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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Methods for Computing Physically Realistic Estimates of Electric Water Heater Demand Response Resource Suitable for Bulk Power System Planning Models

Demand response is commonly called on to reduce load during system peak times or to respond to contingency events. In future power systems with higher shares of wind and solar generation (which we describe together as variable generation [VG]), demand response could have more opportunities to provide energy shifting or operating reserve services. This report evaluates the ability of residential electric water heaters, both electric resistance water heaters (ERWHs) and heat pump water heaters (HPWHs), to provide such services starting from detailed whole-building energy models that realistically represent New England single family home stock. We use a parsimonious surrogate model to represent operational flexibility in a form suitable for linear and mixed integer programming. This enables relatively fast determination of aggregate contingency reserve resource, price-taking energy shifting outcomes, and in some cases the determination of aggregate models at the megawatt (MW) scale that can be directly included in large-scale grid models. After selecting modeling methods and parameters through various computational experiments, we find interquartile ranges of contingency reserve resource in ISO-NE for about 603,400 ERWHs of 45 MW - 69 MW for Claim10 (50 minute responses provided with 10 minutes of advanced notification) and 65 MW - 102 MW for Claim30 (30 minute responses provided with 30 minutes of advanced notification), and for about 619,000 HPWHs of 48 MW - 88 MW for Claim10 and 52 MW - 90 MW for Claim30. The overall reserve resource is up to 32% of total load for ERWHs providing Claim10 service, 47% for ERWHs providing Claim30 service, 93% for HPWHs providing Claim10 service, and 97% for HPWHs providing Claim30 service. More work is required to determine if HPWHs are inherently more suitable than ERWHs for providing contingency reserve or if these results reflect idiosyncrasies of the single family home stock model used in this study. The value of this contingency resource in a Near-term VG model of ISO-NE is $\$ 0.40$ to $\$1.20$ per water heater-year, and significantly larger, $\$ 3.80$ to $\$ 5.30$ per water heater-year in a Mid-term VG model of ISONE. Aggregating surrogate models to the MW-scale for energy shifting service is more challenging than for contingency service and we only present such results for ERWHs, because we were unable to determine satisfactory ways to deal with HPWHs' time-varying and path dependent operational characteristics. Individual surrogate models suitable for evaluating the energy shifting resource from both ERWHs and HPWHs are created, however, and dispatched against day-ahead prices from the Near-Term VG and Mid-Term VG models of ISO-NE. The individual surrogate models are able to access and potentially shift all 640 GWh of HPWH load and 1,547 GWh of ERWH load we modeled in two different single family home stock models. In contrast, the most effective model of aggregate ERWH shifting resource we created only captured 34.7% of the total ERWH load. Energy shifting affected by price-taking dispatch against modeled day-ahead energy prices produces per water heater year profits of $\$19.44$ - $\$22.93$ for individual HPWHs, $\$39.11$ - $\$40.54$ for individual ERWHs, and up to $\$4.00$ - $\$4.24$ for aggregated ERWHs, with the variations mainly due to grid conditions (more or less VG). When the supply-side response to these changes is accounted for, the per water heater year production cost savings for ISO-NE are $\$7.50$ to $\$17.70$ for the most effective set of endogenously dispatched aggregate ERWHs, $\$15.60$ to $\$15.70$ for individual ERWHs dispatched against the DA prices, and $\$10.70$ to $\$11.20$ for individual HPWHs dispatched against DA prices. Those ranges primarily represent the difference between Near-Term VG and Mid-Term VG grid conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1978

An analysis of short haul air passenger demand, volume 2

Several demand models for short haul air travel are proposed and calibrated on pooled data. The models are designed to predict demand and analyze some of the motivating phenomena behind demand generation. In particular, an attempt is made to include the effects of competing modes and of alternate destinations. The results support three conclusions: (1) the auto mode is the air mode's major competitor; (2) trip time is an overriding factor in intermodal competition, with air fare at its present level appearing unimportant to the typical short haul air traveler; and (3) distance appears to underly several demand generating phenomena, and therefore, must be considered very carefully to any intercity demand model. It may be the cause of the wide range of fare elasticities reported by researchers over the past 15 years. A behavioral demand model is proposed and calibrated. It combines the travel generating effects of income and population, the effects of modal split, the sensitivity of travel to price and time, and the effect of alternative destinations satisfying the trip purpose.

Blumer, T. P.↗
DOE OSTI2022

Residential Demand Flexibility: Modeling Occupant Behavior using Sociodemographic Predictors

Demand flexibility (DF) has the potential to increase the saturation of renewables in the grid and reduce operating costs for both utilities and customers. However, less than 8% of U.S. residential electric customers are enrolled in DF programs. A major research gap on this topic is an uneven understanding of behavioral drivers of electricity use and DF program participation at the household level. In this study, we employ machine learning models to predict residential occupant behavior in activities relevant to DF. We model occupants' extensive decisions (i.e., choice of action) and intensive behaviors (i.e., amount of time spent) during peak and off-peak time periods using the publicly available American Time Use Survey, which includes activities data for approximately 200,000 respondents. In our machine learning models, predictions for both extensive and intensive behavior fell within a +/-20% error margin at the aggregate level. We identify 13 key sociodemographic predictors of DF-related intensive behavior using LASSO inference and beta coefficient ranking. However, these top predictors differ by activity, suggesting potential scope for differential user targeting for DF events and technologies during program design. This work also contributes to understanding when and who might adopt these DF technologies based on their daily routine activities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1994

Man-machine Integration Design and Analysis System (MIDAS) Task Loading Model (TLM) experimental and software detailed design report

This is the experimental and software detailed design report for the prototype task loading model (TLM) developed as part of the man-machine integration design and analysis system (MIDAS), as implemented and tested in phase 6 of the Army-NASA Aircrew/Aircraft Integration (A3I) Program. The A3I program is an exploratory development effort to advance the capabilities and use of computational representations of human performance and behavior in the design, synthesis, and analysis of manned systems. The MIDAS TLM computationally models the demands designs impose on operators to aide engineers in the conceptual design of aircraft crewstations. This report describes TLM and the results of a series of experiments which were run this phase to test its capabilities as a predictive task demand modeling tool. Specifically, it includes discussions of: the inputs and outputs of TLM, the theories underlying it, the results of the test experiments, the use of the TLM as both stand alone tool and part of a complete human operator simulation, and a brief introduction to the TLM software design.

Staveland, Lowell↗
DOE OSTI2024

Platform for Integrated Land use And Transportation Experiments and Simulation (PILATES) v1.0

PILATES allows for flexibly and at-scale coupling of multiple models to allow for multi-scale and multi-resolution simulation of regional-scale transport networks. In particular, it couples the MATSim-derived transportation modeling framework for Behavior, Energy, Autonomy and Mobility (BEAM) with other models operating at different time scales. Rather than tightly coupling supply and demand models using shared agents and memory within the same software process, PILATES orchestrates different model runs in a containerized framework. This structure requires passing information from the demand models to BEAM in the format of a synthetic population and agent plans, and from BEAM to the demand models in terms or origin/destination tables (also known as "skims"). This allows it to take advantage of the behavioral sophistication of existing activity-based models as well as the reinforcement learning structure of MATSim replanning and adopted by BEAM, in a way that requires minimal changes to existing models. It also takes advantage of the computational performance of BEAM, which allows for simulations with millions of agents to complete in reasonable time as well as allowing for detailed mechanistic simulation of the operation of on-demand modes.

Needell, Zachary↗
NASA NTRS2020

Demand Forecast Model Development and Scenarios Generation For Urban Air Mobility Concepts

The purpose of this project is to estimate the demand for various Urban Air Mobility Concepts (UAM) of Operations and to generate scenarios for use in analysis and simulations. The demand forecast model, previously developed under NASA/NIA Contract No: NNL13AA08B; Task Order No: NNL16AA36T, for an urban on-demand air-taxi commuter concept is the basis for this work.

M. Rimjha↗
DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grid: Preprint

This paper investigates the impact of Sustainable Aviation Fuel (SAF) production using CO2-to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aimto meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO2-to-Fuels electricity demand model and other carbon capture technologies into power system planning, emphasizing their significance in shaping a sustainable energy future.

BIOMASS FUELS,ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grid

This paper investigates the impact of Sustainable Aviation Fuel (SAF) production using CO 2 -to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aim to meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO 2 -to-Fuels electricity demand model and other carbon capture technologies into power system planning, emphasizing their significance in shaping a sustainable energy future.

capacity expansion model↗
DOE OSTI2024

Evaluating Impacts of Sustainable Aviation Fuel Production with CO2-to-Fuels Technologies on High Renewable Share Power Grids

This paper investigates the impact of Sustainable Aviation Fuel production using CO2-to-Fuels technologies on a future power grid with a high share of renewable energy. We focus on understanding the implications of the 2050 SAF production goal on the U.S. power system's long-term planning, encompassing generation, transmission, and cost analysis. Via the Regional Energy Deployment System (ReEDS) model, we developed a detailed SAF electricity demand model based on a low-temperature electrolysis-syngas fermentation-ethanol pathway. Four SAF target scenarios which aims to meet 10%, 15%, 20%, and 27% of SAF demand by 2050. These scenarios are exhaustively simulated to assess their impact on the power grid. Our results reveal that increasing SAF demand will result in higher electricity requirements, as well as expanded generator and transmission capacities, leading to an overall rise in system costs. However, these impacts are manageable within the broader context of U.S. capacity expansion plans. This study provides valuable insights into incorporating the CO2-to-Fuels electricity demand model and other carbon capture technologies in power system planning, emphasizing their significance in shaping a sustainable energy future.

BIOMASS FUELS,POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2024

Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings

As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

EVI-Equity

EVI-Equity (Electric Vehicle Infrastructure for Equity) is a $200k project, started around in June of 2021, with a funding from the Vehicle Technologies Office (VTO). The motivation was to create a new analytical capability that can enable us to quantify and investigate equitable access to and distribution of existing and future deployment of PEVs and EVSEs in neighborhoods, cities, states, and the nation. EVI-Equity is a bottom-up equity-focused analysis model, built upon individual (synthetic) households, aggregated by census block groups. It consists of four core components - community engagement, environmental profiling, household expenditures, and network design. Although there are some commonalities, EVI-Equity is not a vehicle choice model, charging simulation model, or transportation demand model. EVI-Equity is rather a cross-cutting analysis tool, dedicated for evaluating equitable EV adoption and EVSE deployment, encompassing and bridging a wide variety of related tools, models, and frameworks. Some of the results indicate the importance of used vehicle market for low-income households. The presentation also highlights similarities and variations as to preferred public charging locations. For example, regardless of household income, retail spots are the most preferred location for public charging, followed by curbside/street. However, the results also imply that the importance of workplace charging may vary with income - the lower the income, the less important. Environmental profiling results, with an example of ground-level ozone in Atlanta area, show that the contrast between the haves and the have nots of plug-in electric vehicles depends on location. The assessment of household expenditures illustrates the economic impact of home charging access on an individual household level - the lower the income, the greater the impact is. Lastly, Denver metro area and the state of South Dakota are used to showcase the impact of different network design of charging infrastructure, as well as alternative (vs. baseline/existing) electric vehicle adoption pattern.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗