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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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This report describes the background and implementation of an End Use Savings Shapes measure implementing a packaged water-to-air heat pump, which is tied to a loop conditioned by a ground heat exchanger.
This report describes the implementation of an End Use Savings Shapes measure package implementing geothermal heat pumps across the United States commercial building stock.
Thermal energy storage is anticipated to play an important role in developing the power grid of the future - a power grid that meets increasing demands of users, is resistant to disruptions, but also allows for greater penetration of renewable resources. Specifically, thermal energy storage materials can be integrated into HVAC systems and building envelopes, where they can be used to shift power demands for building climate control from periods of peak demand to periods of low demand. Phase change materials (PCMs) are compelling as low-cost, high energy density thermal energy storage materials for building thermal management. However, there is a lack of high performance low-cost PCMs within the specific temperature ranges which would most effectively allow for power load shifting. Inorganic salt hydrates represent a promising class of PCMs, but their inherent limitations cause them to be currently unavailable for reliable building applications. The overarching goals of this research effort are to: 1) Discover low-cost, high volumetric density salt hydrate eutectic PCMs to store low-quality heat (10 to 40 °C); 2) Introduce a high thermal conductivity matrix to reduce the time constant for energy storage to ~0.1 to 1 hr, incorporate nucleation catalysts to decrease undercooling, and utilize microencapsulation and shape stabilization approaches, minimizing moisture loss/gain, mitigating phase separation, and maintaining stable melting behavior over the lifetime of the compounds; 3) Evaluate the impact these systems have on peak load shifting, and the potential for overall energy savings under different climatic scenarios and building configurations. These goals will be achieved by an integrated research program consisting of six cohesive research subtasks: 1) Materials discovery of eutectic salt hydrate PCMs by using computationally predicted thermodynamic equilibria, coupled with high-throughput experimental validation, 2) Rapid experimental screening of nucleation catalysts identified through robust computational databases, 3) Embedding salt hydrate PCM into a low cost and scalable high conductivity matrix, 4) Microencapsulation of salt hydrate microspheres using hybrid inorganic-polymer microencapsulation approach, 5) Shape stabilization by thermoreversible salt hydrate salogels, and 6) Analysis of end-use using thermal simulations, and characterization of mock-up energy storage finished components.
This documentation focuses on a single end-use savings shape measure—heat pump rooftop units with supplemental heat that matches the original fuel type of the replaced system; if the existing system used electric resistance heating, the supplemental heating source is electric resistance. If it was a natural gas furnace, the supplemental system is modeled as natural gas. This is a modification to the heat pump rooftop unit with electric supplemental heat measure from the Commercial EUSS 2023 Release 1 dataset. This document will primarily discuss the supplemental heating change for the heat pump RTU measure. For a comprehensive overview of the fundamental modeling methodology and background of the heat pump RTU measure, including performance curves and other key assumptions, please review the documentation for the original heat pump rooftop unit with electric supplemental heat.
This report describes the modeling methodology for a single end-use savings shape measure - lighting control for load shedding - and briefly introduces key results. The lighting control for load shedding measure applies lighting dimming control to reduce the lighting load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then adjusts the lighting dimming level by a percentage reduction from the original schedules during the peak window to reduce the peak demand. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 2%-7% daily peak demand reduction performance for applicable buildings, and 0.43% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.
This report describes the modeling methodology for an upgrade package of two end-use savings shape measures - Thermostat Control for Load Shedding and Lighting Control for Load Shedding - and briefly introduces key results. The package combines thermostat control for load shedding and lighting control for load shedding measures to reduce the HVAC and lighting load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then relaxes the thermostat setpoints and dims the lighting level from the original schedules during the peak window to reduce the peak demand. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 3%-10% daily peak demand reduction performance for applicable buildings, and 0.97% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.
This report describes the modeling methodology for an upgrade package of two end-use savings shape measures - Thermostat Control for Load Shedding and Lighting Control for Load Shedding - and briefly introduces key results. The package combines thermostat control for load shedding, lighting control for load shedding, and PV with 40% rooftop coverage measures to reduce the net building load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then relaxes the thermostat setpoints and dims the lighting level from the original schedules during the peak window to reduce the peak demand, while applying the fixed rooftop PV application for onsite electricity generation. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 5%-15% daily peak demand reduction performance for applicable buildings, and around 1% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.
A tremendous commitment of resources is needed to acquire, understand and apply battery data in terms of performance and aging behavior. There are many state of performance (SOP) and state of health (SOH) metrics that are useful to guide alignment of batteries to end-use, yet how these metrics are measured or extracted can make the difference between usable, valuable datasets versus data that lacks the necessary integrity to meet baseline confidence levels for SOP/SOH quantification. This work will speak to 1) types of data that support SOP and SOH evaluations on mechanistic terms, 2) measurement conditions needed to assure high data integrity, 3) equipment limitations that can compromise data high fidelity, and 4) the impact of cell polarization on data quality. A common goal in battery research and field use is to work from a data platform that supports economical paths of data capture while minimizing down-time for battery diagnostics. An ideal situation would be to utilize data obtained during normal daily use (“pulses or cycles of convenience”) without stopping the daily duty cycles to perform dedicated SOP/SOH diagnostic routines. However, difficulties arise in trying to make use of daily duty cycle data (denoted as cycle-by-cycle, CBC) that underscores the need for standardization of conditions: temperature and duty cycles can vary over the course of a day and throughout a week, month and year; polarization can develop within an immediate cycle and throughout successive cycles as a hysteresis. If CBC data is envisioned as a data source to determine performance and aging trends, it should be recognized that polarization is a frequent consequence of CBC and thus makes it difficult to separate reversible and irreversible components to metrics such as capacity loss and resistance increase over aging. Since CBC conditions can have a major impact on data usability, we will devote part of this paper to CBC data conditioning and management. Differential analyses will also be discussed as a means to detect changing trends in data quality. Our target cell chemistries will be lithium-ion types NMC/graphite and LMO/LTO.
The End-Use Load Profiles for the U.S. Building Stock project uses New ResStock and ComStock models to statistically represent the energy use of U.S. buildings. The project's hybrid approach combines best-available ground truth data, such as submetering studies and statistical disaggregation of whole-building interval meter data, with the reach, cost-effectiveness, and granularity of physics-based and data-driven building stock modeling to deliver a nationally-comprehensive data set at a fraction of the historical cost.
Driven by the increased use of direct current (DC) sources (photovoltaics, battery storage) and DC end-use devices (electronics, solid-state lighting, efficient motors), DC power distribution in buildings and DC microgrids have been proposed as a way to achieve greater efficiency, cost savings, and resiliency in a transitioning building sector. Despite these important benefits, several market and technological barriers inhibit the development of DC distribution, and the market for DC in buildings is still largely in the demonstration phase. Therefore, to jumpstart this technology, a clear path forward must emerge at this early stage of deployment. The goal of this paper is to define specific end-use cases for which DC distribution in buildings is a value proposition today by defining clear efficiency and resiliency benefits while addressing barriers to implementation. The paper begins with a technology and market assessment of DC distribution equipment, end uses, and technology standards. That is followed by results from an expert elicitation of DC power and building end-use professionals (e.g., electrical designers, building operators, engineers) and reports on-site visits and lessons learned from successful (and less successful) field deployments of DC distribution projects in North America. We present specific adoption pathways at the community and building level that can be implemented today, and evaluate them using qualitative and quantitative metrics, such as technology and market readiness, energy savings, and resiliency.
Driven by the increased use of direct current (DC) sources (photovoltaics, battery storage) and DC end-use devices (electronics, solid-state lighting, efficient motors), DC power distribution in buildings and DC microgrids have been proposed as a way to achieve greater efficiency, cost savings, and resiliency in a transitioning building sector.Despite these important benefits, several market and technological barriers inhibit the development of DC distribution, and the market for DC in buildings is still largely in the demonstration phase. Therefore, to jumpstart this technology, a clear path forward must emerge at this early stage of deployment. The goal of this paper is to define specific end-use cases for which DC distribution in buildings is a value proposition today by defining clear efficiency and resiliency benefits while addressing barriers to implementation.The paper begins with a technology and market assessment of DC distribution equipment, end uses, and technology standards. That is followed by results from an expert elicitation of DC power and building end-use professionals (e.g., electrical designers, building operators, engineers) and reports on-site visits and lessons learned from successful (and less successful) field deployments of DC distribution projects in North America. We present specific adoption pathways at the community and building level that can be implemented today, and evaluate them using qualitative and quantitative metrics, such as technology and market readiness, energy savings, and resiliency.
Understanding the potential for rooftop solar and other distributed energy resources (DERs) to contribute to power system planning is increasingly relevant for cities, utilities, and other planning entities. Such planning efforts typically require an estimate of technical potential, or the feasible technology potential independent of economic considerations. Currently, best-in-class rooftop solar technical potential methods use Light Detection and Ranging (LiDAR) data which can identify each roof plane tilt, azimuth, and unshaded area. However, LiDAR data is not universally available and, even when available, obtaining and processing this data can be expensive. In contrast, parcel-level data is easy to use and widely available as it is generated by jurisdictions to levy property taxes. Such data universally reports building footprint area, which is highly correlated with roof area suitable (developable) for rooftop solar. Moreover, parcel data identifies building end-use, tenure, and other building characteristics not provided by LiDAR. To explore the feasibility of using parcel data to assess technical potential more broadly, we compare estimates using parcel data in Orlando, Florida (HIFLD 2020) to those generated using LiDAR data (Koebrich et al. 2021). We find that the parcel-based method results in accurate technical potential estimates at a block and city-scale, though only after accounting for shading and other factors that derate developable roof area. The results of this study demonstrate a scalable, low-effort approach to assess rooftop solar technical potential for every city and community in the U.S.
Geothermal Operational Optimization with Machine Learning (GOOML) is a transferable and extensible component-based geothermal asset modeling framework that considers complex steamfield relationships and identifies optimization prospects using a data-driven approach to physics-guided, data-centric machine learning. This framework has been used to develop digital twins that provide steamfield operators with operational environments to analyze and understand historical and forecasted power production, explore new steamfield configuration possibilities, and seek optimal asset management in real world applications. To create, test, and apply the GOOML framework, diverse time-series datasets spanning multiple years were sourced from various geothermal power plant components within several complex real-world geothermal operations. These operations are based in the United States and New Zealand and include a variety of technologies, end-uses and configurations, collectively covering nearly all relevant operating conditions for modern geothermal fields. Datasets were acquired from multiple sources to ensure that machine learning experiments generalized properly to various operating conditions. It was found that the data varied in quality, format, and completeness. To ensure consistency between the various datasets, a standardized data curation process was developed to reliably streamline data preparation. This paper will discuss best practices as learned from the GOOML data curation process which takes the following steps: 1) acquisition of large quantities of data from power plant operators, 2) digestion of data to gain an initial understanding of what is included, 3) data transformation, which includes converting the data into a standardized machine-readable format so that they can be visualized, quality checked, and cleaned, 4) quality assurance and quality control, involving identification of significant data gaps and apparent anomalies through mapping of data features to real world componentry via the GOOML historical model, followed by discussion with modelers and power plant operators to identify additional data needs and to resolve issues, 5) use in machine learning algorithms, and 6) repetition of steps one through five until all data needs are met and data are deemed suitable for producing trustworthy modeling results which may be disseminated, ideally along with the curated dataset. This iterative process is focused on improving the quality of the data rather than tuning machine learning model parameters and supports a shift towards data-centric AI as a means to improving real-world applicability of geothermal machine learning projects.
Synthetic fuels (synfuels), also known as electro-fuels (E-fuels), are hydrocarbon fuels produced from waste CO2 streams and water electrolysis, with electricity as the primary source of energy. To achieve substantial reductions in greenhouse gas (GHG) emissions, electricity sources must release zero carbon or near-zero carbon, as is the case with solar, wind, hydro, and nuclear power. Nuclear power is one of the largest and steadiest domestic sources of clean energy in the United States. Moreover, nuclear power has the potential to produce hydrogen economically for less than $2/kg, reaching the DOE near-term target price. Thus, using nuclear power to produce synfuels has the unique potential to significantly reduce the GHG emissions of hydrocarbon fuels production and end-use applications. Fisher-Tropsch or FT fuel (a mixture of naphtha, jet fuel, and diesel) is of great interest because it is a drop-in fuel that can be blended with conventional petroleum counterparts and is compatible with existing infrastructure. By using the ASPEN Plus model, this report develops FT fuel production models on three scales, corresponding to nuclear plants with capacities of 1000 MWe, 437 Mwe, and 100 MWe, respectively. The FT model case with energy from a 437-MWe nuclear plant is used as a baseline case. This report summarizes the baseline ASPEN Plus model results with a detailed mass and energy analysis. Our modeled facility produces 507 MT/day (185,000 gal/day) of FT fuel by converting 255 MT/day of hydrogen and 1,580 MT/day of CO2. The FT fuel production energy efficiency from hydrogen and electricity energy inputs is 70% (lowerheating-value or LHV-based). Including the high-temperature electrolyzer in the system boundary, the FT fuel production LHV efficiency from electricity and thermal energy inputs is 51%, considering 39.8 kWh/kg of electricity and 6.86 kWh/kg of thermal energy use from a nuclear plant for hydrogen production. The FT production efficiency can potentially be increased by further integrating the heat exchange between nuclear plant and FT process, and this study is underway. The carbon conversion ratio in the baseline case is 99%, with process CO2 capture and recirculation and oxy-combustion using the oxygen by-product from water electrolysis. The hydrogen consumption is 1.38 kg/gal-FT fuel and the CO2 consumption is 8.56 kg/gal-FT fuel in the baseline case. With different FT production scales determined by the nuclear plant capacity, the FT model was scaled using the same operating parameters, which led to the same conversion efficiency regardless of scale. However, the different FT plant scales will impact the economics of FT fuel production; this impact will be examined in the next phase of this study.
Rare Earth Elements (REEs) are critical raw material inputs that are required to enable the energy transition. Constraints on the U.S. supply of conventional REE feedstocks and the desire to diversify U.S. reliance on foreign supply chains have motivated the pursuit of REE recovery from unconventional feedstocks including waste products. Recovery of REEs from unconventional sources is also motivated by environmental and social considerations, including the potential to offset new mining activity and subsidize the remediation of REE-containing wastes from coal, natural gas, and other mineral extraction activities. Sourcing REEs from unconventional feedstocks would reduce supply chain constraints and minimize the environmental impacts of REE extraction, but current processes to exploit these feedstocks have uncertain technical and economic performance and the processes have not been demonstrated at scale. This work aims to reduce uncertainty in evaluating the performance of new REE production pathways. A database of operating and capital expenses for REE recovery and establish consistent process unit costs for common stages in the conventional supply chain are developed. Market prices are used to develop well-defined methods for determining product values for a diverse set of products that can be generated (e.g. oxalates, alloys, end-use products such as magnets) but are not typically considered in technoeconomic analyses. Finally, to provide insight on the effects of economies of scale and feedstock purity on capital and operating costs, process unit level cost functions relating total costs per kilogram of REE produced are reported. These tools can be applied to assess and facilitate scale-up of REE production from diverse sources of REEs.
Residential and commercial buildings accounted for 40% of U.S. energy consumption in 2022 and represent a significant opportunity for decarbonization through energy efficiency and electrification, and for grid planning. Building stock energy modeling is a powerful tool that can evaluate what-if scenarios as utilities, municipalities, policymakers, building owners and others work towards equitable building decarbonization and climate goals. This presentation will highlight several high-impact use cases of the National Renewable Energy Laboratory (NREL)'s highly granular, bottom-up building stock energy modeling tools, ResStock and ComStock. These use cases cover a wide range of project scale, from neighborhood electrification analysis and municipality long-term energy planning, to state energy code development and national policy evaluation. This presentation will showcase specific real-world applications for which ResStock and ComStock have been utilized across the country, including California codes and standards cost-effectiveness analysis, New York City affordable housing electrification cost gap analysis, and California targeted electrification and gas decommissioning analysis. For each use case, this presentation will illustrate how ResStock and ComStock played a crucial role in accurately characterizing regional building stocks, providing discrete and aggregated end-use load shapes, and calculating lifecycle consumption, emissions, and costs for a variety of building electrification strategies and scenarios. Finally, this presentation will demonstrate how the data provided by ResStock and ComStock can help unlock significant outcomes for these use cases, including but not limited to, customer bill impact, incentive and program design, and energy equity analyses.
Grocery stores consumed approximately 3% of total electricity used by commercial buildings in the U.S. in 2018 (EIA 2018), representing a unique end-use load profile characterized by the critical use of refrigerated display cases. Exploring demand response (DR) scenarios in grocery stores presents an opportunity to enhance the efficiency and sustainability of surrounding communities. In addition, recent studies demonstrate that implementing control algorithms considering demand flexibility strategies can lead to load and peak reductions in standalone refrigerated display cases. Because small business grocery stores operate on thin margins, the energy bill cost savings DR might provide could make a positive difference toward continued operations. Still, uncertainty remains about the extent of demand flexibility potential controls could provide when coupling refrigeration with whole building operation. To enhance economic viability and grid stability, it is essential to quantify the load flexibility capability of grocery stores. Advanced controls can optimize energy consumption by responding to load shedding, shifting, and DR events, as well as daily Time-of-Use (TOU) rates without compromising food safety. Using both quantitative data and interviews with community-based organizations, we developed a full-size store model and two small store models with controlled refrigerated cases, HVAC, and lighting systems based on actual grocery store properties. Through simulations, we have assessed load flexibility strategies with varied DR events. The results highlight potential for energy and peak reduction with advanced or basic controls. However, interviews and data indicate that more support is needed to make DR strategies consistently accessible to small grocery stores.