Commercial PV
Units using capacity above represent kWDC.
2024 ATB data for commercial solar photovoltaics (PV) are shown above, with a base year of 2022. The base year estimates rely on modeled capital expenditures (CAPEX) and operation and maintenance (O&M) cost estimates benchmarked with industry and historical data. The 2024 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 year 2023 reflects the most recent historical data, derived from benchmarks made in the first quarter of 2022 plus adjustments for market changes occurring in the first quarter of 2023. Specific projections are made for 2035 and 2050 only. Straight lines interpolate between the 2023 and 2035 values and between the 2035 and 2050 values. Cost fluctuations that may occur between each set of years are not represented, such as the fluctuations because of policy and market conditions that occurred after the first quarter of 2023.
The three scenarios for technology innovation are as follows:
- Conservative Technology Innovation Scenario (Conservative Scenario): lower levels of research and development (R&D) investment with minimal technology advancement and global module pricing consistent with the base year
- 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 2024 ATB provides the average capacity factor for 10 resource categories in the United States, binned by mean global horizontal irradiance (GHI). Average capacity factors are calculated using county-level capacity factor averages from the Renewable Energy Potential (reV) model for 2012 from the National Solar Radiation Database (NSRDB). The NSRDB provides modeled spatiotemporal solar irradiance resource data at 4-kilometer (km) spatial resolution and 0.5-hour temporal resolution. The county-level mean GHI is calculated by aggregating each NSRDB point’s multiyear mean GHI to provide a county’s mean GHI for all years included in the analysis. The U.S. average capacity factor for each resource category is weighted by the land area (square miles) of each county within the GHI resource category. The county estimated land area is provided by geospatial and tabular data from the U.S. Census. The map below shows average annual GHI in the United States.
The following table summarizes the estimated 2019 capacity factors (in the first year of operation) for each resource category and each resource category's associated population.
| Resource Class | GHI Bin (kilowatt-hours/square meters/day [kWh/m2/day]) | Mean Direct Current (DC) Capacity Factor | Population |
|---|---|---|---|
| 1 | >5.75 | 19.8% | 12,554,678 |
| 2 | 5.5–5.75 | 19.1% | 21,403,290 |
| 3 | 5.25–5.5 | 18.0% | 13,476,871 |
| 4 | 5–5.25 | 17.1% | 30,603,630 |
| 5 | 4.75–5 | 16.3% | 45,176,116 |
| 6 | 4.5–4.75 | 16.1% | 39,880,837 |
| 7 | 4.25–4.5 | 15.3% | 31,742,606 |
| 8 | 4–4.25 | 14.6% | 80,155,804 |
| 9 | 3.75–4 | 14.0% | 40,755,023 |
| 10 | <3.75 | 12.7% | 10,255,830 |
| U.S. Mean | 15.8% |
Scenario Descriptions
| Scenario | Module Efficiency1 | Inverter and Power Electronics | Installation Efficiencies | Energy Yield Gain1 |
|---|---|---|---|---|
| Conservative Scenario | Technology Description: A U.S. minimum sustainable price (MSP) is assumed, which excludes import tariffs, plus a supply chain premium for local installers. Module efficiency is based on the lowest projected efficiency for monocrystalline silicon technologies from the International Technology Roadmap for Photovoltaic (ITRPV) in 2032, resulting in a price of $0.39/watts direct current (WDC). Justification: This scenario represents the low end of long-term module efficiency-improvement expectations applied to a U.S. MSP, including a supply chain premium for local installers. | N/A | N/A | Although bifacial modules have begun to be deployed in substantial quantities, monofacial modules are assumed in this scenario owing in part to a lack of data in the changing industry. |
| Moderate Scenario | Technology Description: The module price is halfway between the price in the Conservative Scenario and the price in the Advanced Scenario, or $0.32/WDC. Module efficiency is based on the projected efficiency consistent with TOPCon (tunnel oxide passivated contact) and SHJ (silicon heterojunction) modules in the ITRPV in 2032. Justification: This scenario represents a moderate long-term module efficiency-improvement expectation applied to a module basis halfway between a U.S. MSP and a global spot price, including a supply chain premium for local installers. | N/A | Technology Description: This scenario assumes 30% labor and hardware balance of system (BOS) cost improvements through automation, preassembly of racking, mounting, and wiring efficiencies as well as improvements in wind load design. Justification: This scenario represents lower levels of improvement than the historical average (Feldman et al., 2021). With increased global deployment and a more efficient supply chain, preassembly of mounting, racking, and wiring is possible. Best practices for permitting interconnection and PV installation (e.g., subdivision regulations, new construction guidelines, and design requirements) are being developed. | Technology Description: This scenario assumes a degradation rate reduction from 0.7%/yr to 0.5%/yr plus a 6% energy gain through lower system losses, increased use of bifacial modules, and improvements in bifaciality. 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, and bifacial modules are already becoming a significant part of the global and U.S. supply chain. The ITRPV estimates bifacial modules' world market share will grow from 10% in 2018 to over 60% by 2032. Industry participants have already demonstrated bifacial energy gains of 5%–33%, depending on the system configuration. Under real-world field conditions, a gain of around 5%–15% would be expected. |
| Advanced Scenario | Technology Description: A global module spot price is assumed as the price basis, plus a supply chain premium for local installers. Module efficiency is based on the projected efficiency halfway between the efficiency of TOPCon and SHJ modules and tandem modules in the ITRPV in 2032, resulting in a price of $0.26/WDC. Justification: This scenario represents a relatively high long-term module efficiency-improvement expectation applied to a global spot price, including a supply chain premium for local installers. | Technology Description: This scenario assumes design simplification and manufacturing automation. Justification: The power electronics industry already has roadmaps to simplify and automate current products, and there is more potential with increased industry size. | Technology Description: This scenario assumes 40% labor and hardware BOS cost improvements through automation and preassembly of racking, mounting, and wiring efficiencies. The use of carbon fiber, which becomes low-cost, cuts mounting costs. Justification: This scenario represents lower levels of improvement than the historical average (Feldman et al., 2021). With increased global deployment and a more efficient supply chain, preassembly of racking, mounting, and wiring is possible. Reduction of supply chain margins (e.g., profit and overhead charged by suppliers, manufacturers, distributors, and retailers) will likely occur as the U.S. PV industry grows and matures. Also, streamlining of installation practices through improved workforce development and training and developing standardized PV hardware is assumed. | Technology Description: This scenario assumes a degradation rate reduction from 0.7%/yr to 0.2%/yr plus a 9% energy gain through 1) lower system losses and 2) increased use of bifacial modules, improvements in bifaciality, and albedo enhancement to 0.3 (a white roof has an albedo of 0.7, and suboptimal spacing is likely because of tight row spacing causing shade). Justification: In addition to the justifications listed above, industry participants have already demonstrated bifacial energy gains of 5%–33%, depending on the module mounting. Under real-world field conditions, a gain of around 5%–15% would be expected. |
| Impacts of Technology Advancements |
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| References |
1 Module efficiency improvements represent an increase in energy production over the same area, in this case the dimensions of a PV module. Energy yield gain represents an improvement in capacity factor relative to the rated capacity of a PV system. In the case of bifacial modules, the increase in energy production between two modules with the same dimensions does not currently change the capacity rating of the module under standard test conditions because the rating is based on light from one direction.
Scenario Assumptions
The technology improvement scenarios for commercial PV described above result in CAPEX reductions of 9% (Conservative Scenario), 33% (Moderate Scenario), and 50% (Advanced Scenario) between 2023 and 2035. We assume these CAPEX reductions follow straight lines between 2023 and 2035. The average annual reduction rates are 0.8% (Conservative Scenario), 3.3% (Moderate Scenario), and 5.6% (Advanced Scenario).
Similarly, we assume straight-line CAPEX reductions between 2035 and 2050, as described below. Between these 2 years, the CAPEX reductions are 26% (2.0% per year average) for the Conservative Scenario, 25% (1.9% per year average) for the Moderate Scenario, and 17% (1.2% per year average) for the Advanced Scenario.
Although we did not create our CAPEX projections based on rates of deployment, commercial PV deployment is expected to increase substantially over our analysis period. For example, in the National Renewable Energy Laboratory's (NREL's) Standard Scenarios Mid-case, U.S. distributed PV deployment (including commercial and other distributed systems such as residential systems) grows by 222% between 2024 and 2035 (from 41 GW to 131 GW) and by 32% between 2035 and 2050 (from 131 GW to 173 GW) (Gagnon et al., 2024).
Representative Technology
For the 2024 ATB, commercial PV systems are modeled for a 200-kWDC, flat-roof-mounted system with a 1.23 DC-to-AC (alternating current) ratio, or inverter loading ratio (ILR) (Ramasamy et al., 2022). Flat-plate PV can use direct or indirect insolation, so PV modules need not directly face and track incident radiation. The county-level capacity factors are calculated for specific locations, which are based on representative agents selected in the Distributed Generation Market Demand Model (dGen) 2020 Standard Scenarios agent database (Sigrin et al., 2016). At each location, various tilt/azimuth combinations are evaluated, and the optimal combination is chosen for modeling. The ability to use direct and indirect insolation gives rooftop PV systems a broad geographical application. A study of rooftop PV technical potential (Gagnon et al., 2016) estimated as much as 731 gigawatts (GW) (926 terawatt-hours per year [TWh/yr]) of potential exists for small buildings (< 5,000-m2 footprint) and 386 GW (506 TWh/yr) exists for medium (5,000–25,000 m2) and large buildings (> 25,000 m2).
Methodology
This section describes the methodology to develop assumptions for CAPEX, O&M, and capacity factor. For standardized assumptions, see regional cost variation, materials cost index, scale of industry, policies and regulations, and inflation. The PV-specific and standardized assumptions for labor costs differ; the PV analysis assumes the use of nonunion labor only.
Currently, CAPEX—not levelized cost of energy (LCOE)—is the most common metric for PV costs. Because of different assumptions in long-term incentives, system location and production characteristics, and cost of capital, LCOE can be confusing and often noncomparable for different estimates. Though CAPEX also has many assumptions and interpretations, managing it involves fewer variables. Therefore, PV projections in the 2024 ATB are driven entirely by plant and operating cost improvements.
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 10 resource categories in the United States, binned by mean GHI.
Future year projections are derived from bottom-up benchmarking of PV CAPEX and bottom-up engineering analysis of O&M costs. Three projections are developed for scenario modeling as bounding levels (see the scenario list above).
Capital Expenditures (CAPEX)
Definition: Capital expenditures (CAPEX) are expenditures required to achieve commercial operation in a given year. For commercial PV, CAPEX is modeled only for a host-owned business model with access to debt.
For the 2024 ATB—and based on the NREL PV cost model (Ramasamy et al., 2022)—the distributed PV plant envelope is defined to include items noted in the Components of CAPEX table below.
Base Year: In the chart below, reported historical commercial-scale PV installation CAPEX (Barbose et al., 2023) is shown in box-and-whiskers format through 2021 along with benchmarked CAPEX in 2022 (Ramasamy et al., 2022) and 2023 and projections from the 2024 ATB; the 2023 benchmark is based on (Ramasamy et al., 2022), with market adjustments from (Ramasamy et al., 2023). The installed-price data in (Barbose et al., 2023) represent 38% of all U.S. residential PV and commercial PV capacity installed through 2022.
Historical Sources: (Barbose et al., 2023); (Ramasamy et al., 2022); (Ramasamy et al., 2023)
Future Projections: 2024 ATB
All prices quoted in WAC are converted to WDC (1 WAC = ILR × WDC).
Reported and benchmarked prices can differ for a variety of reasons, as outlined by Barbose and Darghouth (Barbose et al., 2019), Bolinger, Seel, and Robson (Bolinger et al., 2019), and (Ramasamy et al., 2022), including the following:
- Timing-Related Issues: For example, the time between contract completion and project placement in service may vary.
- System Variations: The size, technology, installer margin, and design of systems installed in a given year vary over time.
- Cost Categories: There are variations in which cost categories are included in CAPEX (e.g., financing costs and initial operation and maintenance [O&M] expenses).
Federal investment tax credits provide an incentive to include costs in the up-front CAPEX to receive a higher tax credit; these included costs may have otherwise been reported as operating costs. The bottom-up benchmarks are more reflective of overnight capital costs, which is in line with the ATB methodology of inputting overnight capital costs and calculating construction financing to derive CAPEX.
Commercial 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. This is because kWDC is the unit that most 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 2023 reflect analysis of recent system costs and pricing for projects that became operational in 2023 (Ramasamy et al., 2022); (Ramasamy et al., 2023).
The chart above shows the range in historical CAPEX that reflects the heterogeneous composition of the commercial PV market in the United States. The chart includes a representative commercial-scale PV installation. Although commercial PV systems vary dramatically in size and application, typical installation costs are represented with a single estimate per innovation scenario. Also, commercial PV CAPEX does not correlate well with solar resources.
Although the technology market share may shift over time with new developments, the typical installation costs are represented with the projections above.
System prices of $1.99/WDC in 2022 and $1.78/WDC in 2023 are based on bottom-up benchmark analysis reported by (Ramasamy et al., 2022), with market adjustments for 2023 from (Ramasamy et al., 2023). The 2022 and 2023 bottom-up benchmarks are reflective of overnight capital costs, which are in line with the ATB methodology of inputting overnight capital costs and calculating construction financing to derive CAPEX.
The base year CAPEX estimates should tend toward the low end of observed costs because no regional impacts are included. These effects are represented in the historical market data.
Future Years: Projections of 2035 commercial PV plant CAPEX are based on bottom-up cost modeling, with 2023 values from (Ramasamy et al., 2022)—adjusted for the 2023 market using (Ramasamy et al., 2023)—and a straight-line change in price in the intermediate years between 2023 and 2035. 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.
The values in the chart above represent overnight capital costs, which exclude construction financing costs.
We assume each scenario's 2050 CAPEX is the equivalent of the 2035 CAPEX of the scenario but one degree more aggressive, with a straight-line change in price in the intermediate years between 2035 and 2050. In the table below, asterisks and daggers indicate corresponding cells, where scenarios use the same values but are 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 Advanced Scenario assumes the following:
- Module efficiency of 28%
- Further inverter simplification and manufacturing automation
- 50% labor and hardware BOS cost improvements through automation and preassembly of racking, mounting, and wiring efficiencies
- Carbon fiber becomes low-cost, replacing steel and aluminum, which cuts material costs in half.
| Year | Advanced Scenario (Increased R&D) | Moderate Scenario (Current R&D) | Conservative Scenario (Decreased R&D) |
|---|---|---|---|
| 2035 | * Commercial PV CAPEX: $0.93/WDC | † Commercial PV CAPEX: $1.24/WDC | Commercial PV CAPEX: $1.68/WDC |
| 2050 | $0.77/WDC | * $0.93/WDC | † $1.24/WDC |
More-aggressive scenarios reach given CAPEX sooner, as indicated by the asterisks and daggers.
We compare the CAPEX scenarios over time to three analyst projections—one from Energy Information Administration (EIA) and two from private analysts—adjusted for inflation. The median of the private projections is displayed in the figure below through 2030, along with the EIA projection through 2050. The 2024 ATB Advanced Scenario CAPEX projection generally aligns with the median analyst projection through 2030 whereas all ATB projections are lower than the EIA projection through 2050.
Source for CAPEX: 2024 ATB; (BNEF, 2023b); (Wood Mackenzie, 2023); (EIA, 2023)
All prices quoted in WAC are converted to WDC (1 WAC = ILR × WDC).
Use the following table to view the components of CAPEX.
Operation and Maintenance (O&M) Costs
Definition: O&M costs represent the annual expenditures required to operate and maintain a PV plant over its lifetime, including items noted in the table below.
Base Year: The initial figure on this page shows the base year estimate and future year projections for fixed O&M (FOM) costs. Three technology innovation scenarios are represented. The estimate for a given year represents annual average FOM costs expected over the technical lifetime of a new plant that reaches commercial operation in that year.
The FOM cost of about $21/kWDC-yr is based on modeled pricing for a commercial PV system quoted in 2022 as reported by (Ramasamy et al., 2022). Lawrence Berkeley National Laboratory collected feedback from U.S. solar industry professionals (Wiser et al., 2020). The wide range in reported prices 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, site security, cleaning, vegetation removal, and component failure. Not all these practices are performed for each system; also, some factors depend on the quality of the parts and construction. NREL analysts estimate O&M costs can range from $0/kWDC-yr to $40/kWDC-yr.
Future Years: The FOM cost of about $19/kWDC-yr for 2023 is based on pricing reported by (Ramasamy et al., 2022) and (Ramasamy et al., 2023), which can be divided into system-related expenses ($15.5/kWDC-yr) and administration-related expenses ($3.3/kWDC-yr). From 2023 to 2050, system-related FOM is based on the ratio of O&M costs ($/kW-yr) to CAPEX costs ($/kW), which was 0.87:100 in 2023 based on (Ramasamy et al., 2022) and (Ramasamy et al., 2023). Historical data suggest O&M and CAPEX cost reductions are correlated; from 2010 to 2020, benchmark commercial PV O&M and CAPEX costs fell 46% and 69%, respectively, 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 for commercial PV systems 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 commercial PV pricing in the 2024 ATB is represented in $/WDC, commercial 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), bifaciality of the module, shading, expected downtime, and inverter losses to transform from DC power to AC power. The DC-to-AC ratio is a design choice that influences the capacity factor. The baseline PV plant capacity factor incorporates an assumed degradation rate of 0.7%/yr in the annual average calculation. R&D could increase energy yield through bifaciality, increased albedo, better soil removal, improved cell temperature, lower system losses, O&M practices that improve uptime, and lower degradation rates of PV plant capacity factor; future projections assume energy yield gains of 0%–14%, depending on the location and scenario.
Base Year: In the interactive data chart at the top of this page, select Technology Detail = All to add filters showing a range of capacity factors based on variation in solar resource across the contiguous United States. The range of the base year estimates illustrates the effect of locating a commercial PV plant in places with lower or higher solar irradiance. 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 reV model using solar resource data from the 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 NSRDB is generated based on nearest distance. The azimuth and tilt as well as the resource GHI are used to generate a System Advisor Model (SAM) configuration file and 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.
Because of the change in methodology in calculating capacity factors in the 2024 ATB, they are not directly comparable to some previous editions of the ATB. In the 2024 ATB, 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 19.8% for Class 1 (for locations with an average annual GHI greater than 5.75).
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 2022 ATB Base Year range from 12.0% for Class 10 (for locations with an average annual GHI less than 3.75) to 19.1% 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 an increase in energy yield from the module (improved cell temperature, bifaciality, and improved albedo), reduced system losses (improved soil removal, improved O&M uptime, and more-efficient inverters), and a reduction in degradation rates. The table below summarizes the technology improvements we use to calculate indicative improvements in capacity factor in each scenario.
| Performance Area | 2019* | 2035 Conservative Scenario | 2035 Moderate Scenario | 2035 Advanced Scenario |
| Bifaciality | None | None | 0.85 | 0.85 |
| Albedo | 0.2 | 0.2 | 0.3 | 0.3 |
| AC and DC losses | 14.3% | 14.3% | 10.4% | 7.5% |
| Annual degradation rate | 0.7% | 0.7% | 0.5% | 0.2% |
* The year 2019 is used here because improvement assumptions are not changed from the 2021 ATB with a base year of 2019.
The technology improvements summarized above would not necessarily result in the estimated capacity factor improvements, given the 2024 ATB assumption of a constant ILR. PV system ILR choice is based on an optimization exercise to maximize profits (or offer the lowest energy price), trading off the extra cost and increased clipping losses of additional modules with improvements in inverter operation and a higher, flatter electricity production curve. All things being equal, the optimal ILR of PV systems in higher resource classes or those that use bifacial modules will be lower than the optimal ILR of systems in lower resource classes or those with monofacial modules, particularly without the use of energy storage. Because of the complexity of optimizing CAPEX and ILR for each resource class for each year, and with and without storage, ATB PV system CAPEX and capacity factor benchmarks are calculated using a fixed ILR, independent of system location, performance improvements over time, or the incorporation of storage. Also, we assume performance improvements over time are not location-dependent, even though a PV system with the same ILR in a higher-resource area will experience more clipping and thus fewer performance improvements. However, in reality, PV systems in those areas would reduce their clipping losses by installing fewer PV panels and would thus have a lower up-front cost (trading off the marginally greater production with reduced CAPEX).
The following table summarizes the difference in average capacity factor in 2035 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 2035 capacity factor of the scenario but one degree more aggressive, with a straight-line change in price in the intermediate years between 2035 and 2050. The table below summarizes capacity factors for each ATB scenario by resource class.
| Scenario | Average Capacity Factor in 2035 (Class 10–Class 1) | Percentage Improvement from Base Year (2022) |
|---|---|---|
| Advanced Scenario | 13.6%–21.2% | 11% |
| Moderate Scenario | 13.0%–20.2% | 7% |
| Conservative Scenario | 12.0%–18.6% | 0% |
We also develop and model a scenario one degree more aggressive than the Advanced Scenario to estimate its 2050 capacity factor. The 2050 Advanced Scenario assumes a 14% improvement over 2022 capacity factors.
References
The following references are specific to this page; for all references in this ATB, see References.