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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Strategies for Energy-Efficient Resource Management of Hybrid Programming Models

Many scientific applications are programmed using hybrid programming models that use both message passing and shared memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared memory or message passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. Furthermore, we apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Commercial Midstream Energy Efficiency Incentive Programs: Guidelines for Future Program Design, Implementation, and Evaluation

Many electric utilities operate energy efficiency incentive programs that encourage increased dissemination and use of energy-efficient (EE) products in their service territories. The programs can be segmented into three broad categories—downstream incentive programs target product end users, midstream programs target product distributors, and upstream programs target product manufacturers. Traditional downstream programs have had difficulty engaging Small Business/Small Portfolio (SBSP) audiences, and an opportunity exists to expand Commercial Midstream Incentive Programs (CMIPs) to reach this market segment instead.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Retrofitting a 1960s Split-Level, Cold-Climate Home

National programs such as Home Performance with ENERGY STAR® and numerous other utility air sealing programs have brought awareness to homeowners of the benefits of energy efficiency retrofits. Yet, these programs tend to focus on the low-hanging fruit: air-sealing the thermal envelope and ductwork where accessible, switch to efficient lighting, and low-flow fixtures. At the other end of the spectrum, deep-energy retrofit programs are also being encouraged by various utilities across the country. While deep energy retrofits typically seek 50% energy savings, they are often quite costly and most applicable to gut-rehab projects. A significant potential for lowering energy usage in existing homes lies between the low hanging fruit and deep energy retrofit approaches - retrofits that save approximately 30% in energy over the existing conditions. A key is to be non-intrusive with the efficiency measures so the retrofit projects can be accomplished in occupied homes. This cold climate retrofit project involved the design and optimization of a home in Connecticut that sought to improve energy savings by at least 30% (excluding solar PV) over the existing home's performance. This report documents the successful implementation of a cost-effective solution package that achieved performance greater than 30% over the pre-retrofit - what worked, what did not, and what improvements could be made.

1960 split level↗
DOE OSTI2015

Existing Whole-House Solutions Case Study: Retrofitting a 1960s Split-Level Cold-Climate Home

National programs such as Home Performance with ENERGY STAR® and numerous other utility air sealing programs have brought awareness to homeowners of the benefits of energy efficiency retrofits. Yet, these programs tend to focus on the low-hanging fruit: air-sealing the thermal envelope and ductwork where accessible, switch to efficient lighting, and low-flow fixtures. At the other end of the spectrum, deep-energy retrofit programs are also being encouraged by various utilities across the country. While deep energy retrofits typically seek 50% energy savings, they are often quite costly and most applicable to gut-rehab projects. A significant potential for lowering energy usage in existing homes lies between the low hanging fruit and deep energy retrofit approaches - retrofits that save approximately 30% in energy over the existing conditions.

1960 split level↗
DOE OSTI2015

Building America Case Study: Retrofitting a 1960s Split-Level, Cold-Climate Home, Westport, Connecticut; Whole-House Solutions for Existing Homes, Energy Efficiency & Renewable Energy (EERE)

??National programs such as Home Performance with ENERGY STAR(R) and numerous other utility air sealing programs have brought awareness to homeowners of the benefits of energy efficiency retrofits. Yet, these programs tend to focus on the low-hanging fruit: air-sealing the thermal envelope and ductwork where accessible, switch to efficient lighting, and low-flow fixtures. At the other end of the spectrum, deep-energy retrofit programs are also being encouraged by various utilities across the country. While deep energy retrofits typically seek 50% energy savings, they are often quite costly and most applicable to gut-rehab projects. A significant potential for lowering energy usage in existing homes lies between the low hanging fruit and deep energy retrofit approaches - retrofits that save approximately 30% in energy over the existing conditions. A key is to be non-intrusive with the efficiency measures so the retrofit projects can be accomplished in occupied homes. This cold climate retrofit project involved the design and optimization of a home in Connecticut that sought to improve energy savings by at least 30% (excluding solar PV) over the existing home's performance. This report documents the successful implementation of a cost-effective solution package that achieved performance greater than 30% over the pre-retrofit - what worked, what did not, and what improvements could be made. Confirmation of successfully achieving 30% source energy savings over the pre-existing conditions was confirmed through energy modeling and comparison of the utility bills pre- and post- retrofit.

1960 split level↗
DOE OSTI2020

Policies and Programs Available in the United States in Support of Carbon Capture and Utilization

The article seeks to compile policies and programs that provide revenue and financing support for carbon capture and utilizations projects, which are available in the United States. Often technically minded entrepreneurs and investors new to this space are unaware of available support, which could help successful development of carbon capture and utilization projects.Covered in this article are the 45Q tax credit, green bonds, loan guarantee programs, the regional greenhouse gas initiative, and low carbon fuel standards.1This article covers the eligibility and effect of each policy and program. The article also briefly reviews the current state of technology and summarizes how each technology pathway pairs with the policies covered in the article.The goal of the article is to serve as a primer for lawyers, corporate development professionals, and practitioners, who seek to learn about policies and programs available to support carbon capture and utilization (CCU) projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

New Pathways for Equitable Solar Adoption in Texas

As part of the SEIN Round 3 program, a diverse group of energy stakeholders in Texas set out to develop and pilot new pathways to increase rooftop solar adoption at no upfront cost to lowincome households. One potentially promising pathway we identified involved combining funding from existing utility and federal programs promoting weatherization and energy efficiency. Our objective was to demonstrate pilot projects that equitably deployed rooftop solar to properties owned or rented by families in disadvantaged communities. Our goal was to create a clear path for lowincome households to achieve electricity bill relief and to access clean energy by leveraging existing utility energy efficiency programs with existing federal resources, such as the U.S. Department of Health and Human Services (HHS) Low-Income Home Energy Assistance Program (LIHEAP) and/or U.S. Department of Energy (DOE) Weatherization Assistance Program (WAP) funding in Texas. This has been attempted in a few other states with varying degrees of success, but not yet in Texas.

14 SOLAR ENERGY↗
DOE OSTI2017

Keys to the House: Unlocking Residential Savings With Program Models for Home Energy Upgrades

After more than 40 years of effort, energy efficiency program administrators and associated contractors still find it challenging to penetrate the home retrofit market, especially at levels commensurate with state and federal goals for energy savings and emissions reductions. Residential retrofit programs further have not coalesced around a reliably successful model. They still vary in design, implementation and performance, and they remain among the more difficult and costly options for acquiring savings in the residential sector. If programs are to contribute fully to meeting resource and policy objectives, administrators need to understand what program elements are key to acquiring residential savings as cost effectively as possible. To that end, the U.S. Department of Energy (DOE) sponsored a comprehensive review and analysis of home energy upgrade programs with proven track records, focusing on those with robustly verified savings and constituting good examples for replication. The study team reviewed evaluations for the period 2010 to 2014 for 134 programs that are funded by customers of investor-owned utilities. All are programs that promote multi-measure retrofits or major system upgrades. We paid particular attention to useful design and implementation features, costs, and savings for nearly 30 programs with rigorous evaluations of performance. This meta-analysis describes program models and implementation strategies for (1) direct install retrofits; (2) heating, ventilating and air-conditioning (HVAC) replacement and early retirement; and (3) comprehensive, whole-home retrofits. We analyze costs and impacts of these program models, in terms of both energy savings and emissions avoided. These program models can be useful guides as states consider expanding their strategies for acquiring energy savings as a resource and for emissions reductions. We also discuss the challenges of using evaluations to create program models that can be confidently applied in multiple jurisdictions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Enabling communication concurrency through flexible MPI endpoints

MPI defines a one-to-one relationship between MPI processes and ranks. This model captures many use cases effectively; however, it also limits communication concurrency and interoperability between MPI and programming models that utilize threads. This paper describes the MPI endpoints extension, which relaxes the longstanding one-to-one relationship between MPI processes and ranks. Using endpoints, an MPI implementation can map separate communication contexts to threads, allowing them to drive communication independently. Endpoints also enable threads to be addressable in MPI operations, enhancing interoperability between MPI and other programming models. These characteristics are illustrated through several examples and an empirical study that contrasts current multithreaded communication performance with the need for high degrees of communication concurrency to achieve peak communication performance.

Communication↗
DOE OSTI2016

Aggregate modeling of fast-acting demand response and control under real-time pricing

This paper develops and assesses the performance of a short-term demand response (DR) model for utility load control with applications to resource planning and control design. Long term response models tend to underestimate short-term demand response when induced by prices. This has two important consequences. First, planning studies tend to undervalue DR and often overlook its benefits in utility demand management program development. Second, when DR is not overlooked, the open-loop DR control gain estimate may be too low. This can result in overuse of load resources, control instability and excessive price volatility. Our objective is therefore to develop a more accurate and better performing short-term demand response model. We construct the model from first principles about the nature of thermostatic load control and show that the resulting formulation corresponds exactly to the Random Utility Model employed in economics to study consumer choice. The model is tested against empirical data collected from field demonstration projects and is shown to perform better than alternative models commonly used to forecast demand in normal operating conditions. The results suggest that (1) existing utility tariffs appear to be inadequate to incentivize demand response, particularly in the presence of high renewables, and (2) existing load control systems run the risk of becoming unstable if utilities close the loop on real-time prices.

Aggregate Modeling↗
DOE OSTI2022

Development and Validation of Algorithms That Analyze Communicating Thermostat Data to Identify Enclosure Retrofit Opportunities

Annual energy savings of up to $\$ 4$ to $\$ 5$ billion could be achieved nationwide through basic insulation and heating system retrofits of existing homes. However, current utility energy efficiency programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and online advertising that lack specificity about home-specific retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments (HEAs) that are inconvenient to homeowners, expensive, and of variable accuracy. We developed computational algorithms that automatically analyze communicating thermostat (CT) heating data that could be used to increase the customer uptake of insulation and air sealing energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. The algorithms are based on an extended second-order grey-box model that characterizes a building’s thermal response using lumped elements, coupled with an empirical model of infiltration that accounts for both wind and stack effects. The basic parameters of the model correspond to actual physical parameters of the home, i.e., the home’s overall R-value of and the building envelope ACH50. Unlike the conventional approach, which estimates model parameters based on the best fit to the observed time-dependent room temperature, our approach derives correlations between the daily heating system runtime and temperature difference (indoor-outdoor) that are more robust to data quality issues in real-world applications. We also used HEA data for algorithm development and validation. With the help of our utility partners, Eversource and National Grid, we obtained data sets for hundreds of Massachusetts homes. For each home, these data sets included three sets of information anonymized by the utility: (1) CT data (HVAC runtime, room temperature, and, for some vendors, outdoor temperature and wind speed) collected by the CT vendor (one of three) over a heating season, (2) HEA report performed by the HEA vendor (same vendor for all homes), (3) Monthly utility gas bills coincident with the CT data (3 to 24 per home, depending on availability). For some homes, we also obtained blower-door test results. Initially, we applied the algorithms developed to homes with a single CT and then extended them to homes with two CTs by using an equivalent home approach. Finally, we developed algorithms for prediction of energy savings and a methodology of comparing our predictions with those generated by HEAs. The main technical results indicate that we can reliably identify homes with insulation and/or air sealing retrofit opportunities and provide accurate savings predictions. Our hypothesis is that the algorithms could be applied to utility energy efficiency programs to identify homes that could realize significant energy savings from insulation and/or air sealing retrofits. This information could then be used to reach out to those homes with highly customized outreach, thereby delivering increased program energy savings and cost-effectiveness. This would: Significantly increase the uptake rate of on-site HEAs, and Significantly increase the fraction of HEAs resulting in ECM implementation. To test these hypotheses, we designed and conducted a randomized controlled trial (RCT). The RCT results suggest that personal messaging leads to a two- to five-fold increase in the HEA uptake rate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Charging Decision Making for Automated Electric Vehicles

Abstract: Overview/Significance With the emerging of autonomous driving and its application in electric vehicles, the charging decision making should be taken over by the vehicles. Autonomous electric vehicles will require the capability to make charging decisions according to the battery energy state, the travel demand and also the available charging infrastructure. The introduction of autonomous driving technology would remove the challenge of co-locating charging infrastructure with driver destinations and presents a driver-free method for EVs to reach nearby charging stations. This will significantly change the charging behavior of electric vehicles. EV driver will no longer need to be present at charging stations for charging actions. Automated EVs can drive to nearby charging stations to perform charging actions by themselves when necessary. Meanwhile, connected vehicles technology makes electric vehicles have the capability to sense and obtain pertinent information from nearby charging station networks and then calculate the corresponding costs and availability for charging. All these promising capabilities bring new opportunities to construct an automatic charging decision making system and improve energy efficiency of charging actions for automatic electric vehicles. Abstract: Methodology This research introduces optimization tools and case studies on charging decision making for automated electric vehicles under personal usage scenario. The designed optimization tools aim to provide charging strategies, i.e. the optimal choice of charging station outside home and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. Abundant real world EV data is utilized to construct stochastic energy consumption prediction model under realistic driving conditions. Advanced dynamic programming technology is utilized to model and solve the potential multi-stage decision making process by considering realistic uncertainties of EV energy cost and dynamics of itinerary information. Large-scale simulation studies using Chicago itinerary data are demonstrated to illustrate energy benefits from automatic charging decision making for future autonomous EVs. Abstract: Results/Conclusions Optimal automatic charging decision making and its energy benefits have been studied for personal automated electric vehicles. A data-driven method based on large amount of real-world data and the corresponding real-time update algorithm have been proposed to construct multi-channel stochastic energy consumption prediction model. Based on the energy cost prediction, dynamic multi-stage charging decision-making models are established for optimal strategies during a daily itinerary. Derived charging strategies can minimize the monetary and energy cost of charging actions for autonomous vehicles. Simulations and case studies show the potential ability of autonomous electric vehicles to reduce the range anxiety and charging infrastructure dependency. Automatic charging decision making can mitigate the pain from electric vehicle charging necessity outside home. In conclusion, automated electric vehicles equipped with an appropriate automatic charging decision making system will help EV driver to relieve from heavy time cost of recharging, reduce the energy cost of performing charging actions and improve the energy efficiency in future electrified transportation system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Energy Efficiency and Conservation Block Grant (EECBG): Better Buildings Neighborhood Program Final Report

The Neighbor to Neighbor Energy Challenge (N2N) brought together a consortium of 14 leading clean energy rural, suburban, and low income communities throughout Connecticut. N2N was awarded $4.2 million from the U.S. Department of Energy (DOE) competitive BetterBuildings Neighborhood Program on August 10, 2010 to run a two-year pilot program (plus one year of transition and evaluation) (Award No. EMCBC- 00969-10). N2N tested innovative program models and hypotheses for improving Connecticut’s existing residential energy efficiency programs that are overseen by the ratepayer fund board and administered by CT utilities. N2N’s original goal was to engage 10 percent of households in participating communities to reduce their energy usage by 20 percent through energy upgrades and clean energy measures. N2N planned for customers to complete more comprehensive whole-home energy efficiency and clean energy measures and to achieve broader penetration than existing utility-administered regulated programs. Since this was an ARRA award, we report the following figures on job creation in Table 1. Since N2N is not continuing in its current form, we do not provide figures on job retention. Table 1 N2N Job Creation by Quarter Jobs Created 2010 Q4 6.65 2011 Q1 7.13 2011 Q2 4.98 2011 Q3 9.66 2011 Q4 5.43 2012 Q1 11.11 2012 Q2 6.85 2012 Q3 6.29 2012 Q4 6.77 2013 Q1 5.57 2013 Q2 8.35 2013 Q3 6.52 Total 85.31 The N2N team encountered several gaps in the existing efficiency program performance that hindered meeting N2N’s and DOE’s short-term program goals, as well as the State of Connecticut’s long-term energy, efficiency, and carbon reduction goals. However, despite the slow program start, N2N found evidence of increasing upgrade uptake rates over time, due to delayed customer action of one to two years from N2N introduction to completion of deeper household upgrades. Two main social/behavioral principles have contributed to driving deeper upgrades in CT: 1. Word of mouth, where people share their experience with others, which leads to others to take action; and 2. Self-herding, where people follow past behavior, which leads to deeper and deeper actions within individual households.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

American Recovery and Reinvestment Act of 2009: Final Report on Customer Acceptance, Retention, and Response to Time-Based Rates from Consumer Behavior Studies

Time-based rate programs, enabled by utility investments in advanced metering infrastructure (AMI), are increasingly being considered by utilities as tools to reduce peak demand and enable customers to better manage consumption and costs. Under the Smart Grid Investment Grant Program (SGIG), the U.S. Department of Energy (DOE) partnered with several electric utilities to conduct consumer behavior studies (CBS). The goals involved applying randomized and controlled experimental designs for estimating customer responses more precisely and credibly to advance understanding of time-based rates and customer systems, and provide new information for improving program designs, implementation strategies, and evaluations. The intent was to produce more robust and credible analysis of impacts, costs, benefits, and lessons learned and assist utility and regulatory decision makers in evaluating investment opportunities involving time-based rates.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Low-Income Energy Affordability: Conclusions From A Literature Review

This paper examines the persistent problem of high energy burdens among low-income households, based on a review of more than 180 publications that pointed to several promising opportunities to address energy affordability including inclusive solar programs, leveraged health care benefits, and behavioral economics. using an equity and affordability lens. Even after decades of weatherization and bill-payment programs, low-income households, on average, continue to spend a higher share of their income on electricity and natural gas bills than any other income group. Energy burden for low-income households is not declining, and it remains persistently high, particularly in the South, in rural America, among minority households, and those with children and elderly residents. On a per household basis, utility companies spend less on energy-efficiency programs for low-income households than for other income groups. In addition, government and utility programs that promote rooftop solar power, electric vehicles, and home energy storage are largely inaccessible to low-income households. Our review identifies promising opportunities to address energy affordability including inclusive solar programs, leveraged health care benefits, behavioral economics, data analytics, advanced information and communication technologies, and grid resiliency. Scalable approaches require linking implementing agencies, programs and policies to tackle the complex web of causes and impacts on low-income households with high energy burdens.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Sustainable Energy Solutions for Rural Alaska

The state of Alaska recognizes the challenges these rural communities face and provides financial support via the Power Cost Equalization (PCE) program. The PCE subsidizes the electricity prices paid by customers of these high-cost utilities. The PCE program is designed to spread the benefits of Alaska’s natural resources more evenly throughout the state. Yet even with this subsidy, electricity is still much more expensive for these rural customers. And beyond the PCE, other forms of assistance to rural utilities are becoming scarce given the state’s current fiscal environment. Nearly 90 percent of Alaska’s unrestricted budget funds in recent years have been tied to oil royalties—a sector experiencing significant declines in production and oil prices. Consequently, as Alaska looks to tighten budgets, the challenge of lowering rural utility costs, while encouraging self-sufficiency, has become more urgent.This study examines reliability, capital and strategic planning, management, workforce development, governance, financial performance and system efficiency in the various communities visited by the research team. Using those attributes, a tier system was developed to categorize rural Alaska utilities into Leading and Innovating Systems (Tier I), Advanced Diesel Systems (Tier II), Basic Systems (Tier III), and Underperforming Systems (Tier IV). The tier approach is not meant to label specific utilities, but rather to provide a general set of benchmarks and guideposts for improvement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

The OpenEfficiency Initiative: Piloting an open source solution to overcome market barriers to commercial energy savings

The OpenEfficiency Initiative supported the deployment of federal energy efficiency tools and data standards into energy efficiency programs operated by utilities and local governments, with the goal of reducing the cost and improving the savings performance of whole buildings programs. Over more than three years, the effort supported a range of pilots testing the integration of these tools into a range of energy efficiency program types. The effort also produced a set of open source tools that can be used to support program management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗