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NASA NTRS2000

Propellant Densification Ground Testing Conducted for Launch Vehicles

The NASA Glenn Research Center at Lewis Field has taken the lead in the development of practical densified cryogenic propellants for launch vehicle applications. The technology of subcooling cryogenic propellants below their normal boiling point to produce a denser fluid is one of the key process technologies necessary to meet the challenge of single-stage-to-orbit and reusable launch vehicles. Densified propellants are critical to lowering launch costs because they enable more propellant to be packed into a given unit volume, thus improving the performance by reducing the overall size and weight of the launch vehicle. This two-pronged research and test program has evolved into (1) conducting tank loading tests using densified liquid hydrogen and (2) developing two large-scale propellant densification systems that will be performance tested next year at Glenn. The propellant-loading test program was undertaken at Glenn in coordination with Lockheed Martin Michoud Space Systems. In this testing, the liquid hydrogen recirculation and densification process was simulated, and the thermal stratification of the densified propellant was recorded throughout the tank. The test article was a flight-weight tank constructed from composite materials similar to those to be used on the X-33 launch vehicle. The tank geometry as designed by Lockheed Martin had two cylindrical lobes with a center septum. Liquid hydrogen flow rate, pressure data, and temperature data plotted over time were collected while the subscale tank was filled with 27 R (15 K) densified liquid hydrogen propellant. This testing has validated mathematical models and demonstrated the readiness of densified propellant technology for near-term use. It marks the first time that such a process has been carried out with a multiple-lobe, flight-similar tank. Glenn researchers have also been working on providing a process and critical test data for the continuous production of densified liquid hydrogen (LH2) and densified liquid oxygen (LO2). Each densification production process uses a high-efficiency, subatmospheric boiling bath heat exchanger to cool the working fluid. A near triple-point hydrogen boiling bath is used to condition and subcool hydrogen to 27 R (15 K), and a nitrogen boiling bath is used to cool the liquid oxygen to 120 R (66.7 K). Multistage centrifugal compressors operating at cryogenic inlet conditions maintain the heat exchanger bath vapor pressure below 1 atm. The LO2 propellant densification unit shown in the photograph has a 30 lb/sec capacity, whereas the LH2 unit was designed to process 8 lb/sec of propellant. Each densification unit will be transported to Glenn's South Forty area after all fabrication work is completed sometime late next year. There the LO2 and LH2 densifier performance tests will be conducted with another larger Lockheed Martin tank designated the Structural Test Article (STA). This liquid oxygen tank is a full-scale, flight-weight, prototype aluminum tank designed for the X-33. It has a capacity of 20,000 gallons of LO2. The tank loading and recirculation testing planned for next year with STA will provide the data necessary for full-scale development of propellant densification technology.

Tomsik, Thomas M.↗

DOE OSTI2024

Reconstruction of the 4D beam matrix

The widely used transverse parameters characterizing particle beams are the Twiss parameters. These parameters can be measured experimentally but they do not fully characterize the beam since they do not account for possible correlations in particle distribution between two transverse coordinates. These correlations may occur due to uncompensated magnetic field at the cathode or misalignment of focusing quadrupoles in the transport beamline. We test a novel diagnostic for diagnosing full 4D beam matrix which may be used to identify such imperfections. The diagnostic is based on transporting the beam through the beamline which includes a quadrupole and a skew quadrupole magnets and measuring the resulting 2D beam distribution at the screen downstream. Such a measurement can be viewed as measuring a 2D projection of the 4D distribution. Different settings of the quads provide measurements of different slices of the phase space. The reconstruction of the original beam matrix from a number of measurements is done using machine learning algorithm, which provides a fast and reliable way of reconstruction for an arbitrary configuration of the scanning beamline. In August 2024, we set up the diagnostic beamline to perform a quadrupole scan of the beam. The setup includes a skew quadrupole, a regular quadrupole, and a screen. The images on the screen were post-processed to remove experimental artifacts and enhance contrast by eliminating background noise outside the core of the distribution=. The rms parameters of the distribution were then calculated and used as inputs for the reconstruction algorithm. This algorithm attempts to determine the initial beam matrix that produces expected images on the screen closely matching the observed images across all quadrupole settings. The algorithm found a solution in which the expected rms parameters closely align with the observations. Validation of the results is planned for FY25.

43 PARTICLE ACCELERATORS↗

DOE OSTI2018

Energy-Water Microgrid Opportunity Analysis at the University of Arizona's Biosphere 2 Facility

Microgrids provide reliable and cost-effective energy services in a variety of conditions and locations. There has been minimal effort invested in developing energy-water microgrids that demonstrate the feasibility and leverage synergies of operating renewable energy and water systems in a coordinated framework. Water systems can be operated in ways to provide ancillary services to the electrical grid and renewable energy can be utilized to power water-related infrastructure, but the potential for co-managed systems has not yet been quantified or fully characterized. Energy-water microgrids could be a promising solution to improve energy and water resource management for islands, rural communities, distributed generation, Defense operations, and many parts of the world lacking critical infrastructure. NREL and the University of Arizona have been jointly researching energy-water microgrid opportunities at the University's Biosphere 2 (B2) research facility. B2 is an ideal case study for an energy-water microgrid test site, given its size, its unique mission and operations, the criticality of water and energy infrastructure, and its ability to operate connected to or disconnected from the local electrical grid. Moreover, the B2 is a premier facility for undertaking agricultural research, providing an excellent opportunity to evaluate connections and tradeoffs at the food-energy-water nexus. In this study, NREL used the B2 facility as a case study for an energy-water microgrid test site, with the potential to catalyze future energy-water system integration research. The study identified opportunities for energy and water efficiency and estimated the sizes of renewable energy and storage systems required to meet remaining loads in a microgrid, identified dispatchable loads in the water system, and laid the foundation for an in-depth energy-water microgrid analysis. The foundational work performed at B2 serves a model that can be built upon for identifying relevant energy-water microgrid data, analytical requirements, and operational challenges associated with development of future energy-water microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

SCENARIOS FOR MEETING CALIFORNIA'S 2050 CLIMATE GOALS California's Carbon Challenge Phase II Volume I: Non-Electricity Sectors and Overall Scenario Results

This study provides an updated analysis of long-term energy system scenarios for California consistent with the State meeting its 2050 climate goal, including detailed analysis and assessment of electricity system build-out, operation, and costs across the Western Electricity Coordinating Council (WECC) region. Four key elements are found to be critical for the State to achieve its 2050 goal of 80 percent greenhouse (GHG) reductions from the 1990 level: aggressive energy efficiency; clean electricity; widespread electrification of passenger vehicles, building heating, and industry heating; and large-scale production of low-carbon footprint biofuels to largely replace petroleum-based liquid fuels. The approach taken here is that technically achievable energy efficiency measures are assumed to be achieved by 2050 and aggregated with the other key elements mentioned above to estimate resultant emissions in 2050. The energy and non-energy sectors are each assumed to have the objective of meeting an 80 percent reduction from their respective 1990 GHG levels for the purposes of analysis. A different partitioning of energy and non-energy sector GHG greenhouse reductions is allowed if emission reductions in one sector are more economic or technically achievable than in the other. Similarly, within the energy or non-energy sectors, greater or less than 80 percent reduction from 1990 is allowed for sub-sectors within the energy or non-energy sectors as long as the overall target is achieved. Overall emissions for the key economy-wide scenarios are considered in this report. All scenarios are compliant or nearly compliant with the 2050 goal. This finding suggests that multiple technical pathways exist to achieve the target with aggressive policy support and continued technology development of largely existing technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

A review of privacy in energy applications

As the distribution system continues to experience an increase in distributed energy resource (DER) and electric vehicle (EV) penetration, so does the need for new solutions that can help grid operators manage and leverage their capabilities. This will undoubtedly lead to new operational schemes and business opportunities that will transform the traditional consumer into a prosumer who will be more actively engaged in grid operations. Although the field is still under active development, many of the potential use cases presented in literature or industry are built upon edge computing, two-way communications, and other innovative computational constructs to attain their goals. However, at their core, many use cases assume a great level of data access to aid with the decision-making process, an assumption that may need to be revised to ensure fair and equitable operational processes are maintained. This may be particularly true as edge resources are predicted to participate in retail-side, many-to-many, or peer-to-peer markets and thus may lead to financial impacts if data access considerations are ignored. The need to revise data access mechanisms can be further justified by the introduction of new participants into the operational process, who do not have the same level of trust, nor the incentives to focus on energy delivery as their primary objective. At the same time, more consumers are becoming aware of their own data, and the potential impacts of its abuse. To help solution developers better understand these risks, this report has been developed to offer an initial introduction to the topic of privacy. This is achieved by 1) Highlighting the need for privacy-aware solutions; 2) Encouraging system designers to be inquisitive about the status quo; 3) Documenting the existing threat space; 4) Presenting and evaluating tools that may be helpful towards enabling better privacy postures; and 5) Making recommendations to encourage the adoption of better practices. From a technical perspective, the report focuses on evaluating two potential techniques by applying them to the Transactive Energy Space. Based on the obtained results, it can be established that differential privacy (DP) methods may have limited applicability when highly correlated, time-series data records need to be protected. However, DP may be a powerful tool when it is used to aggregate and analyze mid-size and large-size data sets in a more traditional statistical environment. The second tool under evaluation is threshold cryptography, which can guarantee complete secrecy (and thus privacy) but requires the establishment of key management procedures and dedicated communication channels for key coordination. Therefore, due to its increased computational overhead, the use of threshold cryptography must be weighted using a cost/benefit analysis on a per-application basis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

How can probabilistic solar forecasts lower costs and improve reliability for independent system operator (ISO) markets? We tackle this question in three steps. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty in those forecasts to error distributions for net load ramps for the California ISO (CAISO) using statistical and machine learning methods. Projected net load errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Finally, a multi-period look-ahead production cost model quantifies how conditional ramp requirements can a) decrease operating costs by lowering requirements compared to often conservative unconditional methods, and b) reduce generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. In addition to the products just described (quantification of solar uncertainty, its translation into requirements for ramp capability product, and quantification of the benefits of more accurate ramp requirements), this project also developed a visualization system that alerts system operators of ramp and uncertainty conditions within the network based on solar forecasts. The system is called Resource Forecast and Ramp Visualization for Situational Awareness (RaVIS). These four products represent significant advances in the state-of-the-art of probabilistic solar forecasting, development of weather-informed reserve requirements, production costing methods for estimating the benefits of more accurate reserve requirements, and visualization of system status, respectively. Yet the products are also practical and can be immediately implemented, potentially enabling system operators to save millions of dollars in ramp product procurement costs per year.

14 SOLAR ENERGY↗

DOE OSTI2012

Modeling, Analysis, and Control of Demand Response Resources

While the traditional goal of an electric power system has been to control supply to fulfill demand, the demand-side can plan an active role in power systems via Demand Response (DR), defined by the Department of Energy (DOE) as “a tariff or program established to motivate changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized” [29]. DR can provide a variety of benefits including reducing peak electric loads when the power system is stressed and fast timescale energy balancing. Therefore, DR can improve grid reliability and reduce wholesale energy prices and their volatility. This dissertation focuses on analyzing both recent and emerging DR paradigms. Recent DR programs have focused on peak load reduction in commercial buildings and industrial facilities (C&I facilities). We present methods for using 15-minute-interval electric load data, commonly available from C&I facilities, to help building managers understand building energy consumption and ‘ask the right questions’ to discover opportunities for DR. Additionally, we present a regression-based model of whole building electric load, i.e., a baseline model, which allows us to quantify DR performance. We use this baseline model to understand the performance of 38 C&I facilities participating in an automated dynamic pricing DR program in California. In this program, facilities are expected to exhibit the same response each DR event. We find that baseline model error makes it difficult to precisely quantify changes in electricity consumption and understand if C&I facilities exhibit event-to-event variability in their response to DR signals. Therefore, we present a method to compute baseline model error and a metric to determine how much observed DR variability results from baseline model error rather than real variability in response. We find that, in general, baseline model error is large. Though some facilities exhibit real DR variability, most observed variability results from baseline model error. In some cases, however, aggregations of C&I facilities exhibit real DR variability, which could create challenges for power system operation. These results have implications for DR program design and deployment. Emerging DR paradigms focus on faster timescale DR. Here, we investigate methods to coordinate aggregations of residential thermostatically controlled loads (TCLs), including air conditioners and refrigerators, to manage frequency and energy imbalances in power systems. We focus on opportunities to centrally control loads with high accuracy but low requirements for sensing and communications infrastructure. Specifically, we compare cases when measured load state information (e.g., power consumption and temperature) is 1) available in real time; 2) available, but not in real time; and 3) not available. We develop Markov Chain models to describe the temperature state evolution of heterogeneous populations of TCLs, and use Kalman filtering for both state and joint parameter/state estimation. We present a look-ahead proportional controller to broadcast control signals to all TCLs, which always remain in their temperature dead-band. Simulations indicate that it is possible to achieve power tracking RMS errors in the range of 0.26–9.3% of steady state aggregated power consumption. Results depend upon the information available for system identification, state estimation, and control. We find that, depending upon the performance required, TCLs may not need to provide state information to the central controller in real time or at all. We also estimate the size of the TCL potential resource; potential revenue from participation in markets; and break-even costs associated with deploying DR-enabling technologies. We find that current TCL energy storage capacity in California is 8–11 GWh, with refrigerators contributing the most. Annual revenues from participation in regulation vary from $10 to $220 per TCL per year depending upon the type of TCL and climate zone, while load following and arbitrage revenues are more modest at $2 to $35 per TCL per year. These results lead to a number of policy recommendations that will make it easier to engage residential loads in fast timescale DR.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Initial Development of A Digital Twin Model for an Electrified Aircraft Propulsion Emulation Rig

In support of aviation fuel burn and emission reduction goals, NASA is pursing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP), which relies on the generation, storage, transmission, and use of electrical power for producing thrust and optimizing propulsion system efficiency. Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes advances in propulsion controls, which will be vital for ensuring coordinated efficient operation of the complex integrated subsystems that comprise EAP architectures. To support EAP controls research, the NASA Glenn Research Center has developed the Hybrid Propulsion Emulation Rig (HyPER). The HyPER laboratory hardware includes shaft-mounted electric machines, power converters, power supplies, power distribution cables, and an energy storage device that can be reconfigured to represent a variety of EAP architectures. It also includes an integrated real-time computer system that hosts developed EAP control software and turbomachinery simulations. This enables the electrical system and rotating shafts of EAP designs to be implemented in actual hardware and integrated with turbomachinery simulations and system-level EAP control logic implemented in software. In this form, the HyPER laboratory provides a partially simulated, partially hardware-in-the-loop test environment enabling the initial development and evaluation of EAP control technology. A prerequisite for the development of EAP control designs is the availability of a system model that accurately reflects the operation of the electrical system hardware. To support this need, a digital twin model of the HyPER electrical system hardware is under development. This model is being coded in the MATLAB Simulink environment and uses the NASA-developed Electrical Modeling and Thermal Analysis Toolbox (EMTAT) to construct a digital twin framework. EMTAT contains generic electrical component building blocks that are simulated at turbomachinery timescales. Associated inputs and outputs allow the blocks to be combined to model complete electrical systems. The EMTAT blocks also contain adjustable internal maps and parameters that can be set to reflect the operation of a specific electrical component. For the HyPER digital twin, the settings of these EMTAT block internal maps and parameters is determined through machine learning approaches applied to characterization run data collected from the laboratory. During characterization runs the laboratory electrical system hardware is subjected to a full range of torque, speed, and power settings. Acquired data is then used to estimate EMTAT block parameters using a variety of machine learning techniques. The resulting digital twin model is found to match the operation of actual HyPER hardware with an accuracy suitable for control development purposes. It also holds promise for other applications including modeling the performance of HyPER laboratory reconfigurations and model-based anomaly detection. Planned follow-on work to automate post-processing of acquired laboratory data to update the HyPER digital twin model will also be presented and discussed.

Electrified Aircraft Propulsion↗

DOE OSTI2016

Associate Directorate Environmental Management Infrastructure Plan for Area G and Area L Domes

Technical Area 54, at Los Alamos National Laboratory (LANL) is situated in the east-central portion of the Laboratory on the Mesita del Buey between Pajarito Canyon to the south and Cañada del Buey to the north. TA-54 includes four MDAs designated as G, H, J, and L; a waste characterization, container storage, and transfer facility; active TRU waste and MLLW waste storage and low-level waste (LLW) disposal operations at Area G; active hazardous and mixed low-level (MLLW) waste storage operations at Area L; and administrative and support areas. MDA J has previously under-gone closure. Area G is a waste management and disposal area, used for the disposal and storage of radioactive wastes since 1957. Since August 2015, Area G has been in warm standby and provides minimal operations to support safety, compliance, and nitrate salt remediation. Located within Area G, MDA G covers 63-acres. MDA G contains 334 active and inactive waste management units, which include 36 pits, 294 shafts, and 4 trenches. In 1971, Area G began use for the retrievable storage of TRU waste. There are two pits, four trenches and 60 shafts that contain retrievable TRU waste. Thirty-three of the shafts contain TRU waste that may present unique problems for retrieval. In 1986, segregation of MLLW was initiated at Area G for treatment and temporary storage or for off-site disposal. Area G is the only active LLW disposal facility at the Laboratory. Current operations at Area G include storage and characterization of TRU and mixed TRU waste destined for off-site disposal at the Waste Isolation Pilot Plant (WIPP) in southeastern New Mexico and the storage of MLLW destined for off-site treatment and/or disposal. Several above-ground container storage units (CSUs) are currently used for storage of containerized MLLW and/or mixed TRU wastes. These consist of asphalt pads and associated fabric domes or other structures. As defined by the Consent Order, MDA G contains 229 of the 334 subsurface waste management units at Area G. These MDA G disposal units include 32 pits, 193 shafts, and 4 trenches and contain LLW, MLLW and TRU waste. The remaining 105 solid waste management units (SWMUs) include RCRA-regulated landfill and storage units and DOE-regulated LLW disposal units. The TA-54 closure project must ensure that continuing waste operations at Area G and their transition to an interim or enduring facility are coordinated with closure activities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Austin Sustainable and Holistic Integration of Energy Storage and Solar PV [Austin SHINES]. Final Report, Version 2

The Austin SHINES project and solution is a software management platform, for an electric grid with a high penetration of dispersed photovoltaic (PV) solar generation sites, which maintains the traditional power quality and reliability associated with grid service. This project developed and deployed the platform as a Distributed Energy Resource Management System (DERMS), engaging multiple advanced controls, to evaluate operation and optimization of a fleet of diverse DER assets, installed at several locations among Austin Energy’s customers and distribution system. The project also produced a methodology to create a replicable DERMS template, adaptable to other regions and market structures. Last, Austin SHINES aimed to demonstrate the solution’s methodology would enable the DER grid ecosystem to serve load at a technical cost (System Levelized Cost of Electricity, or System LCOE) of less than the U.S. Department of Energy SHINES program metric of $0.14/kWh, in a defined boundary, while enabling a high penetration of distributed PV. Research was categorized in 6 reports (Final Deliverables = FD) listed below, with titles and descriptions indicating which area of understanding was investigated: FD-1: System Levelized Cost of Electricity (System LCOE) Methodology The creation and use of the System LCOE to Serve Load metric that encompasses the holistic, system-level costs and benefits of all resources, and enables them to be evaluated based on their ability to support an efficient and low-cost integrated grid ecosystem. FD-2: Software Platform Product Description The creation of new DER control methodologies deployable within a utility-grade software platform that enable DER's to maximize their benefit within a grid, that is capable of serving load enabling a high penetration of distributed PV generation. FD-3: Optimal Design Methodology Optimal design methodologies for individual DER installations that enable utilities to determine the optimal combinations and sizing for individual DER sites. FD-4: Austin SHINES Ownership and Operation Models for DER System Performance A comparison of multiple DER aggregation and ownership methodologies including direct utility control, third-party aggregator, and autonomous. FD-5: Economic Modeling & Optimization A comparison of multiple DER technology mixes and configurations within the distribution system, providing insight into an optimal blend of technologies that best enable the distribution system to serve load at the lowest cost at high penetrations of solar. FD-6: Fielded Assets Deployed DER assets within the Austin Energy SHINES circuits. Austin SHINES provided an opening for state-of-the-art technology products to be deployed, providing a rich opportunity for improving how each of the products perform as stand-alone products, and in concert with other complementary products. The Austin SHINES project comprised of two key metrics for System LCOE: SystemLCOE_SHINES<$0.14/kWh Modeled ΔSystemLCOE_SHINES/ΔSystemLCOE_Base≥20% at same solar penetration The System LCOE calculation uses the costs of the utility-owned infrastructure as it exists today, the cost of the DERs that exist in the system today, and the cost of the purchase of energy from ERCOT wholesale markets over the course of the calendar year. All costs are on an annualized basis. The capital and operating costs are derived from the rate case, which produces a yearly cost. The net cost of energy and services imported to the system is integrated over the test year, as is the load served and solar penetration. The first metric was easily achieved by every scenario considered. The goal was set when the Department of Energy’s SHINES Funding Opportunity Announcement was written in 2015 and was a more difficult target at the time. Due mostly to rapidly declining costs for DERs and the significant decrease in the Electric Reliability Council of Texas (ERCOT) energy market prices, which results in lower net cost of energy purchases, the System LCOE is well below this target for all scenarios considered. A fleet of DERs can assume different mixtures, each of which serves the load at a different LCOE. The optimal mixture of DERs serves load at the smallest System LCOE. The second metric (hereinafter %delta metric) asks that the holistic DERMS controls reduce the incremental cost above the baseline of going to a high solar penetration future by at least 20% as compared to the case of a DER deployment with no sophisticated controls (autonomous). Many comparison sets were created throughout this project. Physical technology was installed for informing utility engineering and testing several types of operational control schemes, through the DERMS. The types of operational control which were compared for valuation of the System LCOE Metric were: Holistic control = using the full suite of the DERMS platform to decide and optimize how/why the systems operate depending on weather, market, and reliability signal input. Autonomous control = a local mode at the asset site, wherein a schedule operates the asset, with visibility into performance only No control = the baseline for comparing value against the other two types of control The types of ownership control included: Direct Utility control = the utility dispatches a signal to each asset Third-Party Aggregator = a third party aggregates a fleet of assets and the utility dispatches one signal for all Autonomous = a local mode is set for operation at the asset site, wherein a schedule operates the asset, with visibility into performance only The types of control methodologies deployable within a utility-grade software platform included: Utility Peak Load Reduction = Lower transmission cost obligation Day-Ahead Energy Arbitrage = Realize economic value through price differential Real-Time Price Dispatch = Realize economic value from real-time price spikes Voltage support = Reduce losses and increase solar generation Distribution Congestion Management = Increase local grid reliability Demand Charge Reduction = Lower customer bills and realize system benefit The fielded assets deployed for the project were: Utility Scale Kingsbery Energy Storage System: 1.5 MW / 3 MWh Li-Ion battery storage Mueller Energy Storage System: 1.75 MW / 3.2 MWh Li-Ion battery storage, 7 Energy Storage Units (250 kW each) La Loma Community Solar: 2.6 MW Commercial Scale Aggregated storage installations at 3 sites, with existing solar (300+ kW): One 18 kW / 36 kWh Li-Ion battery storage Two 72 kW / 144 kWh Li-Ion battery storage Residential Scale Aggregated storage installations: -Six stationary battery storage systems (10 kWh each) at homes with existing solar -One Electric Vehicle installed as Vehicle-to-Grid (V2G) Utility-Controlled Solar via Smart Inverters at 12 homes Autonomously-Controlled Smart Inverters at 6 homes Over the course of the project, Austin SHINES undertook installing more than 3 MW of distributed battery energy storage, smart PV inverters, a DER control platform, and other enabling technologies utilizing customer and utility locations and aggregation models. All of these resources were to be integrated and optimized at the utility level. DER assets and control methodologies were designed to achieve a credible pathway to a System LCOE for energy delivered to load of $0.14//kWh or less by 2020, while maximizing distributed solar generation and maintaining acceptable standards of power quality. The project also established a template for other regions to follow, to maximize the adoption of distributed solar PV in support of an economic and efficient grid. In total, the Austin SHINES project added value to the DER subject area in each layer of integration. From utility, to commercial to residential scales, the sheer hierarchy of communication and coordination was a significant accomplishment in addition to learnings from what these communications revealed was unique to each. Economically, the most effective method demonstrated was the criticality of planning phases. Contingencies and multiple projection scenarios helped guide the project to deploy optimal design as close as feasible, in real world conditions. The project and reports will serve public benefit by outlining specific areas of DER strategy and installation where many stakeholders and needs can be addressed with improved efficiency. Overall, communities and utilities should use the results to guide the increasing options available for powering the grid with DER, renewables, and carbon considerate energy.

14 SOLAR ENERGY↗

DOE OSTI2018

Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Science Plan

General circulation models and downscaled regional models exhibit persistent biases in deep convective initiation location and timing, cloud top height, stratiform area and precipitation fraction, and anvil coverage. Despite important impacts on the distribution of atmospheric heating, moistening, and momentum, nearly all climate models fail to represent convective organization, while system evolution is not represented at all. Improving representation of convective systems in models requires characterization of their predictability as a function of environmental conditions, and this characterization depends on observing many cases of convective initiation, non-initiation, organization, and non-organization. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) experiment in the Sierras de Córdoba mountain range of north-central Argentina is designed to improve understanding of cloud life cycle and organization in relation to environmental conditions so that cumulus, microphysics, and aerosol parameterizations in multiscale models can be improved. The Sierras de Córdoba range has a high frequency of orographic boundary-layer clouds, many reaching congestus depths, many initiating into deep convection, and some organizing into mesoscale systems uniquely observable from a single fixed site. Some systems even grow upscale to become among the deepest, largest, and longest-lived in the world. These systems likely contribute to an observed regional trend of increasing extreme rainfall, and poor prediction of them likely contributes to a warm, dry bias in climate models downstream of the Sierras de Córdoba range in a key agricultural region. Many environmental factors influence the convective life cycle in this region, including orographic, low-level jet, and frontal circulations, surface fluxes, synoptic vertical motions influenced by the Andes, cloud detrainment, and aerosol properties. Local and long-range transport of smoke resulting from biomass burning as well as blowing dust are common in the austral spring, while changes in land surface properties as the wet season progresses impact surface fluxes and boundary-layer evolution on daily and seasonal time scales that feed back to cloud and rainfall generation. This range of environmental conditions and cloud properties coupled with a high frequency of events makes this an ideal location for improving our understanding of cloud-environment interactions. The following primary science questions will be addressed through coordinated first U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF1), mobile C-band Scanning ARM Precipitation Radar (C-SAPR2), ARM Aerial Facility (AAF) Gulfstream-1 (G-1), and guest instrument observations: How are the properties and life cycles of orographically generated cumulus humulis, mediocris, and congestus clouds affected by environmental kinematics, thermodynamics, aerosols, and surface properties? How do these cloud types alter these environmental conditions? How do environmental kinematics, thermodynamics, and aerosols impact deep convective initiation, upscale growth, and mesoscale organization? How are soil moisture, surface fluxes, and aerosol properties altered by deep convective precipitation events and seasonal accumulation of precipitation? This multi-faceted experiment involves a long-term 7-month extended operational period (EOP, 1 October, 2018–30 April, 2019) as well as a 1.5-month intensive operational period (IOP, 30 October–13 December) that will include G-1 observations coinciding with the international multi-agency Remote Sensing of Electrification, Lightning, and Meso-scale/Micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign.

Convection, Andes, Argentina, cloud life cycle, cl↗

DOE OSTI2025

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html 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 The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. 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 OSTI2018

Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts

The increased penetration of renewable energy has significantly changed the conditions and the operational timing of the electricity grid. More flexible, faster ramping resources are needed to compensate for the uncertainty and variability introduced by renewable energy. Distributed energy resources (DERs) such as distributed generators, energy storage, and controllable loads could help manage the power grid in terms of both economic efficiency and operational reliability. In order to realize the benefits of DERs, coordination and control approaches must be designed to enable seamless integration of DERs into the power grid. Transactive coordination and control is a new approach for DER integration, where individual resources are automated and engaged through market interaction. Transactive approaches use economic signals—prices or incentives—to engage DERs. These economic signals must reflect the true value of the DER contributions, so that they seamlessly and equitably compete for the opportunities that today are only available to grid-owned assets. Value signals must be communicated to the DERs in near-real time, the assets must be imbued with new forms of distributed intelligence and control to take advantage of the opportunities presented by these signals, and they must be capable of negotiating and transacting a range of market-driven energy services. The concepts of transactive energy systems are not new, but build upon evolutionary economic changes in financial and electric power markets. These concepts also recognize the different regional structures of wholesale power markets, electricity delivery markets, retail markets, and vertically integrated service provider markets. Although transactive energy systems are not revolutionary, they will be transformational in their ability to provide flexibility and operational efficiency. A main goal of this research is to establish useful foundation for analysis of transactive energy systems and to facilitate new transactive energy system design with demonstrable guarantees on stability and performance. Specifically, the goals are to (1) establish a theoretical basis for evaluating the performance of different transactive systems, (2) devise tools to address canonical problems that exemplify challenges and scenarios of transactive systems, and (3) provide guidelines for design of future transactive systems. This report, Part 1 of a two part series, advances the above-listed research objectives by reviewing existing transactive systems and identifying a theoretical foundation that integrates payoff functions, control decisions, information privacy, and mathematical solution concepts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2017

Evaluating the Subjective Straight Ahead Before and After Spaceflight

Introduction. This joint European Space Agency/NASA pre- and post-flight study investigates the influence of exposure to microgravity on the subjective straight ahead (SSA) in crewmembers returning from long-duration expeditions to the International Space Station (ISS). The SSA is a measure of the internal representation of body orientation and to be influenced by stimulation of sensory systems involved in postural control. The use of a vibrotactile sensory aid to correct the representation of body tilted relative to gravity is also tested as a countermeasure. This study addresses the sensorimotor research gap to "determine the changes in sensorimotor function over the course of a mission and during recovery after landing." Research Plans. The ISS study will involve eight crewmembers who will participate in three pre-flight sessions (between 120 and 60 days before launch) and then three post-flight sessions on R plus 0/1 day, R plus 4 days, and R plus 8 days. Sixteen control subjects were also tested during three sessions to evaluate the effects of repeated testing and to establish normative values. The experimental protocol includes measurements of gaze and arm movements during the following tasks: (1) Near & Far Fixation: The subject is asked to look at actual targets in the true straight-ahead direction or to imagine these targets in the dark. Targets are located at near distance (arm's length) and far distance (beyond 2 meters). This task is successively performed with the subject's body aligned with the gravitational vertical, and with the subject's body tilted in pitch relative to the gravitational vertical using a tilt chair. Measures are then compared with and without a vibrotactile sensory aid that indicates how far one has tilted relative to the vertical; (2) Eye and Arm Movements: The subject is asked to look and point in the SSA direction in darkness and then make horizontal and vertical eye or arm movements, relative to Earth coordinates (allocentric) and to the subject's head/body reference (egocentric). This task is successively performed with the subject's body aligned with the gravitational vertical, and with subject's body tilted in roll using a tilt chair; (3) Linear Vestibulo-Ocular Reflex: The subject is asked to fixate actual visual targets at near and far distances in the true straight-ahead direction, and to evaluate the distance of these targets. The subject is asked to continue fixating the same imagined targets in darkness while he/she is passively accelerated up and down on a spring-loaded vertical linear accelerator. Results. In the control subject population, the perceived tilt angles, translations, and distances were remarkably close to the actual values. The pointing tasks indicated that the orientation of arm saccades was influenced by both the gravitational vertical and the body idiotropic vector. Repeating the testing did not reveal any significant changes. Preliminary results obtained in three crewmembers before and after flight will also be presented. Applications. A change in an individual's egocentric reference might have negative consequences on evaluating the direction of an approaching object or on the accuracy of reaching movements or locomotion. Consequently, investigating how microgravity affects the target location will have theoretical, operational, and even clinical implications for future space exploration missions. The use of vibrotactile feedback as a sensorimotor countermeasure is applicable to balance therapy applications for patients with vestibular loss and the elderly to mitigate risks due to loss of spatial orientation.

Campbell, D. J.↗

DOE OSTI2018

Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2024

Flight Mechanics Modeling and Simulation of the Earth Entry System

Introduction: The Mars Sample Return (MSR) Campaign being planned by NASA and ESA has the ambitious goal to return Mars samples back to Earth. This international collaboration had developed a concept of operations that included a ESA-designed Earth Return Orbiter (ERO) and NASA-designed Capture, Containment, and Return System (CCRS). The Earth Entry System (EES), consisting of a protective aeroshell that houses the samples as well as sample containment vessels, would conduct entry, descent, and landing (EDL) on a direct Earth trajectory. The EES would enter on a spin-stabilized ballistic trajectory with the goal to passively achieve aerodynamic stability throughout all regions of flight. The EDL sequence would end with the EES impacting the soft playa soil of the Utah Test and Training Range (UTTR). As of the submission of this abstract, the MSR campaign is undergoing a re-architecture leading to a pause in EES development. However, the novel approaches developed in flight mechanics modeling and simulation can significantly benefit the greater IPPW community in the development of Earth return vehicles. This paper will present the latest state of EES flight mechanics modeling and simulation. The paper will highlight the simulation architecture developed and key lessons learned from understanding of EDL trajectory sensitivities. Modeling and Simulation: Figure 1 provides a high-level concept of operations for the approach, entry, descent, and landing (AEDL) phase of the CCRS-portion of MSR. The objective of EES flight mechanics is to model and simulate the EES trajectory from ERO separation to ground impact at UTTR. A variety of flight mechanics simulation models were utilized to model both exo-atmopsheric and atmospheric portions of flight. 42, a 6-DOF simulation developed at Goddard Space Flight Center, is utilized for propagating the attitude of EES during exo-atmospheric flight. 42 allows for a variety of spin eject mechanism scenarios to be simulated for analysis. 10 minutes prior to entry, the 42 states are handed off to the EDL sims. The prime EDL sim utilized by EES is the Program to Optimize Simulated Trajectories II (POST2), a 6-DOF sim developed at Langley Research Center, and the independent verification and validation EDL sim utilized is DSENDS, a 6-DOF sim developed at Jet Propulsion Laboratory. Figure 2 provides a visualization of the flight mechanics simulation model flow through various points in the AEDL phase. Due to the existence of a variety of sim models, the EES flight mechanics team developed processes for data hand-off. These processes included the development of a centralized coordinate frame document, utilization of a single, centralized simulation input document for all sims to reference, and hand-off files containing both the technical data to be ingested by other flight mechanics sims as well as annotations of modeling assumptions utilized to generate the data. Figure~\ref{fig:post2simarchitecture} provides an overview of the POST2 sim architecture wherein POST2 ingests numerous subsystem models and input files. The dispersed state file generated by MONTE provides the position/velocity state of the trajectory while the 42 Handoff file provides the attitude. The aerodynamics database, delivered by the EES aeroscience team, is utilized to simulate the aerodynamic forces and moments experienced during EDL. A custom atmosphere model, developed by EES atmosphere team, is utilized to simulate the anticipated atmosphere environment around the region of Earth through which the EES trajectory flys. These inputs and subsystem models can be varied depending on the AEDL flight mechanics scenario being simulated. Monte Carlo simulations are utilized to generate statistical AEDL performance metrics in the form of scorecards and violin plots. Furthermore, outputs from the POST2 simulation are utilized for follow-on analyses including aerothermal and landing performance. \section{Flight Mechanics Lessons Learned} Though the EES flight mechanics team uncovered a variety of lessons learned through the analysis conducted to support CCRS through preliminary design review, this paper will highlight the most important lessons. A key AEDL performance goal is to ensure the landing footprint of EES remains on the UTTR south range. A common modeling strategy used in EDL analysis is One-Variable-At-a-Time (OVAT). OVAT analysis provides insight into the key drivers that affect AEDL performance metrics. Figure 3 shows the landing ellipses for single dispersion sources as compared to the baseline aggregate of all dispersions. The figure shows that atmosphere winds alone dominate the size of the footprint ellipse (note: EES does not use a parachute unlike previous Earth-return missions and is in wind-driven free fall for ~5min). The significance of the wind led the EES flight mechanics team to pursue the development of a Custom Atmosphere Model [4], in lieu of EarthGRAM [1], built on actual radiosonde wind measurements around the UTTR-region. This decision was driven by the realism in the generated footprint ellipses and lessons-learned from Stardust [5]. These findings will be invaluable for future Earth-return missions in providing an early understanding of the key drivers affecting footprint size and modeling considerations for which to account. Another lesson learned is tied to the AEDL performance goal of achieving passive stability throughout all regions of flight. It is well understood that blunt-body aeroshells are less stable as they transition from supersonic to subsonic. Eliminating a backshell does help improvestability; however, other phenomena such as roll-induced instability during terminal descent can still arise. The EES flight mechanics team developed stability metrics as tools to better understand the causes of and better predict the onset of dynamic instability. These tools were built upon analytical models developed by Jaffe [3] and Murphy [2]. The tools were shown to both be very accurate in correlation with actual unstable cases and useful in developing stability margin policies based on the vehicle design and simulation considerations (e.g. sphere-cone angle change, mass change, wind turbulence). These tools allowed for the current EES design to demonstrate the ability to achieve passive stability and can be an invaluable tool for consideration in the design of parachute-less Earth-return vehicles.

Rohan Deshmukh↗

NASA NTRSDate not supplied

Neuro-Vestibular Examination During and Following Spaceflight (Vestibular Health)

BACKGROUND Adaptation to microgravity during spaceflight causes neurological disturbances that are either directly or indirectly mediated by the vestibular system. These disturbances could include space motion sickness, spatial disorientation, and cognitive impairment, as well as changes in head-eye coordination, vestibulo-ocular reflexes, and control of posture and locomotion. Otolith-mediated reflex gains appear to adapt rapidly during spaceflight and after landing. However, animal studies have shown that structural modifications of the vestibular sensory apparatus develop during long-duration spaceflight. To date, no studies have characterized the severity of vestibular syndromes experienced by astronauts as a function of the duration of spaceflight or whether the effects are caused by changes at the peripheral end organs, midbrain, cerebellum, or vestibular cortex. OBJECTIVES We will investigate temporal vestibular changes in crewmembers of short, 6-month, and one-year missions to identify trends in adaptation of vestibular health and performance in orbit and after landing. We will also determine whether the vestibular organs and/or the central vestibular system undergo structural changes during long-duration exposure to microgravity, which could cause vestibular disorders when transitioning to a different gravitational environment. METHODS Recordings of eye, head, and body movements, as well as subjective reports of perception of motion, will be used to determine the presence of abnormal eye movements, dysmetria, motion sickness symptoms, and illusions of motion during head or body movements. This includes characterization of temporal trends in central compensation for vestibular (otolith) asymmetry. Pre-flight data will be collected 90 days before the flight. In-flight tests will be performed early in the mission and once every one or two months thereafter. Post-flight examinations will be performed on the following days after return (R) from the mission: R+0, R+4, R+9, and R+30. Ground-based control tests will be performed on healthy volunteers in the laboratory to estimate mean normative responses. CONTROL RESULTS Thirty-two healthy (non-astronaut) control subjects performed the same ground test procedures as planned for crewmembers. In addition to establishing a normative database, these data were used to calculate vestibular asymmetries from perceptual reports during unilateral centrifugation, oculomotor responses during visual-alignment tasks, vestibulo-ocular reflex gain during head-impulse tests, and body rotation during stepping tests. A significant correlation was observed between asymmetries of subjective visual vertical and verbal report during unilateral centrifugation. Another significant correlation was observed between the asymmetries of ocular alignment, vestibulo-ocular reflex gain, and body rotation. These findings in a healthy cohort may help us better understand changes in vestibular asymmetries in crewmembers during and following spaceflight. RELEVANCE If the observed symptoms in crewmembers are more deleterious after the year-long missions than those documented after 6-month missions, then relevant countermeasures will be required to maintain the health and operational performance of astronauts during longer missions. Depending on the etiology of the vestibular syndrome revealed by these tests, countermeasures will be proposed based on vestibular rehabilitation therapies currently used in patients with vestibular disorders, such as habituation, gaze stabilization, and/or balance training exercises. ACKNOWLEDGEMENT This work is supported by the NASA’s Human Research Program Human Health Countermeasures Element.

T R Macaulay↗

DOE OSTI2026

IM3 Open Source Data Center Atlas

IM3 Open Source Data Center Atlas Description This dataset contains locations of existing data center facilities in the United States. Data center locations were derived from OpenStreetMap (OSM), a crowd-sourced database. Data points from OSM are processed in various ways to determine additional variables provided in the data including: facility area (square feet), associated US county, and US state. This dataset can be used to identify areas of concentrated data center development and inform government and private sector planning strategies for future buildout of data centers and the infrastructure necessary to support it. Usage Notes Validation of OSM-derived data center locations is an ongoing development under the IM3 project, and the database will be updated as new information becomes available. In some instances, both the data center area (e.g., campus) and individual data center buildings are included as overlapping areas in the database. Both values are retained. Data center points, buildings, and campus areas are provided as separate layers in the downloadable data package. Note that data items are not necessarily complete across layers. That is, a specific data center may only be present as a single point geometry in the "point" layer while other data centers are represented in both the campus and building layers. In some cases, data center campuses and/or buildings straddle a county boundary line. Mappings to both counties are retained in the database as separate rows. These data rows will have the same data center id information, but each will have different county information. Crowd-sourced data, by nature, relies on individuals and communities to provide information. As a result, some data may be missing where it has not yet been reported. As we collect information on additional data center locations and as OSM receives additional contributions, the database will be updated to capture additional data points not yet shown. Data items will occasionally be removed from OSM if they are misidentified, if they no longer exist, if they are duplicates of another item, or similar. For that reason, updated versions of this database may not contain all data center locations included in previous versions. Technical Information Data is available for download under the following formats: GeoPackage (GPKG) CSV Geospatial data is provided in the WGS84 (EPSG:4326) coordinate reference system. The GeoPackage download contains the following layers. See usage notes for more information. "point" "building" "campus" The "point" layer includes all data from OSM that had POINT geometry type (i.e., individual coordinates). The "building" layer includes all OSM data that did not have POINT geometry and where the building tag in the OSM export was neither equal to "no" or null. Data that did not meet the "point" or "building" qualification was assumed to be a facility campus and included in the "campus" layer. The dataset contains the following parameters. Variables provided by OSM are labeled with (OSM-provided). id - unique identification number (OSM-provided with prefix of "node/", "relation/" and similar attributes removed) state - name of US state state_abb - two letter US state abbreviation state_id - state ID number county - name of US county county_id - county ID number ref - reference numbers or codes (OSM-provided) operator - the name of the company, corporation, or person in charge facility (OSM-provided) name - name of facility (OSM-provided) sqft - surface area of facility polygon, measured in square feet. Only available for "building" and "campus" layers lat - latitude of data centroid point lon - longitude of data centroid point type – represented spatial information. One of "point", "building", or "campus". geometry – POLYGON geometry of area footprint (in "campus" and "building" layers) or POINT geometry of locations (in "point" layer). This parameter is not included in the csv download. Attribution Data center locations were derived from OpenStreetMap, which is made available at openstreetmap.org under the Open Database License (ODbL). US state and county boundary information was collected from the US Census Bureau for the year 2024, which is made publicly available at https://www.census.gov/geographies/mapping-files.html 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 The IM3 Open Source Data Center Atlas is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. 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↗