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This profile presents a snapshot of the electricity generation and reduction technologies available to Turks and Caicos - a British overseas territory consisting of two groups of islands located southeast of the Bahamas.
This profile provides a snapshot of the energy landscape of the British Virgin Islands (BVI), one of three sets of the Virgin Island territories in an archipelago making up the northern portion of the Lesser Antilles.
This profile provides a snapshot of the energy profile of the Cayman Islands, a British Overseas Territory, encompasses 3 islands in the western Caribbean Sea. Grand Cayman, Cayman Brac, and Little Cayman.
Animals within social groups respond to costs and benefits of sociality by adjusting the proportion of time they spend in close proximity to other individuals in the group (cohesion). Variation in cohesion between individuals, in turn, shapes important group-level processes such as subgroup formation and fission–fusion dynamics. Although critical to animal sociality, a comprehensive understanding of the factors influencing cohesion remains a gap in our knowledge of cooperative behavior in animals. We tracked 574 individuals from six species within the genus Canis in 15 countries on four continents with GPS telemetry to estimate the time that pairs of individuals within social groups spent in close proximity and test hypotheses regarding drivers of cohesion. Pairs of social canids (Canis spp.) varied widely in the proportion of time they spent together (5%–100%) during seasonal monitoring periods relative to both intrinsic characteristics and environmental conditions. The majority of our data came from three species of wolves (gray wolves, eastern wolves, and red wolves) and coyotes. For these species, cohesion within social groups was greatest between breeding pairs and varied seasonally as the nature of cooperative activities changed relative to annual life history patterns. Across species, wolves were more cohesive than coyotes. For wolves, pairs were less cohesive in larger groups, and when suitable, small prey was present reflecting the constraints of food resources and intragroup competition on social associations. Pair cohesion in wolves declined with increased anthropogenic modification of the landscape and greater climatic variability, underscoring challenges for conserving social top predators in a changing world. We show that pairwise cohesion in social groups varies strongly both within and across Canis species, as individuals respond to changing ecological context defined by resources, competition, and anthropogenic disturbance. Our work highlights that cohesion is a highly plastic component of animal sociality that holds significant promise for elucidating ecological and evolutionary mechanisms underlying cooperative behavior.
Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.
The risks of long-duration, widespread interruptions (LDWIs) in electrical power are a concern of U.S. regulators, the electric power industry, and stakeholders. Those responsible for making decisions about increasingly costly investments to prevent such interruptions and to facilitate rapid recovery when they do occur need relevant information on which to base those decisions. Although a number of studies have examined the physical and engineering impacts of extreme weather and other precipitating events on the bulk power system, decision makers evaluating investments in preventive strategies need information on the costs of past power interruptions and the benefits of preventing them in the future. This paper contributes to addressing this need by offering six case studies that detail the economics, at the level of the utility service territory, of power interruptions caused by extreme weather and lasting from a few days to several weeks. These intermediate-duration interruptions have been, and will continue to be, the most common type of major electric power disruption. They are longer than the short-term disruptions addressed in utility reliability planning, but not as long or widespread geographically as the national-scale interruptions with durations of many weeks, months, or longer that have been examined in some recent studies.
In the summer of 2020, the U.S. Department of Energy (DOE) awarded funding to Exelon’s Maryland utilities—Baltimore Gas and Electric (BGE), Delmarva Power & Light (DPL), and Potomac Electric Power Company (Pepco)—to implement the Smart Charge Management (SCM) pilot. This initiative aimed to design and implement managed electric vehicle (EV) charging strategies, evaluate the grid impacts of EV charging, and assess the utilities' ability to control EV load based on real-time grid conditions. The SCM pilot explored four aspects for continued improvement: (1) cybersecurity and managed charging functionality testing of two vendor platforms—WeaveGrid (telematics-based) and Shell Recharge Solutions (network-based)—which pursued charge scheduling and optimization through distinct approaches; (2) an analysis by Argonne National Laboratory (ANL) modeling team of three potential SCM enrollment scenarios within BGE and Pepco service territories over the next decade to assess future scalability; (3) employing customer engagement strategies, including surveys and a responsive pricing approach; and (4) the launch and implementation of pilots in Exelon’s Maryland territories in collaboration with WeaveGrid.
The National Aeronautics and Space Administration (NASA) is developing new launch systems and preparing to retire the Space Shuttle by 2010, as directed in the United States (U.S.) Vision for Space Exploration. The Ares I Crew Launch Vehicle (CLV) and the Ares V heavy-lift Cargo Launch Vehicle (CaLV) systems will build upon proven, reliable hardware derived from the Apollo-Saturn and Space Shuttle programs to deliver safe, reliable, affordable space transportation solutions. This approach leverages existing aerospace talent and a unique infrastructure, as well as legacy knowledge gained from nearly 50 years' experience developing space hardware. Early next decade, the Ares I will launch the new Orion Crew Exploration Vehicle (CEV) to the International Space Station (ISS) or to low-Earth orbit for trips to the Moon and, ultimately, Mars. Late next decade, the Ares V's Earth Departure Stage will carry larger payloads such as the lunar lander into orbit, and the Crew Exploration Vehicle will dock with it for missions to the Moon, where astronauts will explore new territories and conduct science and technology experiments. Both Ares I and Ares V are being designed to support longer future trips to Mars. The Exploration Launch Projects Office is designing, developing, testing, and evaluating both launch vehicle systems in partnership with other NASA Centers, Government agencies, and industry contractors. This paper provides top-level information regarding the genesis and evolution of the baseline configuration for the Ares V heavy-lift system. It also discusses riskbased, management strategies, such as building on powerful hardware and promoting common features between the Ares I and Ares V systems to reduce technical, schedule, and cost risks, as well as development and operations costs. Finally, it summarizes several notable accomplishments since October 2005, when the Exploration Launch Projects effort officially kicked off, and looks ahead at work planned for 2007 and beyond.
The National Aeronautics and Space Administration (NASA) is developing new launch systems in preparation for the retirement of the Space Shuttle by 2010, as directed in the United States (U.S.) Vision for Space Exploration. The Ares I Crew Launch Vehicle (CLV) and the Ares V heavy-lift Cargo Launch Vehicle (CaLV) systems will build upon proven, reliable hardware derived from the Apollo Saturn (1961 to 1975) and Space Shuttle (1972 to 2010) programs to deliver safe, reliable, affordable space transportation solutions. This approach leverages existing aerospace talent and a unique infrastructure, as well as the vast amount of legacy knowledge gained from almost a half-century of hard-won experience in the space enterprise. Beginning early next decade, the Ares I will launch the new Crew Exploration Vehicle (CEV) to the International Space Station (ISS) or to low-Earth orbit for trips to the Moon and, ultimately, Mars. Late next decade, the Ares V's Earth Departure Stage will carry larger payloads such as the lunar lander into orbit, and the Crew Exploration Vehicle will dock with it for missions to the Moon, where astronauts will explore new territories and conduct science and technology experiments. Both the Ares I and Ares V systems are being designed to support longer future trips to Mars. The Exploration Launch Projects Office, located at NASA's Marshall Space Flight Center, is designing, developing, testing, and evaluating both launch vehicle systems in partnership with other NASA Centers, Government agencies, and industry contractors. This paper provides top-level information regarding the genesis and evolution of the baseline configuration for the Ares V heavy-lift system. It also touches on risk-based management strategies, such as building on powerful hardware and promoting common features between the Ares I and Ares V systems to reduce technical, schedule, and cost risks, as well as development and operations costs. Finally, it gives a summary of several notable accomplishments over the past year, since the Exploration Launch Projects effort officially kicked off in October 2005, and looks ahead at work planned for 2007 and beyond.
Statement of the problem or situation that is being addressed in your application. The DOE and its national laboratories developed the Home Energy Score™ (HES) to encourage homeowners to improve their energy performance, lower costs and to share energy information through the MLS listing, appraisal, and financing channels. While the HES is an instrumental tool, it is currently underutilized and consists of technical, structural and sector barriers which need to be addressed in order to scale and many energy efficiency contractors are understandably overwhelmed by the added time and effort and lack of incentive to sell and deliver deep retrofit projects while simultaneously meeting the DOE HES program requirements; consequently, contractors may decide to forgo participation. Home Energy Rating System (HERS) Raters have the opportunity to play the critical Assessor role in producing a Home Energy Score (HES); this role has immense potential but currently is unfulfilled. Lastly, while utilities are interested in their customer base achieving greater energy efficiency, especially to help offset growing residential loads in states like California that are accelerating electrification, utilities do not have access to the market actors who are on the front line of influence to homeowners or review and approve their permits: HERS Raters, assessors, contractors and building departments. General statement of how this problem is being addressed: ConSol will integrate the Home Energy Score™ (HES) to its State of California, approved home energy rating services (HERS) platform (CHEERS) to develop a single tool for contractors nationwide to assess, record and install recommended cost, energy, and emissions saving measures to the 140 million single-family homes throughout the U.S. and 14 million homes in California (CHEERS+HES). The CHEERS high fidelity energy code permitting data will be integrated with HES for simple, accurate, easy-to-use home energy estimation and analysis and will directly gain access to the retrofit and renovations markets with the same upgraded platform. This innovative project will assist the utilities in supporting existing homes in their jurisdictions with HES and develop measures to improve energy efficiency and reduce emissions. How is this problem being addressed? What is the overall project approach? In effort to expand the Home Energy Score™ (HES) by increasing the use of aggregable home energy asset data, ConSol proposes to integrate the DOE HES via Application Programming Interface (API) to its State of California approved home energy rating services platform (CHEERS). Once the CHEERS platform and HES are integrated (CHEERS+HES), this enhanced platform will be instantly available and actively deployed via Phase 1 pilot to HERS Raters, assessors and contractors in California to market-test the solution, understand the rate of adoption and identify opportunities for improvement prior to scaling nationally. The CHEERS high fidelity energy code permitting data will be integrated with HES for simple, accurate, easy-to-use home energy estimation and analysis and will directly gain access to the retrofit and renovations markets with the same upgraded platform. This innovative project will assist the building industry and homeowners with an easy-to-use assessment if energy and carbon impacts of existing homes, and assist the utilities in supporting existing homes in their jurisdictions with HES to improve energy efficiency and reduce emissions. What is to be done in Phase I? During Phase I of this proposed project, ConSol will (1) design software architecture that links CHEERS to the Home Energy ScoreTM via API, (2) solicit partnership from one or more California utilities for a regional pilot, (3) test the new software with its HERS Raters and contractor network in the partnership utility jurisdiction, (4) launch a pilot version of the newly developed software with HERS Raters and contractors in the utility territory, and (5) explore California’s GoGreen energy efficiency homeowner lending program in parallel with the pilot. Commercial Applications and Other Benefits. Summarize the future applications or public benefits if the project is carried over into Phase II or Phase III and beyond. The CHEERS+HES commercialized product will be ready for national market scale following a successful Phase 1 performance. The CHEERS+HES adoption is estimated to reach a 5% adoption growth rate versus the 110,000 baseline, starting in Year 1 after Phase I completion, and continuing each year. As a direct benefit to the DOE, CHEERS will set a goal of 100,000 Home Energy Score assessments for existing home alterations within the first 10 years following Phase 1 performance. The technical benefits of this proposed project include the harmonized, automated, and seamless integration of the DOE HES into the widely used and market leading California energy registry, CHEERS. The social benefits include the aggregate energy, cost and GHG savings by allowing the broader public streamlined access to the CHEERS+HES measurement and the energy efficiency recommended measures that may result. Key Words: Home Energy ScoreTM (HES); Application Programming Interface (API); Home Energy Rating Services (HERS); HERS Raters; contractors; assessors; existing homes, energy asset data; cost, energy, and emissions saving measures; energy code (Title 24) compliance; document repository; utilities; pilot; newly developed software; energy efficiency; homeowner. Summary for Members of Congress: The DOE Home Energy Score™ (HES) is a tool to encourage homeowners to improve their energy performance, lower costs and share energy information but is underutilized and consists of barriers which need to be addressed in order to scale. In effort to expand the HES, CHEERS, Inc. will integrate the HES to its State of California, approved home energy rating services (HERS) platform (CHEERS) to develop a single tool for contractors nationwide to assess, record and install recommended cost, energy, and emissions saving measures to the 140 million single-family homes throughout the U.S. and 14 million homes in California.
Groups, like individuals, often develop habitual routines for dealing with frequently encountered stimuli. Although such routines are consequential for group life and work, little is known about them. This paper reconnoiters the territory of habitual behavior in groups that perform work within organizations. We offer a definition of group habits, identify their functions and dysfunctions, suggest how they develop and are maintained, and identify the circumstances when they are likely to be altered or abandoned. Throughout, we give special attention to the social nature of habitual routines in groups, to the interaction between habitual behavior and group life cycle phenomena, and to the role of the organizational context in prompting, shaping, and terminating habitual routines.
PURPOSE: To study the effect of the interaction distance on the frequency of inter- and intrachromosome exchanges in individual chromosomes with respect to their DNA content. Assumptions: Chromosome exchanges are formed by misrejoining of two DNA double-strand breaks (DSB) induced within an interaction distance, d. It is assumed that chromosomes in G(0)/G(1) phase of the cell cycle occupy a spherical domain in a cell nucleus, with no spatial overlap between individual chromosome domains. RESULTS: Formulae are derived for the probability of formation of inter-, as well as intra-, chromosome exchanges relating to the DNA content of the chromosome for a given interaction distance. For interaction distances <1 microm, the relative frequency of interchromosome exchanges predicted by the present model is similar to that by Cigarran et al. (1998) based on the assumption that the probability of interchromosome exchanges is proportional to the "surface area" of the chromosome territory. The "surface area" assumption is shown to be a limiting case of d-->0 in the present model. The present model also predicts that the probability of intrachromosome exchanges occurring in individual chromosomes is proportional to their DNA content with correction terms. CONCLUSION: When the interaction distance is small, the "surface area" distribution for chromosome participation in interchromosome exchanges has been expected. However, the present model shows that for the interaction distance as large as 1 microm, the predicted probability of interchromosome exchange formation is still close to the surface area distribution. Therefore, this distribution does not necessarily rule out the formation of complex chromosomal aberrations by long-range misrejoining of DSB.
The goal of this study was to survey and characterize the scope of current technical and programmatic knowledge pertaining to EV charge management technologies and practices in the US and relevant international jurisdictions. This characterization of existing field demonstrations and knowledge derived were used to determine gaps in the SCM demonstration landscape. Addressing these gaps through research and demonstration could increase confidence in the U.S. that load management and EV charge control could achieve overarching societal benefits. A survey of charge management deployments and input from stakeholders was completed to determine the state-of-the-art of smart charge management (SCM) where SCM is defined as controlling the amount of power exchanged between chargers and EVs to meet customers' charging needs while also responding to external power demand or pricing signals to provide load management, resilience, or other benefits to the customer and electric grid. The survey was the basis of the gap analysis in this report and determines which areas are well understood, with high confidence, and which areas need further investigation. Existing examples of EV charge management are characterized here to determine aspects that are ready for widespread deployment and have been demonstrated in the field. These include demonstration studies, pilots, programs, and EV-specific tariffs. In all, 110 examples of charge management were characterized. The data sources were public literature and utility filings as well as targeted interviews. In addition, 43 interviews with stakeholders were conducted with a consistent set of questions used in each interview. This study prioritized gaps in demonstrated SCM capabilities based on 1) Urgency of the particular use-case to offset traditional grid assets, 2) Impact, extensibility, and scaling of results across the entire spectrum of 3000+ utility service territories including projected technical and market potential for a given grid service, and 3) Value of federal funding in addressing the gap, including potential to leverage and/or add scope to existing field demonstrations funded by other non-federal funding mechanisms.
This report examines how battery energy storage systems (BESS) can help utilities accommodate large load growth while protecting affordability for existing ratepayers. Rapid growth in electricity demand from artificial intelligence (AI) data centers is straining the U.S. grid. Furthermore, many new data centers are entering rural markets, which could offer economic benefits but may also pose implementation challenges for smaller utilities. At the same time, retail electricity prices are increasing faster than inflation, elevating customer affordability as a key challenge. While data centers have not been the primary driver of increases in residential prices to date, they have pushed wholesale energy and capacity prices higher in several markets. Fundamental utility cost-allocation principles show that data center growth can be rate-positive for existing customers only if new peak demand grows faster than the costs a utility must incur to serve it. Several factors, including a utility’s degree of wholesale market exposure, forecast uncertainty and stranded-asset risk, and tariff design can determine the outcome of load growth on retail rates. Energy storage can make several affordability contributions in the face of this landscape of uncertainty and market volatility, including deferral of higher-cost grid investments through improved utilization of existing assets and flexibility of new large loads, insulation from volatile wholesale prices through peak shaving, and reliability support to address grid risks stemming from the behavior of AI data center loads. Different potential BESS deployment pathways—utility-scale front-of-the-meter systems, aggregated small-scale storage installations, and data center-sited behind-the-meter storage—are compared against each other and against conventional capacity alternatives. This framework is intended as a conceptual resource to utilities, particularly smaller public utilities with rural service territories, who may be considering the role that energy storage can play in insulating existing ratepayers from data center cost shifts.
Kennedy Space Center (KSC), located on Merritt Island on the east coast of central Florida, is one of three remaining major populations of the Florida Scrub Jay (Aphelocoma coerulescens coerulescens), listed as threatened by the U.S. Fish and Wildlife Service (USFWS) since 1987. Construction of new facilities by the National Aeronautics and Space Administration (NASA) on KSC over the next five years has the potential to impact up to 193 ac (78.1 ha) of Scrub Jay habitat. Under an early consultation process with the Endangered Species Office of the USFWS, NASA agreed to a compensation plan for loss of Scrub Jay habitat. The compensation plan required NASA to restore or create scrub on KSC at a 2:1 ratio for that lost. The compensation plan emphasized restoration of scrub habitat that is of marginal or declining suitability to Scrub Jays because it has remained unburned. Although prescribed burning has been conducted by the USFWS Merritt Island National Wildlife Refuge (MINWR) for more than ten years, significant areas of scrub remain unburned because they have been excluded from fire management units or because landscape fragmentation and a period of fire suppression allowed scrub to reach heights and diameters that are fire resistant. For such areas, mechanical cutting followed by prescribed burning was recommended for restoration. A second part of the restoration plan is an experimental study of scrub reestablishment (i.e., creation) on abandoned, well drained agricultural sites by planting scrub oaks and other scrub plants. The compensation plan identified 260 ac (105 ha) of scrub restoration in four areas and a 40 ac (16 ha) scrub creation site. Monitoring of restoration sites required under the plan included: establishing permanent vegetation sample transects before treatment and resampling annually for ten years after treatment, and color banding Scrub Jays to determine territories prior to treatment followed by monitoring reproductive success and survival for ten years after treatment. Monitoring scrub creation sites included determining survival of planted material for five years and establishing permanent transects to follow vegetation development for ten years after planting. Scrub Jay monitoring of creation sites is incorporated with that of adjacent restoration sites.
A biophysical model has been developed that is based on the assumptions that an interphase chromosome occupies a spherical territory and that chromosome exchanges are formed by the misrejoining of two DNA double-strand breaks induced within a defined interaction distance. The model is used to explain the relative frequencies of inter- and intrachromosomal exchanges and the relationship between radiation-induced aberrations in individual chromosomes and the DNA content of the chromosome. Although this simple model predicts a higher ratio of inter- to intrachromosomal exchanges for low-LET radiation than for high-LET radiation, as has been suggested by others, we argue that the comparison of the prediction of the model with experimental results is not straightforward. With the model, we also show that the probability of the formation of interchromosomal exchanges is proportional to the "surface area" of the chromosome domain plus a correction term. The correction term is small if the interaction distance is less than 1 microm for both low- and high-LET radiations.
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
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