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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability

“Stay-at-home” orders and other health precautions enacted during the COVID-19 pandemic have led to substantial changes in residential electricity usage. Here, we conduct a case study to analyze data from 390 apartments in New York City (NYC) to examine the impacts of two key drivers of residential electricity usage: COVID-19 case-loads and the outdoor temperature. We develop a series of regression models to predict two characteristics of residential electricity usage on weekdays: The average occupied apartment’s consumption (kWh) over a 9am-5pm window and the hourly peak demand (Watt) over a 12pm-5pm window. Via a Monte Carlo simulation, we forecast the two usage characteristics under a possible scenario in which stay-at-home orders in NYC, or a similar metropolitan region, coincide with warm summer weather. Under the scenario, the 9am-5pm residential electricity usage on weekdays is predicted to be 15% – 24% higher than under prior, pre-pandemic conditions. This could lead to substantially higher utility costs for residents. Additionally, we predict that the residential hourly peak demand between 12pm and 5pm on weekdays could be 35% – 53% higher than that under pre-pandemic conditions. We conclude that the projected increase in peak demand - which might arise if stay-at-home guidelines coincided with hot weather conditions - could pose grid management challenges, especially for residential feeders. We also note that, if there is a longer lasting shift towards work and study-from-home, utilities will have to rethink load profile considerations. The applications of our predictive models to managing future smart-grid technology are also highlighted.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Performance evaluation of underground thermal storage integrated dual-source heat pump systems

The increasing demand for electricity stresses the existing electric grids. Buildings consume 73% of all U.S. electricity and are responsible for 30% of U.S. greenhouse gas emissions. Integrating thermal energy storage (TES) in building heating/cooling systems, which consume considerable electricity, can mitigate the challenges to electric grids. Here, this study reports on a novel thermal energy storage device integrated heat pump system to reshape the building electricity demand profile while maintaining thermal comfort. The annual performance of the proposed system has been evaluated through a dynamic system simulation with high fidelity in the Modelica platform. The dynamic model of the novel hybrid component named ‘dual purpose underground thermal battery’ was developed and validated. It was then incorporated into the system model. Given a time-of-use tariff, a rule-based control strategy was designed to shift the electric demand and switch the heat pump source for a typical single-family house in different climate zones of the United States. The system performance of the new TES-integrated dual-source heat pump was compared with that of a conventional air-source heat pump system. The results indicate that the proposed system can reduce the annual HVAC electricity cost by up to 52% while saving 45.2% on electricity consumption. In the Northern areas, the annual peak load of the HVAC system can be reduced by 64.9%. However, this reduction is less in the Southern areas as the system’s higher efficiency in winter dominates the overall energy-saving potential.

25 ENERGY STORAGE↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
DOE OSTI2018

Effects of long-term climate change on global building energy expenditures

This paper explores potential future implications of climate change on building energy expenditures around the globe. Increasing expenditures result from increased electricity use for cooling, and are offset to varying degrees, depending on the region, by decreased energy consumption for heating. The analysis is conducted using a model of the global buildings sector within the GCAM integrated assessment model. The integrated assessment framework is valuable because it represents socioeconomic and energy system changes that will be important for understanding building energy expenditures in the future. Results indicate that changes in net expenditures are not uniform across the globe. Net expenditures decrease in some regions, such as Canada and Russia, where heating demands currently dominate, and increase the most in areas with less demand for space heating and greater demand for space cooling. We explain these results in terms of the basic drivers that link building energy expenditures to regional climate.

Climate change impacts↗
DOE OSTI2023

A comprehensive review on regeneration strategies for direct air capture

Direct air capture (DAC), which removes CO 2 directly from ambient air, is a critical negative emission technology for mitigating global climate change. Efficiency and the source of energy are crucial considerations for DAC to enable negative emissions. Substantial technological progress has been made in DAC technologies, and promising opportunities exist for commercial-scale deployments. However, DAC technologies require high regeneration energy to release CO 2 from sorbents. Various approaches have been tested and optimized for different DAC systems. This review demonstrates that the work equivalent regeneration energy demand (supported by either the electric grid or fossil fuel combustion) ranges from 0.5–18.75 GJ/t-CO 2 for solid sorbent DAC systems and 0.62–17.28 GJ/t-CO 2 for liquid solvent DAC systems. The regeneration process is the energy-demanding process in DAC that is a key step for efficient operation. Potential methods to lower the regeneration energy demand include microwave, ultrasound, magnetic particle heating, and electric swing. Although the potential methods to date are still at the lab scale, significant work is being done to optimize DAC system processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach

This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs involved in scheduling and allocating time to various activities across days of the week. Both limited survey data availability and methodological challenges in modeling week-long activity-travel schedules have precluded the development of multi-day activity-travel demand models. With passive and technology-based data collection methods increasingly in vogue, the collection of multi-day travel data may become increasingly commonplace in the years ahead. This paper addresses the methodological challenge associated with modeling multi-day activity-travel demand by formulating a multivariate multiple discrete-continuous probit (MDCP) model system. The comprehensive framework ties together two MDCP model components, one corresponding to weekday time allocation and the other to weekend activity-time allocation. By tying the two MDCP components together, the model system also captures relationships in activity-time allocation between weekdays on the one hand and weekend days on the other. Model estimation on a week-long travel diary data set from the United Kingdom shows that there are significant inter-relationships between weekdays and weekend days in activity-travel scheduling behavior. In conclusion, the model system presented in this paper may serve as a higher-level multi-day activity scheduler in conjunction with existing daily activity-based travel models.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

U.S. lithium resources from geothermal and extraction feasibility

As the demand for electric vehicles (EVs) increases, demand for lithium (Li) is expected to grow over the coming decade. To satisfy the demand for rechargeable batteries, it is imperative to devise solutions for sufficient Li production. Additionally, even though commercial Li currently comes from conventional saline salar brine and spodumene ore extractions, efforts to retrieve Li from geothermal brines are increasing in popularity. In this paper, we evaluate the United States’ potential to supply Li from geothermal brines using Systems Dynamics modeling. The main goals are to assess (1) the viability of US Li supply from geothermal brines and (2) the potential supply chain impact of extracting Li from this source. Results show that Li supply from geothermal brines could (1) reach around 4% to 8% of total US Li supply and (2) be economically viable.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Electricity rates for electric vehicle direct current fast charging in the United States

While several efforts are promoting a widespread and convenient network of direct current fast charging (DCFC) stations to support electric vehicles, there is limited understanding of the magnitude and variability of the cost of electricity for these applications. This information gap may hinder optimal investing and planning for charging station placement and in turn affect electric vehicle adoption and usage. Here, we assess the electricity cost for different scenarios of DCFC station size and use based on over 7500 commercial and industrial electricity rates available for 2017 across the United States. Results show that the cost of electricity for DCFC varies dramatically, ranging from less than 0.10 cents to over 2 dollars per kilowatt-hour, depending on station design and high uncertainty in use. The main driver of cost is low utilization, which results from a combination of few charging events and limited energy recharged during each event. Low utilization leads to significantly higher electricity cost, particularly for rates with demand charges; however, cost decreases rapidly as utilization increases. For high-utilization stations, selecting rates with demand charges can actually reduce electricity costs compared to non-demand-charge rates. Furthermore, significant opportunities for cost savings based on existing rates include preferential charging during off-peak hours and limiting multi-plug station power so that not all plugs can be used simultaneously at maximum power.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Optimal strategies for a cost-effective and reliable 100% renewable electric grid

This paper explores cost-optimal pathways to 100% renewable power systems for the U.S. building stock. We show that long-duration misalignments of supply and demand, spanning from multi-day to seasonal timescales, present a dominant challenge that must be addressed to meet real-time 100% renewable targets. While long-duration misalignments can be addressed through energy storage, we show that alternative and readily available solutions that are more cost-effective should be considered first. Through a techno-economic analysis, we identify cost-optimal, region-dependent, supply-side, and demand-side strategies that reduce, and in some U.S. regions eliminate, the otherwise substantial capacities and associated costs of long-duration energy storage. Investigated supply-side strategies include optimal mixes of renewable portfolios and oversized generation capacities. Considered demand-side strategies include building load flexibility and building energy efficiency investments. Our results reveal that building energy efficiency measures can reduce long-duration storage requirements at minimum total investment costs. In addition, oversizing and diversifying renewable generation can play a critical role in reducing storage requirements, remaining cost effective even when accounting for curtailed generation. We identify regionally dependent storage cost targets and show that for emerging long-duration energy storage innovations to achieve broad adoption, their costs will need to compete with the decreasing cost of renewables. The findings of this research are particularly important given that most long-duration storage technologies are currently either uneconomical, geologically constrained, or still underdeveloped.

100% renewable↗
DOE OSTI2024

An Empirical Validation of a Constrained Bin Packing Algorithm for a Home Energy Management System

The increasing number of intelligent electrical appliances and home energy management systems provide a big opportunity for demand response services from residential and small commercial buildings to the grid. Simultaneously, direct control of individual devices by utilities can cause communication bottlenecks, as well as coordination and privacy concerns. These challenges can be addressed by combining the constituent devices into a single house battery equivalent for the purposes of demand response, using Minkowski sum and a 2d bin packing problem. However, the well-studied traditional problems have not been tested in a real house, as implementation carries significant challenges of its own. We deploy the packing problem on residential devices in a controllable house. We report the barriers we found, such as charge forecast and scalability of the algorithm, and discuss our solutions. The study serves as an intermediate step between existing theoretical research and possible future steps, such as prototype deployments of systems that provide residential demand response.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

EV Forecasting-Based Model Predictive Control for Distribution System Congestion Mitigation

The uncoordinated charging of electric vehicles (EVs) in time and space brings congestion issues to the distribution network. This paper proposes an EV charging demand forecasting-based model predictive control (MPC) method for distribution system congestion management. To effectively forecast the time-series EV station charging demand, a hybrid forecasting model that integrates the long short-term memory network (LSTM) and Transformer is proposed. The Transformer-LSTM model is trained using a one-year real historical charging dataset of EV stations to forecast future charging demand in 15-minute intervals. This informs the MPC for distribution network congestion management and minimization of PV curtailment. Numerical results carried out on the modified IEEE 123-bus distribution system demonstrate that the proposed method can effectively resolve line congestion issues through EV smart charging and PV curtailment while outperforming other benchmarks.

ADVANCED PROPULSION SYSTEMS,SOLAR ENERGY↗
DOE OSTI2023

Demographic Information Incorporated Household Energy Consumption Analysis

The high energy consumption from residential buildings provides them large potential to participate in demand response programs. To design appropriate demand response programs for residential buildings, it is important for electric utilities to know the energy consumption characteristics for different types of households so that utilities can send requests to the groups with a higher possibility to successfully respond. In this paper, we develop a load model to generate synthetic load profiles for different types of households incorporating demographical information including Current Population Survey data set and American Time Use Survey data set. The details of each data set and the details of the load models are presented. The synthetic household load profiles are generated by the load model and clustered into different groups based on state, age, number of occupants, income level, and city of the household. The average energy consumption characteristics for different groups of households are analyzed and compared, which will help electric utilities issue demand response signals to appropriate households.

building loads↗
DOE OSTI2017

Optimal Load-Side Control for Frequency Regulation in Smart Grids

Frequency control rebalances supply and demand while maintaining the network state within operational margins. It is implemented using fast ramping reserves that are expensive and wasteful, and which are expected to become increasingly necessary with the current acceleration of renewable penetration. The most promising solution to this problem is the use of demand response, i.e., load participation in frequency control. Yet it is still unclear how to efficiently integrate load participation without introducing instabilities and violating operational constraints. In this paper, we present a comprehensive load-side frequency control mechanism that can maintain the grid within operational constraints. In particular, our controllers can rebalance supply and demand after disturbances, restore the frequency to its nominal value, and preserve interarea power flows. Furthermore, our controllers are distributed (unlike the currently implemented frequency control), can allocate load updates optimally, and can maintain line flows within thermal limits. We prove that such a distributed load-side control is globally asymptotically stable and robust to unknown load parameters. We illustrate its effectiveness through simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Study of Cost-Saving Potential of Load Flexibility Measures in Grid-Interactive Multifamily Buildings

With recent advances in smart technologies, more and more smart devices are penetrating the residential and commercial buildings market. The introduction of these smart devices is also helping IoT companies emerge with load aggregator roles in the sector. With more utility companies on the track of supporting OpenADR protocols, the aggregators could play a significant role in providing load flexibilities by automatically responding to demand response (DR) events and coordinating load flexibility measures between customers. This would benefit utility companies by reducing stress on the grid during critical peak demand hours as well as customers by allowing them to utilize utility rate structures advantageous to those able to reduce electric usage during high-demand hours. This study evaluates cost and energy savings from adopting multiple load flexibility measures in multifamily buildings. Combinations of different load flexibility measures, including space temperature floating, light dimming, automatic window shading, and water heater temperature floating, are considered. The simulations are performed using OpenStudio®, an open-source U.S. Department of Energy (DOE) simulation platform. For the case study, we used a midrise apartment building with weather conditions from Denver, Colorado. To compare climate zone differences, simulations were also performed for Los Angeles, California, and Chicago, Illinois. Initial results indicate that the application of automated load flexibility measures without careful consideration of dispatching strategies and DR program enrollments could significantly affect the savings. To get meaningful cost savings, aggregators need to encourage tenant awareness to curtail energy usage through occupant behavior in addition to dispatching automatic load flexibility measures. The outcomes from this study are believed to help load aggregators understand the risks and benefits of load flexibility opportunities.

building energy modeling↗
DOE OSTI2016

WE-G-204-00: Post-Graduate Training of the Next Generation of Academic Medical Physicists

While many indicators for academic medical physics are distressing – jobs are tight, demands on clinical time are high (and getting worse) and national funding has been flat for several years (meaning less money in reality) the present is perhaps one of the most exciting times in cancer research history, and medical physicists have an opportunity to make a difference. Many of us predict the impact of medical physics on cancer research over the next decade to be more significant than ever. Why is that? First, medical imaging is used for every cancer patient in developed countries. Every improvement in the acquisition, processing or analysis of radiological images has the potential to impact patients. The use of radiation therapy is at an all-time high – and virtually cannot be performed without medical physics. Many of the advances in both biomedical imaging and radiation oncology are the result of the hard work of academic medical physicists who are thinking of the next generation of technologies that will be used against cancer or an even broader spectrum of diseases. A career in academic medical physics is demanding, particularly for those with clinical responsibilities. As the demands for justification of their clinical effort become increasingly metricized, the ability to do “unfunded research” will become even more difficult. This means that many will have to generate external salary support to justify their efforts in research and development. This comes at a time when funding for research is compressed and harder to obtain. Generally speaking, if you are not contributing 50% or more of your effort to research, you are competing at a disadvantage and it is very unlikely you will get an NIH/NCI/NIBIB grant. Furthermore, in the ongoing effort to improve patient care and safety, we have developed credentialing pathways that now require at least two-years of residency training. This full-time clinical training creates a gap in the research trajectory of graduate students who aspire to academic positions with an expectation for extramural funding. To address this, several residency programs have created hybrid programs where the two-years of clinical training is combined with one or two years of research effort to allow candidates to further establish an academic identity and to ensure adequate academic productivity to compete for a beginning faculty position. In conclusion, while the path to a successful career in academic medical physics is steep and sometimes hard to follow, reaching the apex is worth the journey. Different paths to a career in medical physics are available, you just have to decide which one is right for you. If improving cancer care is your goal as a physicist, then academic medical physics is the job for you!.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-G-204-01: Building a Career in Academic Medical Physics: The Hardest and Best Job You Will Ever Have!

While many indicators for academic medical physics are distressing – jobs are tight, demands on clinical time are high (and getting worse) and national funding has been flat for several years (meaning less money in reality) the present is perhaps one of the most exciting times in cancer research history, and medical physicists have an opportunity to make a difference. Many of us predict the impact of medical physics on cancer research over the next decade to be more significant than ever. Why is that? First, medical imaging is used for every cancer patient in developed countries. Every improvement in the acquisition, processing or analysis of radiological images has the potential to impact patients. The use of radiation therapy is at an all-time high – and virtually cannot be performed without medical physics. Many of the advances in both biomedical imaging and radiation oncology are the result of the hard work of academic medical physicists who are thinking of the next generation of technologies that will be used against cancer or an even broader spectrum of diseases. A career in academic medical physics is demanding, particularly for those with clinical responsibilities. As the demands for justification of their clinical effort become increasingly metricized, the ability to do “unfunded research” will become even more difficult. This means that many will have to generate external salary support to justify their efforts in research and development. This comes at a time when funding for research is compressed and harder to obtain. Generally speaking, if you are not contributing 50% or more of your effort to research, you are competing at a disadvantage and it is very unlikely you will get an NIH/NCI/NIBIB grant. Furthermore, in the ongoing effort to improve patient care and safety, we have developed credentialing pathways that now require at least two-years of residency training. This full-time clinical training creates a gap in the research trajectory of graduate students who aspire to academic positions with an expectation for extramural funding. To address this, several residency programs have created hybrid programs where the two-years of clinical training is combined with one or two years of research effort to allow candidates to further establish an academic identity and to ensure adequate academic productivity to compete for a beginning faculty position. In conclusion, while the path to a successful career in academic medical physics is steep and sometimes hard to follow, reaching the apex is worth the journey. Different paths to a career in medical physics are available, you just have to decide which one is right for you. If improving cancer care is your goal as a physicist, then academic medical physics is the job for you!.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-G-204-02: So You Want to Do Research: Two Approaches to Beginning a Career in Medical Physics Research

While many indicators for academic medical physics are distressing – jobs are tight, demands on clinical time are high (and getting worse) and national funding has been flat for several years (meaning less money in reality) the present is perhaps one of the most exciting times in cancer research history, and medical physicists have an opportunity to make a difference. Many of us predict the impact of medical physics on cancer research over the next decade to be more significant than ever. Why is that? First, medical imaging is used for every cancer patient in developed countries. Every improvement in the acquisition, processing or analysis of radiological images has the potential to impact patients. The use of radiation therapy is at an all-time high – and virtually cannot be performed without medical physics. Many of the advances in both biomedical imaging and radiation oncology are the result of the hard work of academic medical physicists who are thinking of the next generation of technologies that will be used against cancer or an even broader spectrum of diseases. A career in academic medical physics is demanding, particularly for those with clinical responsibilities. As the demands for justification of their clinical effort become increasingly metricized, the ability to do “unfunded research” will become even more difficult. This means that many will have to generate external salary support to justify their efforts in research and development. This comes at a time when funding for research is compressed and harder to obtain. Generally speaking, if you are not contributing 50% or more of your effort to research, you are competing at a disadvantage and it is very unlikely you will get an NIH/NCI/NIBIB grant. Furthermore, in the ongoing effort to improve patient care and safety, we have developed credentialing pathways that now require at least two-years of residency training. This full-time clinical training creates a gap in the research trajectory of graduate students who aspire to academic positions with an expectation for extramural funding. To address this, several residency programs have created hybrid programs where the two-years of clinical training is combined with one or two years of research effort to allow candidates to further establish an academic identity and to ensure adequate academic productivity to compete for a beginning faculty position. In conclusion, while the path to a successful career in academic medical physics is steep and sometimes hard to follow, reaching the apex is worth the journey. Different paths to a career in medical physics are available, you just have to decide which one is right for you. If improving cancer care is your goal as a physicist, then academic medical physics is the job for you!.

60 APPLIED LIFE SCIENCES↗