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Residential PV

Units using capacity above represent kWDC.

2021 ATB data for residential solar photovoltaics (PV) are shown above. The Base Year estimates rely on modeled capital expenditures (CAPEX) and operation and maintenance (O&M) cost estimates benchmarked with industry and historical data. Capacity factor is estimated based on hours of sunlight at latitude for 10 resource categories in the United States, binned by mean global horizontal irradiance (GHI). The 2021 ATB presents capacity factor estimates that encompass a range associated with advanced, moderate, and conservative technology innovation scenarios across the United States. Future year projections are derived from bottom-up benchmarking of PV CAPEX and bottom-up engineering analysis of O&M costs.

The three scenarios for technology innovation are:

  • Conservative Technology Innovation Scenario (Conservative Scenario): lower levels of R&D investment with minimal technology advancement and current global module pricing
  • Moderate Technology Innovation Scenario (Moderate Scenario): R&D investment continuing at similar levels as today, with no substantial innovations or new technologies introduced to the market
  • Advanced Technology Innovation Scenario (Advanced Scenario): an increase in R&D spending that generates substantial innovation, allowing historical rates of development to continue.

Resource Categorization

The ATB provides the average capacity factor for 10 resource categories in the United States, binned by mean GHI. The annual average capacity factor for the contiguous United States is calculated using the Renewable Energy Potential (ReV) model using solar resource data for 2012 from the National Solar Radiation Database (NSRDB). The county-level capacity factors are calculated for specific locations with azimuth and tilt, which are based on representative agents selected in the Distributed Generation Market Demand Model (dGen) 2020 Standard Scenarios agent database (Sigrin et al., 2016). A lookup table for these locations and the National Solar Radiation Database (NSRDB) is generated based on nearest distance. The azimuth and tilt as well as the resource GID are used to generate a System Advisor Model (SAM) config file and to run ReV, which outputs the annual average capacity factor at each evaluated location. The U.S. average capacity factor for each resource category is weighted by the population of each county within the GHI resource category. The county estimated populations are provided by geospatial and tabular data from the U.S. Census.

The map below shows average annual GHI in the United States.

Map of annual average daily GHI in the United States

The following table summarized estimated 2019 (Base Year), year 1 capacity factors per resource category and each resource category's associated population.

Residential PV Resource Classes

Resource Class GHI BinMean DC Capacity FactorPopulation
1>5.7519.6% 12,554,678
25.5–5.7519.3% 21,403,290
35.25–5.518.0% 13,476,871
45–5.2517.0% 30,603,630
54.75–516.1% 45,176,116
64.5–4.7515.9% 39,880,837
74.25–4.515.2% 31,742,606
84–4.2514.5% 80,155,804
93.75–413.9% 40,755,023
10<3.7512.7% 10,255,830
 Mean15.7% 

DOE’s Solar Energy Technologies Office sets its PV cost targets for a location centered geographically within the continental U.S., in resource class 7, whereas the ATB benchmark is class 5, representing the national-average solar resource.

Scenario Descriptions

Summary of Technology Innovations by Scenario (2030) 

ScenarioModule Efficiency1Inverter and Power ElectronicsInstallation EfficienciesEnergy Yield Gain1
Conservative Scenario

Technology Description: Tariffs expire, as scheduled, though some form of friction still remains, keeping U.S. panel pricing halfway between current U.S. and global pricing. Efficiency gains for panels are consistent with one standard deviation below that of the International Technology Roadmap for Photovoltaic (ITRPV—an annual document prepared by many leading international poly-Si producers, wafer suppliers, c-Si solar cell manufacturers, module manufacturers, PV equipment suppliers, and production material providers, as well as PV research institutes and consultants) to 2030 —well below the historical monofacial average gains and below the leveling off point to 21.5% in 2030 -$0.3/WDC (Feldman et al., 2021).

Justification: This scenario represents the low end of manufacturers' expectations and additional friction despite the scheduled removal of the tariff.

Technology Description: This scenario assumes a Larger market size.

Justification: The global PV industry expected to continue to expand.

N/AN/A
Moderate Scenario

Technology Description: Tariffs expire, as scheduled, and efficiency gains are consistent with median ITRPV road map to 2030 —well below historical monofacial average gains and below the leveling off point to 22.5% in 2030 -$0.19/W

Justification: This scenario represents manufacturers' expectations for 2030.

Technology Description: This scenario assumes design simplification and manufacturing automation

Justification: Industry is currently switching to this practice.

Technology Description: This scenario assumes 20% labor and hardware balance-of-system (BOS) cost improvements through automation, preassembly of racking, mounting, and wiring efficiencies, and improvements in wind load design. It also assumes a switch in deployments to be a standard feature of the construction of a house or during a normally scheduled reroofing event.

Justification: Because residential PV systems represent a smaller investment in absolute dollars, soft costs—such as customer acquisition, permitting, interconnection, and overhead—contribute a significantly higher percentage of CAPEX than they do with larger PV systems. Incorporating the PV system into a larger related construction project, such as a reroofing, or the construction of a new house could create significant efficiencies to customer acquisition costs and to permitting, inspection, and interconnection costs, as well as reduced costs through efficiencies in labor and structural BOS. (Feldman et al., 2021) found that PV systems in new homes were 24% less expensive than in retrofit residential PV systems.

Technology Description: This scenario assumes as 2% energy gain and a degradation rate reduction from 0.7%/yr to 0.5%/yr

Justification: Significant R&D is currently spent on improved cell temperatures and lower degradation rates. Companies will likely continue to focus on improved uptime to maximize profitability. Degradation rates of 0.5% per year are already common for some developers.

Advanced Scenario

Technology Description: Modules maintain the historical average of 0.5% improvement per year to reach 25% in 2030 -$0.17/W.

Justification: Manufactures reported mass produced cell efficiencies will increase from 20%–23% in 2018 to 21%–24% by 2021. Mass production-monocrystalline and silicon heterojunction have already achieved cell efficiency records in a laboratory of 26.1% and 26.7% respectively.

 

Technology Description: This scenario assumes design simplification and manufacturing automation

Justification: The power electronics industry already has road maps to simplify and automate current products and there is more potential with increased industry size.

Technology Description: This scenario assumes 20% labor and hardware BOS cost improvements through automation and preassembly of racking, mounting, and wiring efficiencies; it also assumes a switch in deployments to be a standard feature of the construction of a house or during a normally scheduled reroofing event (e.g., new construction or reroofing).

Justification: In addition to the justifications listed above, there is potential for a national streamlined permitting process, with efforts currently underway. For example, the Solar Automated Permit Processing platform (SolarAPP) is being designed to be an instant online solar permitting tool for code compliant residential systems.

Technology Description: This scenario assumes a 4% energy gain degradation rate reduction from 0.7%/yr to 0.2%/yr

Justification: Significant R&D is currently spent on improved cell temperatures and lower degradation rates. Companies will likely continue to focus on improved uptime to maximize profitability. Degradation rates of 0.2% per year is currently being pursued.

Impacts
  • Lower module cost per watt
  • Reductions in PV system labor and BOS material, shipping, and warehousing
  • Reduced costs
  • Higher efficiency power conversion
  • Lower costs
  • Fewer building errors
  • Higher capacity factors
Scenario

1 Module efficiency improvements represent an increase in energy production over the same area of space, in this case the dimensions of a photovoltaic module. Energy yield gain represents an improvement in capacity factor, relative to the rated capacity of a PV systems. The rated capacity of a system does not increase with fewer system losses (e.g., panel cleanings). 

Representative Technology

For the 2021 ATB, residential PV systems are modeled for a 7-kWDC, fixed tilt, roof-mounted system. Flat-plate PV can utilize 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.

Methodology

This section describes the methodology to develop assumptions for CAPEX, O&M, and capacity factor. For standardized assumptions, see labor costregional cost variationmaterials cost indexscale of industrypolicies and regulations, and inflation.

Currently, CAPEX—not LCOE—is the most common metric for PV cost. Because of different assumptions in long-term incentives, system location and production characteristics, and cost of capital, LCOE can be confusing and often incomparable for different estimates. Though CAPEX also has many assumptions and interpretations, it involves fewer variables to manage. Therefore, PV projections in the 2021 ATB are driven entirely by plant and operational cost improvements.

Three projections are developed for scenario modeling as bounding levels:

  • Conservative Scenario: lower levels of R&D investment with minimal technology advancement and current global module pricing.
  • Moderate Scenario: R&D investment continuing at similar levels as today, with no substantial innovations or new technologies introduced to the market.
  • Advanced Scenario: an increase in R&D spending that generates substantial innovation, allowing historical rates of development to continue.

Capital Expenditures (CAPEX)

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

For the 2021 ATB—and (EIA, 2016) and the NREL Solar-PV Cost Model—the distributed residential solar PV plant envelope is defined to include items noted in the table above.

Base Year: Reported residential PV installation CAPEX (Barbose et al., 2020) is shown (see chart below) in box-and-whiskers format for comparison to historical residential PV benchmark overnight capital cost (Feldman et al., 2021) and the 2021 ATB estimates of future CAPEX projections. The data in  (Barbose et al., 2020) represent 82% of all U.S. residential PV and commercial PV capacity installed through 2019.

Historical Sources: (Barbose et al., 2020)(Feldman et al., 2021)

Future Projections: 2021 ATB

The difference in each year's price between the market and benchmark data reflects differences in methodologies. Reported and benchmark prices can differ for a variety of reasons, as outlined by Barbose and Darghouth (Barbose et al., 2019) and Bolinger, Seel, and Robson (Bolinger et al., 2019), including:

  • Timing-related issues: For instance, the time between contract completion and project placement 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).

Federal investment tax credits provide an incentive to include costs in the upfront CAPEX to receive a higher tax credit, and these included costs 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.

Residential PV pricing and capacities are quoted in kWDC (i.e., module rated capacity) unlike other generation technologies (including utility-scale PV), which are quoted in kWAC. For PV, this would correspond to the combined rated capacity of all inverters. This is because kWDC is the unit that the majority of the residential 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 2019 and 2020 reflect a continued rapid decline in pricing supported by analysis of recent system cost and pricing for projects that became operational in 2019 and 2020 (Feldman et al., 2021).

For illustration in the 2020 ATB, a representative residential-scale PV installation is shown. Although the PV technologies vary, typical installation costs are represented with a single estimate per innovations scenario because residential PV CAPEX does not correlate well 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.

System prices of $2.77/WDC in 2019 and $2.71/WDC in 2020 are based on bottom-up benchmark analysis reported in U.S. Solar Photovoltaic System Cost Benchmark: Q1 2020 (Feldman et al., 2021).

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 Years:

Projections of 2030 residential PV plant CAPEX are based on bottom-up cost modeling, with a straight-line change in price in the intermediate years between 2020 and 2030. The system design and price changes made in the models are summarized and described in the Summary of Technology Innovations by Scenario table. See below for the details of changes to components of system price in the different ATB scenarios.

Cost Details by Scenario

We assume each scenario's 2050 CAPEX is the equivalent of the 2030 CAPEX of the scenario but one degree more aggressive, with a straight-line change in price in the intermediate years between 2030 and 2050. Asterisks indicate corresponding cells, where scenarios use the same values but shifted in time. We also develop and model a scenario one degree more aggressive than the Advanced Scenario to estimate its 2050 CAPEX. The 2050 Advances Scenario assumes a module efficiency of 30%; further inverter simplification and manufacturing automation; 35% labor and hardware BOS cost improvements through automation and preassembly of racking, mounting, and wiring; carbon fiber is assumed to achieve low costs, replacing steel and aluminum and cuts material costs in half; and that PV systems become a standard feature of the construction of a house or during a normally scheduled reroofing event (i.e., new construction/reroof).

More Aggressive Scenarios Reach Given CAPEX Sooner

YearAdvanced (Increased R&D)Moderate (Current R&D)Conservative (Decreased R&D)
2030*Residential PV CAPEX: $0.77/WDCResidential PV CAPEX: $1.00/WDCResidential CAPEX: $2.26/WDC
2050$0.54/WDC*$0.77/WDC† $1.00/WDC

More aggressive scenarios reach given CAPEX sooner, as indicated by the asterisks and daggers

We compare the CAPEX scenarios over time to four analysts' projections, adjusted for inflation. The 2021 ATB CAPEX projections are fairly in-line with other analysts' projections through 2030, with the exception of the analysts' maximum projection, which starts at a much higher CAPEX. After 2030, other analysts' projections level off to a greater degree than ATB projections. Two of the four analyst projections do not go beyond 2030, so data points to compare the ATB projections are limited; however, the Advanced Scenario is in-line with the minimum analyst projection.

Sources: (BNEF, 2019)(BNEF, 2020)(Cox, 2020)(EIA, 2021)

Use the following table to view the components of CAPEX.

Operation and Maintenance (O&M) Costs

Definition: Operation and maintenance (O&M) costs represent the annual expenditures required to operate and maintain a solar PV plant over its lifetime, including items noted in the table below.

Base Year: Fixed O&M (FOM) of $27/kWDC-yr is based on modeled pricing for a commercial PV system quoted in Q1 2019 as reported by (Feldman et al., 2021). The values in the 2021 ATB are higher than those from the 2020 ATB because we include costs in the 2021 ATB not previously calculated. These include three additional line measures (property taxes, insurance, and asset management) that are added based on feedback collected by Lawrence Berkeley National Laboratory (LBNL) from U.S. solar industry professionals (Wiser et al., 2020). A wide range in reported prices exists in the market that depends in part on the maintenance practices that exist for a particular system. These cost categories include asset management (including compliance and reporting for incentive payments), insurance products, cleaning, vegetation removal, and component failure. 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 Years: FOM of $29/kWDC-yr for 2020 is also based on pricing reported by (Feldman et al., 2021), which can be divided into system-related expenses ($25/kWDC-yr) and administration-related expenses ($4/kWDC-yr). From 2020 to 2050, FOM is based on the historical average ratio of O&M costs ($/kW-yr) to CAPEX costs ($/kW), 0.9:100, as reported by (Feldman et al., 2021). Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2020, benchmark residential PV O&M fell 49% and PV CAPEX fell 64%, as reported by (Feldman et al., 2021). Administrative expenses are kept constant.

Use the following table to view the components of O&M.

Capacity Factor

Definition: 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.

Residential PV system capacity factor is not directly comparable to other technologies' capacity factors. Other technologies' capacity factors (including utility-scale PV) are represented exclusively in AC units (see Solar PV AC-DC Translation). However, because residential PV pricing in the 2020 ATB is represented in $/WDC, residential 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 or crystalline silicon), 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 0.7%/yr (Feldman et al., 2021) in the annual average calculation.

Base Year: R&D could lower degradation rates of PV plant capacity factor; future projections for the Moderate Scenario and the Advanced Scenario reduce degradation rates by 2030, using a straight-line basis, to 0.5%/yr, and 0.2%/yr respectively. The Conservative Scenario assumes no improvement in degradation rates through 2030.

In the interactive data chart at the top of this page, select Technology Detail = All to add filters to the initial figure on to show a range of capacity factors based on variation in solar resource in the contiguous United States. The ATB provides the average capacity factor for 10 resource categories in the United States, binned by mean GHI. The annual average capacity factor for the contiguous United States is calculated using the Renewable Energy Potential (ReV) model using solar resource data for 2012 from the National Solar Radiation Database (NSRDB). The county-level capacity factors are calculated for specific locations with azimuth and tilt, which are based on representative agents selected in the dGen 2020 Standard Scenarios agent database (Sigrin et al., 2016). A lookup table for these locations and the National Solar Radiation Database (NSRDB) is generated based on nearest distance. The azimuth and tilt as well as the resource GID are used to generate a System Advisor Model (SAM) config file and to run ReV, which outputs the annual average capacity factor at each evaluated location. U.S. average capacity factor for each resource category is weighted by the population of each county within the GHI resource category. The county estimated populations are provided by geospatial and tabular data from the U.S. Census.

First-year operation capacity factors as modeled range from 12.7% for Class 10 (for locations with an average annual GHI less than 3.75) to 19.6% for Class 1 (for locations with an average annual GHI greater than 5.75). Actual systems will vary significantly depending on location and system configuration (e.g., south-facing or west-facing).

Over time, PV installation output is reduced because of degradation in module quality, which is accounted for in ATB estimates of capacity factor over the 30-year lifetime of the plant. The adjusted average capacity factor values in the 2019 ATB Base Year range from 12.0% for Class 10 (for locations with an average annual GHI less than 3.75) to 18.5% for Class 1 (for locations with an average annual GHI greater than 5.75).

Future Years: Projections of capacity factors for plants installed in future years increase over time because of reduced system losses, and a straight-line reduction in PV plant capacity degradation rates from 0.7%/yr that reach 0.5%/yr and 0.2%/yr by 2030 for the Moderate Scenario and the Advanced Scenario respectively. The Conservative Scenario assumes no improvement in degradation rates through 2030. The following table summarizes the difference in average capacity factor in 2030 caused by these changes in the technology innovation scenarios. Similar to our CAPEX assumptions, we assume each scenario's 2050 capacity factor is the equivalent of the 2030 capacity factor of the scenario but one degree more aggressive, with a straight-line change in price in the intermediate years between 2030 and 2050.

2030 Residential PV DC Capacity Factors by Technology Innovation Scenario

ScenarioAverage Capacity (Class 10 - Class 1)Percentage Improvement from 2019
Advanced Scenario (0.20%/yr Degradation Rate)12.5%–19.3%4.3%
Moderate Scenario (0.50%/yr Degradation Rate)12.2%–18.8%1.7%
Conservative Scenario (0.7%/yr Degradation Rate)12.0%–18.5%0%

Solar PV plants have very little downtime, and inverter efficiency is already optimized. Even so, 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.

References

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

ITRPV. “ITRPV 2020: International Technology Roadmap for Photovoltaic (ITRPV).” International Technology Roadmap for Photovoltaic, April 2020. https://itrpv.vdma.org/.

Feldman, David, Vignesh Ramasamy, Ran Fu, Ashwin Ramdas, Jal Desai, and Robert Margolis. “U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2020.” National Renewable Energy Lab. (NREL), Golden, CO (United States), January 27, 2021. https://doi.org/10.2172/1764908.

Barbose, Galen, Naïm Darghouth, Salma Elmallah, Sydney Forrester, Kristina LaCommare, Dev Millstein, Joe Rand, Will Cotton, and Eric O’Shaughnessy. “Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States: 2019 Edition.” Tracking the Sun. Berkeley, CA: Lawrence Berkeley National Laboratory, October 30, 2019. https://doi.org/10.2172/1574343.

Fuscher, Moritz, and Ehrler Bruno. “Efficiency Limit of Perovskite/Si Tandem Solar Cells.” ACS Energy Lett. 1, no. 4 (October 3, 2016): 863–868. http://dx.doi.org/10.1021/acsenergylett.6b00405.

Cox, Molly. “H2 2020 US Solar PV System Pricing.” Wood Mackenzie, December 2020.

EIA. “Annual Energy Outlook 2021.” Energy Information Administration, January 2021. https://www.eia.gov/outlooks/aeo/.

Barbose, Galen, Naïm Darghouth, Eric O’Shaughnessy, and Sydney Forrester. “Distributed Solar Data Update.” Lawrence Berkeley National Laboratory (LBNL), December 2020. https://emp.lbl.gov/tracking-the-sun.

BNEF. “2H 2020 U.S. Renewable Energy Market Outlook.” BNEF, October 2020.

Bolinger, Mark, Joachim Seel, and Dana Robson. “Utility-Scale Solar: Empirical Trends in Project Technology, Cost, Performance, and PPA Pricing in the United States: 2019 Edition.” Utility-Scale Solar. Berkeley, CA: Lawrence Berkeley National Laboratory, December 2019. https://eta-publications.lbl.gov/sites/default/files/lbnl_utility_scale_solar_2019_edition_final.pdf.

BNEF. “2H 2019 US PV Market Outlook.” Bloomberg New Energy Finance, 2019.

EIA. “Annual Energy Outlook 2016 Early Release: Annotated Summary of Two Cases.” Washington, D.C.: U.S. Energy Information Administration, 2016. https://www.eia.gov/outlooks/archive/aeo16/er/.

Wiser, Ryan, Mark Bolinger, and Joachim Seel. “Benchmarking Utility-Scale PV Operational Expenses and Project Lifetimes: Results from a Survey of U.S. Solar Industry Professionals.” Berkeley, CA: Lawrence Berkeley National Laboratory., June 2020. https://emp.lbl.gov/publications/benchmarking-utility-scale-pv.

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