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
For each electricity generation technology in the ATB, this website provides:
Electricity generation technologies are selected on the left side of the screen, and the topics highlighted above can be selected using the drop-down menu at the top-right of the screen.
Guidelines for using and interpreting ATB content are provided. LCOE captures the energy component of electric system planning and operation, but the electric system also requires capacity and flexibility services to operate reliably. These services are represented in electric sector models such as the Regional Energy Deployment Systems (ReEDS) model and corresponding analysis results such as the NREL Standard Scenarios.
The NREL Standard Scenarios, a companion product to the ATB, provides a suite of electric sector scenarios and associated assumptions—including technology cost and performance assumptions from the ATB.
ATB data sources and references are also provided for each technology. All dollar values are presented in 2015 U.S. dollars, unless noted otherwise.
Additional information about the 2017 ATB—available via links in the ATB website footer below—includes:
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.
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.
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").
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):
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:
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.
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.
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.
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 |
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.
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:
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.
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.
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:
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.
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 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.
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.
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.
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).
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.
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.
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:
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.
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) 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.
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:
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.
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.
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.
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).
Renewable energy technical potential, as defined by Lopez et al. (2012), represents the achievable energy generation of a particular technology given system performance, topographic limitations, and environmental and land-use constraints. The primary benefit of assessing technical potential is that it establishes an upper-boundary estimate of development potential. It is important to understand that there are multiple types of potential-resource, technical, economic, and market (Lopez et al. 2012; NREL, "Renewable Energy Technical Potential").
The 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:
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.
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.
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.
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.
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:
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.
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).
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:
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.
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 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.
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.
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:
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%.
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.
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.
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.
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) 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.
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:
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.
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.
FOM cost reduction represents optimized O&M strategies, reduced component replacement costs, and lower frequency of component replacement.
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.
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).
Renewable energy technical potential, as defined by Lopez et al. (2012), represents the achievable energy generation of a particular technology given system performance, topographic limitations, and environmental and land-use constraints. The primary benefit of assessing technical potential is that it establishes an upper-boundary estimate of development potential. It is important to understand that there are multiple types of potential-resource, technical, economic, and market (Lopez et al. 2012; NREL, "Renewable Energy Technical Potential").
The 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:
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.
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).
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.
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.
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:
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.
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).
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:
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.
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 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.
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.
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:
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%.
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.
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.
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.
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) 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.
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:
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.
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.
FOM cost reduction represents optimized O&M strategies, reduced component replacement costs, and lower frequency of component replacement.
Concentrating solar power (CSP) technology is assumed to be molten-salt power towers. Thermal energy storage (TES) is accomplished by storing hot molten-salt in a two-tank system, which includes a hot-salt tank and a cold-salt tank. Stored hot salt can be dispatched to the power block as needed, regardless of solar conditions. In the ATB, CSP plants with 10 hours of TES are illustrated.
The first large molten-salt power tower plant (Crescent Dunes 110 MWe with 10 hours of storage) was commissioned in 2015 with a reported installed CAPEX of $8.96/WAC (Danko 2015; Taylor 2016 ).
Solar resource is prevalent throughout the United States, but the Southwest is particularly suited to CSP plants. The direct normal irradiance (DNI) resource across the Southwest is some of the best in the world and ranges from 2,000 to 2,800 kWh/m2/year. The solar resource for the Southwest was found in Ballaben, Poliafico, and Hashem (2015). The raw resource technical potential of seven western states (Arizona, California, Colorado, Nevada, New Mexico, Utah, and Texas) exceeds 11,000 GW (almost tenfold current total U.S. electricity generation capacity), assuming an annual average resource > 6.0 kWh/m2/day and after accounting for exclusions such as land slope (>1%), urban areas, water features, and parks, preserves, and wilderness areas (Mehos, Kabel, and Smithers 2009).
Renewable energy technical potential, as defined by Lopez et al. (2012), represents the achievable energy generation of a particular technology given system performance, topographic limitations, and environmental and land-use constraints. The primary benefit of assessing technical potential is that it establishes an upper-boundary estimate of development potential. It is important to understand that there are multiple types of potential - resource, technical, economic, and market (Lopez et al. 2012; NREL, "Renewable Energy Technical Potential").
The Solar Programmatic Environmental Impact Statement identified 17 solar energy zones for priority development of utility-scale solar facilities in six western states. These zones total 285,000 acres and are estimated to accommodate up to 24 GW of solar potential. The program also allows development, subject to a more rigorous review, on an additional 19 million acres of public land. Development is prohibited on approximately 79 million acres.
According to NREL's Concentrating Solar Power Projects website, 15 of the 17 currently operational CSP plants in the United States use parabolic trough technology. And, two power tower facilities - Ivanpah (392 MW) and Crescent Dunes (110 MW), are operational. One small 5-MW linear Fresnel plant is non-operational in California (NREL's Concentrating Solar Power Projects). This 5-MW solar-enhanced oil recovery site was a development site.
For the ATB, three representative sites were chosen based on resource class to demonstrate the range of cost and performance across the United States:
The Base Year estimates are made for 2015 (via an updated index of the ATB 2016) and for 2018, which has utilized a recent assessment of the industry and has expected project completion in 2018.
Future year projections are informed by published literature and technology pathway assessments to inform CAPEX and O&M cost reductions. Three different projections were developed for scenario modeling as bounding levels:
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. These expenditures include the generation plant, the balance of system (e.g., site preparation, installation, and electrical infrastructure), and financial costs (e.g., development costs, 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.
CAPEX is unchanged for resource class because the same plant is assumed to be built in each location. The capacity factor will change with resource.
TES increases plant CAPEX but also increases capacity factor and annual efficiency. TES lowers LCOE for power towers.
The CAPEX estimate (2015) is approximately $8,130/kW. It is for a representative power tower with 10 hours of storage (and a solar multiple of 2.4). Based on recent assessment of the industry and expected project completion in 2018, the CAPEX estimate is $7,037/kW.
Three cost projections are developed for CSP technologies:
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.
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
The ATB represents the year in which a plant starts commercial operation. Accordingly, for plants whose construction duration exceeds one year, CAPEX costs will represent technology costs that are lagging current-year estimates by at least one year. For CSP plants, the construction period is typically three years.
For the ATB - and based on EIA (2016a), Turchi (2010), and Turchi and Heath (2013) - the CSP generation plant envelope is defined to include:
CAPEX can be determined for a plant in a specific geographic location as follows:
CAPEX = ConFinFactor*(OCC*CapRegMult+GCC).
(See the Financial Definitions tab in the ATB data spreadsheet.)
Regional cost variations and geographically specific grid connection costs are not included in the ATB (CapRegMult = 1; GCC = 0). In the ATB, the input value is overnight capital cost (OCC) and details to calculate interest during construction (ConFinFactor).
In the ATB, CAPEX represents a typical solar-CSP plant with 10 hours of thermal storage and does not vary with resource. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by EIA (2016a) expand the range of CAPEX. Unique land-based spur line costs based on distance and transmission line costs expand the range of CAPEX even further. The following figure illustrates the ATB representative plant relative to the range of CAPEX including regional costs across the contiguous United States. The ATB representative plants are associated with a regional multiplier of 1.0.
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 2016a).
The ReEDS model determines the land-based spur line (GCC) uniquely for each potential CSP plant based on distance and transmission line cost.
Operations and maintenance (O&M) costs represent the annual expenditures required to operate and maintain a solar CSP plant over its technical lifetime of 30 years (the distinction between economic life and technical life is described here), including:
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.
FOM is assumed to be $66/kW-yr. Variable O&M is approximately $4/MWh until 2018 and $3.50/MWh after (Kurup and Turchi 2015).
Future FOM is assumed to decline to the SunShot target of $50/kW-yr by 2030 in the Mid cost case and $40/kW-yr by 2030 in the Low cost case (DOE 2012).
A detailed description of the methodology for developing future year projections is found in Projections Methodology.
Technology innovations that could impact future O&M costs are summarized in LCOE Projections.
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.
Capacity factors are influenced by power block technology, storage technology and capacity, the solar resource, expected downtime, and energy losses. The solar multiple is a design choice that influences the capacity factor.
The following figure shows a range of capacity factors based on variation in the resource for CSP plants in the contiguous United States. The range of the Base Year estimates illustrates the effect of locating a CSP plant at a site with fair, good, or excellent solar resource. The future projections for the High, Mid, and Low cost scenarios are unchanged from the Base Year. Technology improvements are focused on CAPEX and O&M cost elements.
For illustration in the ATB, a range of capacity factors is associated with three resource locations in the contiguous United States, as represented in the ReEDS model for three classes of insolation:
The CSP technologies are assumed to be power towers, but with different power cycles and operating conditions as time passes:
Over time, CSP plant output may decline. Capacity factor degradation due to mirror and other component degradation is not accounted for in ATB estimates of capacity factor or LCOE.
The ATB capacity factors are slightly down-rated from SAM 2015 projections.
Estimates of capacity factors for CSP in the ATB represent typical operation. The dispatch characteristics of these systems are valuable to the electric system to manage changes in net electricity demand. Actual capacity factors will be influenced by the degree to which system operators call on CSP plants to manage grid services.
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.
CSP plants with TES can be dispatched by grid operators to accommodate diurnal and seasonal load variations and output from variable generation sources (wind and solar PV). Because of this, their annual energy production and the value of that generation are determined by the electric system needs and capacity and ancillary services markets.
When comparing the ATB projections with other projections, note that there are major differences in technology assumptions, radiation conditions, field sizes, storage configurations, and other factors.
The Low ATB projection is based on the SunShot Vision Study (DOE 2012; Mehos et al. 2016 ) and has been vetted with solar industry representatives.
Attempts have been made to clarify the specifics of the other published CSP projections (e.g., number of hours of storage and solar multiple). As yet, this has not been possible in detail for the ATB 2017.
Projections of future utility-scale CSP plant CAPEX and O&M are based on three different projections developed for scenario modeling as bounding levels:
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 CSP plants are associated with Tower - Excellent Resource. Data for all the resource categories can be found in the ATB data spreadsheet.
The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. The three pathways are generally defined as:
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.
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.
The LCOE range shown is based on locations with fair (Abilene, Texas), good (Las Vegas, Nevada), and excellent (Daggett, California) resources. The CAPEX is the same at each resource as the same plant is used.
Nuclear power contributed about 20% of U.S. electricity generation over the past two decades (DOE "Light Water Reactor Sustainability Program").
Nuclear power plants generate electricity in the same way as any other steam-electric power plant. Water is heated, and steam from the boiling water turns turbines and generates electricity. The main difference is that heat from a self-sustaining chain reaction boils the water in a nuclear power plant, as opposed to burning fuels in fossil fuel plants (DOE Office of Nuclear Energy "History").
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 corresponds most closely to fossil reserves, as both can be characterized by the prospect of commercial feasibility and depend strongly on available technology at the time of the resource assessment. Uranium reserves in the United States are assessed by the United States Geological Survey (USGS, "Uranium Resources and Environmental Investigations").
Because nuclear 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.
Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year.
Overnight capital costs are modified from EIA (2017). Capital costs include overnight capital cost plus defined transmission cost, and it removes a material price index.
Overnight Capital Cost ($/kW) | Construction Financing Factor (ConFinFactor) | CAPEX ($/kW) | |
---|---|---|---|
Nuclear: Advanced nuclear power generation | $5,515 | 1.084 | $5,979 |
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 nuclear 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 (Plant*Region). Unique land-based spur line costs based on distance and transmission line costs are not estimated. The following figure illustrates the ATB representative plant relative to the range of CAPEX including regional costs across the contiguous United States. The ATB representative plants are associated with a regional multiplier of 1.0.
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:
Market data for comparison are limited and generally inconsistent in the range of costs covered and the length of the historical record.
The capacity factor represents the assumed annual energy production divided by the total possible annual energy production, assuming the plant operates at rated capacity for every hour of the year. For nuclear plants, the capacity factor is typically the same as (or very close to) their availability factor.
The capacity factor of nuclear units is generally very high (>85%) as they are typically always online except when undergoing maintenance or refueling (NEI "US Nuclear Capacity Factors").
In the United States, nuclear power plants are baseload plants with steady capacity factors. They need to change out their uranium fuel rods about every 24 months. After 18-36 months, the used fuel is removed from the reactor (World Nuclear Association "The Nuclear Fuel Cycle"). The average fueling outage duration in 2013 was 41 days; from 1990 to 1997, the refueling days ranged from 66 to 106, so improvements have helped capacity factors (NEI, "US Nuclear Refueling Outage Days"). See also NEI ("US Nuclear Power Plants: General U.S. Nuclear Info").
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.
The LCOE of nuclear power plants is directly impacted by the cost of uranium, variations in the heat rate, and O&M costs, but the biggest factor is the capital cost (including financing costs) of the plant. The LCOE can also be impacted by the amount of downtime from refueling or maintenance. For a given year, the LCOE assumes that the fuel prices from that year continue throughout the lifetime of the plant.
Fuel prices are based on the 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.
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.
B&V (Black & Veatch). 2012. Cost and Performance Data for Power Generation Technologies. Black & Veatch Corporation. February 2012. http://bv.com/docs/reports-studies/nrel-cost-report.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.
Danko, Pete. 2015. 'SolarReserve: Crescent Dunes Solar Tower Will Power Up in March: Without Ivanpah's Woes.' Breaking Energy. February 10, 2015. http://breakingenergy.com/2015/02/10/solarreserve-crescent-dunes-solar-tower-will-power-up-in-march-without-ivanpahs-woes/.
DOE (U.S. Department of Energy). 2012. SunShot Vision Study. DOE/GO-102012-3037. February 2012. https://www1.eere.energy.gov/solar/pdfs/47927.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.
Entergy. 2015. Entergy Arkansas, Inc.: 2015 Integrated Resource Plan. July 15, 2015. http://entergy-arkansas.com/content/transition_plan/IRP_Materials_Compiled.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.
IEA (International Energy Agency). 2015a. Projected Costs of Generating Electricity: 2015 Edition. Paris: International Energy Agency. https://www.iea.org/media/presentations/150831_ProjectedCostsOfGeneratingElectricity_Presentation.pdf.
IRENA (International Renewable Energy Agency). 2016. The Power to Change: Solar and Wind Cost Reduction Potential to 2025. June 2016. Paris: International Renewable Energy Agency. http://www.irena.org/DocumentDownloads/Publications/IRENA_Power_to_Change_2016.pdf.
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.
Kurup, Parthiv, and Craig S. Turchi. 2015. Parabolic Trough Collector Cost Update for the System Advisor Model (SAM). Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-65228. November 2015. http://www.nrel.gov/docs/fy16osti/65228.pdf.
Lazard. 2016. Levelized Cost of Energy Analysis-Version 10.0. December 2016. New York: Lazard. https://www.lazard.com/media/438038/levelized-cost-of-energy-v100.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.
Mehos, Mark, Craig Turchi, Jennie Jorgenson, Paul Denholm, Clifford Ho, and Kenneth Armijo. 2016. On the Path to SunShot: Advancing Concentrating Solar Power Technology, Performance, and Dispatchability. Golden, CO: National Renewable Energy Laboratory. NREL/TP-5500-65688. May 2016. http://www.nrel.gov/docs/fy16osti/65688.pdf.
Moné, C., et al. 2013. Reference to come.
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.
Taylor, Phil. 2016. 'Nev. Plant Solves Quandary of How to Store Sunshine.' E&E News. March 29, 2016. http://www.eenews.net/stories/1060034748.
Turchi, C. 2010. Parabolic Trough Reference Plant for Cost Modeling with the Solar Advisor Model (SAM). Golden, CO: National Renewable Energy Laboratory. NREL/TP-550-47605. July 2010. http://www.nrel.gov/docs/fy10osti/47605.pdf.
Turchi, Craig S., and Garvin A. Heath. 2013. Molten Salt Power Tower Cost Model for the System Advisor Model (SAM). Golden, CO: National Renewable Energy Laboratory. NREL/TP-5500-57625. February 2013. http://www.nrel.gov/docs/fy13osti/57625.pdf.
Wiser, Ryan, Karen Jenni, Joachim Seel, Erin Baker, Maureen Hand, Eric Lantz, and Aaron Smith. 2016. Forecasting Wind Energy Costs and Cost Drivers: The Views of the World's Leading Experts. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-1005717. June 2016. https://emp.lbl.gov/publications/forecasting-wind-energy-costs-and.
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.