 for Low, Mid, and High projections are used to develop Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

ReEDS Version 2017.1 standard scenario model results use 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.

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 NPD resource and 53%-81% 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 (2016a) and the System Cost Breakdown Structure described by O'Connor et al. (2015b) - 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
    • 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
    • Owner's 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.)

Regional cost variations and geographically specific grid connection costs are not included in the ATB (CapRegMult = 1; GCC = 0). In the ATB, the input value is overnight capital cost (OCC) and details to calculate interest during construction (ConFinFactor).

In the ATB, CAPEX is shown for four representative non-powered dam plants and four representative new stream-reach development plants. CAPEX estimates for all identified hydropower potential (~700 NPD and ~8,000 NSD) results in a CAPEX range that is much broader than that shown in the ATB. It is unlikely that all of 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.

chart: CAPEX definition for hydropower in the 2017 ATB

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

CAPEX in ATB do not represent regional variants (CapRegMult) associated with labor rates, material costs, etc., and neither does ReEDS.

CAPEX in ATB do not include geographically determined spur line (GCC) from plant to transmission grid, and neither does ReEDS.

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 technical lifetime of 50 years (the distinction between economic life and technical life is described here), 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. Three cost reduction 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.

chart: base year estimate and future year projections for fixed O&M costs for hydropower in the 2017 ATB

Base Year Estimates

A statistical analysis of long-term plant operation costs from FERC Form-1 resulted in a relationship between annual, FOM costs, and plant capacity (updated to 2015$ from O'Connor et al. 2015a).

Lesser of (Annual O&M (in 2015$)=227,000xP0.547) 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:

  • High cost: FOM costs unchanged from the Base Year to 2050; consistent with all ATB technologies
  • Mid cost: FOM costs for both NPD and NSD plants are unchanged from 2015 to 2050; consistent with Reference in Hydropower Vision
  • Low cost: FOM costs for both NPD and NSD plants are reduced by 50% in 2035 and 54% in 2050, consistent with Advanced Technology in Hydropower Vision.

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

Technology innovations that could impact future O&M costs are summarized in LCOE Projections.

Capacity Factor: Expected Annual Average Energy Production Over Lifetime

The capacity factor represents the expected annual average energy production divided by the annual energy production, assuming the plant operates at rated capacity for every hour of the year. It is intended to represent a long-term average over the technical lifetime of the plant (the distinction between economic life and technical life is described here). It does not represent interannual variation in energy production. Future year estimates represent the estimated annual average capacity factor over the technical lifetime of a new plant installed in a given year.

The capacity factor is influenced by site hydrology, design factors (e.g., exceedance level), and operation characteristics (e.g., dispatch or run of river). Capacity factors for all potential NPD sites and NSDs are estimated based on design criteria, long-term monthly flow rate records, and run-of-river operation.

The following figure shows a range of capacity factors based on variation in the resource for hydropower plants in the contiguous United States. Historical data from run of river hydropower plants operating in the United States from 2003 through 2012 are shown for comparison with the Base Year estimates. The range of the Base Year estimates illustrates the effect of resource variation. Future projections for High, Mid and Low cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX and O&M cost elements.

chart: capacity factor (annual average energy production over plant lifetime) for hydropower in the 2017 ATB
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.
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 CF 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 2016a) 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 losses. 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
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 $6,169 $112
NPD 2 3-30 10+ 15.9 82.2 0.64 $5,615 $31
NPD 3 30+ 0.5-10 89.6 4.2 0.60 $4,131 $119
NPD 4 30+ 10+ 81.3 44.7 0.60 $3,895 $41
NSD 1 3-30 1-10 15.7 3.7 0.66 $7,270 $125
NSD 2 3-30 10+ 19.6 44.1 0.66 $6,490 $41
NSD 3 30+ 1-10 46.8 4.3 0.62 $6,357 $118
NSD 4 30+ 10+ 45.3 94.0 0.66 $5,722 $29

Future Year Projections

The capacity factor remains unchanged from the Base Year through 2050. Technology improvements are focused on CAPEX and O&M costs.

Standard Scenarios Model Results

ATB CAPEX, O&M, and capacity factor assumptions for the Base Year and future projections through 2050 for High, Mid, and Low projections are used to develop the NREL Standard Scenarios using the ReEDS model. See ATB and Standard Scenarios.

ReEDS Version 2017.1 standard scenario model results use resource/cost supply curves representing estimates at each individual facility (~700 NPD and ~8,000 NSD).

The ReEDS model represents cost and performance for NPD and NSD potential in 5 bins for each of 134 geographic regions, which results in capacity factor ranges of 38%-80% for the NPD resources and 53%-81% for NSD.

Existing hydropower facilities in the ReEDS model provide dispatch capability such that their annual energy production is determined by the electric system needs by dispatching generators to accommodate diurnal and seasonal load variations and output from variable generation sources (e.g., wind and solar PV).

Plant Cost and Performance Projections Methodology

Projections developed for the Hydropower Vision study (DOE 2016) using technological learning assumptions and bottom-up analysis of process and/or technology improvements provide a range of future cost outcomes. Three different projections were developed for scenario modeling as bounding levels:

  • High cost: no change in CAPEX or OPEX from 2015 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid cost: incremental learning, consistent with Reference in the Hydropower Vision study (DOE 2016); CAPEX reductions for 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.
chart: cost and performance projections methodology for hydropower in the 2017 ATB

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 magnitude of order 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 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 focus of the ATB is to define the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. LCOE captures the energy component of electric system planning and operation, but the electric system also requires capacity and flexibility services to operate reliably. 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 the combined impact of CAPEX, O&M, and capacity factor projections across the range of resources present in the contiguous United States. The Current Market Conditions LCOE demonstrates the range of LCOE based on macroeconomic conditions similar to the present. The Historical Market Conditions LCOE presents the range of LCOE based on macroeconomic conditions consistent with prior ATB editions and Standard Scenarios model results. The Normalized LCOE (all LCOE estimates are normalized with the lowest Base Year LCOE value) emphasizes the effect of resource quality and the relative differences in the three future pathways independent of project finance assumptions. The ATB representative plant characteristics that best align with 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.

Current Market Conditions
Historical Market Conditions
Normalized

Values are in 2015$.

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

The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. The three pathways are generally defined as:

  • High = Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
  • Mid = 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 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. For comparison in the ATB, two project finance structures are represented.

  • Current Market Conditions: The values of the production tax credit (PTC) and investment tax credit (ITC) are ramping down by 2020, at which time wind and solar projects may be financed with debt fractions similar to other technologies. This scenario reflects debt interest (4.4% nominal, 1.9% real) and return on equity rates (9.5% nominal, 6.8% real) to represent 2017 market conditions (AEO 2017) and a debt fraction of 60% for all electricity generation technologies. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.
  • Long-Term Historical Market Conditions: Historically, debt interest and return on equity were represented with higher values. This scenario reflects debt interest (8% nominal, 5.4% real) and return on equity rates (13% nominal, 10.2% real) implemented in the ReEDS model and reflected in prior versions of the ATB and Standard Scenarios model results. A debt fraction of 60% for all electricity generation technologies is assumed. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.

These parameters are held constant for estimates representing the Base Year through 2050. No incentives such as the PTC or ITC are included. 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 and economic life, see Project Finance Impact on LCOE. For LCOE estimates for High, Mid, and Low scenarios for all technologies, see 2017 ATB Cost and Performance Summary.

Areas identified as having potential cost reduction opportunities associated with the Low cost projection include:

  • 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 roadmap actions that result in lower-cost technology.

Biopower Plants

In a biopower plant:

  1. Heat is created: Biomass (sometimes co-fired with coal) is pulverized, mixed with hot air, and burned in suspension.
  2. Water turns to steam: The heat turns purified water into steam, which is piped to the turbine.
  3. Steam turns the turbine: The pressure of the steam pushes the turbine blade, turns the shaft in the generator, and creates power.
  4. Steam is turned back into water: Cool water is drawn into a condenser where the steam turns back into water that can be reused in the plant.
Joseph C. McNeil Generating Station in Burlington, Vermont (a biomass gasifier that operates on wood chips)
Joseph C. McNeil Generating Station in Burlington, Vermont
(a biomass gasifier that operates on wood chips)
Photo by David Parsons, NREL 06905
NIPSCO generating station
NIPSCO generating station
Photo by Kevin Craig, NREL 08928

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. Technical resource potential for biopower is based on estimated biomass quantities from the Billion Ton Update study (DOE 2011).

CAPital EXpenditures (CAPEX): Historical Trends, Current Estimates, and Future Projections

Because biopower plants are well-known and perform close to their optimal performance, EIA expects capital expenditures (CAPEX) will incrementally improve over time and slightly more quickly than inflation.

The exception is new biomass cofiring, which is expected to have costs that decline a bit more than existing cofiring project technologies.

chart: Current estimates and future projections calculated from EIA (2017), modified as described in the CAPEX section.
Current estimates and future projections calculated from EIA (2017), modified.

CAPEX Definition

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

Overnight capital costs are modified from EIA (2014). Capital costs include overnight capital cost plus defined transmission cost, and it removes a material price index. The overnight capital costs for cofired units are not the cost of upgrading a plant but the total cost of the plant after the upgrade.

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

Overnight Capital Cost ($/kW) Construction Financing Factor (ConFinFactor) CAPEX ($/kW)
Dedicated: Dedicated biopower plant $3,737 1.041 $3,889
CofireOld: Pulverized coal with sulfur dioxide (SO2) scrubbers and biomass co-firing $3,856 1.041 $4,013
CofireNew: Advanced supercritical coal with SO2 and NOx controls and biomass co-firing $3,856 1.041 $4,013

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 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 (2016a) 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.

chart: CAPEX range for ATB representative plant and regional costs across U.S. for biopower plants.

Operation and Maintenance (O&M) Costs

Operations and maintenance (O&M) costs represent the annual expenditures required to operate and maintain a plant over its technical lifetime (the distinction between economic life and technical life is described here), including:

  • Insurance, taxes, land lease payments, and other fixed costs
  • Present value and annualized large component replacement costs over technical life
  • Scheduled and unscheduled maintenance of power plants, transformers, and other components over the technical lifetime of the plant.

Market data for comparison are limited and generally inconsistent in the range of costs covered and the length of the historical record.

Joseph C. McNeil Generating Station in Burlington, Vermont (a biomass gasifier that operates on wood chips)
Joseph C. McNeil Generating Station in Burlington, Vermont (a biomass gasifier that operates on wood chips)
Photo by Warren Gretz, NREL 06382
chart: Coal plant fixed O&M projections.

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 2015, as reported by EIA.

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

chart: Biopower net capacity factor (dedicated and cofire).
Current estimates and future projections calculated from EIA (2017) and modified.

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 focus of the ATB is to define the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. LCOE captures the energy component of electric system planning and operation, but the electric system also requires capacity and flexibility services to operate reliably. 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 the combined impact of CAPEX, O&M, and capacity factor projections across the range of resources present in the contiguous United States. The Current Market Conditions LCOE demonstrates the range of LCOE based on macroeconomic conditions similar to the present. The Historical Market Conditions LCOE presents the range of LCOE based on macroeconomic conditions consistent with prior ATB editions and Standard Scenarios model results. The Normalized LCOE (all LCOE estimates are normalized with the lowest Base Year LCOE value) emphasizes the relative effect of fuel price and heat rate independent of project finance assumptions. Data for all the resource categories can be found in the ATB data spreadsheet.

Current Market Conditions
Historical Market Conditions
Normalized
The ATB representative plant characteristics that best align with recently installed or anticipated near-term biopower plants are associated with Dedicated.

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 prices are based on the EIA's Annual Energy Outlook 2017 (EIA 2017).

To estimate LCOE, assumptions about the cost of capital to finance electricity generation projects are required. For comparison in the ATB, two project finance structures are represented.

  • Current Market Conditions: The values of the production tax credit (PTC) and investment tax credit (ITC) are ramping down by 2020, at which time wind and solar projects may be financed with debt fractions similar to other technologies. This scenario reflects debt interest (4.4% nominal, 1.9% real) and return on equity rates (9.5% nominal, 6.8% real) to represent 2017 market conditions (AEO 2017) and a debt fraction of 60% for all electricity generation technologies. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.
  • Long-Term Historical Market Conditions: Historically, debt interest and return on equity were represented with higher values. This scenario reflects debt interest (8% nominal, 5.4% real) and return on equity rates (13% nominal, 10.2% real) implemented in the ReEDS model and reflected in prior versions of the ATB and Standard Scenarios model results. A debt fraction of 60% for all electricity generation technologies is assumed. An economic life, or period over which the initial capital investment is recovered, of 20 years is assumed for all technologies. These assumptions are one of the project finance options in the ATB spreadsheet.

These parameters are held constant for estimates representing the Base Year through 2050. No incentives such as the PTC or ITC are included. 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 and economic life, see Project Finance Impact on LCOE. For LCOE estimates for High, Mid, and Low scenarios for all technologies, see 2017 ATB Cost and Performance Summary.

References

Augustine, C. 2011. Updated U.S. Geothermal Supply Characterization and Representation for Market Penetration Input. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A2-47459. October 2011. http://www.nrel.gov/docs/fy12osti/47459.pdf.

Barbose, Galen, and Naïm Dargouth. 2016. Tracking the Sun IX: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-1006036. August 2016. https://emp.lbl.gov/sites/default/files/tracking_the_sun_ix_report.pdf.

Beiter, Philipp, Walter Musial, Aaron Smith, Levi Kilcher, Rick Damiani, Michael Maness, Senu Sirnivas, Tyler Stehly, Vahan Gevorgian, Meghan Mooney, and George Scott. 2016. A Spatial-Economic Cost-Reduction Pathway Analysis for U.S. Offshore Wind Energy Development from 2015-2030. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-66579. September 2016. http://www.nrel.gov/docs/fy16osti/66579.pdf.

Bolinger, Mark, and Samantha Weaver. 2014. Utility Scale Solar 2013: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States. Berkeley, CA: Lawrence Berkeley National Laboratory. September 2014. https://emp.lbl.gov/sites/default/files/lbnl_utility-scale_solar_2013_report.pdf.

Cole, Wesley, Haley Lewis, Ben Sigrin, and Robert Margolis. 2016. 'Interactions of Rooftop PV Deployment with the Capacity Expansion of the Bulk Power System.' Applied Energy 168 (April):473–481. doi:10.1016/j.apenergy.2016.02.004.

DOE (U.S. Department of Energy). 2011. U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry. Perlack, R.D., and B.J. Stokes, eds. Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/TM-2011/224. August 2011. https://www.osti.gov/scitech/biblio/1023318.

DOE (U.S. Department of Energy). 2016. Hydropower Vision: A New Chapter for America's Renewable Electricity Source. Washington, D.C.: U.S. Department of Energy. DOE/GO-102016-4869. July 2016. https://energy.gov/sites/prod/files/2016/10/f33/Hydropower-Vision-10262016_0.pdf.

DOI (U.S. Department of the Interior, Bureau of Reclamation). 2010. Assessment of Potential Capacity Increases at Existing Hydropower Plants: Hydropower Modernization Initiative. Sacramento, CA: U.S. Department of the Interior. October 2010. https://www.usbr.gov/power/AssessmentReport/USBRHMICapacityAdditionFinalReportOctober2010.pdf.

EIA (U.S. Energy Information Administration). 2014. Annual Energy Outlook 2014 with Projections to 2040. Washington, D.C.: U.S. Department of Energy. DOE/EIA-0383(2014). April 2014. http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf.

EIA (U.S. Energy Information Administration). 2015. Annual Energy Outlook with Projections to 2040. Washington, D.C.: U.S. Department of Energy. DOE/EIA-0383(2015). April 2015. http://www.eia.gov/outlooks/aeo/pdf/0383(2015).pdf.

EIA (U.S. Energy Information Administration). 2016a. Capital Cost Estimates for Utility Scale Electricity Generating Plants. Washington, D.C.: U.S. Department of Energy. November 2016. https://www.eia.gov/analysis/studies/powerplants/capitalcost/pdf/capcost_assumption.pdf.

EIA (U.S. Energy Information Administration). 2017. Annual Energy Outlook 2017 with Projections to 2050. Washington, D.C.: U.S. Department of Energy. January 5, 2017. http://www.eia.gov/outlooks/aeo/pdf/0383(2017).pdf.

Feldman, David, Robert Margolis, Paul Denholm, and Joseph Stekli. 2016. Exploring the Potential Competitiveness of Utility-Scale Photovoltaics plus Batteries with Concentrating Solar Power, 2015–2030. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-66592. August 2016. http://www.nrel.gov/docs/fy16osti/66592.pdf.

Fu, Ran, Donald Chung, Travis Lowder, David Feldman, Kristen Ardani, and Robert Margolis. 2016. U.S. Photovoltaic (PV) Prices and Cost Breakdowns: Q1 2016 Benchmarks for Residential, Commercial, and Utility-Scale Systems. Golden, CO: National Renewable Energy Laboratory. NREL/PR-6A20-67142. September 2016. http://www.nrel.gov/docs/fy16osti/67142.pdf.

Gagnon, Pieter, Robert Margolis, Jennifer Melius, Caleb Phillips, and Ryan Elmore. 2016. Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-65298. https://www.nrel.gov/docs/fy16osti/65298.pdf.

Hadjerioua, Boualem, Yaxing Wei, and Shih-Chieh Kao. 2012. An Assessment of Energy Potential at Non-Powered Dams in the United States. Oakridge, TN: Oakridge National Laboratory. GPO DOE/EE-0711. April 2012. http://www1.eere.energy.gov/water/pdfs/npd_report.pdf.

Hall, Douglas G., Richard T. Hunt, Kelly S. Reeves, and Greg R. Carroll. 2003. Estimation of Economic Parameters of U.S. Hydropower Resources. Idaho Falls, ID: Idaho National Laboratory. INEEL/EXT-03-00662. June 2003. http://www1.eere.energy.gov/water/pdfs/doewater-00662.pdf.

IEA (International Energy Agency). 2017. Reference to come.

Jordan, Dirk C., and Sarah R. Kurtz. 2013. Photovoltaic Degradation Rates: An Analytical Review. Golden, CO: National Renewable Energy Laboratory. NREL/JA-5200-51664. http://onlinelibrary.wiley.com/doi/10.1002/pip.1182/full.

Kao, Shih-Chieh, Ryan A. McManamay, Kevin M. Stewart, Nicole M. Samu, Boualem Hadjerioua, Scott T. DeNeale, Dilruba Yeasmin, M. Fayzul, K. Pasha, Abdoul A. Oubeidillah, and Brennan T. Smith. 2014. New Stream-Reach Development: A Comprehensive Assessment of Hydropower Energy Potential in the United States. Oakridge, TN: Oakridge National Laboratory. April 2014. GPO DOE/EE-1063. http://nhaap.ornl.gov/sites/default/files/ORNL_NSD_FY14_Final_Report.pdf.

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

Mines, Greg. 2013. Geothermal Electricity Technology Evaluation Model. Geothermal Technologies Office. 2013 Peer Review. Washington, D.C: U.S. Department of Energy. April 22, 2013. https://energy.gov/sites/prod/files/2014/02/f7/mines_getem_peer2013.pdf.

MIT (Massachusetts Institute of Technology), and INL (Idaho National Laboratory). 2006. The Future of Geothermal Energy Impact of Enhanced Geothermal Systems (EGS) on the United States in the 21st Century. Idaho Falls, ID: Idaho National Laboratory. INL/EXT-06-11746. November 2006. https://energy.mit.edu/wp-content/uploads/2006/11/MITEI-The-Future-of-Geothermal-Energy.pdf.

Moné, C., et al. 2013. Reference to come.

Montgomery, Watson, and Harza. 2009. Hydropower Modernization Initiative, Phase I, Needs and Opportunities Evaluation and Ranking. Prepared for the U.S. Army Corps of Engineers Northwest Division Hydroelectric Design Center. Contract No. W9127N-08-D-0003. Task Order 001.

Musial, Walt, Donna Heimiller, Philipp Beiter, George Scott, and Caroline Draxl. 2016. 2016 Offshore Wind Energy Resource Assessment for the United States. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-66599. September 2016. http://www.nrel.gov/docs/fy16osti/66599.pdf.

O'Connor, Patrick W., Qin Fen (Katherine) Zhang, Scott T. DeNeale, Dol Raj Chalise, and Emma Centurion. 2015. Hydropower Baseline Cost Modeling. Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/TM-2015/14. January 2015. http://info.ornl.gov/sites/publications/files/Pub53978.pdf.

O'Connor, Patrick W., Scott T. DeNeale, Dol Raj Chalise, Emma Centurion, and Abigail Maloof. 2015. Hydropower Baseline Cost Modeling, Version 2. Oak Ridge, TN: Oak Ridge National Laboratory. ORNL/TM-2015/471. September 2015. http://info.ornl.gov/sites/publications/files/Pub58666.pdf.

Williams, Colin F., Marshall J. Reed, Robert H. Mariner, Jacob DeAngelo, S. Peter Galanis, Jr. 2008. 'Assessment of Moderate- and High-Temperature Geothermal Resources of the United States.' Menlo Park, CA: U.S. Geological Survey. Fact Sheet 2008-3082. September 2008. https://pubs.usgs.gov/fs/2008/3082/.

Wiser, Ryan, Karen Jenni, Joachim Seel, Erin Baker, Maureen Hand, Eric Lantz, and Aaron Smith. 2016. Forecasting Wind Energy Costs and Cost Drivers: The Views of the World's Leading Experts. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-1005717. June 2016. https://emp.lbl.gov/publications/forecasting-wind-energy-costs-and.

Woodhouse, Michael, Rebecca Jones-Albertus, David Feldman, Ran Fu, Kelsey Horowitz, Donald Chung, Dirk Jordan, and Sarah Kurtz. 2016. On the Path to SunShot: The Role of Advancements in Solar Photovoltaic Efficiency, Reliability, and Costs. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-65464. May 2016. http://www.nrel.gov/docs/fy16osti/65872.pdf.