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2019 ATB

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

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

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

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

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

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

Additional information is available here: About the 2019 ATB.

Residential PV Systems

Representative Technology

For the ATB, residential PV systems are modeled for a 5.0-kW_DC fixed tilt (25°), roof-mounted system. Flat-plate PV can take advantage of direct and indirect insolation, so PV modules need not directly face and track incident radiation. This gives PV systems a broad geographical application, especially for residential PV systems.

Resource Potential

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

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

/electricity/2019/images/solar/pv-map-mean-us-resource.jpg

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

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

Base Year and Future Year Projections Overview

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

Future year projections are derived from analysis of published projections of PV CAPEX and bottom-up engineering analysis of O&M costs. Three different technology cost scenarios were developed for scenario modeling as bounding levels:

Constant Technology Cost Scenario: no change in CAPEX, O&M, or capacity factor from 2017 to 2050; consistent across all renewable energy technologies in the ATB
Mid Technology Cost Scenario: based on the median of literature projections of future CAPEX; O&M technology pathway analysis
Low Technology Cost Scenario: based on the low bound of literature projections of future CAPEX and O&M technology pathway analysis.

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

Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. These expenditures include the hardware, the balance of system (e.g., site preparation, installation, and electrical infrastructure), and financial costs (e.g., development costs, onsite electrical equipment, and interest during construction) and are detailed in CAPEX Definition. In the ATB, CAPEX reflects typical plants and does not include differences in regional costs associated with labor, materials, taxes, or system requirements. The related Standard Scenarios product uses Regional CAPEX Adjustments. The range of CAPEX demonstrates variation with resource in the contiguous United States.

The following figure shows the Base Year estimate and future year projections for CAPEX costs. Three cost scenarios are represented: Constant, Mid, and Low technology cost. 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.

/electricity/2019/images/solar-res/chart-solar-res-capex-RD-2019.png

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

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

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

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

Recent Trends

Reported residential PV installation CAPEX (Barbose and Dargouth 2018) is shown in box-and-whiskers format for comparison to historical residential PV benchmark overnight capital cost (Fu, Feldman, and Margolis 2018) and the ATB estimates of future CAPEX projections. The data in Barbose and Dargouth (2018) represent 81% of all U.S. residential and commercial PV capacity installed through 2016 and 75% of capacity installed in 2017.

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

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

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

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

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

Base Year Estimates

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

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

A system price of $2.77/W_DC in 2017 and $2.64/W_DC in 2018 are based on bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2018 (adjusted for inflation) (Fu, Feldman, and Margolis 2018). These figures are in line with other estimated system prices reported in Q2/Q3 2018 Solar Industry Update (Feldman and Margolis 2018).

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

Future Year Projections

Projections of future residential PV installation CAPEX are based on seven system price projections from six separate institutions made in the last two years. We adjusted the "min," "median," and "max" projections in a few different ways. All 2017 and 2018 pricing are based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2018 (adjusted for inflation) (Fu, Feldman, and Margolis 2018). These figures are in line with other estimated system prices reported in Q2/Q3 2018 Solar Industry Update (Feldman and Margolis 2018).

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

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

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

CAPEX Definition

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

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

Hardware
Module supply
Power electronics, including inverters
Racking
Foundation
AC and DC wiring materials and installation
Balance of system (BOS)
Site and/or roof preparation
Permitting, inspection, and interconnection costs
Project indirect costs, including costs related to engineering, distributable labor and materials, construction management start up and commissioning, and contractor overhead costs, fees, and profit
Financial costs
Owners costs, such as development costs, legal fees, insurance costs.

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

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)

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

Regional cost variations are not included in the ATB (CapRegMult = 1). Because distributed PV plants are located directly at the end use, there are no grid connection costs (GCC = 0). In the ATB, the input value is overnight capital cost (OCC) and details to calculate interest during construction (ConFinFactor).

In the ATB, CAPEX represents a typical distributed residential/commercial PV plant and does not vary with resource. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by EIA (2016b) expand the range of CAPEX. Unique land-based spur line costs based on distance and transmission line costs are not estimated. The following figure illustrates the ATB representative plant relative to the range of CAPEX including regional costs across the contiguous United States. The ATB representative plants are associated with a regional multiplier of 1.0.

/electricity/2019/images/solar-res/chart-solar-res-capex-definition-RD-2019.png

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

Standard Scenarios Model Results

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

CAPEX in the ATB does not represent regional variants (CapRegMult) associated with labor rates, material costs, etc., but dSolar does include 134 regional multipliers (EIA 2016b).

Operation and Maintenance (O&M) Costs

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

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

The following figure shows the Base Year estimate and future year projections for fixed O&M (FOM) costs. Three cost scenarios are represented. The estimate for a given year represents annual average FOM costs expected over the technical lifetime of a new plant that reaches commercial operation in that year.

/electricity/2019/images/solar-res/chart-solar-res-operation-maintenance-2019.png

Base Year Estimates

FOM of $23/kW_DC - yr based on modeled pricing for a commercial PV system quoted in Q1 2017 as reported by Fu, Feldman, and Margolis (2018), adjusted for inflation. The values in this report (ATB 2019) are higher than those from ATB 2018 to better align with the benchmarks reported in Fu, Feldman, and Margolis (2018); the previous edition relied solely on an O&M-to-CAPEX ratio, derived from multiple reports. 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 from $0 to $40/kW_DC - yr.

Future Year Projections

FOM for 2018 is also based on pricing reported in Fu, Feldman, and Margolis (2018), adjusted for inflation. From 2019-2050, FOM is based on the historical average ratio of O&M costs ($/kW-yr) to CAPEX costs ($/kW), 0.8:100, as reported by Fu, Feldman, and Margolis (2018). Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2018 benchmark residential PV O&M and CAPEX costs fell 60% and 63% respectively, as reported by Fu, Feldman, and Margolis (2018).

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

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

Capacity Factor: Expected Annual Average Energy Production Over Lifetime

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

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

The capacity factor is influenced by the hourly solar profile, technology (e.g., thin-film versus crystalline silicon), axis type (e.g., none, one, or two), expected downtime, and inverter losses to transform from DC to AC power. The DC-to-AC ratio is a design choice that influences the capacity factor. PV plant capacity factor incorporates an assumed degradation rate of 0.75%/year (Fu, Feldman, and Margolis 2018) in the annual average calculation. R&D could lower degradation rates of PV plant capacity factor; future projections for Mid and Low cost scenarios reduce degradation rates by 2050, using a straight-line basis, to 0.5%/year and 0.2%/year respectively.

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

/electricity/2019/images/solar-res/chart-solar-res-capacity-factor-2019.png

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

Base Year Estimates

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

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

First-year operation capacity factors as modeled range from 13.4% to 22.2%, though these depend significantly on geography and system configuration (e.g., fixed-tilt versus single-axis tracking).

Over time, PV installation output is reduced due to degradation in module quality. This degradation is accounted in ATB estimates of capacity factor over the 30-year lifetime of the plant. The adjusted average capacity factor values in the ATB Base Year are 12.6%, 14.8%, 16.2%, 18.2%, and 20.8%.

Future Year Projections

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

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

	Seattle, WA	Chicago, IL	Kansas City, MO	Los Angeles, CA	Daggett, CA
Low Cost (0.30% degradation rate)	13.1%	15.4%	16.8%	18.9%	21.6%<
Mid Cost (0.50% degradation rate)	12.9%	15.1%	16.6%	18.6%	21.3%
Constant Cost (0.75% degradation rate)	12.6%	14.8%	16.2%	18.2%	20.8%

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

Standard Scenarios Model Results

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

Plant Cost and Performance Projections Methodology

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

We created Constant, Mid, and Low technology cost CAPEX cases to explore the range of possible outcomes of future PV cost improvements. The Constant technology 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 seven system price projections from six separate institutions. Projections include short-term U.S. price forecasts and long-term global and U.S. price forecasts made in the past two 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.

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

In instances in which literature projections did not include all years, a straight-line change in price was assumed between any two projected values. To generate Mid and Low technology cost scenarios we took the "median" and "min" of the data sets; however, we only included short-term U.S. forecasts until 2030 as they focus on near-term pricing trends within the industry. Starting in 2030, we include long-term global and U.S. forecasts in the data set, as they focus more on long-term trends within the industry. It is also assumed after 2025 U.S. prices will be on par with global averages. 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 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 Mid and Low technology cost scenarios between 2020 and 2030 are interpolated on a straight-line basis.

We adjusted the "median" and "min" projections in a few different ways. All 2017 and 2018 pricing are based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2018 (adjusted for inflation) (Fu, Feldman, and Margolis 2018). These figures are in line with other estimated system prices reported in Q2/Q3 2018 Solar Industry Update (Feldman and Margolis 2018).

From 2019-2050, FOM is based on the historical average ratio of O&M costs ($/kW-yr) to CAPEX costs ($/kW), 0.8:100, as reported by Fu, Feldman, and Margolis (2018). Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2018 benchmark residential PV O&M and CAPEX costs fell 60% and 63% respectively, as reported by Fu, Feldman, and Margolis (2018).

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

Levelized Cost of Energy (LCOE) Projections

Levelized cost of energy (LCOE) is a summary metric that combines the primary technology cost and performance parameters: CAPEX, O&M, and capacity factor. It is included in the ATB for illustrative purposes. The ATB focuses on defining the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. The LCOE accounts for the energy component of electric system planning and operation. The LCOE uses an annual average capacity factor when spreading costs over the anticipated energy generation. This annual capacity factor ignores specific operating behavior such as ramping, start-up, and shutdown that could be relevant for more detailed evaluations of generator cost and value. Electricity generation technologies have different capabilities to provide such services. For example, wind and PV are primarily energy service providers, while the other electricity generation technologies provide capacity and flexibility services in addition to energy. These capacity and flexibility services are difficult to value and depend strongly on the system in which a new generation plant is introduced. These services are represented in electric sector models such as the ReEDS model and corresponding analysis results such as the Standard Scenarios.

The following three figures illustrate LCOE, which includes the combined impact of CAPEX, O&M, and capacity factor projections for residential PV across the range of resources present in the contiguous United States. For the purposes of the ATB, the costs associated with technology and project risk in the U.S. market are represented in the financing costs but not in the upfront capital costs (e.g., developer fees and contingencies). An individual technology may receive more favorable financing terms outside of the United States, due to less technology and project risk, caused by more project development experience (e.g., offshore wind in Europe) or more government or market guarantees. The R&D Only LCOE sensitivity cases present the range of LCOE based on financial conditions that are held constant over time unless R&D affects them, and they reflect different levels of technology risk. This case excludes effects of tax reform, tax credits, and changing interest rates over time. The R&D + Market LCOE case adds to these financial assumptions: (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform and tax credits. The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term residential PV plants are associated with Res PV: Kansas City. Data for all the resource categories can be found in the ATB Data spreadsheet; for simplicity, not all resource categories are shown in the figures. In the R&D + Market LCOE case, there is an increase in LCOE from 2018-2020, caused by an increase WACC, and an increase from 2023-2024, caused by the reduction in tax credits.

R&D Only | R&D + Market

R&D Only

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R&D + Market

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

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

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

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

The methodology for representing the CAPEX, O&M, and capacity factor assumptions behind each pathway is discussed in Projections Methodology. In general, the degree of adoption of technology innovation distinguishes the Constant, Mid, and Low technology cost scenarios. These projections represent trends that reduce CAPEX and improve performance. Development of these scenarios involves technology-specific application of the following general definitions:

Constant Technology Cost Scenario: Base Year (or near-term estimates of projects under construction) equivalent through 2050 maintains current relative technology cost differences
Mid Technology Cost Scenario: Technology advances through continued industry growth, public and private R&D investments, and market conditions relative to current levels that may be characterized as "likely" or "not surprising"
Low Technology Cost Scenario: technology advances that may occur with breakthroughs, increased public and private R&D investments, and/or other market conditions that lead to cost and performance levels that may be characterized as the "limit of surprise" but not necessarily the absolute low bound.

To estimate LCOE, assumptions about the cost of capital to finance electricity generation projects are required, and the LCOE calculations are sensitive to these financial assumptions. Two project finance structures are used within the ATB:

R&D Only Financial Assumptions: This sensitivity case allows technology-specific changes to debt interest rates, return on equity rates, and debt fraction to reflect effects of R&D on technological risk perception, but it holds background rates constant at 2017 values from AEO 2019 (EIA 2019b) and excludes effects of tax reform and tax credits.
R&D Only + Market Financial Assumptions: This sensitivity case retains the technology-specific changes to debt interest, return on equity rates, and debt fraction from the R&D Only case and adds in the variation over time consistent with AEO2019 as well as effects of tax reform and tax credits. For a detailed discussion of these assumptions, see Project Finance Impact on LCOE.

A constant cost recovery period – over which the initial capital investment is recovered – of 30 years is assumed for all technologies throughout this website, and can be varied in the ATB data spreadsheet.

The equations and variables used to estimate LCOE are defined on the Equations and Variables page. For illustration of the impact of changing financial structures such as WACC, see Project Finance Impact on LCOE. For LCOE estimates for the Constant, Mid, and Low technology cost scenarios for all technologies, see 2019 ATB Cost and Performance Summary.

In general, differences among the technology cost cases reflect different levels of adoption of innovations. Reductions in technology costs reflect the cost reduction opportunities that are listed below.

The 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.

Modules

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

Balance of system (BOS)

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

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

Improved power electronics

Improvement of inverter prices and performance, possibly by integrating microinverters

Decreased installation costs and margins

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

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