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DOE OSTI2023

Evaluation of thermostat location for multizone commercial building performance

In multi-zone buildings, it is often found that a single shared thermostat controls more than one conditioned zones. Although these shared zones are supposed to have similar thermal needs (e.g., cooling and heating load), in reality, they are not mainly due to different orientations, sizes of windows, occupancy, space types, etc. This can cause unnecessary energy waste or thermal discomfort for the occupants. How to quantify this impact in multizone buildings remains a research gap. Therefore, this study aims to evaluate the impact of different sensor (i.e., thermostat) locations for multizone commercial buildings through a comprehensive modeling study. Here, two different scenarios for the sensor locations were selected to evaluate the impact in terms of energy and thermal comfort. The scenario (1) is that one to five sensors distributed among the five zones, but the sensor readings from selected zones will be used for no-sensor zones, which is no-mean sensor scenario. The scenario (2) is that one to five sensors distributed among the five zones, but the average temperature from the shared zones will be used for each of the shared zones, which is a mean sensor scenario. The uncertainty analysis was performed for different sensor location scenarios.(a)The major findings from an energy perspective, for scenario (1), the differences of cooling energy go as high as 17% more or 12% less, compared with the baseline. For heating energy consumption, the discrepancies go as high as 51% more or 52% less, compared with baseline. For site energy consumption, the discrepancies go as high as 3.2% more or 3.2% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.2% more or as low as 1.0% less, compared with baseline. For scenario (2), the discrepancies of cooling energy go as high as 3% more, or 0.5% less, compared with the baseline. For heating energy consumption, the discrepancies, are go as high as 10.1% more or as low as 3.0% less, compared with baseline. For site energy consumption, the discrepancies go as high as 1.3% more or 0.3% less, compared with baseline. For fan energy consumption, the discrepancies go as high as 3.1% more or as low as 1.0% less, compared with baseline.(b) In terms of the indoor thermal comfort, for the no-mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 1,200 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 740 h, compared with the baseline. For the mean-sensor scenarios, the discrepancies of unmet hours for cooling mode can be as high as 750 h, compared with the baseline. The discrepancies of unmet hours for heating mode can be as high as 50 h, compared with the baseline.

42 ENGINEERING↗

DOE OSTI2021

Evaluation of Electrical Resistivity Tomography to Monitor the Transport of Past Releases Beneath Tank Farms

Underground storage tanks at the Hanford Site, in southeastern Washington State, hold radioactive waste generated from four decades of plutonium production. The 149 single-shell tanks and the 28 double-shell tanks have all exceeded their initial design life of approximately 25 years. At least 67 tanks are assumed to have leaked in the past, resulting in radioactive releases into the vadose zone. Gamma ray logging within dry monitoring wells is currently the primary method for tracking the migration of leaked tank waste through the vadose zone. While this approach provides an accurate assessment of radioactive contamination, that information is only provided near (within ~1m) the borehole, leaving most of the vadose zone unmonitored, particularly the important region directly beneath the tank. This report describes a numerical study that investigates the feasibility and performance of time-lapse 3D electrical resistivity tomography (ERT) for long-term monitoring of a hypothetical tank waste location and migration through the vadose zone. ERT is a method of remotely imaging the bulk electrical conductivity (EC) of the subsurface, which is significantly impacted by the presence of conductive solid and liquid tank waste. The release of liquid tank wastes increases subsurface fluid conductivity and saturation over time, creating a target to use time-lapse ERT for long-term monitoring. Although the presence of metallic infrastructure can cause ERT interference, recent advancements in ERT data processing enable the deleterious effects of buried metallic infrastructure (e.g. pipes, wellbore casings, tanks) to be removed to better determine the liquid tank waste migration over time. Three hypothetical realistic scenarios were simulated in the ERT evaluation. The first two scenarios assume the same leak amount and rate (i.e., between 1/1/1951 and 12/31/1951 at the rate of 347 m 3 per year) but different leaky tanks. Scenario 1 assumes leaks under tank B-102, which is located on the edge of the B-tank farm and surrounded by a few metallic infrastructure including cased pipes/wells/tanks. Scenario 2 assumes leaks under tank B-108, which is located near the center of the B-tank farm and surrounded by larger amount of metallic infrastructure than B-102. Scenario 3 assumes the same metallic infrastructure as B-102, with a more recent contaminant leak that was simulated to have occurred between 1/1/2018 and 12/31/2023 at a rate of 1.89 m 3 per year. The leak time in Scenarios 1 and 2 corresponds to a historical overfill event in 1951 and Scenario 3 corresponds to a recent found tank leak in 2019. In each scenario, a “true” bulk EC model vs. time reflecting contaminant migration was generated. ERT data was simulated from these “true” bulk EC models and a time-lapse ERT inversion produced “imaged” bulk EC vs. time. Three electrode configurations in two, four and eight boreholes surrounding the leak tank were used in the ERT simulations in each scenario. These borehole configurations were considered logistically feasible and cost-effective for monitoring. The hypothetical ERT boreholes are assumed to have non-metallic casing. By comparing the “imaged” bulk EC with the “true” bulk EC, it was demonstrated that the three configurations of wells used (two, four, and eight wells) were able to successfully monitor the migration of tank leaks through the vadose zone, with bulk EC resolution increasing with the number of down borehole ERT arrays for all scenarios. Therefore, the use of eight boreholes to perform ERT monitoring beneath the tanks provided the best spatiotemporal information.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. 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 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on 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. License This data is made available under a CCBY4 License 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

Mongird, Kendall [Pacific Northwest National Labor↗

DOE OSTI2024

Assessment of the polygeneration approach in wastewater treatment plants for enhanced energy efficiency and green hydrogen/ammonia production

Wastewater treatment plants (WWTPs) offer opportunities to optimize resource utilization and enhance energy efficiency. Here, this study provides a comprehensive analysis of using the polygeneration approach in WWTPs to reduce grid energy dependence, optimize energy distribution, and utilize surplus energy for hydrogen (H 2 ) and ammonia (NH 3 ) production. Several models were employed, including photovoltaic (PV) cells, parabolic trough collectors (PTCs), steam methane reforming, and polymer electrolyte membranes, to assess the feasibility of this approach. Three scenarios were evaluated and compared: Scenario 1 (Baseline) represents the current situation, Scenario 2 maximizes the Net Present Value (NPV), and Scenario 3 minimizes NH 3 production costs. Real data from As-Samra WWTP in Jordan was used to accurately assess the feasibility of each scenario. The results show that Scenario 2 offers the highest profitability and efficiency, with a NPV of 87.48 million USD and an annual NH 3 production of 15,417 tons, reducing both grid dependency and biogas fuel consumption. Both Scenarios 2 and 3 demonstrate the ability to meet thermal demands efficiently while generating significant revenue from NH 3 production. Scenario 3, in particular, achieves competitive H 2 and NH 3 production costs. Environmentally, Scenario 2 significantly reduces annual greenhouse gas emissions by 12.66 kilotons of CO 2eq , with near-zero carbon intensity for thermal energy due to solar reliance. In conclusion, the polygeneration approach offers a promising pathway for WWTPs to achieve greater sustainability, economic gains, and reduced environmental impact, providing valuable insights for decision-makers.

42 ENGINEERING↗

DOE OSTI2023

Regional Economic Impacts of the Los Angeles 100% Renewable Energy Transition

To help mitigate greenhouse gas (GHGs) generation from burning fossil fuels, many state and local governments are requiring utilities to dramatically increase the share of electricity generated from renewable sources. The City of Los Angeles has set a target of 100% renewable energy by 2045 and has formulated a plan that considers nine potential alternative scenarios that differ by technology, location, and timing. Each scenario has a unique set of local investments, operating and maintenance (O&M) costs, and concomitant rate structures. In this study we develop and apply a computable general equilibrium (CGE) model built specifically for LA to estimate and compare the economic impacts for each of the scenarios over time relative to a reference case. We find differences in economic impacts across scenarios, depending on the level and timing of investment and O&M expenditures, as well as differences in the relative rate changes across scenarios. Results show that employment and economic output are positively correlated with greater capital and O&M spending, while higher electricity rates can dampen economic activity. Several scenarios generate positive economic impacts relative to the reference case, showing that the transition need not have harmful economic impacts, and all scenarios generate a number of other positive co-benefits, such as reduced damage to health from the reduction of ordinary air pollutants. The net employment impacts from 2026 to 2045 across the scenarios range from a low of 3,600 job-year losses annually to 4,700 job-year gains, both around only 0.1% of the baseline average annual employment in the city over that period. The analysis also indicates that lower-income households are relatively more affected than others by the scenarios. Overall, even in the most negatively impactful case, the economic output and employment effects are quite small when taken in the context of the overall size of the regional economy and the large reduction in GHGs.

economic impact modeling↗

DOE OSTI2024

Simulation of triggering and evolution of ELM by pellet injection in EAST under BOUT++ framework

A BOUT ++ three-field magnetohydrodynamic model is employed to study the triggering and evolution of edge localized mode (ELM) by Li pellets injected along the outer mid-plane in the EAST configuration. The linear simulation shows that compared with a large deposition on the pedestal top (scenario I), a smaller deposition within the steep-gradient pedestal region (scenario II) can stimulate much larger linear growth rates of all-n peeling-ballooning modes (PBMs). The nonlinear simulation shows that there exists a pellet size threshold for ELM triggering for two deposition locations; the threshold for scenario I predicted in the present study matches the EAST observation well. Comparison of the two scenarios reveals that a smaller deposition is sufficient to trigger an ELM in a much shorter time in scenario II, whose ELM size is comparable to that in scenario I. This conclusion confirms previous DIII-D and ASDEX-Upgrade observations, suggesting that the steep-gradient pedestal region is a favorable deposition location for ELM triggering with minimum pellet size. Simulation analyses also find that the positive radial gradient of the hump-like pressure profile in the outer mid-plane induced by the pellet deposition plays a different role in the two scenarios. In scenario I, the force resulting from the gradient hinders the outflow of core plasmas and in return, the perturbation is suppressed from spreading inwards after ELM crashes. In scenario II, with a sizable deposition, the gradient results in another competitive perturbation growth region during the linear phase, thus dispersing the free energy and reducing the efficiency of destabilizing PBMs by pellet injection. The suppressing effect of saturated zonal flow on other modes, the short ELM fast crash phase, and the restricting transport effect of the positive radial pressure gradient work together to constrain the pedestal energy loss, especially when the pellet deposition amount is high.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Grid Strength Assessment for High Levels of Inverter-based Resources in the Puerto Rico Power

A system-wide assessment of the Puerto Rico power system grid strength is studied when considering high levels of inverter-based resource penetration. This study is carried out in PSSE and assesses the impact of inverter-based resource (IBR) contributions in response to three-phase ground faults. A hypothetical 100% inverter-based generation scenario is created to assess the extreme impact on short circuit levels and a realistic scenario is considered to assess the impact to short circuit ratios. The overall conclusion is that short circuit capacity and current will drastically decrease in a high IBR scenario, an effect that is quantified in this paper. The short circuit ratio metric is typically used to assess the impact of IBR but generally overestimates grid strength due to not accounting for the impact from multiple IBRs. Other methods such as equivalent circuit based short circuit ratio and weighted short circuit ratio offer a more comprehensive consideration for multiple IBRs and can better account for their mutual interactions. The work presented in this paper is part of the PR100 study.

inverter based resources, PR100, grid strength↗

DOE OSTI2023

Grid Cost and Total Emissions Reductions Through Mass Deployment of Geothermal Heat Pumps for Building Heating and Cooling Electrification in the United States

This report presents the results of a study on the potential grid impacts of national-scale mass deployment of geothermal heat pumps (GHPs) coupled with weatherization in single-family homes (SFHs) from 2022 to 2050. GHPs are a technology readiness level 10, commercially available technology across the United States. This study is an impact analysis only; installed costs and available land areas for installing GHPs are not accounted for in determining their estimated deployment. The three scenarios studied were (1) continuing to operate the grid as it is today (the Base scenario), (2) a scenario to reach 95% grid emissions reductions by 2035 and 100% clean electricity by 2050 (the Grid Decarbonization scenario), and (3) a scenario in which the Grid Decarbonization scenario is expanded to include the electrification of wide portions of the economy, including building heating (the Electrification Futures Study or EFS scenario). The analysis team modeled each of these three scenarios with and without GHP deployment to a large percentage of US building floor space. In all cases, deployment of approximately 5 million GHPs per year demonstrated system cost savings on the grid, consumer fuel cost savings through eliminated fuel combustion for space heating, and CO 2 emission reductions from avoided on-site fuel combustion—and, in the case of the Base scenario, CO 2 emissions reductions from the electric power sector. GHPs have traditionally been viewed as a building energy technology. The most notable result of this study, however, is the demonstration that GHPs coupled with weatherization in SFHs are primarily a grid cost reduction tool and technology that, when deployed at a national scale, also substantially reduces CO 2 emissions, even in the absence of any other decarbonization policy.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Wind Turbine Sound Setbacks and Supply Curves: Ordinances and Extrapolated Trends, 110 Hub Height, 130 Rotor Diameter

This dataset provides a comprehensive set of wind turbine sound setbacks from every residential structure in the contiguous United States (CONUS). A sound setback is defined as the minimum required distance between a residential structure and a hypothetical turbine installation site to ensure that modeled sound levels received at the residence do not exceed local sound ordinances, which are commonly expressed in A-weighted decibels (dBA). Therefore, sound setbacks are a local spatial assessment combining multiple factors, including the sound pressure curve as a function of the observer location (distance and direction) relative to the turbine, local sound regulations, and the geographical distribution of residential structures. The dataset is organized into multiple scenario-based products, detailed as follows: 1. Existing and extrapolated sound setbacks. An existing scenario characterizes sound setbacks only in states or counties that have implemented sound regulations as of 2022. The extrapolated scenarios extend a constant sound threshold to counties that lack explicit sound regulations, with thresholds ranging from 35 to 60 dBA, in 5-dBA increments reflecting the variation observed in current sound ordinances. 2. Sound setbacks in directional and worst scenarios. The directional scenario accounts for the distance and orientation of residential structures relative to a hypothetical turbine location, utilizing the turbine's sound emissions in that specific direction. In contrast, the worst scenario takes loudest sound level at each distance step from the turbine, irrespective of directional considerations, which aligns with current industry practice. 3. Supply curves for Open and Reference Access scenarios. This dataset includes supply curves generated by the reV model, which integrates each of the above sound setbacks into both Open and Reference siting scenarios. In addition, two Open and Reference baselines scenarios were included which do not consider sound setbacks for comparative analysis. All sound setback data are stored in TIF files, with partial maps of the data provided in PNG format. The values in the sound setback raster range from 0 to 1, representing the fraction of developable land within a 90 meter by 90 meter pixel due to sound ordinances. A value of 0 indicates areas where wind energy development is prohibited, while a value of 1 signifies areas fully permissible. The wind turbine parameters used in the sound modeling are based on the land-based turbine from International Energy Agency (IEA), featuring a rated electrical power of 3.4 MW, a rotor diameter of 130 meters, and a hub height of 110 meters. The atmospheric conditions, including wind speed/direction, turbulence, air temperature, relative humidity, and air pressure, that drive the sound generation are obtained from the WIND Toolkit dataset.

Array↗

DOE OSTI2022

IM3/HyperFACETS Thermodynamic Global Warming (TGW) Simulation Datasets

Publication For a thorough description of the methods, see the peer-reviewed paper: Jones, A.D., Rastogi, D., Vahmani, P. et al. Continental United States climate projections based on thermodynamic modification of historical weather. Sci Data 10, 664 (2023). https://doi.org/10.1038/s41597-023-02485-5 Overview The IM3 / HyperFACETS climate simulations provide 40-year historical (1980-2019) as well as four 80-year future simulations (2020-2099) over the U.S. The future simulations are split into near (2020-2059) and far future (2060-2099) segments. The future scenarios span a range of plausible changes in future climate (both Global Circulation Model (GCM) and Representative Concentration Pathways/Shared Socioeconomic Pathway (RCP/SSP) dimensions). The simulations provide climate variables with high spatiotemporal resolution (25 hourly variables and 207 3-hourly variables at 12 km2). The datasets are generated using dynamical downscaling with the WRF (Weather Research and Forecasting) model (version 4.2.1) and therefore preserve physical consistency across variables. WRF is a state-of-the-art, fully compressible, non-hydrostatic, mesoscale numerical weather prediction model. WRF is coupled with an urban canopy model (UCM), which resolves urban surfaces. The future scenarios were developed using a thermodynamic global warming approach where past events are replayed under a range of future warming conditions. These scenarios therefore provide a perspective on potential increases in extreme event intensity, geographic scope, and duration, with previously non-extreme conditions potentially crossing new thresholds to be considered extreme by today's standards. This approach is not intended to estimate future changes in extreme event frequency that might result from changes in large-scale atmospheric dynamics. This dataset has NOT been bias corrected. A bias corrected version of selected variables is under development and will be released here when available. Scenarios Files Data for each scenario is provided in weekly NetCDF files. 25 variables are available at hourly resolution, and 207 variables are available at three-hourly resolution. Spatial resolution is 12km and spans the conterminous United States (CONUS), including some areas of Canada and Mexico, resulting in a grid of 424 by 299 cells. The spatial projection is a Lambert Conformal Conic with the following proj-string: "+proj=lcc +lat_0=40.0000076293945 +lon_0=-97 +lat_1=30 +lat_2=45 +x_0=0 +y_0=0 +R=6370000 +units=m +no_defs". The available scenarios and simulation periods are listed below: historical | 1980 - 2019 rcp45cooler | 2020 - 2059 rcp45cooler | 2060 - 2099 rcp45hotter | 2020 - 2059 rcp45hotter | 2060 - 2099 rcp85cooler | 2020 - 2059 rcp85cooler | 2060 - 2099 rcp85hotter | 2020 - 2059 rcp85hotter | 2060 - 2099 * The first year (1979, 2019, and 2059) of data within each scenario represents a model warmup period and should not be used. These are located in the `spinup_files` directory. Historical year 2020 is considered an extra year of data beyond the simulation period and can be found in the `additional_files` directory. For information on specific variables and a more in-depth discussion of methodology, please refer to the data landing page at https://tgw-data.msdlive.org. Delta Warming Files The global and CONUS warming deltas for each scenario are provided in degrees Celsius annually and monthly. Restart Files Yearly restart files are provided for each scenario which can be used to restart the WRF model at a particular point in time. Spinup Files The first year of data within each simulation period represents a model warmup period and should not be used. The files are provided here for the sake of reproducibility. Additional Files Additional years of data are provided as an extension of the historic simulation.

Jones, Andrew D.↗

DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility 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. License This data is made available under a CCBY4.0 License 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

Mongird, Kendall (ORCID:0000000328077088)↗

DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility 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. License This data is made available under a CCBY4.0 License 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

Mongird, Kendall (ORCID:0000000328077088)↗

DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗

DOE OSTI2021

Systems and methods for real-time data processing and for emergency planning

Systems and methods are described herein for real-time data processing and for emergency planning. Scenario test data may be collected in real-time based on monitoring local or regional data to ascertain any anomaly phenomenon that may indicate an imminent danger or of concern. A computer-implemented method may include filtering a plurality of different test scenarios to identify a sub-set of test scenarios from the plurality of different test scenarios that may have similar behavior characteristics. A sub-set of test scenarios is provided to a trained neural network to identify one or more sub-set of test scenarios. The one or more identified sub-set of test scenarios may correspond to one or more anomaly test scenarios from the sub-set of test scenarios that is most likely to lead to an undesirable outcome. The neural network may be one of: a conventional neural network and a modular neural network.

Yilmaz, Alper↗

DOE OSTI2023

Systems and methods for real-time data processing and for emergency planning

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Evaluating Nationwide Supply Chain for Circularity of PET and Olefin Plastics

PET (Polyethylene Terephthalate, #1) and olefin plastics including HDPE (High Density Polyethylene, #2), LDPE/LLDPE (Low Density/Linear Low-Density Polyethylene, #4) and PP (Polypropylene, #5) together comprise nearly 80% of the U.S. plastic market. Due to their high market share and extensive application in packaging these polymers have better potential for circularity than other polymer types. To understand the potential of future scenarios with higher recycling rates, supply chain scenarios for PET and olefin plastic packaging need to be analyzed with increased availability and collection of plastics. Currently there exists a knowledge gap to understand the circular supply chain on a national level. Performing a nationwide analysis introduces certain challenges such as variability in the plastic mix of the recycling stream in different municipalities, lack of collection of some types of plastics, regional differences in cost and estimating a national supply curve based on state level access rate and participation rate. Currently, a limited number of Plastic Reclaimers recycle the nation’s collected plastic involving transportation over long distances surpassing state boundaries. Modeling a nationwide scenario instead of regional/state based scenario will facilitate transportation beyond state boundaries for the development of a circular economy. A Mixed-integer Linear Programming model was developed, to identify the optimal location and capacity of the Material Recovery Facilities (MRFs) nationwide subject to maximizing the profit margin of the industrial entities within the model. We considered three different scenarios including the base case scenario (S1) collecting 2.27 million metric tons/year as well as two additional scenarios with available plastic supply of 1.8 times (S2) and 2.2 times (S3) of the base case scenario. The model identified 166, 275 and 319 counties as potential MRF locations for scenarios S1, S2 and S3 respectively. Compared to the existing number of counties having MRFs in the US, the model results indicated a reduction of 38% of MRFs for S1. For S2 and S3, the number of counties with MRFs increased 3% and 20% respectively compared to counties currently with MRFs. The average profit remains constant between $180-$181/ton regardless of the increased plastic collection.

54 - ENVIRONMENTAL SCIENCES/GLOBAL CLIMATE CHANGE ↗

DOE OSTI2020

Time-Lapse Gravity Monitoring of CO2 Migration Based on Numerical Modeling of a Faulted Storage Complex

In this study, the performance of both surface and borehole time-lapse gravity monitoring to detect CO2 leakage from a carbon storage site is evaluated. Several hypothetical scenarios of CO2 migration in a leaky fault, and thief zones at different depths at the Kimberlina site (California, USA) constitute the basis of the approach. The CO2 displacement is simulated using the TOUGH2 simulator applied to a detailed geological model of the site. The gravity responses to these CO2 plumes are simulated using forward modeling with sensors at ground surface and in vertical boreholes. Results of inversion on one scenario are also presented. The surface-based gravity responses obtained for the different leakage scenarios demonstrate that leakage can be detected at the surface in all the scenarios but the time to detection is highly variable (10 to 40 years) and dependent on the detection threshold considered. Borehole measurements of the vertical component of gravity provide excellent constrains in depth when they are located in proximity of the density anomaly associated with the presence of CO2, thus discriminating multiple leaks in different thief zones. Joint inversion of surface and borehole data can bring valuable information of the occurrence of leakages and their importance by providing a reasonable estimate of mass of displaced fluids. This study demonstrates the importance of combining multiphase flow simulations with gravity modeling in order to define if and when gravity monitoring would be applicable at a given storage site.

Time-lapse gravity monitoring, leak detection, CCS↗

DOE OSTI2022

Assessing Impacts on Pressure Stabilization and Leasing Acreage for CO 2 Storage Utilizing Oil Migration Concepts

Favorable geological storage for CO 2 has long been pictured as large anticlines with thick sandstones, similar to oil reservoirs in the petroleum system. Unlike oil, however, stored CO 2 does not need to be recoverable, which raises the possibility of using dissolution and residual trapping to augment the capacity of buoyant traps and tap more of the bulk rock volume. The work presented builds on that idea, asking the following question: If we inject CO 2 down to a syncline – analogous to the carrier bed in the petroleum system – how would this injection mechanism impact storage capacity and plume shape, migration, and stabilization? To address this question, we built a reservoir model, based on seismic interpretation of Middle Miocene strata, offshore Galveston, Texas. 3-D seismic and well logs were used to characterize key intervals. Reservoirs chosen for modeling are progradational-aggradational sands with mud intercalation. They have a higher degree of heterogeneity than the more conventional reservoirs commonly targeted for CO 2 storage. Modeling investigated how far the CO 2 plume would migrate under two scenarios: (1) injecting CO 2 at the base of the salt withdrawal basin (syncline scenario) and (2) injecting CO 2 at the base of the structural closure, similar to a common injection well location for EOR purposes (base scenario). For each scenario, we separately simulated injection of 30 MT of CO 2 and 60 MT of CO 2 continuously for 30 years and observe the plume and pressure evolution for 100 years after the injection stops. The simulation shows that injecting the CO 2 into a syncline limits the vertical migration of CO 2 , thus making synclinal injection more secure. In the syncline scenario, the geological layer around the injection point is more heterogeneous than the layer in the base scenario; thus, the CO 2 tends to migrate laterally. Additionally, in the syncline scenario, the plume does not even reach the upper part of the anticline, allowing us to safely store an additional amount of CO 2 into the reservoir. Furthermore, the simulation also shows that in the syncline scenario, the times needed for the reservoir to reach its stabilized pressure after the end of injections are faster. To summarize, CO 2 injection at the base of a syncline could provide additional storage, increase the safety of the project from the limited vertical plume migration, and expedite plume stabilization, which could result in the decrease of monitoring frequency as the project runs, thus lowering the operating cost of the project in the long run.

58 GEOSCIENCES↗