Utility-Scale PV
ATB data for utility-scale solar photovoltaics (PV) are shown above. 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 locations in the United States. The 2020 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
Solar resources in the 2020 ATB are categorized according to the range of solar irradiance across the range of latitudes 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 23.1% to 37.3%, though these depend significantly on location and system configuration (e.g., fixed-tilt or single-axis tracking).
Scenario Descriptions
Scenario | Module Efficiency1 | Inverter Power Electronics | Installation Efficiencies | Energy Yield Gain1 |
Conservative | 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 International Technology Roadmap for Photovoltaic (ITRPV—an annual document prepared by 55 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 2030for PERC n-type mono module; well below historical mono average gains and below leveling off point (21.5%) -$0.30/WDC
Justification:Represents the low end of manufacture expectations and additional friction despite the scheduled removal of the tariff. | Medium-voltage power transmission or centralized power conversion center for inverters: $0.03/WDC (from $0.04/WDC today)
Justification: industry currently switching to this practice. | n/a | n/a |
Moderate | Tariffs expire, as scheduled, and efficiency gains consistent with median ITRPV roadmap to 2030 for PERC n-type mono module; well below historical mono average gains and below leveling off point (22.5%) - $0.19/W
Justification:Represents manufacture expectations for 2030. | Medium-voltage power transmission or centralized power conversion center for inverters: $0.03/WDC
Justification: industry currently switching to this practice. | 30% labor cost improvements through automation, pre-assembly efficiencies, and improvements in wind load design
Justification: represents lower levels of improvement than historical average (Feldman et al. Forthcoming). With increased global deployment and more efficient supply chain, pre-assembly is possible. Development of best practices for permitting interconnection and PV installation, such as subdivision regulations, new construction guidelines, and design requirements. | 4%–7% energy gain (10% energy yield gain at module), lower system losses, and degradation rate reduction from 0.7%/yr to 0.5%/yr
Justification: Significant R&D is currently spent on better tracking, 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. ITRPV estimates bifacial module's world market share will grow from 10% in 2018 to over 60% by 2030.. Industry participants have already demonstrated bifacial energy gain of 5%-33%, depending on module mounting. |
Advanced | Modules maintain historical average of 0.5% improvement per year (25%) - $0.17/W
Justification: Manufactures reported that mass produced cell efficiencies will increase from 20%-23% in 2018 to 21%-24% by 2021. Mass production-monocrystalline and silicon heterojunctionhave already achieved cell efficiency records in a laboratory of 26.1% and 26.7% respectively.
| Inverter design simplification and manufacturing automation: $0.02/WDC
Justification: power electronics industry already has roadmaps to do simply and automate for current products and there is more potential with increased industry size. | 40% labor cost improvements through automation and pre-assembly efficiencies; the use of low-cost carbon fiber cuts mounting costs
Justification: represents lower levels of improvement than historical average (Feldman et al. Forthcoming). With increased global deployment and more efficient supply chain, pre-assembly is possible. Reduction of supply chain margins (e.g., profit and overhead charged by suppliers, manufacturers, distributors, and retailers), will likely occur naturally as the U.S. PV industry grows and matures. Additionally streamlining of installation practices through improved workforce development and training and developing standardized PV hardware | 8%–14% energy gain (20% energy yield gain at module), lower system losses, and degradation rate reduction from 0.7%/yr to 0.2%/yr
Justification: In addition to the justification above, industry participants have already demonstrated bifacial energy gain of 5%-33%, depending on module mounting. |
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 |
References |
NREL, "Best Research-Cell Efficiencies (Rev. 04-06-20)" Feldman et al. (Forthcoming) | Feldman et al. (Forthcoming) | Feldman et al. (Forthcoming) |
Representative Technology
Utility-scale PV systems in the 2020 ATB are representative of one-axis tracking systems with performance and pricing characteristics in line with a 1.34 DC-to-AC ratio-or inverter loading ratio (ILR) for current and future years (Fu, Feldman, and Margolis 2018).
Methodology
This section describes methodology to develop assumptions for CAPEX, O&M, and capacity factor. Click on these links for standardized assumptions for labor cost, regional cost variation, materials cost index, scale of industry, policies and regulations, and inflation.
PV projections in the 2020 ATB are driven primarily by CAPEX cost improvements, along with improvements in energy yield, operational cost, and cost of capital (for the Market + Policies Financial Assumptions Case [Market + Policies Case]).
While CAPEX is one of the drivers of lower costs, R&D efforts continue to focus on other areas to lower the cost of energy from utility-scale PV, such as longer system lifetime and improved performance. Three 2030 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.
Use the following table to view the components of CAPEX.
Capital Expenditures (CAPEX)
Definitions: For a PV system, the rated capacity in the denominator is reported in terms of the aggregated capacity of either (1) all its modules or (2) all its inverters. PV modules are rated using standard test conditions and produce direct current (DC) energy; inverters convert DC energy/power to alternating current (AC) energy/power. Therefore, the capacity of a PV system is rated either in MWDC via the aggregation of all modules' rated capacities or in MWAC via the aggregation of all inverters' rated capacities. The ratio of these two capacities is referred to as the inverter loading ratio (ILR). The 2020 ATB assumes current estimates and future projections use an inverter loading ratio of 1.34.
The PV industry typically refers to PV CAPEX in units of $/MWDC based on the aggregated module capacity. The electric utility industry typically refers to PV CAPEX in units of $/MWAC based on the aggregated inverter capacity; for the 2020 ATB, we have switched to using $/MWAC for utility-scale PV.
Plant costs are represented with a single estimate per innovations scenario, because CAPEX does not correlate well with solar resource.
For the 2020 ATB, and based on EIA (2016a) and the NREL Solar PV Cost Model (Feldman et al. Forthcoming), the utility-scale solar PV plant envelope is defined to include items noted in the table above.
Base Year: A system price of $1.57/WAC in 2018 is based on modeled pricing for a 100-MWDC, one-axis tracking systems quoted in Q1 2018 as reported by, adjusted from $/WDC to $/WAC by an ILR of 1.34. The $1.38/WAC price in 2020 is based on modeled pricing for a 100-MWDC, one-axis tracking systems quoted in Q1 2019 as reported by Feldman et al. (2018).
We focus on larger systems for the 2018 and 2019 values to better align with the current trends in utility-scale installations. (EIA 2020) reported 92 PV installations (greater than 5 MWAC in capacity) totaling 4.4 GWAC were placed in service in 2019 in the United States. While this represents an average of approximately 48 MWAC, 76% of the installed capacity in 2018 came from systems greater than 50 MWAC, and 39% came from systems greater than 100 MWAC.
In the historical data chart below, reported historical utility-scale PV plant CAPEX (Bolinger and Seel 2018) is shown in box-and-whiskers format for comparison to the historical benchmarked utility-scale PV plant overnight capital cost and future CAPEX projections. Bolinger, Seel, and Robson (2018) provide statistical representation of CAPEX for 93% of all utility-scale PV capacity.
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 enumerated by Barbose and Darghouth (2018) and Bolinger, Seel, and Robson (2018), including:
- Timing-related issues: For instance, the time between PPA contract completion and project placement in service may vary, and a system can be reported as installed in separate sections over time or when an entire complex is complete. For example, in 2014, the reported capacity-weighted average system price was higher than 80% of system prices in 2014 due to very large systems with multiyear construction schedules being installed in that year. Developers of these large systems negotiated contracts and installed portions of their systems when module and other costs were higher.
- 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.
Future Years
Projections of 2030 utility-scale PV plant CAPEX are based on bottom-up cost modeling, with a straight-line change in price in the intermediate years between 2019 and 2030. ILR is assumed to remain at a constant 1.34 over time. A summary of the system design and price changes made in the models are 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.
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 developed and modeled 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%, achieved through double-junction cells (perovskite on top of c-Si); further inverter simplification and manufacturing automation; 50% labor cost improvements through automation and pre-assembly efficiencies; and that low-cost carbon fiber cuts material costs in half.
Year | Advanced (Increased level of R&D) | Moderate (Current level of R&D) | Conservative (Decreased level of R&D) |
2030 | **Utility-scale CAPEX: $0.67/WAC | ***Utility-scale CAPEX: $0.82/WAC | Utility-scale CAPEX: $1.20/WAC |
2050 | $0.51/WAC | **$0.67/WAC | ***$0.82/WAC |
However, it is important to keep in mind that analyst projections represent analyst expected outcomes, while the Advanced Scenario represents NREL's estimate of an unlikely outcome (20%-50% likelihood). We compared the CAPEX scenarios over time to five analysts' projections, adjusted for inflation and ILR. The 2030 CAPEX Conservative, Moderate, and Advanced Scenarios line up fairly well with the maximum, median, and minimum analyst projections (see chart below). The 2050 CAPEX Conservative and Moderate Scenarios exceed all analysts' other projections; however, it is important to keep in mind that analysts' projections represent analyst expected outcomes, while the Conservative and Moderate Scenarios represent NREL's estimate of an outcome which will likely be exceeded.
Operation and Maintenance (O&M) Costs
Definition: Operation and maintenance (O&M) costs represent the annual fixed expenditures required to operate and maintain a solar PV plant over its lifetime, including items noted in the table above.
Base Year: O&M of $19/kWAC – yr is based on modeled pricing for a 100-MWDC, one-axis tracking systems quoted in Q1 2018 as reported by Feldman et al. (Forthcoming) adjusted from DC to AC. The values in the 2020 ATB are higher than those from the 2019 ATB because of the switch from using $/WDC to using $/WAC.
The wide range in reported prices in part depends on the range in maintenance practices for various systems. These cost categories include asset management (including compliance and reporting for incentive payments), different insurance products, site security, 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 $40/kWDC – yr.
Future Years: FOM for 2019 is also based on pricing reported by Feldman et al. From 2020 to 2050, FOM is based on the historical average ratio of O&M costs ($/kW-yr) to CAPEX costs ($/kW) of 1.2:100, as reported by Feldman et al (Forthcoming). This ratio is higher than the ratio of O&M costs to historically reported CAPEX costs of 0.8:100, which is derived from 2011–2018 historical data reported by Bolinger, Seel, and Robson (2018), as well as the ratio of O&M costs to CAPEX costs of 1.0:100, which is derived from IEA (2016) and Lazard (2018). Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2011 to 2018, fleetwide average O&M and CAPEX costs fell 43% and 64% respectively, as reported by Bolinger, Seel, and Robson (2018).
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.
PV system inverters, which convert DC energy/power to AC energy/power, have AC capacity ratings; therefore, the capacity of a PV system is rated in MWAC, or the aggregation of all inverters' rated capacities, or MWDC, or the aggregation of all modules' rated capacities. Other technologies' capacity factors are represented exclusively in AC units; however, in previous years of the ATB, PV pricing is represented in $/kWDC. In the 2020 ATB, we represent utility-scale PV (though not commercial PV or residential PV) in $/KWAC; for this reason, values in this year's report are not directly comparable to previous years of the ATB without adjusting previous versions from WDC to WAC.
The capacity factor is influenced by the hourly solar profile, technology (e.g., thin-film or crystalline silicon), the bifaciality of the module, axis type (i.e., none, one, or two), shading, 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. Forthcoming) in the annual average calculation. R&D could increase energy yield through bifaciality, 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% to 27% depending on the location and scenario.
Base Year: Click here and select Tech Detail = All to add filters to the initial figure on this page to display 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 System Advisor Model using PV system characteristics from Feldman et al. (Forthcoming).
Because we switched from using DC ratings to using AC ratings, and because we incorporated technology trends in energy yield gain, capacity factors in the 2020 ATB are higher than those reported in previous ATBs. In the 2020 ATB, we use capacity factors of 21.8%, 25.2%, 27.2%, 31.9%, and 35.1% in the Base Year. The 2020 ATB capacity factor assumptions are based on ILR = 1.34. The figure below shows historical data for capacity factor as a function of ILR.
Base Year: At the end of 2018, the cumulative capacity-weighted average AC capacity factor for all U.S. projects installed at the time was 25.0% (including fixed-tilt systems), but individual project-level capacity factors exhibited a wide range (12.1%–34.8%).
Over time, PV plant output is reduced. This degradation (at 0.7%/yr) is accounted for in ATB estimates of capacity factor. The 2020 ATB capacity factor estimates represent estimated annual average energy production over a 30-year lifetime.
These AC capacity factors are for a one-axis tracking system with a DC-to-AC ratio of 1.34, and therefore is not representative of the lower capacity factors reported by fixed-tilt systems.
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, 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 and advanced technology innovation scenarios 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. The table below summarizes capacity factors for each ATB scenario by resource class.
Seattle, WA | Chicago, IL | Kansas City, MO | Los Angeles, CA | Daggett, CA | |
Advanced Scenario | 25.9% | 29.7% | 31.7% | 36.1% | 39.5% |
Moderate Scenario | 23.7% | 27.2% | 29.3% | 33.9% | 37.2% |
Conservative Scenario | 21.8% | 25.2% | 27.2% | 31.9% | 35.1% |
We also developed and modeled a scenario one degree more aggressive than the Advanced Scenario to estimate its 2050 capacity factor. The 2050 Advances Scenario assumes: 18%-27% energy gain, depending on location, through a 30% energy yield gain at the module and lower system losses; and a 0.2%/year degradation rate. This is achieved through bifaciality, better tracking, soil removal, improved cell temperature, and improved O&M uptime.
References
The following references are specific to this page; for all references in this ATB, see References.
Barbose, Galen, & Darghouth, Naïm. (2019). Tracking the Sun: Pricing and Design Trends for Distributed Photovoltaic Systems in the United States: Tracking the Sun. Lawrence Berkeley National Laboratory. https://emp.lbl.gov/sites/default/files/tracking_the_sun_2019_report.pdf
Barbose, Galen, & Dargouth, Naïm. (2018). Tracking the Sun XI: The Installed Price of Residential and Non-Residential Photovoltaic Systems in the United States. (No. LBNL-2001062). Lawrence Berkeley National Laboratory. https://eta-publications.lbl.gov/sites/default/files/tracking_the_sun_2018_edition_final.pdf
BNEF (2019). 2H 2019 US PV Market Outlook. BNEF (Bloomberg New Energy Finance).
BNEF (2019). New Energy Outlook 2019. https://about.bnef.com/new-energy-outlook/
Bolinger, Mark, & Seel, Joachim. (2018). Utility-Scale Solar: An Empirical Trends in Project Technology, Cost, Performance, and PPA Pricing in the United States (2018 Edition). Lawrence Berkeley National Laboratory. https://emp.lbl.gov/sites/default/files/lbnl_utility_scale_solar_2018_edition_report.pdf
EIA (2016). Annual Energy Outlook 2016 Early Release: Annotated Summary of Two Cases. (No. AEO2016). U.S. Energy Information Administration. https://www.eia.gov/outlooks/archive/aeo16/er/
EIA (2019). Annual Energy Outlook 2019 with Projections to 2050. (No. AEO2019). U.S. Energy Information Administration. https://www.eia.gov/outlooks/aeo/pdf/AEO2019.pdf
EIA (2020). Annual Energy Outlook 2020 with Projections to 2050. (No. AEO2020). U.S. Energy Information Administration. https://www.eia.gov/outlooks/aeo/pdf/AEO2020.pdf
Feldman, David, Vignesh Ramasamy, Ran Fu, Ashwin Ramdas, Jal Desai, and Robert Margolis. (Forthcoming). U.S. Solar Photovoltaic System and Energy Storage Cost Benchmark: Q1 2020. Golden, CO: National Renewable Energy Laboratory.
Fu, Ran, Feldman, David, & Margolis, Robert. (2018). U.S. Solar Photovoltaic System Cost Benchmark: Q1 2018. National Renewable Energy Laboratory. https://www.nrel.gov/docs/fy19osti/72399.pdf
Fuscher, Moritz, & Bruno, Ehrler. (2016). Efficiency Limit of Perovskite/Si Tandem Solar Cells. ACS Energy Lett., 1(4), 863–868.
IEA (2016). World Energy Outlook 2016. (No. WEO2016). International Energy Agency. https://webstore.iea.org/world-energy-outlook-2016
ITRPV (n.d.). International Technology Roadmap for Photovoltaic (ITRPV). International Technology Roadmap for Photovoltaic (ITRPV) i. https://itrpv.vdma.org/
Lazard (2018). Lazard's Levelized Cost of Energy Analysis: Version 12.0. Lazard. https://www.lazard.com/media/450784/lazards-levelized-cost-of-energy-version-120-vfinal.pdf
Satpathy, Rabindra (n.d.). Additional Energy Yield Using Bifacial Solar PV Modules and Dependency on Albedo. https://www.ises.org/sites/default/files/webinars/Presentation Rabi Satpathy_ISESWebinar_0.pdf
Wood Mackenzie (2019). U.S. PV System Pricing H2 2019: System Pricing, Breakdowns and Forecasts. https://www.woodmac.com/reports/power-markets-us-solar-pv-system-pricing-h2-2019-361390
Developed with funding from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy.