Utility-Scale PV
Units using capacity above represent kWAC.
2024 ATB data for utility-scale 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. Capacity factor is estimated for 10 resource classes, binned by mean global horizontal irradiance (GHI) in the United States. 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 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. In the Advanced Scenario, Markets Case, the levelized cost of building and operating the project can be lower than the levelized value of the federal tax credit, which results in some levelized cost of energy (LCOE) values below zero.
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 current industry technology roadmaps achieved but 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.
Detailed descriptions of the scenarios are presented in the Scenario Descriptions section.
Resource Categorization
The 2024 ATB provides the average capacity factor for 10 resource categories in the United States, binned by mean GHI. Average capacity factors are calculated using county-level capacity factor averages from the Renewable Energy Potential (reV) model for 1998–2021 (inclusive) of 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 following map shows average annual GHI in the United States.
The following table summarizes the estimated 2021 capacity factor (in the first year of operation) for each resource category and each resource category's associated U.S. land area.
| Resource Class | GHI Bin (kWh/m2/day) | Mean Alternating Current (AC) Capacity Factor | Area (km2) |
| 1 | >5.75 | 34.0% | 285,955 |
| 2 | 5.5–5.75 | 32.9% | 405,147 |
| 3 | 5.25–5.5 | 31.3% | 351,716 |
| 4 | 5–5.25 | 29.8% | 428,289 |
| 5 | 4.75–5 | 28.3% | 469,396 |
| 6 | 4.5–4.75 | 26.9% | 687,604 |
| 7 | 4.25–4.5 | 25.5% | 646,344 |
| 8 | 4–4.25 | 24.5% | 697,356 |
| 9 | 3.75–4 | 23.3% | 537,441 |
| 10 | <3.75 | 21.4% | 97,193 |
Scenario Descriptions
| Scenario | Module Efficiency1 | Inverter Power Electronics | Installation Efficiencies | Energy Yield Gain1 |
|---|---|---|---|---|
| Conservative Scenario | Technology Description: A U.S. minimum sustainable price (MSP) is assumed, which excludes import tariffs. 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 $0.31/WDC. Justification: This scenario represents the low end of long-term module efficiency-improvement expectations applied to a U.S. MSP. | N/A | N/A | N/A |
| Moderate Scenario | Technology Description: The module price is halfway between the price in the Conservative Scenario and the price in the Advanced Scenario, or $0.25/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. | N/A | Technology Description: This scenario includes 30% labor and hardware balance of system (BOS) cost improvements through automation, preassembly efficiencies (e.g., module mounting and wiring), and 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 module mounting 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 lower system losses, improvements in bifaciality (bifaciality factor increasing from 0.65 to 0.85), and a degradation rate reduction from 0.7%/yr to 0.5%/yr. Justification: Significant R&D is currently spent on better tracking, improving cell temperatures, and lowering degradation rates. Companies will likely continue to focus on improving uptime to maximize profitability. The ITRPV projects higher bifaciality factors for n-type module technologies (compared with the established p-type PERC [passivated emitter and rear cell] module technology) as well as increased deployment of n-type modules over the next decade. Industry participants have already demonstrated bifacial energy gains of 5%–33%, depending on the module mounting and other factors such as albedo. 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. 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 $0.19/watts direct current (WDC). Justification: This scenario represents a relatively high long-term module efficiency-improvement expectation applied to a global spot price.
| Technology Description: This scenario assumes inverter design simplification and manufacturing automation result in an inverter price of $0.03/WDC. 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 efficiencies (e.g., module mounting and wiring); the use of carbon fiber—which is assumed to have achieved low cost, replacing steel and aluminum—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 PV module 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 additional reductions in system losses, improvements in bifaciality (bifaciality factor increasing from 0.65 to 0.85), and improved siting to increase average albedo from 0.2 to 0.3 (similar to dirt, gravel, and concrete), without a significant increase in site preparation costs, as well as a reduction in degradation rate from 0.7%/yr to 0.2%/yr. Justification: The types of technology improvements are similar to those for the Moderate Scenario above, with similar justification but more aggressive. |
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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 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 utility-scale PV described above result in CAPEX reductions of 26% (Conservative Scenario), 44% (Moderate Scenario), and 58% (Advanced Scenario) between 2023 and 2035. We assume these CAPEX reductions follow straight lines between 2023 and 2035. The average annual reduction rates are 2.5% (Conservative Scenario), 4.8% (Moderate Scenario), and 7.0% (Advanced Scenario).
Similarly, we assume straight-line CAPEX reductions between 2035 and 2050, as described below. Between these 2 years, the CAPEX reductions are 25% (1.9% per year average) for the Conservative Scenario, 24% (1.8% per year average) for the Moderate Scenario, and 18% (1.3% per year average) for the Advanced Scenario.
Although we did not create our CAPEX projections based on rates of deployment, utility-scale 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 (which uses CAPEX assumptions from the 2023 ATB), U.S. utility-scale PV deployment grows by 192% between 2024 and 2035 (from 148 gigawatts [GW] to 432 GW) and by 113% between 2035 and 2050 (from 432 GW to 922 GW) (Gagnon et al., 2024).
Representative Technology
Utility-scale PV systems in the 2024 ATB represent 100-MWDC (74.6-MWAC) one-axis tracking systems with performance and pricing characteristics in line with bifacial modules and a DC-to-AC ratio, or inverter loading ratio (ILR), of 1.34 for the Base Year and future years (Ramasamy et al., 2023). We recognize that ILR is likely to change, particularly with the adoption of bifacial modules, and to greatly depend on location. However, allowing for this change would require the optimization of ILR and CAPEX by resource bin and year, causing a range of prices, independent of other regional factors. We believe this would create less transparency and more confusion regarding the impact of technology changes on these individual LCOE categories.
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 cost differ; the PV analysis assumes the use of nonunion labor only.
PV projections in the 2024 ATB are driven primarily by CAPEX cost improvements but also by improvements in energy yield, operating cost, and cost of capital (for the Market + Policies Financial Assumptions Case).
Though CAPEX is one driver 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 projections are developed for scenario modeling as bounding levels (see the Scenario Descriptions section of this page).
Capital Expenditures (CAPEX)
Definitions: The rated capacity used to calculate CAPEX for PV systems is reported in terms of the aggregated capacity of either all its modules or all its inverters. PV modules are rated using standard test conditions and produce DC energy; inverters convert DC energy/power to AC energy/power. Therefore, the capacity of a PV system is rated either in units of MWDC via the aggregation of all modules' rated capacities or in units of MWAC via the aggregation of all inverters' rated capacities. The ratio of these two capacities is referred to as the ILR. The 2024 ATB assumes the base year estimates and future projections use an ILR of 1.34.
The PV industry typically refers to PV CAPEX in units of $/kWDC based on the aggregated module capacity. The electric utility industry typically refers to PV CAPEX in units of $/kWAC based on the aggregated inverter capacity; starting with the 2020 ATB, we use $/kWAC for utility-scale PV.
Plant costs are represented with a single estimate per innovation scenario because CAPEX does not correlate well with solar resources.
For the 2024 ATB—and based on the NREL PV cost model (Ramasamy et al., 2023)—the utility-scale PV plant envelope is defined to include items noted in the Components of CAPEX table below.
Base Year: An overnight capital cost (plus grid connection cost) of $1.43/WAC in 2022 is based on modeled pricing for a 100-MWDC, one-axis tracking system quoted in Q1 2022 as reported by (Ramasamy et al., 2022), adjusted from $/WDC to $/WAC by an ILR of 1.34. The $1.56/WAC overnight capital cost (plus grid connection cost) in 2023 is based on modeled pricing for a 100-MWDC, one-axis tracking system quoted in Q1 2023 as reported by (Ramasamy et al., 2023), adjusted by an ILR of 1.34.
We focus on these system sizes to align with recent trends in utility-scale installations. (EIA, 2023a) reported that 140 PV installations (greater than 5 MWAC in capacity) totaling 10.3 GWAC were placed in service in 2022 in the United States. This represents an average of approximately 73 MWAC; 86% of the installed capacity in 2022 came from systems greater than 50 MWAC, and 52% came from systems greater than 100 MWAC.
In the chart below, reported historical utility-scale PV plant CAPEX (Bolinger et al., 2023) is shown in box-and-whiskers format for comparison to the historical benchmarked and future CAPEX projections for utility-scale PV plants. (Bolinger et al., 2023) provide statistical representation of CAPEX for projects larger than 5 MWAC.
Historical Sources: (Bolinger et al., 2023) (Ramasamy et al., 2022) (Ramasamy et al., 2023)
Future Projections: 2024 ATB
All prices quoted in WDC are converted to WAC (1 WDC = ILR × WAC).
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. (Ramasamy et al., 2022), including the following:
- Timing-Related Issues: For example, the time between power purchase agreement contract completion and project placement in service can vary, and a system can be reported as being 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 because very large systems with multiyear construction schedules were being installed that year. Developers of these large systems negotiated contracts and installed portions of their systems when module and other costs were higher.
- 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 O&M expenses).
When federal investment tax credits are taken, they provide an incentive to include costs in the up-front CAPEX to receive a higher tax credit; these included costs might 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 costs and calculating construction financing to derive CAPEX.
Use the following table to view the components of CAPEX.
Future Years
Projections of utility-scale PV plant CAPEX for 2035 are based on bottom-up cost modeling, with 2023 values from (Ramasamy et al., 2023) and a straight-line change in price in the intermediate years between 2023 and 2035. ILR is assumed to remain at a constant 1.34. 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 CAPEX in 2050 is the equivalent of the CAPEX in 2035 but is one degree more aggressive (e.g., in 2035 the Moderate Scenario's CAPEX is $0.90/WAC, and in 2050 it is $0.68/WAC, which is equivalent to the Advanced Scenario's CAPEX in 2035) and CAPEX, with a straight-line change in price in the 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 Advanced Scenario assumes the following:
- Module efficiency of 28% achieved by 2050
- Further inverter simplification and manufacturing automation
- 50% labor and hardware BOS cost improvements through automation and preassembly of module mounting and wiring efficiencies
- Carbon fiber replaces 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 | * Utility-scale CAPEX: $0.68/WAC | † Utility-scale CAPEX: $0.90/WAC | Utility-scale CAPEX: $1.19/WAC |
| 2050 | $0.56/WAC | * $0.68/WAC | † $0.90/WAC |
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 and ILR. The median of the private projections is displayed in the figure below through 2030, along with the EIA projection through 2050. The 2024 ATB Moderate Scenario CAPEX projection generally aligns with the analyst projections shown.
Sources: 2024 ATB; (BNEF, 2023b); (Wood Mackenzie, 2023); (EIA, 2023b)
All prices quoted in WDC are converted to WAC (1 WAC = ILR × WDC).
Operation and Maintenance (O&M) Costs
Definition: O&M costs represent the annual fixed expenditures required to operate and maintain a PV plant over its lifetime, including items noted in the table below.
Base Year: The O&M cost of $24/kWAC-yr in 2022 is based on modeled pricing for a 100-MWDC, one-axis tracking system quoted in Q1 2022 as reported by Ramasamy et al. (Ramasamy et al., 2022), adjusted from DC to AC. Lawrence Berkeley National Laboratory collected feedback on O&M costs from U.S. solar industry professionals (Wiser et al., 2020). The wide range in reported prices depends in part on the range in maintenance practices for various systems and on cost categories that 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, and 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 ($0/kWAC-yr to $54/kWAC-yr at an ILR of 1.34).
Future Years: The fixed O&M (FOM) cost of $22/kWAC-yr for 2023 is based on pricing reported by (Ramasamy et al., 2023), which can be divided into system-related expenses ($14.4/kWAC-yr), property-related expenses ($5.4/kWAC-yr), and administration-related expenses ($2.4/kWAC-yr). From 2024 to 2050, system-related FOM is based on the ratio of system-related O&M costs ($/kW-yr) to CAPEX costs ($/kW) of 0.9:100 in 2023, as reported by (Ramasamy et al., 2023). This ratio is higher than the ratio of O&M costs to historically reported CAPEX costs of 0.8:100, which is derived from 2011–2022 historical data reported by (Bolinger et al., 2023), but lower than the ratio of O&M costs to CAPEX costs of 1.0:100, which is derived from (IEA, 2018) and Lazard (Lazard, 2021). Historically reported data suggest O&M and CAPEX cost reductions are correlated; from 2011 to 2021, fleetwide average O&M and CAPEX costs fell 59% and 75%, respectively, as reported by (Bolinger et al., 2023). From 2024 to 2050, property-related expenses are reduced by the inverse ratio of the increase in module efficiency because less space will be required on a per watt basis. Administrative expenses are kept constant.
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 units of 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 some previous editions of the ATB, PV pricing is represented in $/kWDC. In the 2024 ATB, utility-scale PV (though not commercial PV or residential PV) is represented in $/kWAC; for this reason, values in the 2024 ATB are not directly comparable to values in the 2019 ATB or earlier editions 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, albedo, axis type (i.e., none, one, or two), shading, expected downtime, ILR, and inverter losses to transform from DC power to AC power. The ILR (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, improved 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%–18.8%, depending on the scenario.
From 2007 to 2021, the cumulative median AC capacity factor for utility-scale U.S. projects installed at the time (including fixed-tilt systems) was 24%, but individual project-level capacity factors exhibited a wide range (9%–35%) (Bolinger et al., 2023). The reported U.S. system capacity factors encompass the range of estimated capacity factors in the 2024 ATB (first year capacity factors of 21%–34% in 2021, see Resource Categorization section above). The figure below shows historical data for capacity factor as a function of ILR, solar resource, and mounting type.
Source: (Bolinger et al., 2023)
Over time, PV plant output is reduced. This degradation is accounted for in ATB estimates of capacity factor (see table below). The 2024 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 are therefore not representative of the lower capacity factors reported by fixed-tilt systems.
Base Year: In the interactive data chart at the top of this page, select Technology Detail = All to add filters 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 illustrates the effect of locating a utility-scale 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. Average capacity factors are calculated using county-level capacity factor averages from the reV model for 1998–2021 (inclusive) of the NSRDB.
The NSRDB provides modeled spatiotemporal solar irradiance resource data at 4-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 the 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.
Because of the methodology for calculating capacity factors in the 2024 ATB, they are not directly comparable to those in some previous editions of the ATB. In the 2024 ATB, we use capacity factors ranging from 21.4% for Class 10 (for locations with an average annual GHI less than 3.75) to 34.0% for Class 1 (for locations with an average annual GHI greater than 5.75) in 2021. The 2024 ATB capacity factor assumptions are based on ILR = 1.34.
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 (better tracking, improved cell temperature, bifaciality, and better siting/practices to improve albedo), reduced system losses (improved soil removal, improved O&M uptime, and more-efficient inverters), and a reduction in degradation rates. These projections do not account for factors such as the potential need to increase PV curtailment or use less-favorable sites as PV deployment increases in the future. The table below summarizes the technology improvements we use to calculate indicative improvements in capacity factor in each scenario.
| Performance Area | 2021 | 2035, Conservative Scenario | 2035, Moderate Scenario | 2035, Advanced Scenario |
|---|---|---|---|---|
| Bifaciality factor | 0.65 | 0.65 | 0.85 | 0.85 |
| Albedo | 0.2 | 0.2 | 0.2 | 0.3 |
| DC losses | 14.1% | 14.1% | 10.4% | 7.5% |
| Inverter efficiency | 96% | 96% | 98% | 98% |
| Annual degradation rate | 0.7% | 0.7% | 0.5% | 0.2% |
The technology improvements summarized above would not necessarily result in the estimated capacity factor improvements, given the 2024 ATB assumption of a constant ILR of 1.34. PV system ILR choice is based on an optimization exercise to maximize profits (or offer the lowest energy price), trading off the extra costs 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 for those that use bifacial modules will be lower than the optimal ILR of systems in lower resource classes or for 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 of 1.34, 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 three 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.
| Scenario | Average Capacity Factor in 2035 (Class 10–Class 1) | Percentage Improvement From Base Year (2022) |
|---|---|---|
| Advanced Scenario | 23.0%–35.7% | 12.8%–15.4% |
| Moderate Scenario | 21.6%–33.8% | 7.3%–9.0% |
| Conservative Scenario | 19.7%–31.3% | 0.0% |
We also develop and model a scenario one degree more aggressive than the Advanced Scenario to estimate capacity factors in 2050. In the Advanced Scenario, the capacity factors in 2050 are assumed to have an 18.8% improvement over 2022 capacity factors.
References
The following references are specific to this page; for all references in this ATB, see References.