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Annual Technology Baseline 2018

National Renewable Energy Laboratory


Recommended Citation:
NREL (National Renewable Energy Laboratory). 2018. 2018 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

Commercial PV

Representative Technology

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

Resource Potential

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

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

map: mean U.S. solar resource available to PV systems
Map of the mean solar resource available to PV systems in the United States

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

Base Year and Future Year Projections Overview

The Base Year estimates rely on modeled CAPEX and O&M estimates benchmarked with industry and historical data. Capacity factor is estimated based on hours of sunlight at latitude for 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:

  • Constant Technology Cost Scenario: no change in CAPEX, O&M, or capacity factor from 2016 to 2050; consistent across all renewable energy technologies in the ATB
  • Mid Technology Cost Scenario: based on the median of literature projections of future CAPEX and O&M technology pathway analysis
  • Low Technology Cost Scenario: based on the low bound of literature projections of future CAPEX and O&M technology pathway analysis.

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

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

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

Historical data shown in box-and-whiskers format where a bar represents the median, a box represents the 20th and 80th percentiles, and whiskers represent the minimum and maximum.
Year represents Commercial Online Date for a past or future plant.
CAPEX is shown as a single value because it does not vary with solar resource.
R&D Only Financial Assumptions (constant background rates, no tax or tariff changes)

Recent Trends

Reported historical commercial-scale PV installation CAPEX (Barbose and Dargouth (2017)) is shown in box-and-whiskers format for comparison to the ATB current CAPEX estimates and future projections. The data in Barbose and Dargouth (2017) represent 83% of all U.S. residential and commercial PV capacity installed through 2016 and 76% of capacity installed in 2016.

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 2016 reflect continued rapid decline in pricing supported by analysis of recent system pricing for projects that became operational in 2016 (Feldman et al. (2017)).

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

Base Year Estimates

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

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

A system price of $2.56/WDC in 2016 represents the capacity-weighted average reported price of a commercial PV system installed in 2016 reported in Tracking the Sun X (Barbose and Dargouth (2017)) and adjusted to remove regional cost multipliers based on geographic location of projects installed in 2016. The $1.95/WDC in 2017 is based on bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2017 (adjusted for inflation, and accounting for $0.1/W higher than expected module prices due to tariff concerns in the R&D + Market sensitivity case) (Fu et al. (2017)). These figures are in line with other estimated system prices reported in Feldman et al. (2017).

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

Future Year Projections

Projections of future commercial PV installation CAPEX are based on 12 system price projections from 6 separate institutions. We adjusted the " min," " median," and " max" projections in a few different ways. All 2016 pricing is based on the capacity-weighted average historically reported commercial PV prices reported in Tracking the Sun X (Barbose and Dargouth (2017)). All 2017 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2017 (adjusted for inflation, and accounting for $0.1/W higher than expected module prices due to tariff concerns in the R&D + Market sensitivity case) (Fu et al. (2017)). These figures are in line with other estimated system prices reported in Feldman et al. (2017).

We adjusted the Mid and Low projections for 2018-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. In addition, because the projections were made before the Section 201 proclamation implementing a tariff on imported PV modules and cells, we adjusted projections to incorporate Section 201 tariff per pricing from internal NREL analysis in the R&D + Market sensitivity case. For the Constant technology cost scenario the 2017 CAPEX value is held constant, assuming no improvements beyond2017.

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

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

CAPEX Definition

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

For the ATB-and based on EIA 2016a and the NREL Solar-PV Cost Model (Fu et al. (2017)) - the distributed solar PV plant envelope is defined to include:

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

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

CAPEX = ConFinFactor × (OCC × CapRegMult + GCC)
(See the Financial Definitions tab in the ATB data spreadsheet)

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

In the ATB, CAPEX represents a typical distributed residential/commercial PV plant and does not vary with resource. Regional cost effects associated with labor rates, material costs, and other regional effects as defined by EIA 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.

R&D Only Financial Assumptions (constant background rates, no tax or tariff 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 state-level cost multipliers (EIA 2016a).

Natural Gas Internal Combustion Engine Vehicle

Operations and maintenance (O&M) costs represent the annual expenditures required to operate and maintain a solar PV plant over its lifetime of 30 years:

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

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

Base Year Estimates

FOM of $18/kWDC - yr is based on the average ratio of O&M costs to CAPEX costs, 0.7%, as reported in Lazard (2017). This ratio is consistent with the ratio of O&M costs to CAPEX costs of 0.7%, which are derived from 2010 to 2017 bottom-up benchmarks in Fu et al. (2017) but are lower than the ratio of O&M costs to CAPEX costs for PV systems on buildings of 1.0%, which are derived from IEA (2016). A wide range in reported prices exists in the market, in part depending on what maintenance practices exist for a particular system. These cost categories include asset management (including compliance and reporting for incentive payments), different insurance products, site security, cleaning, vegetation removal, and failure of components. Not all these practices are performed for each system; additionally, some factors depend on the quality of the parts and construction. NREL analysts estimate O&M costs can range from $0 to $40/kWDC - yr.

Future Year Projections

We derive future FOM based on the same 0.7% ratio of O&M to CAPEX, used to estimate Base Year O&M costs. Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2015 benchmark commercial PV O&M and CAPEX costs fell 42% and 58% respectively, as reported in Fu et al. (2017).

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 $/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 0.75%/year (Fu et al. (2017)) 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 0.3%/year respectively.

The following figure shows a range of capacity factors based on variation in solar resource in the contiguous United States. The range of the Base Year estimates illustrate the effect of locating a utility-scale PV plant in places with lower or higher solar irradiance. These five values use specific locations as examples of high (Daggett, California), high-mid (Los Angeles, California), mid (Kansas City, Missouri), low-mid (Chicago, Illinois), and low (Seattle, Washington) resource areas in the United States as implemented in the SAM model using PV system characteristics from Fu et al. (2017).

The legend labels refer to the first-year operation capacity factors. The data illustrated modifies the first-year capacity factor by including estimated degradation over the lifetime of a PV plant.

Base Year Estimates

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

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

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

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

Future Year Projections

Projections of capacity factors for plants installed in future years are unchanged from the Base Year for the Constant technology cost scenario. Capacity factors for Mid and Low cost scenarios are projected to increase over time, caused by a straight-line reduction in PV plant capacity degradation rates, reaching 0.5%/year and 0.3%/year by 2050 for the Mid and Low cost scenarios respectively. The following table summarizes the difference in average capacity factor in 2050 caused by different degradation rates in the Constant, Mid, and Low cost scenarios.

2050 Commercial PV DC Capacity Factors by Resource Location and Cost Scenario
Seattle, WA Chicago, IL Kansas City, MO Los Angeles, CA Daggett, CA
Low Cost (0.30% degradation rate) 12.3% 14.5% 15.8% 18.0% 20.3%<
Mid Cost (0.50% degradation rate) 12.1% 14.3% 15.5% 17.7% 20.0%
Constant Cost (0.75% degradation rate) 11.9% 14.0% 15.2% 17.3% 19.6%

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

Standard Scenarios Model Results

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.

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

Plant Cost and Performance Projections Methodology

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

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

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

Projections of future commercial PV installation CAPEX are based on 12 system price projections from 6 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 Constant, Mid, and Low technology 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 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 Constant, Mid, and Low technology cost forecast between 2020 and 2030 are interpolated on a straight-line basis.

We adjusted the " min," " median," and " max" projections in a few different ways. All 2016 pricing is based on the capacity-weighted average historically reported commercial PV price reported in Tracking the Sun X (Barbose and Dargouth (2017)). All 20167 pricing is based on the bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark Q1 2017 (adjusted for inflation, and accounting for $0.1/W higher than expected module prices due to tariff concerns in the R&D + Market sensitivity case) (Fu et al. (2017)). These figures are in line with other estimated system prices reported in Feldman et al. (2017).

We adjusted the Mid and Low cost projections for 2018-2050 to remove distortions caused by the combination of forecasts with different time horizons and based on internal judgment of price trends. In addition, because the projections were made before the Section 201 proclamation implementing a tariff on imported PV modules and cells, we adjusted projections to incorporate Section 201 tariff per pricing from internal NREL analysis in the R&D + Market sensitivity case. The Constant cost projection case is kept constant at the 2017 CAPEX value, assuming no improvements beyond2017.

All prices quoted in euros are converted to USD (1 € = $1.25).
All prices quoted in WAC are converted to WDC (1 WAC =1.2 WDC).

We derive future FOM based on the same 0.7% ratio of O&M to CAPEX, used to estimate Base Year O&M costs. Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2015 benchmark commercial PV O&M and CAPEX costs fell 42% and 58% respectively, as reported in Fu et al. (2017).

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 from 0.75%, reaching 0.5%/year and 0.3%/year by 2050 for the Mid and Low cost scenarios respectively.

Levelized Cost of Energy (LCOE) Projections

Levelized cost of energy (LCOE) is a simple metric that combines the primary technology cost and performance parameters: CAPEX, O&M, and capacity factor. It is included in the ATB for illustrative purposes. The ATB focuses on defining the primary cost and performance parameters for use in electric sector modeling or other analysis where more sophisticated comparisons among technologies are made. The LCOE accounts for the energy component of electric system planning and operation. The LCOE uses an annual average capacity factor when spreading costs over the anticipated energy generation. This annual capacity factor ignores specific operating behavior such as ramping, start-up, and shutdown that could be relevant for more detailed evaluations of generator cost and value. Electricity generation technologies have different capabilities to provide such services. For example, wind and PV are primarily energy service providers, while the other electricity generation technologies 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 commercial PV across the range of resources present in the contiguous United States. For the purposes of the ATB, the costs associated with technology and project risk in the U.S. market are represented in the financing costs, not in the upfront capital costs (e.g. developer fees, contingencies). An individual technology may receive more favorable financing terms outside of the U.S., due to less technology and project risk, caused by more project development experience (e.g. offshore wind in Europe), or more government or market guarantees. The R&D Only LCOE sensitivity cases present the range of LCOE based on financial conditions that are held constant over time unless R&D affects them, and they reflect different levels of technology risk. This case excludes effects of tax reform, tax credits, technology-specific tariffs, and changing interest rates over time. The R&D + Market LCOE case adds to these the financial assumptions (1) the changes over time consistent with projections in the Annual Energy Outlook and (2) the effects of tax reform, tax credits, and tariffs. The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term commercial PV plants are associated with Comm (commercial) PV: Kansas City. Data for all the resource categories can be found in the ATB data spreadsheet.

R&D Only | R&D + Market

R&D Only
The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term commercial PV plants are associated with Comm PV: Kansas City
R&D Only Financial Assumptions (constant background rates, no tax or tariff changes)
R&D + Market
The ATB representative plant characteristics that best align with those of recently installed or anticipated near-term commercial PV plants are associated with Comm PV: Kansas City
R&D Only + Market Financial Assumptions (dynamic background rates, taxes, and tariffs)

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. Three 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 2016 values from AEO 2018 and excludes effects of tax reform, tax credits, and tariffs.
  • R&D Only + Market Financial Assumptions: This sensitivity case retains the technology-specific changes to debt interest, return on equity rates, and debt fraction from the R&D Only case and adds in the variation over time consistent with AEO 2018, as well as effects of tax reform, tax credits, and technology-specific tariffs. For a detailed discussion of these assumptions, see Changes from 2017 ATB to 2018 ATB.
  • ReEDS Financial Assumptions: ReEDS uses the R&D Only + Market Financial Assumptions for the "Mid" technology cost scenario.

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

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.

  • 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.

References

Lazard's Levelized Cost of Energy Analysis: Version 11.0. November 2017. New York: Lazard. https://www.lazard.com/perspective/levelized-cost-of-energy-2017.

Barbose, Galen, and Naïm Dargouth. 2017. Tracking the Sun X: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-2001062. September 2017. http://eta-publications.lbl.gov/sites/default/files/tracking_the_sun_10_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.

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). 2018. Annual Energy Outlook 2018 with Projections to 2050. Washington, D.C.: U.S. Department of Energy. February 6, 2018. https://www.eia.gov/outlooks/aeo/pdf/AEO2018.pdf.

Feldman, David, Jack Hoskins, and Robert Margolis. 2017. Q2/Q3 2017 Solar Industry Update. U.S. Department of Energy. NREL/PR-6A42-70406. November 13, 2017. https://www.nrel.gov/docs/fy18osti/70406.pdf.

Fu, Ran, David Feldman, Robert Margolis, Mike Woodhouse, and Kristen Ardani. 2017. U.S. Solar Photovoltaic System Cost Benchmark: Q1 2017. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-68925. https://www.nrel.gov/docs/fy17osti/68925.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). 2016. World Energy Outlook 2016. Paris: International Energy Agency. December 2016.

Labastida, Roberto, and Dexter Gauntlett. 2016. Next-Generation Solar PV High Efficiency Solar PV Modules and Module-Level Power Electronics: Global Market Analysis and Forecasts. Chicago: Navigant Consulting. 3Q 2016.

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.