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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Impacts of Increasing Electrification on State Fleet Operations and Charging Demand

State fleets represent an enticing opportunity to explore the near-term feasibility of fleet electrification. In many instances, state fleet operations encompass a wide geographic area with fleet locations for many vehicles. Serving these wide areas will require a significant amount of energy and, in the case of electric vehicles (EVs), a significant level of charging power. The peak demand as a result of this charging demand is of interest for fleets, with impacts on both utility bills and installation costs ranking among some of the greatest concerns. The combination of a wide operational area and multiple fleet locations positions state fleets as ideal candidates to understand the impacts of vehicle charging on fleet operations. As the availability of electric drivetrains expands beyond light-duty sedans, fleets need to understand when it will be appropriate operationally and financially to start adding electric drivetrains to their fleets. Throughout this process, it will also be important to understand the charging implications of fleet electrification and the resulting impacts to facility electrical systems. To better understand these considerations, NREL contracted Sawatch Labs to analyze the role that increasing state fleet electrification may have on the charging demand at fleet parking facilities.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Insights Into On-Demand Transit: A Case Study of Houston METRO's curb2curb Service

As modern on-demand transit (ODT) deployments increase, the insights gained from studying real-world systems prove more useful. In this study, a shared ODT system called "METRO curb2curb" in the suburbs of Houston, Texas was studied, with a particular focus on the Missouri City zone, which has the highest ridership out of the four total zones. The Missouri City zone is 18 square miles with an average daily ridership of nearly 400 as of November 2024. In order to gain a passenger perspective, data collection was performed through using the ODT system first-hand, completing 50 rides in March and April of 2023. These rides included a mix of three different types of on-demand travel accommodations - 1.) advance bookings (30 min - 2 hours ahead), 2.) as-soon-as-possible / real-time dispatch bookings, and 3.) no-reservation trips at two designated "anchor points." Wait and travel time were collected for each ride. Anecdotal findings related to ease of locating the driver/vehicle, the process of booking a ride, and popular trip purposes were also documented. This study provides an overview of the METRO curb2curb system, including its history, service provider, service area, ridership, booking options, wait time, and travel time. Intricacies related to different definitions of wait time are included. Finally, relevant considerations for automating the on-demand transit system and discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Addressing Rising Energy Demand Through Innovation

The U.S. is facing a significant increase in energy demand, driven by AI advancements, the rapid expansion of data centers, manufacturing and industrial growth, and the electrification of transportation and buildings. Buildings alone account for approximately 75% of U.S. electricity consumption and 40% of total energy use. To address these challenges, NLR leverages its state-of-the-art research facilities, advanced energy modeling, hardware-in-the-loop emulation, and real-world demonstrations to provide data-driven insights that de-risk emerging energy solutions, increase efficiency and demand flexibility, optimize grid controls, and identify vulnerabilities to enhance energy security. This presentation will highlight our research ecosystem and its role in supporting a more reliable, affordable, and adaptive energy infrastructure in the face of accelerating demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Hourly Electricity Demand Profiles for Each County in the Contiguous United States

This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.

14 SOLAR ENERGY↗
DOE OSTI2026

Meeting the Challenges of Modern Transit through the Integration of Traditional Transit, On-Demand Micro-Transit Services and Vehicle Automation - A Case Study in Chandler, Arizona

Current technology trends of vehicle automation, electrification and on-demand transit are providing new tools to develop effective public mobility services, yet need to be balanced with traditional modes to serve the spectrum of demand effectively and efficiently. Automated vehicle transportation network company (TNC) services, better known as 'Robotaxis' are beginning to proliferate and scale across the US, with Waymo as the lead commercial entity. Chandler, AZ was the first fully automated deployment of Waymo technology. Chandler was also one of the first municipalities in Arizona to roll out micro-transit service to its citizens in 2022. At present the Chandler Flex micro-transit program is beginning to partner with Waymo to augment micro-transit services during peak demands, another first in the nation. However, the mix of services in Chandler relies not only on new technology, but also on traditional modes including fixed route transit and para-transit services. These are blended and balanced such that both old and new modes are applied within the context in which each excel. This presentation will provide background on the Chandler, AZ public mobility services, and the performance metrics that govern their use and selection within the spectrum of development and population density as well as socio-demographics within Chandler. Additionally, the behavioral response of the use of unmanned, automated vehicles employed to augment micro-transit is novel, the first in the nation - and initial feedback from public transit constituents in Chandler will be shared.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2017

Development and Validation of an Automated Simulation Capability in Support of Integrated Demand Management

Integrated Demand Management (IDM) is a near- to mid-term NASA concept that proposes to address mismatches in air traffic system demand and capacity by using strategic flow management capabilities to pre-condition demand into the more tactical Time-Based Flow Management System (TBFM). This paper describes an automated simulation capability to support IDM concept development. The capability closely mimics existing human-in-the-loop (HITL) capabilities, automating both the human components and collaboration between operational systems, and speeding up the real-time aircraft simulations. Such a capability allows for parametric studies that will inform the HITL simulations, identifying breaking points and parameter values at which significant changes in system behavior occur. This paper also describes the initial validation of individual components of the automated simulation capability, and an example application comparing the performance of the IDM concept under two TBFM scheduling paradigms. The results and conclusions from this simulation compare closely to those from previous HITL simulations using similar scenarios, providing an initial validation of the automated simulation capability.

Integrated Demand Management↗
DOE OSTI2019

A Machine-Learning Decision-Support Tool for Travel-Demand Modeling

Utility maximization(UM) models are the lifeblood of virtually all travel demand models (TDM) in practice. Be it the traditional travel demand models or more advanced activity-based models, utility maximization models are used extensively to model and predict myriad travel choices such as location choice, mode choice, route choice etc. More recently machine learning (ML) models are being applied in a variety of contexts to predict choice patterns (product suggestions on Amazon, restaurant suggestions on Yelp etc.,). In the TDM arena, there has been interest in incorporating ML models where they can enhance prediction accuracy. Though there have been sporadic efforts at comparing specific utility maximization models to machine learning models, there is a need for a standard comparison tool which can evaluate ML models against UM models for a given choice context. Addressing this need, we present a tool for applying an array of models including logit, nested logit, neural network, Naive Bayes and decision tree classifiers. The tool is specifically tailored to aid in the deciding the best model for a given choice context and can be used to choose an appropriate model family or to construct a model ensemble to improve upon current modeling standards in travel demand modeling. We test our proposed system on household vehicle count and work schedule targets from the 2017 National Household Travel Survey. Results demonstrate that for some variables, logit are not the most effective models, and the proposed system can aid in selecting a better model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Modeling the Interaction Between Energy Efficiency and Demand Response on Regional Grid Scales: Preprint

With increasing penetration of intermittent renewable generation at grid and distributed scales, flexible building loads can provide significant system value and support the evolving needs of the grid. The growing value of load flexibility may complicate the traditional separation between energy efficiency (EE) and demand response (DR). EE measures may compete in some cases with a building’s DR capabilities but complement one another in other cases. EE can also increase or decrease the need for DR at the system level and change the availability of DR to meet system needs. In this study we present a bottom-up approach to modeling interactive effects between EE and DR in buildings within two regions of the US electricity grid. From a library of building simulation models for different buildings and climates, we synthesize system-level demand profiles and the impacts of potential future EE portfolios. Coupling the underlying building models with a database of DR-enabling technologies, we then compute the quantity of DR that can be delivered in each scenario. The results show that EE and DR interactions are largely driven by the timing of EE savings that are measure-specific and the coincidence with system peak demand that is region-specific. We also find that perspective of the impacts matters – for instance that some EE measures reduce the system need for DR but also reduce the DR potential. Our results imply that utility EE and DR programs developed without considering interactive effects may lead to increased grid-management challenges over the long term.

buildings↗
DOE OSTI2022

Sustainability, Scalability and Resiliency of the Town of Innisfil Mobility-on-Demand Experiment: Preliminary Results, Analyses, and Lessons Learned: Preprint

In 2017, the Town of Innisfil, Ontario launched Innisfil Transit in partnership with Uber, a transportation network company, to provide a subsidized on-demand public mobility service as an alternative to investing in a new fixed-route bus service. The performance of Innisfil Transit is documented in a 2021 Ryerson University report which shows greater cost effectiveness of the mobility provided over the proposed bus alternative (Sweet, Mitra, and Benaroya 2021). This paper expands on those findings by assessing Innisfil Transit with respect to sustainability, scalability, and resiliency. First, we quantify the energy and emissions of this program relative to traditional transit and driving alone across varying powertrains. We then characterize a conservative first-order estimate of the percentage of US communities that fall within a similar spatial-demographic tier as Innisfil. Replicability also hinges on service cost and performance in comparison to average values for low-density transit in the US. Lastly, most transit agencies experienced a significant drop in demand (as much as 90%) with slowly rebounding ridership since the onset of the COVID-19 pandemic. The resiliency of the Innisfil program to the pressures induced by the pandemic is examined in comparison to other transit operations. The lessons learned across these three dimensions complement prior work to better understand the efficiency and sustainability of on-demand public mobility service for low-density communities like Innisfil.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Projecting Electric Vehicle Electricity Demands and Charging Loads

After over a century of petroleum dominance, electric vehicles are rapidly disrupting the transportation energy landscape. At the same time, the electric power systems are undergoing profound changes as variable renewables replace dispatchable fossil generators. It is critically important to understand how transportation electrification will impact electricity demand, including changes in the load shapes that characterize the system, and the value of flexible electric vehicle charging to better balance electricity demand and supply. This talk focuses on methods to project electricity demand for EV charging with high spatiotemporal fidelity to enable electricity system modeling and analysis and summarizes results from recent NREL studies in this area.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Frozen Freedom: Unleashing Grocery Store Demand Flexibility: Preprint

Grocery stores consumed approximately 3% of total electricity used by commercial buildings in the U.S. in 2018 (EIA 2018), representing a unique end-use load profile characterized by the critical use of refrigerated display cases. Exploring demand response (DR) scenarios in grocery stores presents an opportunity to enhance the efficiency and sustainability of surrounding communities. In addition, recent studies demonstrate that implementing control algorithms considering demand flexibility strategies can lead to load and peak reductions in standalone refrigerated display cases. Because small business grocery stores operate on thin margins, the energy bill cost savings DR might provide could make a positive difference toward continued operations. Still, uncertainty remains about the extent of demand flexibility potential controls could provide when coupling refrigeration with whole building operation. To enhance economic viability and grid stability, it is essential to quantify the load flexibility capability of grocery stores. Advanced controls can optimize energy consumption by responding to load shedding, shifting, and DR events, as well as daily Time-of-Use (TOU) rates without compromising food safety. Using both quantitative data and interviews with community-based organizations, we developed a full-size store model and two small store models with controlled refrigerated cases, HVAC, and lighting systems based on actual grocery store properties. Through simulations, we have assessed load flexibility strategies with varied DR events. The results highlight potential for energy and peak reduction with advanced or basic controls. However, interviews and data indicate that more support is needed to make DR strategies consistently accessible to small grocery stores.

demand flexibility↗
DOE OSTI2022

Factors Affecting Override Behavior during Demand Flexibility from High-Resolution Smart Thermostat Data

In this study, we delineate key weather and demographic predictors of override behavior in residential buildings during connected thermostats demand response events. Anticipating and reducing overrides is critical to demand flexibility (DF) program success. We use high- dimensional fixed effects linear regression techniques on a large ecobee dataset for about 5,000 enrolled households in the United States. We identify critical weather (indoor and outdoor temperature), building (housing type), and occupant (previous DF overrides and previous DF event exposure) factors influencing the override patterns of customers during thermostat demand response. We also differentiate DF events by season to understand the different indoor and outdoor conditions that might influence seasonal override rates. We found significant differences in override rates between building types, with single-family semi-detached homes generally having the highest overrides. Having a history of overrides was additionally a critical factor in predicting occupant response to future DF events. Understanding these override differences is necessary for rural electric cooperatives and emerging DF programs without access to large DR historical data. Overall, we provide critical information on technical and local demographic characteristics that may be correlated to building type to influence strategies to reduce overrides and improve the adoption of DF technologies.

connected thermostats (CTs)↗
DOE OSTI2025

Insights Into On-Demand Transit: A Case Study of Houston METRO's curb2curb Transit Services: Preprint

As modern on-demand transit (ODT) deployments increase, the insights gained from studying real-world systems prove more useful. In this study, a shared ODT system called "METRO curb2curb" in the suburbs of Houston, Texas was studied, with a particular focus on the Missouri City zone, which has the highest ridership out of the four total zones in which the system operates. The Missouri City zone spans 18 square miles with an average daily ridership of nearly 400 as of November 2024. In order to gain a passenger perspective, data collection was performed through using the ODT system first-hand, completing 50 rides in March and April of 2023. These rides included a mix of three different types of on-demand travel accommodations - 1.) advance bookings (30 min - 2 hours ahead), 2.) as-soon-as-possible / real-time dispatch bookings, and 3.) no-reservation trips at two designated "anchor points." Wait and travel time were collected for each ride. Anecdotal findings related to ease of locating the driver/vehicle, the process of booking a ride, and popular trip purposes were also documented. This study provides an overview of the METRO curb2curb system, including its history, service provider, service area, ridership, booking options, wait time, and travel time. Intricacies related to different definitions of wait time are included. Finally, relevant considerations for automating the on-demand transit system and discussed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

U.S. Heat Demand by Sector for Potential Application of Direct Use Geothermal

This dataset includes heat demand for potential application of direct use geothermal broken down into 4 sectors: agricultural, commercial, manufacturing and residential. The data for each sector are organized by county, were disaggregated specifically to assess the market demand for geothermal direct use, and were derived using methodologies customized for each sector based on the availability of data and other sector-specific factors. This dataset also includes a paper containing a full explanation of the methodologies used.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

A Demand Bidding Model for Multi-Product Industrial Plants

The growing contribution of renewable energy sources has increased volatility and uncertainty in electricity markets, challenging traditional grid operation paradigms. Demand bidding (DB), a market participation model where (large) electricity users communicate their willingness to pay for electricity to the grid operator, was shown in previous work to enhance grid stability and lower generation cost. We present a DB model for multi-product industrial plants, based on an extended optimal power flow problem where the plant dynamics are represented using autoregressive with extra inputs (ARX) models. We compare DB to price-based demand-side management, showing that, under certain assumptions, the two approaches are equivalent, while DB provides more transparency and predictability to the grid operator. A case study based on an industrial air separation unit is discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Accuracy of hourly energy predictions for demand flexibility applications

Decarbonization goals in the United States electricity sector are increasing the levels of renewable energy generation in the electricity supply system, and are driving increased attention to building electrification, which will increase the magnitude and shift the timing of the electricity system peak. These changes are motivating new approaches to coordinate building electricity demand with low-carbon renewable generation, elevating the importance of demand flexibility (DF) in buildings and the need to quantify the temporal impacts of DF. In this paper, we first characterize the hourly predictive accuracy of six commonly used baseline models in an application context of quantifying building-level load shift. Our analysis revealed insights such as hours of the day (afternoons), periods of the week (weekends), and seasons (summer) that were predicted with more accuracy than other time periods. In addition, the analysis showed tendencies toward overprediction or underprediction of load. Secondly, we provide the first published investigation of baseline erosion from repeated dispatch of building load shifting. We observed that as the baseline period is pushed back further from the prediction day, the distribution of errors across baseline model predictions increases, with notable inflection points near the three-week erosion point for two of the three models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Updated U.S. Low-Temperature Heating and Cooling Demand by County and Sector

This dataset includes U.S. low-temperature heating and cooling demand at the county level in major end-use sectors: residential, commercial, manufacturing, agricultural, and data centers. Census division-level end-use energy consumption, expenditure, and commissioned power database were dis-aggregated to the county level. The county-level database was incorporated with climate zone, numbers of housing units and farms, farm size, and coefficient of performance (COP) for heating and cooling demand analysis. This dataset also includes a paper containing a full explanation of the methodologies used and maps. Residential data were updated from the latest Residential Energy Consumption Survey (RECS) dataset (2015) using 2020 census data. Commercial data were baselined off the latest Commercial Building Energy Consumption Survey (CBECS) dataset (2012). Manufacturing data were baselined off the latest Manufacturing Energy Consumption Survey (MECS) dataset (2021).

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Modular supply chain optimization considering demand uncertainty to manage risk

Supply chain under demand uncertainty has been a challenging problem due to increased competition and market volatility in modern markets. Flexibility in planning decisions makes modular manufacturing a promising way to address this problem. We report the problem of multiperiod process and supply chain network design is considered under demand uncertainty. A mixed integer two-stage stochastic programming problem is formulated with integer variables indicating the process design and continuous variables to represent the material flow in the supply chain. The problem is solved using a rolling horizon approach. Benders decomposition is used to reduce the computational complexity of the optimization problem. To promote risk-averse decisions, a downside risk measure is incorporated in the model. The results demonstrate the several advantages of modular designs in meeting product demands. A pareto-optimal curve for minimizing the objectives of expected cost and downside risk is obtained.

42 ENGINEERING↗