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DOE OSTI2026

Marginal Soils Index Analysis & Geospatial Data

This data package contains output files associated with Mongird et al. (in prep) organized into four dataset directories. Each dataset is described in more detail below. 1. Marginal Soils Index Analysis Description: This folder contains a csv file with land needs and availability by state, power generating technology type, and scenario in 2050 when suitable siting areas are additionally constrained to areas with increasing levels of soil marginality. Files: msi_constrained_siting_availability_2050.csv Variables: Scenario - Projected 2050 scenario name State - US state abbreviation Technology - Generating technology type solar = solar photovoltaic gas_cc_re = natural gas combined cycle (recirculating cooling) wind = onshore wind gas_cc_ccs_re = natural gas combined cycle with carbon capture sequestration (recirculating cooling) gas_cc_dry = natural gas combined cycle with (dry cooling) gas_cc_pond = natural gas combined cycle with (pond cooling) coal_conv_ccs_re = conventional coal with carbon capture sequestration (recirculating cooling) Req_Capacity_MW - The amount of rated capacity required in 2050 of the given technology type in the given state and under the given scenario from the capacity expansion plan Req_Capacity_Factor - The assumed capacity factor (fraction between 0 and 1) for the given technology type in the given state and under the given scenario by the capacity expansion plan Req_Land_km2 - The amount of land required (in km-squared) to host the required generating capacity that is capable of meeting the specified capacity factor for the given technology type in the given state and under the given scenario Req_Energy_TWh - Product of Req_Capacity_MW, Req_Capacity_Factor, and 8760/1e6 for the given technology type in the given state and under the given scenario MSI_Case - The level of MSI that siting the given technology is additionally constrained to, where >0 means siting is additionally constrained to suitable land areas that have an MSI value greater than 0 >=1 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 1 >=2 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 2 >=3 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 3 Soil Attribute Rasters Description: This folder contains geospatial raster files for individual soil parameters upscaled to the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of non-missing 30m resolution values. All raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. Files: avg_cond_raster_ .tif - Average conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm max_cond_raster_ .tif - Maximum conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm min_ph_raster_ .tif- Min pH values across all soil horizons within a depth of 40 inches. avg_ph_raster_ .tif - Average pH value across all soil horizons within a depth of 40 inches. max_ph_raster_ .tif- Max pH value across all soil horizons within a depth of 40 inches. erosion_factor_raster_ .tif - Product of k-factor and percent slope flood_freq_raster_ .tif - Number of months of the year during which the area is commonly, frequently, or very frequently flooded. max_sar_raster_ .tif - Maximum sodium adsorption ratio across all horizons within a depth of 40 inches rock_frac_raster_ .tif - Fraction of the upper 6 inches of soil composed of rock fragments larger than 3 inches. temp_regime_raster_ .tif - Soil temperature regime with the following key: 0 = pergelic 1 = gelic 2 = cryic 3 = frigid 4 = isofrigid 5 = mesic 6 = isomesic 7 = thermic 8 = isothermic 9 = hyperthermic 10 =isohyperthermic Marginal Soils Index Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index at the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of 30m resolution. Both raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. A value of 0 indicates that there were no soil attributes present that indicate marginal soil. NA values indicate that data was unavailable or bodies of water. Files: marginal_soils_index_30m_raster.tif marginal_soils_index_1km_raster.tif Marginal Soils Index Resource Potential Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index + Resource Potential (MSI+RP) score at 1km resolution for geothermal, solar, and wind technologies. Raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. NA values indicate that the location is not suitable for siting the given technology due to policy, environmental, socioeconomic, topological, and other constraints regardless of soil marginality level. Areas with values greater than or equal to zero represent the product of the normalized MSI value and the normalized resource potential value. Files: geothermal_msi_ep_score_raster.tif solar_msi_ep_score_raster.tif wind_msi_ep_score_raster.tif Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Agriculture↗

DOE OSTI2026

Marginal Soils Index Analysis & Geospatial Data

This data package contains output files associated with the Mongird et al. paper entitled "Can US power grid expansion avoid prime agricultural lands?" and is organized into four dataset directories. Each dataset is described in more detail below. 1. Marginal Soils Index Analysis Description: This folder contains a csv file with land needs and availability by state, power generating technology type, and scenario in 2050 when suitable siting areas are additionally constrained to areas with increasing levels of soil marginality. Files: msi_constrained_siting_availability_2050.csv Variables: Scenario - Projected 2050 scenario name State - US state abbreviation Technology - Generating technology type solar = solar photovoltaic gas_cc_re = natural gas combined cycle (recirculating cooling) wind = onshore wind gas_cc_ccs_re = natural gas combined cycle with carbon capture sequestration (recirculating cooling) gas_cc_dry = natural gas combined cycle with (dry cooling) gas_cc_pond = natural gas combined cycle with (pond cooling) coal_conv_ccs_re = conventional coal with carbon capture sequestration (recirculating cooling) Req_Capacity_MW - The amount of rated capacity required in 2050 of the given technology type in the given state and under the given scenario from the capacity expansion plan Req_Capacity_Factor - The assumed capacity factor (fraction between 0 and 1) for the given technology type in the given state and under the given scenario by the capacity expansion plan Req_Land_km2 - The amount of land required (in km-squared) to host the required generating capacity that is capable of meeting the specified capacity factor for the given technology type in the given state and under the given scenario Req_Energy_TWh - Product of Req_Capacity_MW, Req_Capacity_Factor, and 8760/1e6 for the given technology type in the given state and under the given scenario MSI_Case - The level of MSI that siting the given technology is additionally constrained to, where >0 means siting is additionally constrained to suitable land areas that have an MSI value greater than 0 >=1 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 1 >=2 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 2 >=3 means siting is additionally constrained to suitable land areas that have an MSI value greater than or equal to 3 Soil Attribute Rasters Description: This folder contains geospatial raster files for individual soil parameters upscaled to the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of non-missing 30m resolution values. All raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. Files: avg_cond_raster_ .tif - Average conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm max_cond_raster_ .tif - Maximum conductivity of the saturation extract across all soil horizons within a depth of 40 inches, measured in mmhos/cm min_ph_raster_ .tif- Min pH values across all soil horizons within a depth of 40 inches. avg_ph_raster_ .tif - Average pH value across all soil horizons within a depth of 40 inches. max_ph_raster_ .tif- Max pH value across all soil horizons within a depth of 40 inches. erosion_factor_raster_ .tif - Product of k-factor and percent slope flood_freq_raster_ .tif - Number of months of the year during which the area is commonly, frequently, or very frequently flooded. max_sar_raster_ .tif - Maximum sodium adsorption ratio across all horizons within a depth of 40 inches rock_frac_raster_ .tif - Fraction of the upper 6 inches of soil composed of rock fragments larger than 3 inches. temp_regime_raster_ .tif - Soil temperature regime with the following key: 0 = pergelic 1 = gelic 2 = cryic 3 = frigid 4 = isofrigid 5 = mesic 6 = isomesic 7 = thermic 8 = isothermic 9 = hyperthermic 10 =isohyperthermic Marginal Soils Index Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index at the listed grid resolution (30m or 1 km). 1 km resolution files are a spatial average of 30m resolution. Both raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. A value of 0 indicates that there were no soil attributes present that indicate marginal soil. NA values indicate that data was unavailable or bodies of water. Files: marginal_soils_index_30m_raster.tif marginal_soils_index_1km_raster.tif Marginal Soils Index Resource Potential Rasters Description: This folder contains geospatial raster files of the Marginal Soils Index + Resource Potential (MSIxRP) score at 1km resolution for geothermal, solar, and wind technologies. Raster files use the USA Contiguous Albers Equal Area Conic (ESRI:102003) projection. NA values indicate that the location is not suitable for siting the given technology due to policy, environmental, socioeconomic, topological, and other constraints regardless of soil marginality level. Areas with values greater than or equal to zero represent the product of the normalized MSI value and the normalized resource potential value. Files: geothermal_msi_rp_score_raster.tif solar_msi_rp_score_raster.tif wind_msi_rp_score_raster.tif Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Agriculture↗

DOE OSTI2025

CERF: IM3 Projected Western US Power Plant Locations

Overview The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial python package that provides new power plant locations at a 1km resolution. The model ingests U.S. state or regional-scale electricity system capacity expansion plans, such as those produced by the Global Change Analysis Model (GCAM-USA), and identifies feasible, site-specific locations for individual new power plants (renewable and non-renewable). CERF combines high-resolution geospatial suitability analyses with an economic algorithm that selects individual plant siting locations based on grid interconnection costs and the locational marginal value of new generation. The model incorporates a wide range of dynamic constraints and opportunities, such as protected lands, population density, existing infrastructure, and water availability. This dataset provides CERF power plant siting results for IM3 Phase 2 simulations across eight different scenarios for the Western US through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 CERF siting results in this dataset correspond to capacity expansion plans in the GCAM-USA IM3 Phase 2 simulation data and are available for each of the above scenarios. Data Details Temporal Range: 2015-2055 in 5-year timesteps. Note that 2015 is the experiment base year and 2020 and beyond represent model simulation years. Spatial Range: Plant locations are provided for the eleven states in the Western US including Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Spatial Resolution: 1 km-squared, provided in x and y coordinates Geospatial Projection: Albers Equal Area Conic (ESRI:102003) File Type: csv The dataset contains subdirectories for each of the eight scenarios described in the overview. Each scenario folder contains two subfolders with the following information: 1. Power Plant Data This directory contains a single .csv file of power plant locations for both pre-existing (non-CERF sited plants in operation in 2015) and new (CERF-sited) power plants across the temporal range along with additional CERF model output parameters for CERF-sited plants. Plant with a siting year earlier than 2020 correspond to facilities that are operational leading into the first timestep CERF simulation. For a more detailed description of CERF model output parameters, see the CERF model documentation. Note that the cerf_plant_id parameter is unique within each scenario file but not across scenario files. Parameter Descriptions scenario - Name of scenario cerf_plant_id - Unique siting identifier cerf_sited - If True, indicates that plant was sited by CERF model. If False, indicates pre-existing facility region_name - Name of region (state) tech_id - Technology ID tech_name - Full generation technology name inclusive of cooling type (if applicable) and additional characteristics tech_simple - Simplified generation technology type unit_size_mw - Power plant unit size (MW) xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) index - Index position in the flattend 2D array buffer_in_km - Exclusion buffer around site (km) sited_year - Year of siting retirement_year - Year of retirement lmp_zone - Locational marginal price (LMP) zone ID locational_marginal_price_usd_per_mwh - Locational marginal price ($/MWh) generation_mwh_per_year - Generation output (MWh/yr) operating_cost_usd_per_year - Cost of plant operations ($/yr) net_operational_value - Net operational value based on LMP and and operating costs ($/yr) interconnection_cost - Cost of interconnection for transmission & gas pipeline (if applicable) net_locational_cost -- Difference of interconnection cost and operating value ($/yr) capacity_factor_fraction - Capacity factor (fraction) carbon_capture_rate_fraction - Carbon capture rate (fraction) fuel_co2_content_tons_per_btu - Fuel CO2 content (tons/Btu) fuel_price_usd_per_mmbtu - Fuel price ($/MMBtu) fuel_price_esc_rate_fraction - Fuel price escalation rate (fraction) heat_rate_btu_per_kWh - Heat rate (Btu/kWh) lifetime_yrs - Technology lifetime for annuity (years) operational_life_yrs - Operational lifetime for retirement (years) variable_om_usd_per_mwh - Variable operation and maintenance costs of yearly capacity use ($/MWh) variable_om_esc_rate_fraction - Variable operation and maintenance costs escalation rate (fraction) carbon_tax_usd_per_ton - Carbon tax ($/ton) carbon_tax_esc_rate_fraction - Carbon tax escalation rate (fraction) 2. Storage Data This directory contains information on new and pre-existing energy storage facilities operational in each timestep along with various storage operational parameters. The 2015 timestep provides pre-existing energy storage data and corresponds with facilities that are operational leading into the first model simulation timestep. Note that coordinates in the storage files correspond to the interconnection point on the grid (substation location), not individual energy storage locations. Energy storage is added in a cumulative process at each given interconnection point. That is, each individual file provides the total operational storage capacity interconnected to the specified substation for the given timestep, inclusive of previously installed storage at that location and new storage installed in that timestep at that location. Parameters scenario - Name of scenario timestep - Simulation timestep name - Unique storage identifier s_typ - Type of energy storage technology (battery or pumped storage hydro) s_node - Node ID of interconnecting substation xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) charge_rate - Maximum charge rate (power capacity) of storage system (MW) discharge_rate - Maximum discharge rate (power capacity) of storage system (MW) duration - Duration of storage system (hours) max_SoC - Allowed maximum state of charge (energy capacity) of storage system (MWh) min_SoC -Allowed minimum state of charge (energy capacity) of storage system (MWh) charge_eff - Efficiency of charge (fraction between 0 and 1) discharge_eff - Efficiency of discharge (fraction between 0 and 1) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

CERF↗

DOE OSTI2024

Performance Testing of an Integrated Magnetic Power Take-Off

A wave energy converter (WEC) and the power take-off (PTO) generator system can be represented by using a mass-spring-dampener model. By incorporating a negative stiffness spring within the PTO, the overall stiffness of the PTO can be lowered allowing the impedance of the PTO to be more closely matched with the WEC. By designing for impedance matching the WEC can greatly enhance its power generation capability. This project has involved the design, fabrication and testing of a new type of linear-stroke length and rotary stroke length adjustable negative stiffness magnetic springs for use within a wave energy converter (WEC). The magnetic springs were studied by using 3-D finite element analysis with the objective of creating high energy density and a long linear stroke length. After the construction and testing of both a proof-of-principle adjustable linear and rotary magnetic spring prototype the rotary (torsional) magnetic spring was selected for scaling up analysis. The selected scaled-up proof-of-principle magnetic spring had a peak torque of 850 Nm with a ±45 degree stroke length. By translating the inner rotor relative to the outer rotor, the stiffness could be adjusted to be either negative or positive stiffness. During this project a magnetic lead screw was also studied, and it was shown that by combining the magnetic lead screw with a linearly translating magnetic spring a very long rotary stroke length could be attained. However, as the magnetic lead screw increased complexity and reduced overall energy density relative to a rotary (torsional) spring, this design approach was not further pursed. Dynamometer testing of the variable stiffness magnetic springs was first completed by Portland State University and following this the magnetic spring performance was independently verified by Sandia National Laboratory (Sandia). A WEC analysis when using a variable stiffness magnetic spring was completed by using the WecOptTool. WecOptTool is an open-source WEC optimization software developed by Sandia that supports efficient power take-off (PTO) and control optimization. Wave condition data from the Oregon PacWave test-site was used in this study. The analysis showed that a WEC with a tunable stiffness value could consistently achieve about 80% of its maximum theoretical power production. The use of a tunable stiffness WEC, rather than zero-stiffness or a constant stiffness WEC was also shown to lead to a smaller maximum PTO force requirement. The variable stiffness magnetic spring was integrated into an experimental WEC developed by Sandia, called a Wave-Bot. Sandia successfully completed water-tank testing of the Wave-Bot at the Navy’s Carderock, Maryland, wave-basin test site. The wave-basin testing helped to experimentally demonstrate the operating capabilities and increased power generation capability of a WEC containing an integrated variable stiffness magnetic spring. A WEC capacity factor analysis was also completed. The capacity factor was defined as the ratio of annual average WEC generator power to the maximum (RMS) generated power. This capacity factor provided a means of identifying the ratio of potential revenue to cost. It was calculated that if a tunable variable stiffness magnetic spring has RMS constraints it could operate with a capacity factor above 30%. This is comparable to a wind turbine’s capacity factor.

16 TIDAL AND WAVE POWER↗

DOE OSTI2017

Development of a project on North Unit Irrigation District’s Main Canal at the Monroe Drop, using a novel low-head hydropower technology called the SLH100

Natel Energy is a low-head, distributed hydropower company based out of Alameda, CA. Natel manufactures and sells proprietary hydroelectric turbines called hydroEngines® that are suitable for low-head, high-flow settings, and range from 30kW to 1 MW of capacity per unit. Natel’s hydroEngine is a state-ofthe-art two stage impulse turbine, using blades mounted symmetrically on two belts perpendicular to the axis of travel, and using linearly-moving foils, rather than a rotor, to enable efficient conversion of kinetic energy of large volumes of water at low head with no risk of cavitation. In addition, the hydroEngine can be installed at or above tailwater level, reducing the excavation necessary to build the powerhouse and thus reducing total installed cost and project footprint. Thus, the hydroEngine technology enables a new generation of small hydro installations with low cost of project development, fish-friendly operations, and small project footprint. In September of 2015, Natel Energy formally commissioned its first project installation in Madras, Oregon, installing 1 SLH100 turbine at an existing drop structure on the North Unit Irrigation District (NUID) Main Canal. The water falls between 13.5 feet to 16.5 feet at this structure, depending on flow. The plant has an installed capacity of 250 kW and an expected annual generation of approximately 873 MWh. The plant operates at an annual capacity factor of 40%, and a capacity factor over the irrigation season, or period of available flow, of 80%. Annual capacity factor is calculated as a percentage of plant operating hours relative to a total of 8,760 hours in a year; because the irrigation canal in which the Project is located only runs water from April to October, the available flow capacity factor is higher. Net greenhouse gas reductions from the Monroe Project are estimated to be 602 tCO2/year. The purpose of this report is to provide an overview of the specifications for Natel’s first commissioned project, the project development process, the plant’s performance, project costs, and the construction, installation, and commissioning process. We hope that this report will provide useful context for assessment of the hydroEngine as a viable technology choice for future distributed, low-head hydropower projects, as well as assessment of the bankability, performance, reliability, and cost of the hydroEngine.

13 HYDRO ENERGY↗

DOE OSTI2022

What can surface wind observations tell us about interannual variation in wind energy output?

The past decade of wind power growth was supported by capacity factor improvements and associated cost reductions. But are higher capacity factors a technology success story or, as suggested by recent research, has the influence of technology been overstated by ignoring positive surface wind speed trends? The answer could influence estimates of wind energy's cost and even future deployment rates. We find that US surface wind speed observations imply a 2.6% improvement in capacity factors from 2010 to 2019. Yet newer vintages of wind plants have recorded capacity factors that are ~25% larger than plants built close to 2010. It follows that technological factors and improved site quality, not higher wind speeds, drove most of the improvement in capacity factors. Additionally, we match hundreds of meteorological stations to nearby (< 25 km) wind plants and compare annual estimated generation, based on a function of surface wind speed observations, to annual recorded generation. Researchers rely on this publicly available surface data because measurements co-located with wind plants are generally considered proprietary. Our analysis addresses a research gap: interannual variation in observed surface wind speeds is rarely compared to observed data at wind plant locations and turbine heights. We find that despite its common use for this purpose, generation estimates based on publicly available surface observational data provide a poor proxy for interannual variability in recorded wind generation. These findings suggest that caution is generally needed when researchers use surface wind speed measurements to investigate long-term wind energy trends.

17 WIND ENERGY↗

DOE OSTI2017

Utility-Scale Solar 2016: An Empirical Analysis of Project Cost, Performance, and Pricing Trends in the United States

The utility-scale solar sector has led the overall U.S. solar market in terms of installed capacity since 2012. In 2016, the utility-scale sector installed more than 2.5 times as much new capacity as did the residential and commercial sectors combined, and is expected to maintain its dominant position for at least another five years. This report—the fifth edition in an ongoing annual series—provides data-driven analysis of the utility-scale solar project fleet in the United States. We analyze not just installed project prices, but also operating costs, capacity factors, and power purchase agreement ("PPA") prices from a large sample of utility-scale PV and CSP projects throughout the United States. Highlights from this year's edition include the following: Installation Trends: The use of solar tracking devices dominated 2016 installations, at nearly 80% of all new capacity. In a reflection of the ongoing geographic expansion of the market beyond California and the Southwest, the median long-term average insolation level at newly built project sites declined again in 2016. While new fixed-tilt projects are now seen predominantly in less-sunny regions, tracking projects are increasingly pushing into these same regions. The median inverter loading ratio has stabilized in 2016 at 1.3 for both tracking and fixed-tilt projects. Installed Prices: Median installed PV project prices within a sizable sample have fallen by two-thirds since the 2007-2009 period, to $2.2/WAC (or $1.7/WDC) for projects completed in 2016. The lowest 20th percentile of projects within our 2016 sample were priced at or below $2.0/WAC, with the lowest-priced projects around $1.5/WAC. Overall price dispersion across the entire sample and across geographic regions decreased significantly in 2016. Operation and Maintenance (“O&M”) Costs: What limited empirical O&M cost data are publicly available suggest that PV O&M costs were in the neighborhood of $18/kWAC-year, or $8/MWh, in 2016. These numbers include only those costs incurred to directly operate and maintain the generating plant. Capacity Factors: The cumulative net AC capacity factors of individual PV projects range widely, from 15.4% to 35.5%, with a sample median of 26.3%. This project-level variation is based on a number of factors, including the strength of the solar resource at the project site, whether the array is mounted at a fixed-tilt or on a tracking mechanism, the inverter loading ratio, degradation, and curtailment. Changes in at least the first three of these factors drove mean capacity factors higher from 2010- to 2013-vintage projects, where they’ve remained fairly steady among both 2014- and 2015-vintage projects as an ongoing increase in the prevalence of tracking has been offset by a build-out of lower resource sites. Meanwhile, several of the newer CSP projects in the United States are struggling to match long-term performance expectations. PPA Prices: Driven by lower installed project prices and improving capacity factors, levelized PPA prices for utility-scale PV have fallen dramatically over time. Most recent PPAs in our sample are priced at or below $50/MWh levelized, with a few priced as aggressively as ~$30/MWh. Though impressive in pace and scale, these falling PPA prices have been offset to some degree by declining wholesale market value within high penetration markets like California, where in 2016 a MWh of solar generation was worth just 83% of a MWh of flat, round-the-clock generation. At the end of 2016, there were at least 121.4 GW of utility-scale solar power capacity within the interconnection queues across the nation. The growth within these queues is widely distributed across all regions of the country: California and the Southeast each account for 23% of the 83.3 GW of solar that first entered the queues in 2016, followed by the Northeast (17%), the Southwest (16%), the Central region (12%), Texas (6%) and the Northwest (3%). The widening geographic distribution of solar projects is a clear sign that the utility-scale market is maturing and expanding outside of its traditional high-insolation comfort zones.

14 SOLAR ENERGY↗

DOE OSTI2018

Utility-Scale Solar: Empirical Trends in Project Technology, Cost, Performance, and PPA Pricing in the United States – 2018 Edition

The utility-scale solar sector has led the overall U.S. solar market in terms of installed capacity since 2012. In 2017, the utility-scale sector accounted for nearly 60% of all new solar capacity, and is expected to maintain its market-leading position for at least another six years. Two-thirds of all states, representing all regions of the country, are now home to one or more utility-scale solar projects. This report—the sixth in an ongoing annual series—provides in-depth data-driven analysis of the utility-scale solar project fleet in the United States. Drawing on empirical project-level data from a wide range of sources, this report analyzes technology trends, installed project prices, operating costs, capacity factors, power purchase agreement ("PPA") prices, the levelized cost of solar energy (LCOE), and the market value of solar. Given its current preeminence in the market, utility-scale PV also dominates much of this report, though data from CPV and CSP projects are also presented where appropriate. Highlights from this year's edition include: -Installation and Technology Trends: The use of solar trackers dominated 2017 installations with nearly 80% of all new capacity. In a reflection of the ongoing geographic expansion of the market beyond California and the high-insolation Southwest, the median insolation level at newly built project sites declined again in 2017. While new fixed-tilt projects are now seen predominantly in less-sunny regions, tracking projects are increasingly pushing into these same regions. The median inverter loading ratio grew to 1.32 in 2017, allowing the inverters to operate closer to full capacity for a greater percentage of the day. -Installed Prices: Median installed PV project prices have steadily fallen by two-thirds since the 2007-2009 period, to $2.0/WAC (or $1.6/WDC) for projects completed in 2017. The lowest 20th percentile of projects within our 2017 sample were priced at or below $1.8/WAC, with the lowest-priced projects around $0.9/WAC. Overall price dispersion across the entire sample has decreased steadily every year since 2013; similarly, price variation across regions decreased in 2017. -Operation and Maintenance ("O&M") Costs: PV O&M costs were in the neighborhood of $16/kWAC-year, or $8/MWh, in 2017. These numbers include only those costs incurred to directly operate and maintain the generating plant. -Capacity Factors: The cumulative net AC capacity factors of individual projects vary widely, from 14.3% to 35.2%, with a sample median of 26.3%. This project-level variation is based on a number of factors, including the strength of the solar resource at the project site, whether the array is mounted at a fixed tilt or on a tracking mechanism, the inverter loading ratio, degradation, and curtailment. Changes in at least the first three of these factors drove mean capacity factors higher from 2010-vintage to 2013-vintage projects, where they've remained fairly steady among more-recent project vintages as an ongoing increase in the prevalence of tracking has been offset by a build-out of lower resource sites. -PPA Prices and LCOE: Driven by lower installed project prices and improving capacity factors, levelized PPA prices for utility-scale PV have fallen dramatically over time, by $20-$30/MWh per year on average from 2006 through 2012, with a smaller price decline of ~$10/MWh per year evident from 2013 through 2016. Most recent PPAs in our sample—including many outside of California and the Southwest—are priced at or below $40/MWh levelized (in real 2017 dollars), with a few priced as aggressively as ~$20/MWh. The median LCOE among operational PV projects in our sample has followed PPA prices lower, suggesting a relatively competitive market for PPAs. -Solar's Wholesale Energy Market Value: Falling PPA prices have been offset to some degree by a decline in the wholesale energy market value of solar within higher-penetration markets like California, where solar earned just 79% of the average price across all hours within CAISO's real-time wholesale energy market in 2017 (down from 125% back in 2012). In other markets with less solar penetration, however, solar's hourly generation profile still earns more than the average wholesale price (e.g., 127% in ERCOT, 112% in PJM). -Solar+Storage: Adding battery storage is one way to at least partially restore the value of solar, and three recent PV+storage PPAs in Nevada (each using 4-hour batteries sized at 25% of PV nameplate capacity) suggest that the incremental PPA price adder for storage has fallen to ~$5/MWh, down from ~$15/MWh just a year ago for a similarly configured project. As PV plus battery storage becomes more cost-effective, a number of developers are regularly offering it as a viable upgrade to standalone PV. At the end of 2017, there were at least 188.5 GW of utility-scale solar power capacity within the interconnection queues across the nation. The 2017 growth within these queues is widely distributed across all regions of the country, but is most pronounced in the up-and-coming Midwest region, which accounts for 27% of the 99.2 GW of new queue capacity. The widening geographic distribution is as clear of a sign as any that the utility-scale market is maturing and expanding outside of its traditional high-insolation comfort zones.

14 SOLAR ENERGY↗

DOE OSTI2015

Wind resource quality affected by high levels of renewables

For solar photovoltaic (PV) and wind resources, the capacity factor is an important parameter describing the quality of the resource. As the share of variable renewable resources (such as PV and wind) on the electric system is increasing, so does curtailment (and the fraction of time when it cannot be avoided). At high levels of renewable generation, curtailments effectively change the practical measure of resource quality from capacity factor to the incremental capacity factor. The latter accounts only for generation during hours of no curtailment and is directly connected with the marginal capital cost of renewable generators for a given level of renewable generation during the year. The Western U.S. wind generation is analyzed hourly for a system with 75% of annual generation from wind, and it is found that the value for the system of resources with equal capacity factors can vary by a factor of 2, which highlights the importance of using the incremental capacity factor instead. Finally, the effect is expected to be more pronounced in smaller geographic areas (or when transmission limitations imposed) and less pronounced at lower levels of renewable energy in the system with less curtailment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2016

Test-Based Calibration of Safety Factors for Capacity Models

In this paper, a simple procedure to calibrate the safety factor of capacity models is presented. The calibration can be carried out based on any available database of experimental tests, even of limited size. The procedure aims to assess the model capability of predicting the test results and to calibrate the safety factor so that the capacity equation meets the target reliability level required by the code or sought by the calibrator. After predicting each test of the database with the capacity equation under consideration, the test-prediction pairs are checked for the property of linearity, and the relative error for the properties of homoscedasticity and normality. Once these properties are fulfilled—which may require a nonlinear transformation of the test values and/or the predictions—the closed-form equation proposed in this paper is employed to compute a target design value. The model safety factor is finally obtained by comparing such target design value with the design value obtained from the code. The paper also proposes two approximate analytical equations to compute the tolerance factor, used to attain any given fractile, as a function of the (even small) number of tests, with any assigned confidence level. A fundamental outcome of the procedure is that it yields an objective indicator of the model accuracy, measured by the standard deviation of its error, which may be regarded as a parameter useful for selecting the most reliable model among different competing ones. In the long run, the application of the proposed procedure will allow achieving a uniform reliability level throughout all capacity models used in codes and guidelines. A further advantage is that the partial safety factors so derived can be straightforwardly updated when more experiments become available. Finally, as an example, the proposed procedure is herein applied to the ACI 318 shear design capacity equation for concrete members unreinforced in shear.

42 ENGINEERING↗

DOE OSTI2020

Potential impacts of climate change on wind and solar electricity generation in Texas

Wind and solar energy sources are climate and weather dependent, therefore susceptible to a changing climate. in this paper, we quantify the impacts of climate change on wind and solar electricity generation under high concentrations of greenhouse gases in Texas. We employ mid-twenty-first century climate projections and a high-resolution numerical weather prediction model to generate weather variables in the future and produce wind and solar generation time series. We find that mid-twenty-first century projections based on five global climate models agree on the multiyear average increases across Texas in direct normal irradiance, global horizontal irradiance, surface air temperature, and 100-m wind speed of up to 5%, 4%, 10%, and 1%, respectively. These changes lead to multiyear average relative changes across Texas of -0.6 to + 2.5% and of + 1.3 to + 3.5% in solar and wind capacity factors, respectively, with significant regional, seasonal, and diurnal differences. Areas with low solar resource show an increase in solar capacity factors but reductions in wind capacity factors. Areas with high solar resource show reductions in solar capacity factors. The spatial and temporal differences in our results highlight the importance of using high-resolution data sets to study the potential impacts of climate change on wind and solar power.

14 SOLAR ENERGY↗

DOE OSTI2026

GRIDCERF - Geospatial Raster Input Data for Capacity Expansion Regional Feasibility

The Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package is a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers hundreds of individual suitability layers for use with both renewable and non-renewable power plant technology configurations in a harmonized format that can be easily ingested by geospatially-enabled modeling software. It also provides pre-compiled technology-specific suitability layers and allows for user customization to robustly address science objectives when evaluating varying future conditions. GRIDCERF data can be directly used with the CERF (Capacity Expansion Regional Feasibility) model to site power plants at a 1km resolution. GRIDCERF includes composite technology siting suitability raster layers for the following utility scale technology configurations. Note that, in addition to technology sub-types shown below, various cooling types are also included (recirculating, pond, once-through, recirculating-seawater, dry-hybrid, or dry) for various technologies. Biomass Conventional (with or without CCS) IGCC (with or without CCS) Coal Conventional (with or without CCS) IGCC (with or without CCS) Natural Gas Combined-cycle (CC) (with or without CCS) Turbine Geothermal Enhanced Geothermal Systems (EGS) - Class 1 through Class 5 resource potential Nuclear Gen 2 Light Water Reactor (LWR) Gen 3 Small Modular Reactor (SMR) Gen 3 AP1000 Refined Liquids Combined-cycle (CC) (with or without CCS) Turbine Solar Photovoltaic (PV) - for capacity factors in the range of 6-18% Utility-scale Concentrating Solar Power (CSP) - for capacity factors in the range of 24-46% Tower Wind (Onshore) - for capacity factors in the range of 5-50% 80m hub height 100m hub height 120m hub height 140m hub height Wind (Offshore) - for capacity factors in the range of 25-60% 100m hub height 140m hub height 160m hub height

capacity expansion↗

DOE OSTI2024

GRIDCERF - Geospatial Raster Input Data for Capacity Expansion Regional Feasibility

capacity expansion↗

DOE OSTI2021

Quantifying the Solar Energy Resource for Puerto Rico

After Hurricane Maria, multiple U.S. Department of Energy laboratories studied the state of the electric grid in Puerto Rico and analyzed grid resilience and grid integration of renewable energy. As part of the work done at the National Renewable Energy Laboratory, researchers created new solar resource data, conducted a technical potential and supply curve analysis, and studied the interannual variability of the solar resource. A new methodology was developed to downscale solar resource data from the National Solar Radiation Data Base (NSRDB) from a 4-km x 4-km spatial and 30-minute temporal resolution to a 2 km x 2 km and 5-minute resolution. This methodology primarily used simple physical principles to develop high-resolution cloud properties which were then used to compute solar radiation. The high-resolution datasets were validated against ground measurements and the error metrics were found to be similar to the original lower resolution dataset. Using 20 years of downscaled data from the NSRDB multi-year capacity factors for photovoltaics (PV) were developed for both single-axis tracking and fixed latitude-tilt configurations. Use of the multi-year data provides the ability to understand variability in capacity factors due to variability in weather over a long period of time. For Puerto Rico the coastal regions were found to have significant higher capacity factors than inland. Using land-use and terrain information a technical potential analysis was conducted for Puerto Rico. This analysis restricted single PV plant development to a maximum of 100 MW nameplate capacity. The nameplate capacity for each municipality were then determined. Based on our assumptions, 56 of the 78 total municipalities of Puerto Rico contain some level of solar capacity. Most of the interior municipalities did not have any capacity because of the geographic exclusions used in this study. The lowest capacity for a PV plant observed in a municipality was 10 MW. The maximum capacity within a county was 2,000 MW. Further a supply curve analysis was conducted by taking the results of the technical potential and quantifying system and transmission costs. The levelized cost of energy (LCOE) was calculated for each theoretical PV plant site, and the levelized cost of transmission was added to the LCOE to produce a total cost estimate for each site. The results of the supply curve analysis allow for a relative comparison of the cost for integrating new PV capacity into the grid. This analysis indicates that cheaper total LCOE sites tend to be larger in capacity. The total capacity in this study was found to be far beyond the maximum peak load for the island. However, this study does not consider the economic and market potential for development. The cumulative capacity presented in this study assumes that the best locations are developed first and ignores the complex decision paths for new power plant development. Therefore, this analysis can only be treated as illustrative. Finally this study investigates the impact of inter-annual variability of resource using a variety of metrices including probability of exceedance and variation in capacity factor and LCOE. This study demonstrates that the capacity factor or LCOE could vary by over 10% year to year. This clearly indicates the risks involved in using any particular year of data and clearly points to the use of multi-year data to reduce some of the risks related to variability in weather.

14 SOLAR ENERGY↗

DOE OSTI2025

Assessment of Climate Change Impacts on Renewable Energy Resources in Western North America

We examine a 25 km resolution climate model dataset to evaluate how regional climate change impacts solar and wind energy under a high-emission scenario. Our study considers the Western Electricity Coordinating Council (WECC) region, which covers the western United States and southwestern Canada, focusing specifically on locations with existing solar and wind infrastructure. First, we conduct a historical model comparison of solar and wind energy capacity factors to highlight model uncertainties across the study area. Using future climate projections, we then assess the seasonal patterns of solar and wind capacity factors for three timeframes: historical, mid-century, and end of century. Additionally, we estimate the frequency of solar and wind resource droughts during these periods for the entire WECC and its five operational subregions, finding that certain subregions are more susceptible to energy droughts due to limited renewable resources. Finally, we present day-ahead capacity factor forecasts to support energy storage planning and provide estimates of offshore wind energy capacity within the WECC. Our results indicate that offshore wind capacity factors are nearly twice as high as onshore values, with less seasonal variation, which suggests that offshore wind could offer a more consistent renewable energy supply in the future.

climate change↗

DOE OSTI2024

Technical Potential for Hydropower Capacity at Non-powered Dams

In the last decade, retrofits of existing nonpowered dams (NPDs) have made up the largest share of capacity increases for US hydropower. Accurate estimates of potential capacity and generation at NPDs help identify sites that may be worth investing in detailed feasibility analyses and design exploration. This dataset consists of NPDs in the conterminous US with at least 100kW of theoretical potential based on earlier resource assessments. Historical daily streamflow (modeled or from USGS gauge records), hydraulic head (based on historical observations or primary purpose and dam height) are the main inputs for HydroGenerate which determines design flow and turbine efficiencies and then calculates nominal capacity, daily generation, and capacity factor. These outputs are summarized on a monthly basis (i.e., generation (MWh) and capacity factor averaged for each month from January to December) and overall (i.e., nominal capacity, average annual generation (MWh), and average annual capacity factor). A total of 4.1 GW capacity is estimated across all 2,564 NPDs included in the dataset.

13 HYDRO ENERGY↗

DOE OSTI2019

Hybrid Systems to Enable High Penetration PV

A model for PV plus energy storage and fuel cells was developed in the System Advisor Model (SAM). This model was used to evaluate the ramp rate and interconnection capacity factor for a hypothetical utility-scale plant for several configuration options. For PV only, the plant experienced 3345 violations of a 10%/minute ramp up limitation and a interconnection capacity factor of 17.6%. Adding a battery reduced the violations to 154 per year and the magnitude of the average violation to 2%. Adding a fuel cell to the PV system increased the capacity factor to 78% without improving ramp violations. Using a combined PV plus storage plus fuel cell arrangement maintained the improved capacity factor while reducing ramp up violations to 23, illustrating the potential to operate a hybrid system both to enable grid integration requirements and increase utilization of the interconnection.

14 SOLAR ENERGY↗

DOE OSTI2019

Hybrid Systems to Enable High Penetration PV: Preprint

14 SOLAR ENERGY↗