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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗
DOE OSTI2024

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. Here, the associated notebooks allow to reproduce the reported analyses and apply them to other systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Electrical and Thermal Load Impacts of Three District Heating and Cooling Designs for an Existing Community in Washington, DC

District energy systems that provide building heating and cooling are a promising option to provide low-cost, energy efficient heating and cooling solutions for communities. As some buildings move to electrified heating designs, there may be significant increases in electric grid demand, particularly in the winter. District energy systems may be able to help reduce these high electrical demands on the electric grid, while also providing decreased overall energy consumption with an increased flexibility in electrical energy usage. To evaluate and compare district options for district-based heating and cooling, an existing neighborhood of Washington, DC consisting of 35 existing buildings was selected as a case study. This study models and compares a fourth generation (4G) district heating and cooling systems that provide hot and chilled water from a central plant directly to each prosumer and a fifth generation (5G) district heating and cooling systems that provide near-ambient water via geothermal boreholes to interface with an energy transfer station with a heat pump at each prosumer. These district systems are compared against a baseline of the non-connected buildings with their self-contained and current HVAC systems. The neighborhood was selected as a good candidate for a district system for its diversity of loads (mix of different commercial and property types) and the availability of building characteristics. URBANopt District Energy Systems (DES) was used to model the buildings and create initial Modelica models for the district systems. Finally, the DES models were tuned and simulated in Dymola, and the outputs were post-processed for verification and to calculate the electrical and thermal grid impact metrics. This is the first documentation of this workflow and its complete analysis. The three designed systems are compared via their grid metrics including: total energy consumption, electrical demand peak loads, daily peak-to-valley ratios, and system ramping. A detailed discussion is provided about how each system impacts and interacts with the electric grid.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Adiabatic and radiative cooling are both important causes of aerosol activation in simulated fog events in Europe

Aerosol–fog interactions affect the visibility in, and life cycle of, fog and are difficult to represent in weather and climate models. Here we explore processes that impact the simulation of fog droplet number concentrations (N d ) at sub-kilometer scale horizontal grid resolutions in the UK Met Office Unified Model. We modify the parameterization of aerosol activation to include droplet activation by radiative cooling in addition to adiabatic cooling and determine the relative importance of the two cooling mechanisms. We further test the sensitivity of simulated N d to: (a) interception of droplets by trees and buildings, (b) overestimation of updrafts in temperature inversions (which leads to artificially high N d values), and (c) potential mechanisms for droplet deactivation due to downward fluctuations in supersaturation. We evaluate our model against observations from the ParisFog and LANFEX field campaigns, building on evaluation described in the companion paper. Including radiative cooling in the activation mechanism improves how accurately we represent the liquid water path and the vertical structure of the fog in our LANFEX case study. However, with radiative cooling, the N d are overestimated for most of the ParisFog cases and for the LANFEX case. The time-averaged overestimate exceeds a factor of three (the normalized mean bias factor exceeds 2.0) in 4 out of 11 ParisFog cases. Our sensitivity studies demonstrate how these overestimates can be mitigated. Assuming the overestimate affects both radiative and adiabatic cooling, we find that although radiative cooling is more often the dominant source, both cooling sources can sometimes dominate activation.

Ghosh, Pratapaditya [Carnegie Mellon University, P↗
DOE OSTI2023

Climate-Water-Electricity Interactions in the U.S. Under Alternative Decarbonized Futures

The U.S. electric sector is rapidly evolving, with widespread renewable energy deployment, building and transport electrification, and nuanced decarbonization policies. Climate change influences the supply and demand for electricity by altering water resources for cooling and hydropower, thermal plant efficiencies, and heating and cooling demand. This research combines global climate model data, asset-level water and streamflow modeling, and electric sector capacity expansion modeling to study climate-water-electricity interactions across myriad future climate and electricity scenarios that consider electrification and decarbonization using renewable, hydrogen, carbon capture, and nuclear technologies. Multi-model integration leads to insights into power-water sector interactions and their impacts on grid economics and environmental outcomes.

climate change↗
DOE OSTI2026

CMIP7 data request: land and land ice priorities and opportunities

The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

Li, Yue [Univ. of California, Los Angeles, CA (Uni↗
DOE OSTI2023

Los Angeles Air Force Base Vehicle-to-Grid Demonstration (Final Project Report)

Electrification of non-tactical vehicle fleets represents a key efficiency and energy security objective for the United States Department of Defense. To achieve electrification, the department targeted vehicle-to-grid services as a way to decrease the overall cost of operating the vehicle fleet and achieve rough parity with traditional internal combustion engine vehicle fleets. This report describes efforts to aggregate a fleet of bi-directional electric vehicles and charging stations to provide regulation up and regulation down in the California Independent System Operator ancillary services market. A 29-vehicle electric vehicle demonstration fleet, consisting of mixed purpose and duty vehicles such as sedans, pickups, vans, and medium-duty trucks, was deployed at the Los Angeles Air Force base. The fleet provided frequency regulation to the California Independent System Operator’s wholesale electricity market to determine the capability of recouping some of the additional costs of procuring electric vehicles and their supporting infrastructure. Lawrence Berkeley National Laboratory, with its partner Kisensum, LLC, developed the fleet scheduling, optimization, and control software to allow the vehicle fleet at the air force base to participate in the ancillary services markets. This report focuses on the control software and market interactions, the significant challenges faced and solutions devised to address them, and examines the potential of using the electric vehicle fleet as an energy storage resource for the base buildings, an application known as vehicle-tobuilding, in providing demand response and emergency backup power. The report discusses key findings related to providing frequency regulation to the California Independent System Operator market, electric vehicle fleet performance, compatibility of varying resource parameters of vehicle fleet aggregation, the need for automated methods for communicating hour-ahead energy bidding, challenges related to battery capacity and charge/discharge rates, and monthly settlement revenue.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Artificial Intelligence for Enhancing Multiscale Analysis: Buildings Focus

This project aims to develop multi-scale building energy data, potentially improving the representation of the U.S. buildings sector in GCAM-USA, an U.S.-focused human-energy-Earth systems model. Existing building energy datasets are typically limited to national or regional levels, which constrains the ability of models to capture fine-scale human-energy-Earth systems interactions and reduces their relevance for decision-making on issues such as energy security, resilience, and energy planning. By leveraging AI and advanced data integration methods, this work fuses multiple existing datasets to enhance the physical and geographic representation of both residential and commercial building energy use. So far, progress includes processing residential building data, designing the data structure for commercial buildings, and testing AI approaches for integrating datasets and addressing spatial-temporal gaps. This effort can not only advances GCAM-USA’s capability in modeling the buildings sector but also supports broader DOE missions, such as developing digital testbeds, enhancing grid resilience analysis, and improving building–energy system modeling at decision-relevant scales.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

tell: a Python package to model future total electricity loads in the United States

The purpose of the Total ELectricity Load (tell) model is to generate 21st century profiles of hourly electricity load (demand) across the Conterminous United States (CONUS). tell loads reflect the impact of climate and socioeconomic change at a spatial and temporal resolution adequate for input to an electricity grid operations model. tell uses machine learning to develop profiles that are driven by projections of climate/meteorology and population. tell also harmonizes its results with United States (U.S.) state-level, annual projections from a national- to global-scale energy-economy model. This model accounts for a wide range of other factors affecting electricity demand, including technology change in the building sector, energy prices, and demand elasticities, which stems from model coupling with the U.S. version of the Global Change Analysis Model (GCAM-USA). tell was developed as part of the Integrated Multisector Multiscale Modeling (IM3) project. IM3 explores the vulnerability and resilience of interacting energy, water, land, and urban systems in response to compound stressors, such as climate trends, extreme events, population, urbanization, energy system transitions, and technology change

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Designing Hydropower Flows to Balance Energy and Environmental Needs (HydroWIRES Topic A Final Project Report)

Hydropower is expected to play a new role in the US electricity grid as more variable renewable energy sources like wind and solar come online. Wind and solar generation increase fluctuations in electrical supply increasing the value of flexible generation sources that can quickly ramp generation up and down. The flexible generation hydropower can provide as well as the ancillary services (e.g., frequency and voltage regulation and reserves, black start capability) it provides for balancing and stabilizing the power grid are predicted to be of increased value in these future grid scenarios. Yet, the flexibility of hydropower may come with environmental costs due to up- and down-ramping of hydropower plants (i.e., quickly increasing or decreasing generation flows, respectively) which may strand fish, dewater or scour fish nests, alter habitat, or create unsafe recreational conditions that may be unacceptable to participants in the hydropower regulatory process. These types of environmental impacts are often mitigated through environmental flow requirements that specify minimum or maximum flow releases, or ramp-rates changes allowed at a hydropower facility. While it is not currently known to what degree electrical grid reliability could be affected by environmental flow requirements, gaining a better understanding of these interactions before the grid becomes more deeply decarbonized can help define what policy, regulation, or infrastructure may be needed to support the clean energy transition. As the future grid will rely on hydropower to provide both flexibility and robust environmental protections, the analyses and tools described in this report are centered on making mechanistic linkages between energy and the environment in hydropower systems. This understanding of energy-environment linkages can provide the foundational understanding needed to quantitatively assess the trade-offs between the increased generation flexibility that hydropower will be expected to provide and the environmental impacts of this flexibility. This report seeks to provide an objective foundation for building future science and tools that can be used by a broad spectrum of the hydropower community that is involved in licensing or environmental regulatory proceedings tasked with balancing energy and environmental objectives through flow management.

13 HYDRO ENERGY↗
DOE OSTI2025

Thermal and electric multidomain dynamic model for integration of power grid distribution with behind-the-meter devices

As renewable energy sources like solar and wind power become more integrated into the grid, coordinated control of behind-the-meter devices is crucial for enhancing grid flexibility and reliability and for meeting cost targets, with standardized models being developed to support this transition. The increasing flexibility and uncertainty of integrated renewable energy grids, along with interactions between various subsystems, make traditional steady-state modeling insufficient to capture transient and dynamic behaviors. Current models (e.g., composite load and battery equivalent models) focus on thermodynamic or electrical characteristics but overlook critical electromechanical interactions. This limits the ability to share performance information for grid services and hampers fast dynamic simulations. In addition, motor stalling is usually triggered by a fault event and attributed to the characteristics of the mechanical torque of the motor, resulting in absorption of a large amount of reactive power during the stalling period. Further, this significant withdrawal of reactive power will deteriorate the dynamic voltage stability of power grids and cause delayed voltage recovery. Therefore, an in-depth modeling of the thermodynamics or mechanical torque is essential to study the impacts of the realistic torque characteristics of those behind-the-meter devices on power system voltage stability. This study developed a dynamic multidomain model for building HVAC systems, such as air-source heat pumps, to simulate their thermal and electrical responses to grid transients. The model can accurately predict power metrics with a mean absolute percentage error of 10%, by validating against with power system computer-aided design performance data. Case studies demonstrate the model capability of capturing the transient response to sudden voltage changes, rapid load fluctuations, and system shutdowns respectively. During a sudden voltage drop (30% for 0.1s), a fully loaded heat pump’s motor speed dropped, continued declining, and shut down after 3.6s, with severe power oscillations and a torque spike. A partially loaded unit experienced temporary oscillations but stabilized. Under higher building loads, compressor speed increased from 64% to 100%, with power and torque rising before stabilizing. In safety-triggered shutdowns, power decreased after minor fluctuations, and torque briefly spiked before dropping to zero.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Hidden Flexibility of the Natural Gas Network for Electric Power Operations: A Case Study of a Near-Miss Winter Event

The U.S. power sector has become increasingly reliant on gas pipeline networks to deliver fuel to natural gas power plants. In addition to supplying relatively low-cost fuel, gas networks offer generators flexibility in their operations through the ability to deliver fuel when needed by using gas storage facilities or linepack if the gas network is at an operating point below its design capacity. However, disruptions or stress events on the gas network - like those occurring in the Northeast and Texas in recent years - can result in limitations on gas availability to generators at times when generation is in short supply. Here we examine a period of stress that occurred in the winter of 2022 in the Western United States. Using data on the region's natural gas pipeline network and electric generators, we build an integrated gas and electric model that closely replicates the actual dispatch of the period. We then evaluate the implications of removing flexibility employed by the gas network operator, which during that period curtailed scheduled gas deliveries to other parties to increase deliveries to natural gas power plants, which requested more gas than initially forecasted. We find that without the flexibility supplied by the gas network operator, there would have been curtailment of gas generation due to gas offtake constraints, requiring the power system operator to redispatch relying on more expensive generation or to potentially shed load. A sensitivity exploring a wind drought further exacerbates the strain, illustrating the potential challenge of managing gas and grid interactions as systems move to higher shares of variable renewable electricity. Based on this example, we discuss potential coordination strategies between the two system operators to ensure that the power system can successfully utilize and rely on the flexibility offered by natural gas networks.

03 NATURAL GAS↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2020

Automated Integration of Continental-Scale Observations in Near-Real Time for Simulation and Analysis of Biosphere–Atmosphere Interactions

The National Ecological Observatory Network (NEON) is a continental-scale observatory with sites across the US collecting standardized ecological observations that will operate for multiple decades. To maximize the utility of NEON data, we envision edge computing systems that gather, calibrate, aggregate, and ingest measurements in an integrated fashion. Edge systems will employ machine learning methods to cross-calibrate, gap-fill and provision data in near-real time to the NEON Data Portal and to High Performance Computing (HPC) systems, running ensembles of Earth system models (ESMs) that assimilate the data. For the first time gridded EC data products and response functions promise to offset pervasive observational biases through evaluating, benchmarking, optimizing parameters, and training new machine learning parameterizations within ESMs all at the same model-grid scale. Leveraging open-source software for EC data analysis, we are already building software infrastructure for integration of near-real time data streams into the International Land Model Benchmarking (ILAMB) package for use by the wider research community. We will present a perspective on the design and integration of end-to-end infrastructure for data acquisition, edge computing, HPC simulation, analysis, and validation, where Artificial Intelligence (AI) approaches are used throughout the distributed workflow to improve accuracy and computational performance.

Durden, David J.↗
DOE OSTI2026

Building a Trusted Roaming Hub [Slides]

The Trusted Roaming Hub is a U.S. Department of Energy-backed initiative led by the National Laboratory of the Rockies (NLR) to address one of the most persistent challenges in electric vehicle (EV) charging: fragmented roaming, inconsistent interoperability, and insufficient digital trust across charging networks. As EV adoption accelerates and charging infrastructure scales nationwide, today's many-to-many integration model between eMobility Service Providers (eMSPs) and Charge Point Operators (CPOs) has become increasingly brittle, costly, and difficult to secure. The Trusted Roaming Hub introduces a neutral, cybersecurity-forward "switchboard" architecture that enables standardized, secure, and scalable roaming interactions across the EV charging ecosystem. Rather than replacing existing networks or commercial relationships, the hub acts as a trusted intermediary that enforces consistent identity, authentication, authorization, and routing across participants improving reliability for drivers, lowering integration burden for industry, and creating a foundation for future grid-interactive charging services. This read-ahead provides an overview of the problem the hub is designed to solve, the core functional and security concepts behind the architecture, the value proposition to key stakeholders, and the near-term trajectory of the work.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Common Grid Services: Terms and Definitions Report

This document is prepared as part of the Department of Energy’s Grid Modernization Laboratory Consortium (GMLC) 2.5.2 project, whose goal is to develop and socialize a common set of grid service definitions relevant to grid-related interactions with distributed energy resources (DER: responsive generation, storage, and loads) and to advance the concept and requirements of the Energy Services Interface (ESI) to the point of launching related interface standards and guides that can be implemented in communication protocols and business process definitions. The notion of grid services is associated with the definition of an ESI because a key principle of the ESI is that it permits coordination between grid operators and DER facilities in a way that is service-oriented with an understanding of performance expectations. The project investigated the current state of North American grid service definitions from various market operators and utilities, as well as the Federal Energy Regulatory Commission and the North American Electric Reliability Corporation, actively used today (Liu et al. 2022). This document builds on that work to propose terms and definitions for a set of grid service types that address operational objectives commonly found in power system operations. These grid service types derive from existing grid services used in bulk electric market operations, recognizing that each operational authority uses somewhat different names with variations on service performance parameters. Finding commonality at the wholesale or bulk system operation level will hopefully engender progress in seeking agreement on grid services for DER engagement at the distribution level in the emerging retail marketplace.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters - CRADA 625 (Abstract)

Commercial heat pump water heater (CHPWH) systems significantly decarbonize the commercial and multifamily sectors by eliminating the reliance on gas-fired water heating. CHPWH systems are also well suited to include load shift controls that enable load-up and shed commands for supporting grid reliability and time-of-use pricing structure. However, they have not had wide adoption due to factors including price, complexity, and perceived risk. Although CHPWHs have been available in the US for decades, they have not made significant market gains in part because the systems have required significant and costly engineering design expertise and proved lackluster performance. Successful widespread market adoption requires a different approach; a shift from the current custom specialized expertise project design and installation to a repeatable approach that requires little specialized knowledge or expertise and can deliver persistent performance. Using this type of holistic systems approach requires effectively integrating four CHPWH system key components: primary air-to-water heat pumps; primary thermal storage tanks, a temperature maintenance system, and a control system which has capabilities to manage the primary heat pump cycles, any back-up, supplemental, or temperature maintenance heating, alarms, and grid connectivity allowing for demand response (DR), and/or load shifting. The project team has developed and will implement a suite of tools to support faster, less expensive, and more reliable field installations of CHPWH technology and with the resulting data used to further improve the tool set. These tools include: (1) A tool for optimizing system size and costs. (2) A tool that predicts annual energy use and overall system efficiency. (3) The Advanced Water Heater Specification (AWHS 8.0) defining the components of a full CHPWH system addressing performance requirements by climate zone. (4) The Qualified Products List: (QPL) of approved products that meet the specifications requirements. (5) Training materials including online on-demand modules, instructor-led training, and virtual interactive video tours of CHPWH installations in multifamily buildings. Demonstration site identification in low-income buildings in underserved communities is currently underway. Preliminarily, the team anticipates having three demonstrations in the Pacific Northwest and three in the Northeast for a total of six sites. After the demonstration sites are finalized, and M&V instrumentation installations are complete, the team will gather performance data and confirm whether the CHPWH systems perform as predicted and use the data to improve the existing tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A geo-visual analysis for exploring the socioeconomic benefits of the heating electrification using geothermal energy

In parallel to population growth and climate change, the rapid pace of urbanization worldwide has led to an enormous increase in energy demand and costs in urban areas. The subsequent energy burden has become an increasing concern for many households in the U.S. Previous studies have revealed that geothermal resources can effectively lower the electricity demand and carbon emissions in large cities. In this paper, we focus on the socioeconomic impacts of geothermal energy on urban systems by presenting an interactive visual analytics dashboard. The dashboard allows urban planners to spatially examine geothermal energy's practical benefits on energy affordability, urban livability, and resilience across the U.S. We compiled a list of socioeconomic metrics by integrating the simulation results from multiple geothermal and building models with multi-domain urban datasets (socioeconomic, demographic, and electricity utility). These metrics are created to characterize the benefits of the heating electrification of buildings using Geothermal Heat Pumps (GHP) for lowering the energy burden of middle-and low-income households nationwide. The visual dashboard employs a combination of multivariate, glyph-based, and geospatial visualization to reveal the variability and patterns in our metrics. We present a pilot study to demonstrate the GHPs' potential as a renewable and affordable solution for increasing the economic and energy grid resilience in U.S cities.

Xu, Haowen↗