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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Modeling Framework and Results to Inform Charging Infrastructure Investments

NREL analysis will inform VTO on costs/benefits of DOE investment in an array of electric vehicle charging infrastructure technologies, trade-offs between residential and non-residential solutions, and cost of charging to consumers. Additional analysis will focus on vehicle sales, energy consumption, and emission impacts of meeting DOE technology target goals ahead of or behind schedule (timing assessment).

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

The Cost of Comfort in Your Home: Peak-Time Energy and Cost Savings from Setpoint Adjustment of Space Heating and Cooling

This poster presents the tradeoff between energy costs and thermal comfort in homes. Residential heating and cooling systems use a thermostat control to maintain a comfortable temperature range, but tighter temperature control leads to higher annual energy costs. We estimate the impact of temperature setpoint adjustment on annual energy costs using a residential building model developed at NREL. We show that increasing the allowable temperature range reduces energy costs in all five of the climate zones in the U.S. Annual costs reduced by about $100, with larger savings in areas with more heating and/or cooling loads.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT)

The National Renewable Energy Laboratory (NREL) developed the Puerto Rico Energy Efficiency Scenario Analysis Tool (PREESAT) to allow stakeholders to estimate the long-term aggregate electricity consumption impacts of various energy efficiency measures in Puerto Rico’s residential, commercial, and industrial sectors. PREESAT is an energy accounting framework that addresses electricity use. These presentation slides provide a summary of the electrical sector in Puerto Rico and an overview of how the PREESAT tool can be used to project energy savings from various energy efficiency measures in the residential, commercial, and industrial sectors.

commercial↗
DOE OSTI2022

Load Shifting with Space Conditioning Heat Pumps During Heating Season in the Pacific Northwest

Ductless mini- and multi-split heat pumps are commonly deployed in Pacific Northwest residences, which historically have inefficient space heating and no space cooling. Heat pumps provide these residences with more energy-efficient space heating in the winter and space cooling for increasingly warm summer periods. For many of these applications, heat pumps add a new electrical load at the residence, compelling regional utilities to seek to understand their impacts on the electrical grid and opportunities for load management. In coordination with regional utilities, this study examined the winter load-shifting potential of residential multi-split heat pumps with variable capacity technology. The study included nine residential sites with a total of 20 ductless indoor units, which underwent simulated smart grid control. Grid control functionality was provided through the CTA-2045 communication protocol and included offsetting temperature setpoints for individual indoor units in the study. Field data collection included whole-house and heating, ventilation, and air conditioning electrical power consumption; indoor zone temperatures; occupant surveys; and CTA-2045 curtailment data for both baseline and simulated load-shifting events by site. This paper provides an overview of the load-shifting strategies and aggregated results from winter data collection.

ASHRAE, Heat pump, energy effciency, residential b↗
DOE OSTI2021

Effect of Image Classification Accuracy on Dasymetric Population Estimation

Dasymetric mapping involves the disaggregation of count data, usually relating to population/demographics, from census enumeration areas to smaller target zones with the aid of an ancillary layer related to population density. The ancillary layer is often a binary classification such as developed versus undeveloped, building versus non-building, and residential versus non-residential, in which one class is treated as populated and the other as unpopulated. While dasymetric mapping relies heavily on ancillary data, little research has been done to address the error in ancillary data and its effects on dasymetric mapping accuracy. This chapter reports our research effort to investigate the effect of image classification accuracy on dasymetric population estimates by developing a binomial classification of buildings from high-resolution remote sensor imagery. The classifier was systematically and iteratively manipulated to generate a series of outputs with variegated accuracy characteristics. Lastly, we generated a corresponding series of population estimates based on the mapped building area from each iteration to investigate the relationship between the accuracy of classification and population estimation.

McKee, Jacob↗
DOE OSTI2013

Bounding the Role of Black Carbon in the Climate System: A Scientific Assessment

Black carbon aerosol plays a unique and important role in Earth’s climate system. Black carbon is a type of carbonaceous material with a unique combination of physical properties. Predominant sources are combustion related; namely, fossil fuels for transportation, solid fuels for industrial and residential uses, and open burning of biomass. Total global emissions of black carbon using bottom-up inventory methods are 7500 Gg yr-1 in the year 2000 with an uncertainty range of 2000 to 29000. This assessment provides an evaluation of black-carbon climate forcing that is comprehensive in its inclusion of all known and relevant processes and that is quantitative in providing best estimates and uncertainties of the main forcing terms: direct solar absorption, influence on liquid, mixed-phase, and ice clouds, and deposition on snow and ice. These effects are calculated with models, but when possible, they are evaluated with both microphysical measurements and field observations. Global atmospheric absorption attributable to black carbon is too low in many models, and should be increased by about about 60%. After this scaling, the best estimate for the industrial-era (1750 to 2005) direct radiative forcing of black carbon is +0.43 W m-2 with 90% uncertainty bounds of (+0.17, +0.68) W m-2. Total direct forcing by all black carbon sources in the present day is estimated as +0.49 (+0.20, +0.76) W m-2. Direct radiative forcing alone does not capture important rapid adjustment mechanisms. A framework is described and used for quantifying climate forcings and their rapid responses and feedbacks. The best estimate of industrial-era (1750 to 2005) climate forcing of black carbon through all forcing mechanisms is +0.77 W m-2 with 90% uncertainty bounds of +-0.06 to +1.53 W m-2. Thus, there is a 96% probability that black carbon emissions, independent of co-emitted species, have a positive forcing and warm the climate. With a value of +0.77 W m-2, black carbon is likely the second most important individual climate-forcing agent in the industrial era, following carbon dioxide. Sources that emit black carbon also emit other short- lived species that may either cool or warm climate. Climate forcings from co-emitted species are estimated and used in the framework described herein. When the principal effects of co- emissions, including cooling agents such as sulfur dioxide, are included in net forcing, energy-related sources (fossil-fuel and biofuel) have a net climate forcing of +0.004 (-0.62 to +0.57) W m-2 during the first year after emission. For a few of these sources, such as diesel engines and possibly residential biofuels, warming is strong enough that eliminating all emissions from these sources would reduce net climate forcing (i.e., produce cooling). When open burning emissions, which emit high levels of organic matter, are included in the total, the best estimate of net industrial-era climate forcing by all black- carbon-rich sources becomes slightly negative (-0.08 W m-2 with 90% uncertainty bounds of -1.23 to +0.81 W m-2). The uncertainties in net climate forcing from black-carbon-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both black carbon and co-emitted organic carbon. In prioritizing potential black-carbon mitigation actions, non-science factors, such as technical feasibility, costs, policy design, and implementation feasibility play important roles. The major sources of black carbon are presently in different stages with regard to the feasibility for near-term mitigation. This assessment, by evaluating the large number and complexity of the associated physical and radiative processes in black-carbon climate forcing, sets a baseline from which to improve future climate forcing estimates.

assessment↗
DOE OSTI2017

Measured and estimated performance of a fleet of shaded photovoltaic systems with string and module-level inverters

Shade obstructions can significantly impact the performance of photovoltaic (PV) systems. Although there are many models for partially shaded PV arrays, there is a lack of information available regarding their accuracy and uncertainty when compared with actual field performance. This work assesses the recorded performance of 46 residential PV systems, equipped with either string-level or module-level inverters, under a variety of shading conditions. We compare their energy production data to annual PV performance predictions, with a focus on the practical models developed here for National Renewable Energy Laboratory's system advisor model software. This includes assessment of shade extent on each PV system by using traditional onsite surveys and newer 3D obstruction modelling. The electrical impact of shade is modelled by either a nonlinear performance model or assumption of linear impact with shade extent, depending on the inverter type. When applied to the fleet of residential PV systems, performance is predicted with median annual bias errors of 2.5% or less, for systems with up to 20% estimated shading loss. The partial shade models are not found to add appreciable uncertainty to annual predictions of energy production for this fleet of systems but do introduce a monthly root-mean-square error of approximately 4%-9% due to seasonal effects. Here the use of a detailed 3D model results in similar or improved accuracy over site survey methods, indicating that, with proper description of shade obstructions, modelling of partially shaded PV arrays can be done completely remotely, potentially saving time and cost.

14 SOLAR ENERGY↗
DOE OSTI2020

PV Field Reliability Status--Analysis of 100,000 Solar Systems

In this paper, we analyzed annual production data from 100 000 photovoltaic systems as well as comments relating to their performance and maintenance. Our analysis revealed that 80% to 90% of all systems performed within 10% of the predicted production or better. Also, 56% of the systems were still performing above P50 or the median at 5 years. However, a small but significant tail of about 7000 systems perform below P90 expectations. In general, residential systems have a lower rate of failure than utility or commercial systems. Despite higher rates of component failures, utility systems lose less power than residential or commercial systems. This outcome is likely due to closer monitoring and better operations and maintenance practices. Inverters are still the components that reportedly fail most often (4%-6%), but other failures such as unspecified repair and meters cause more production loss. Reported module failures are relatively rare (0.2%) and are within the range of historical values. Installation quality affects performance and safety as indicated by data showing connector, wiring, breaker, and fuse failures due to undersizing, electrical design, and improper connection. Lastly, early detection of degradation and proactive response resulted in less impact on production than reactive, unplanned repairs.

14 SOLAR ENERGY↗
DOE OSTI2025

LandScan HD: a high-resolution gridded ambient population methodology for the world

Unwarned population distributions accounting for routine human activities are needed to address many global human security challenges, including disasters, conflict, and infrastructure demand. LandScan High Definition (LSHD) supports this need through gridded ambient population estimates that measure average human presence between daytime and nighttime at a high spatial resolution of 3 arcseconds (approximately 90 m). Although LSHD has traditionally been produced on a country-specific basis, advances in global foundational data and computational resources now enable scaling its methodology to the world. Combining aspects of top-down and bottom-up gridded population methods, LSHD allocates subnational population totals from authoritative statistics to built-up areas based on occupancy estimates for multiple facility types (e.g., residential, commercial) and then reaggregates these estimates to a global population grid. We scale this approach by organizing the LSHD data stack into a 1° resolution tileset of vector analytic features, enabling an efficient and repeatable workflow for all countries worldwide. Examining the Philippines as an output of the global LSHD baseline dataset, we contrast unwarned and residential (WorldPop) population distributions by (1) exploring a practical application of flood risk assessment and (2) evaluating their congruence with outcomes of collective human activities (subnational CO 2 emissions). Finally, we discuss plans to address current LSHD limitations through data/modeling and uncertainty quantification improvements and provide outlook for workflow automation and extending the model to social, demographic and economic population characteristics.

Building morphology↗
DOE OSTI2018

A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses

Within the residential building sector, the air-conditioning (AC) load is the main target for peak load shifting and reduction since it is the largest contributor to peak demand. By leveraging its power flexibility, residential AC is a good candidate to provide building demand response and peak load shifting. For realization of accurate and reliable control of AC loads, a building thermal model, which characterizes the properties of a building’s envelope and its thermal mass, is an essential component for accurate indoor temperature or cooling/heating demand prediction. Building thermal models include two types: “Forward” and “Data-Driven”. Due to time-saving and cost-effective characteristics, different data-driven models have been developed in a number of research studies. However, few developed models can predict temperatures in respective zones of a multiple-zone building with an open air path between zones e.g., an open stairwell connecting two floors of a home. In this research, a novel hybrid modeling approach is proposed to predict the average indoor air temperatures of both the upstairs and downstairs. This “hybrid” solution integrates both gray-box, i.e. RC model and black-box models. A developed RC model is used to predict the building mean temperature, and black-box model, in which the supervised machine learning algorithms are leveraged, is used to predict the temperature difference between the downstairs and upstairs. Compared with the measured data from a real house, the results obtained have acceptable/satisfactory accuracy. The method proposed in this study integrates the advantages of black-box and gray-box modeling. As a result, it can be used as a reliable alternative to predict the average temperatures in respective floors of typical detached two-story houses.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics

A distributed energy management system for community microgrids is developed here. Unlike a centralized optimization-based energy management system, it schedules distributed energy resources and energy storage systems, as well as residential appliances, indirectly through iterative interaction between the microgrid central controller and home energy management systems, based on price signals. In each iteration, the microgrid central controller adjusts the scheduling of distributed energy resources and energy storage systems at the microgrid level. Meanwhile, the home energy management system of each house adjusts the scheduling of residential appliances. Then, the energy price at each bus is updated according to the unbalanced power between generation and demand. The optimization converges when the unbalanced power of all buses is close to zero, i.e., the microgrid central controller and home energy management systems reach an agreement on the energy price and generation/consumption. In particular, a detailed thermal dynamic model of the house is integrated into the HEMS scheduling for intelligent control of the heating, ventilation, and air-conditioning system by customers. Lastly, the distribution network is also considered to make the energy price signal locational. Results of case studies validate the efficacy of the proposed distributed energy management system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Impact of installation faults in air conditioners and heat pumps in single-family homes on U.S. energy usage

To ensure that new residential air conditioners and heat pumps operate optimally, care must be taken during the initial system design and installation to avoid missteps that immediately degrade a system’s performance. Laboratory and simulation studies have quantified the impact that faults can have on capacity and efficiency. Field studies have demonstrated that installation-related faults are commonplace. However, the national impact of installation-related faults cannot be accurately estimated by only simulating a limited number of homes at several fault levels because of the variety of home characteristics and climates involved. In our analysis, we use an improved residential building stock simulation tool to predict the annual energy increase and additional utility costs resulting from two common installation faults: indoor airflow rate and refrigerant charge level. Our method considers the wide range of building characteristics and climate zones of the U.S. housing stock. We use existing field data to develop fault intensity probability distributions to inform our analysis. The study shows that these two faults result in approximately 20.7 TWh/y of additional energy use for central air conditioners and air-source heat pumps in U.S. single-family detached homes, which is a 9% increase over baseline (no-fault) usage, costing homeowners approximately $2.5 billion annually on utility bills. Air-source heat pumps are responsible for a disproportionate fraction of this energy use increase because of the larger number of operating hours compared to central air conditioners and the sensitivity of heating mode performance to the faults analyzed compared to cooling mode.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Multiscale Effects Masked the Impact of the COVID-19 Pandemic on Electricity Demand in the United States

Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class.

COVID-19, electricity demand, multiscale, Commonwe↗
DOE OSTI2024

Heating performance of a vapor compression heat pump cascaded with a thermoelectric heat pump

Air source heat pumps (ASHPs) are widely utilized for heating and cooling in residential buildings; however, their effectiveness in heating mode is compromised during extreme weather conditions. Extensive research endeavors have been undertaken to develop, test, and assess a cost-effective vapor compression ASHP suitable for cold climate regions. Further, this study takes an innovative approach by developing component and system prototypes for a cold climate heat pump. This design combines a thermoelectric heat pump (TEHP) with a traditional residential split ASHP to augment heating capacity in low ambient temperature conditions. The component and system prototypes underwent experimental testing in the psychrometric chambers. The experimental findings revealed a 13.6% to 13.7% increase in total heat pump heating capacity, accompanied by a 3.1% to 5.0% decrease in the coefficient of performance (COP) at ambient temperatures of -15°C and -19°C, when compared to the original ASHP. The COP of the TEHP is relatively constant, ranging from 1.63 to 1.76. This prototype offers a solution to address the challenges associated with reduced heat pump capacity at low ambient temperatures. The experimental results indicated that the lower the ambient temperature, thermoelectric heat pump auxiliary heating can increase the heating efficiency 60% in comparison to electric resistance.

42 ENGINEERING↗
DOE OSTI2017

Black carbon emissions from biomass and coal in rural China

Residential solid fuel combustion makes a major contribution to black carbon (BC) emissions in China. A new estimation of BC emissions from rural solid biomass and coal consumption has been derived from field survey data. The following new contributions are made: (1) emission factors are collected and reviewed; (2) household energy data are collected from field survey data and from the literature; (3) a new extrapolation method is developed to extend the field survey data to other locations; (4) the ownership and usage of two stove types are estimated and considered in the emission calculations; and (5) uncertainties associated with the estimation results are quantified. It is shown that rural households with higher income will consume less biomass but more coal. Agricultural acreage and temperature also significantly influence the amount of solid fuel consumed in rural areas. It is estimated that 640±245 Gg BC/y were emitted to the atmosphere due to residential solid fuel consumption in rural China in 2014. Emissions of BC from straw, wood, and coal contributed 42±13%, 36±15%, and 22±10% of the total, respectively. As a result, we show that effective BC mitigation (a reduction of 47%) could be obtained through widespread introduction of improved stoves in rural households

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate

Urban buildings account for up to 75% of total energy use in the United States (U.S.). Understanding urban building energy use is important for developing feasible options to mitigate energy use and greenhouse gas emissions. In this study, an improved bottom-up building energy use model, named City Building Energy Use Model (City-BEUM), was developed to estimate building energy use for all buildings in Polk County, Iowa. First, 28 commercial and 6 residential building prototypes were designed by combing Assessor's parcel data and building footprint data. Then, the EnergyPlus in the CityBEUM was calibrated for all building prototypes using the U.S. Energy Information Administration's survey data, monthly utility meter data, and actual weather data. Finally, spatial and temporal variations of building energy use in the study area were estimated using the CityBEUM. Results indicate that the spatial variation of building energy use in the study area can be captured using the CityBEUM. With the monthly-calibrated model, the temporal pattern of urban building energy use can be well represented. The comparison of building energy use using the Typical Meteorological Year and actual weather data demonstrates the importance of using actual weather data in building energy modeling for an improved temporal representation.

CityBEUM, Urban building energy use, High spatiote↗
DOE OSTI2019

Sensitivity of occupant comfort models to humidity and their effect on cooling energy use

Residential building energy consumption in the United States has decreased steadily during the last several decades largely because of advances in building codes as well as voluntary efficiency and labeling programs, which have resulted in the reduction in building sensible loads. Building latent loads have not decreased by the same amount, and field and analytical research studies have concluded that high-efficiency, low-load homes often have elevated indoor humidity levels, possibly leading to occupant discomfort. There are discrepancies in the literature on occupant comfort as it relates to indoor humidity and recommended upper humidity limits. The choice of humidity limit could potentially change results and conclusions on the energy impacts of a new air conditioner or dehumidifier technology, changing ventilation rates, or an improved building envelope. In this study, we use building simulations to explore the impact of humidity control on cooling energy use in efficient residential buildings for different humidity limits. We look at limits based on humidity ratio (or dew point temperature), wet-bulb temperature, relative humidity, and constant slope lines based on the popular comfort models of both Fanger and Gagge. We quantify the additional energy use required to control humidity below these limits. We look at the sensitivity of the results to different temperature and humidity set points, occupant internal gains, moisture buffering levels, evaporator airflow rates, and dehumidifier energy factors. High-efficiency home predicted energy savings are more sensitive to the assumed humidity limit and cooling and dehumidification set points than the other parameters investigated in this paper.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗