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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Energy Efficiency and Performance Evaluation of Self-Contained, Medium Temperature, Reach-In Refrigerated Display Cases

This project was part of an effort by Commonwealth Edison Company (ComEd) to evaluate the energy and peak demand saving potential of emerging technologies in the Chicago area. This document focuses on the assessment of energy-efficient, medium-temperature, self-contained refrigerated display cases utilizing environmentally friendly refrigerants. The results of this evaluation will be considered by CLEAResult to develop a new energy efficiency rebate measure for ComEd's incentive programs. This rebate measure will become an addition to the Technical Reference Manual (TRM). In 2016, the United Nations passed the Kigali Montreal Protocol Amendment which placed restrictions on certain types of refrigerants. In compliance with this amendment, the US Environmental Protection Agency (EPA) placed a ban on the manufacture of refrigeration systems using hydrofluorocarbons including R134a starting in January 2020. Although the ban has halted manufacture, the EPA continues to allow the use of these refrigerants. Therefore, it is critical to provide incentives for replacing these refrigerants with other environmentally friendly and energy-efficient alternatives. The energy-efficient refrigerator cases evaluated here (referred to as EE Case A and B) contain environmentally-friendly refrigerants in compliance with the EPA hydrofluorocarbon ban. These consist of natural refrigerant propane (R290), and HFC drop-in hydrofluoroolefin R513a, respectively. These cases also contain other energy-efficient components including efficient lighting, more robust evaporator and condenser fans and different-sized heat exchangers. EE Case A is also built with materials that better insulate the case, which improves energy efficiency. To ascertain the energy efficiency contribution of these design components, the consumption of the evaporator and condenser fan motors, compressor, and lighting/controller were evaluated individually. The medium-temperature, self-contained reach-in refrigerated display case was selected due to its widespread use in convenience stores and small supermarkets. Self-contained refrigeration has also seen increased use in restaurants due to curbside pickup during the COVID-19 pandemic. For this study, the refrigerated display cases' performance was evaluated in a controlled environmental chamber at representative indoor dry-bulb and humidity conditions found in supermarkets within ComEd's service territory climate zone. The test method used in this project was foundationally inspired by the ANSI/ASHRAE 72-2018 method of testing. However, modifications to the ANSI/ASHRAE test methodology were implemented to better represent customer operation of the units. In addition to the indoor supermarket conditions, the cases were also evaluated at the "upper target," or environmental conditions used in ANSI/ASHRAE 72-2018. The case total power and case components were metered to obtain their daily power (kW) and energy consumption(kWh). The cases were filled with thermal filler mass to replicate thermal mass of product loading. Additionally, product simulators were used to provide product temperature information. Door actuators were mounted to each of the cases' three doors to replicate regular door openings and effects of shopper traffic.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Engineering bimetallic interfaces and revealing the mechanism for carbon dioxide electroreduction to C 3+ liquid chemicals

Reduction reaction of CO 2 (CO 2 RR) to liquid C 3+ chemicals is a net-zero-carbon process and can increase local resiliency to power outages and fuel consumption. The mechanism and the catalyst design rules of CO 2 RR-to-C 3+ are unknown. Engineering bimetallic interface (e.g., Pd/Au) to tune the intermediate adsorption is promising for promoting C 3+ formation. Our density functional theory (DFT) calculations find that *CH 2 could be the key intermediate and C 1 -CH 2 coupling could be the rate-limiting step to generate C 3+ . High CO surface coverages can promote the bimetallic interfacial sites, lower the energetics of the C 1 -CH 2 coupling step, and enhance the C 3+ formation. We further construct a volcano plot of C 1 -CH 2 kinetics as a function of the binding strength of key intermediate *CH 2 via engineering the d-band center of the interfacial site. Our findings could guide the rational design of bimetallic interfaces and their near-surface microenvironment for enhancing CO 2 RR-to-C 3+ .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Evaluation of Equivalent Battery Model Representations for Thermostatically Controlled Loads in Commercial Buildings

Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.

commercial buildings↗
DOE OSTI2021

A Reconfigurable Neural Network ASIC for Detector Front-End Data Compression at the HL-LHC

Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions are made. We demonstrate that a neural network (NN) autoencoder model can be implemented in a radiation-tolerant application-specific integrated circuit (ASIC) to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile. For our application, we consider the high-granularity calorimeter from the Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider. The advantage of the machine learning approach is in the flexibility and configurability of the algorithm. By changing the NN weights, a unique data compression algorithm can be deployed for each sensor in different detector regions and changing detector or collider conditions. To meet area, performance, and power constraints, we perform quantization-aware training to create an optimized NN hardware implementation. The design is achieved through the use of high-level synthesis tools and the hls4ml framework and was processed through synthesis and physical layout flows based on a low-power (LP)-CMOS 65-nm technology node. The flow anticipates 200 Mrad of ionizing radiation to select gates and reports a total area of 3.6 mm 2 and consumes 95 mW of power. The simulated energy consumption per inference is 2.4 nJ. Furthermore, this is the first radiation-tolerant on-detector ASIC implementation of an NN that has been designed for particle physics applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Digital Twin Technology (“Morpheus”) for Optimized Building Operations [SWR-22-74]

The electrification of buildings is an important step to reducing greenhouse gas emissions across all industries. The management of increasingly electrified buildings is a complex pursuit, and there remains a need for cost-effective software capable of handling the computational burden required of such complexity. Through a partnership with Dallas Fort Worth (DFW) Airport, researchers at NREL have developed a digital twin modeling framework to optimize building operations, called Morpheus. Pairing predictive control with automatic fault detection and diagnostics, Morpheus decreases energy expenditures, costs, and faults for large facilities. Additionally, Morpheus employs artificial intelligence to continuously improve its performance using information provided by sensor systems, human experts with deep industry domain knowledge, and even from other similar machines or fleets of machines. Coupling this novel energy-management software with other digital twins, such as NREL’s Athena software for mobility operations, enables robust decision-making for asset and space management. The implementation of Morpheus at DFW has resulted in significantly improved HVAC system operations and reduced both peak power and overall energy consumption. This enhanced functionality comes at a more affordable price than previously developed digital twins and can be customized for other facilities’ geometries to provide optimal, individualized control of a facility’s energy consumption.

Chinde, Venkatesh↗
DOE OSTI2023

Demonstrating the Transient System Impact of Cyber-Physical Events Through Scalable Transmission and Distribution (T&D) Co-Simulation

Modern power systems become more vulnerable to cyber threats due to their growing interconnectivity, interdependence, and complexity. Widespread deployment of distributed energy resources (DERs) further expands the threat landscape to the grid edge, where fewer cybersecurity protections exist. In this article, a systematic cyber-physical events demonstration, enabled by an integrated transmission, distribution, and communication co-simulation framework, is performed. It analyzes cyber risks to power grid under DER-enabled automatic generation control from different angles. Unlike existing works, the simulation captures millisecond-to-minutes frequency and voltage transient dynamics at a cross-region system scale.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2019 State of Technology and Future Research

NREL’s algae state of technology benchmarking efforts focus both on front-end algal biomass production and separately on back-end conversion to fuels through NREL’s “combined algae processing” (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted under NREL R&D projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model and reports on key process sustainability indicators for the biomass production stage including annual biomass yields, facility power demand, and water consumption.

09 BIOMASS FUELS↗
DOE OSTI2020

Comparative Analysis of Natural Gas Cogeneration Incentives on Electricity Production in Latin America

An analysis to assess the influence of country-dependent variables and incentives on the feasibility of natural gas cogeneration projects in Latin America is presented in this work. The analysis is performed using a hypothetical industrial plant, where the cogeneration solution consists in the recovery of waste heat from the power generation for steam production. The feasibility is evaluated by calculating the Return of Investment (ROI) and the Internal Rate of Return (IRR) of the project. Eight Latin American countries are studied considering their specific natural gas markets, regulation, and macroeconomic variables. Two scenarios, electricity production for self-consumption and electricity production with power surplus sale, are independently analyzed considering the effects of available incentives. In the countries where the project is feasible, the application of incentives leads to a significant reduction in the ROI and, consequently, to an increment in the IRR. The effect of the interest rate and environmental impact were also analyzed. In general terms, along the region, the regulation for cogeneration is incipient while incentives are very standard and similar respect to lowering of import, Value-Added Tax (VAT), and income taxes, which seems to be designed purely to promote capital investment. Following the results from this study, it is of paramount importance to create new policy instruments in the future to advance the regulatory framework for cogeneration in Latin America.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Building Resilience with Distributed Wind

Distributed wind has several properties that can increase resilience of local electric systems. One of the biggest benefits distributed wind provides is the ability to provide power locally without importing fuel and in areas where solar resources are not adequate. In this way, it can be used to offset fossil fuel consumption and support local backup power needs in the case of a wider grid outage. This fact sheet provides an overview of this and other ways that distributed wind supports resilience.

17 WIND ENERGY↗
DOE OSTI2024

Long-term impact of electrification and retrofits of the U.S residential building in diverse locations

The U.S. buildings sector contributes 30% of operational carbon emissions, with residential buildings accounting for 56%. Reducing residential carbon emissions is crucial for achieving net-zero carbon goal. While many studies examine energy efficiency retrofit (EER) and electrification, few explore their long-term impacts across diverse climates and dynamic grid clean energy penetrations, as well as their economic effects on households. Here, this study proposes a method to assess how EER and electrification affect long-term decarbonization and economics across different climates, focusing on carbon emissions, energy burden (the percentage of household income spent on energy), and payback period in four locations: Tampa, San Diego, Denver, and Great Falls. The study also introduces the concept of implicit energy burden by considering investment costs. Results show that while electrification can reduce long-term emissions with increased clean energy penetration, it may not always achieve decarbonization due to mismatches between clean energy availability and demand. In cooling-dominant locations, electrification lowers energy burden and peak demand, but in heating-dominant locations, it increases energy burden to 8.24%, raises peak demand by 632.78%, and shifts it from summer to winter. After integrating investment costs, the implicit energy burden can reach 8.35% in cold climates. For already highly electrified buildings in Denver and Great Falls, the payback period of EER measures can be shortened by up to 48.98%. The study highlights a tradeoff between decarbonization and energy burden alleviation, showing that while EER measures can reduce the energy burden, they only achieve one-quarter of the carbon emission reduction of electrification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Large-signal Stability Analysis of Grid-forming Inverters with Equivalent-circuit Models

Here, this paper proposes an energy function-based direct method for large-signal stability assessment of grid-forming (GFM) inverters leveraging an equivalent-circuit representation of all involved control- and physical-layer dynamics. Three different primary controls, a standard inner-current outer-voltage cascaded-control architecture, output LCL filter, and reference-current saturation limiting are featured in the modeling and analysis framework. A composite energy function for the GFM inverter is obtained by summing up individual energy contributions gleaned from the circuit representation. The approach can readily be generalized to different primary controls, output-filter arrangements, and current limiters since it is based on a circuit-theoretic foundation. Numerical simulations validate the efficacy of the approach in estimating the critical clearing time following a large-signal disturbance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Development and Experimental Validation of a High-Power DC Distribution Testbed for Advanced Charging Infrastructure and Energy Management

This paper presents the development of a hardware testbed for DC-distributed high-power charging (HPC) stations. As DC distributed solutions emerge as a viable solution to optimize HPC site operations, challenges such as interoperability, protection, and seamless integration of distributed energy resources (DER) persist. These issues underscore the need for a robust testing facility to investigate compliance of available commercial off-the-shelf (COTS) market devices. The developed testbed features a DC-distributed charging hub including a charger, emulated energy storage system (ESS), and site level communication and controller implementation. It facilitates the testing of COTS hardware, charger prototypes, standards validation and site energy management system (SEMS) controllers at rated power. This paper details the development of the charging infrastructure platform, implementation of communication system, validation of different SEMS algorithms, and understanding improvements required for future expansion. Using the developed testbed, interoperability gaps for SEMS implementation with multi-vehicle concurrent charging via a multi-port charger are experimentally observed. Aimed at supporting the transition to large-scale EV charging infrastructure deployment and DER integration, this testbed plays a crucial role in conformity testing of COTS device interoperability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Development, calibration, and validation of a novel gray-box energy model for residential split air conditioners

Energy models for vapor compression refrigeration in residential air conditioners have been developed through white-box, gray-box, and black-box methods in decades. However, existing white-box and gray-box models require complicated equations with detailed geometries while black-box models require substantial experimental data. Further, this paper aims to develop and validate a simple gray-box steady-state energy model without the need for detailed geometries, which can accurately predict the cooling capacity and electrical power input based on outdoor and indoor air conditions, and supply airflow rates. First three state variables, including the evaporation and condensation temperatures, and refrigerant mass flowrate, are applied to develop the energy model and are solved by three physical equations, including the energy conservations at the evaporator and condenser, and the refrigerant volume-mass flow correlation of the compressor. Secondly, seven performance property functions related to three state variables and three physical equations are identified and calibrated by simple temperature and power measurements. Finally, field experiments are conducted on a residential air conditioner to calibrate these performance property functions and validate the developed model. The validated results reveal the model can accurately predict the cooling capacity and electrical power input, with the normalized root mean square errors of 2.3% and 0.87% respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Multi-facility analysis using metered power data to quantify MRI energy use and utility bill costs across scanner operating modes

This study quantifies the energy consumption of magnetic resonance imaging (MRI) scanners across discrete operating modes during routine clinical workflows, based solely on electrical power measurements. Although previous studies have investigated MRI energy consumption within single hospitals or specific clinical settings, this research provides a broader and more systematic analysis. Researchers analyzed electrical power data and applied a previously developed semi-automatic method for identifying MRI operating modes using load duration curves for 20 MRI scanners across four different U.S. healthcare facilities, encompassing outpatient, inpatient, and mixed-use clinical settings. A key innovation is the inclusion of localized hourly utility rates to estimate costs, a parameter absent in prior literature. Key findings indicate significant variability in energy and cost profiles between weekdays and weekends. Scanner characteristics, including magnet strength, manufacturer, vintage, location, and clinical setting, influenced average daily energy consumption and power thresholds for operating modes. Notably, the clinical setting of a scanner predominantly determines its energy use. For example, the scanners in outpatient facilities consumed more energy. The breakdown of energy usage and costs by operating modes showed scanners spend between 61% and 93% of their time in nonproductive modes, with one outlier spending 34%. Average daily energy use for the scanners in the study ranged from 160 to 1069 kWh, with energy costs ranging from $\$$9 to $\$$149. This study uses an existing framework to quantify MRI energy behavior, leading to insights that can enable improved performance and cost savings across different healthcare environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Chapter 7: Predictive Analytics in Future Power Systems: A Panorama and State-of-the-Art of Deep Learning Applications

The challenges surrounding the optimal operation of power systems are growing in various dimensions, due in part to increasingly distributed energy resources and a progression towards large-scale transportation electrification. Currently, the increasing uncertainties associated with both renewable energy generation and demand are largely being managed by increasing operational reserves - potentially at the cost of suboptimal economic conditions - in order to maintain the reliability of the system. This chapter looks at the big picture role of forecasting in power systems from generation to consumption and provides a comprehensive review of traditional approaches for forecasting generation and load in various contexts. This chapter then takes a deep dive into the state-of-the-art machine learning and deep learning approaches for power systems forecasting. Furthermore, a case study of multi-time-horizon solar irradiance forecasting using deep learning is discussed in detail. Smart grids form the backbone of the future interdependent networks. For addressing the challenges associated with the operations of smart grid, development and wide adoption of machine learning and deep learning algorithms capable of producing better forecasting accuracies is urgently needed. Along with exploring the implementation and benefits of these approaches, this chapter also considers the strengths and limitations of deep learning algorithms for power systems forecasting applications. This chapter, thus, provides a panoramic view of state-of-the-art of predictive analytics in power systems in the context of future smart grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Cut–and–chip harvester material capacity and fuel performance on commercial-scale willow fields for varying ground and crop conditions

Shrub willow (Salix spp.) is capable of producing commercially attractive amounts of biomass in short rotations, but harvesting costs and logistics remain a concern. There is a particular need for information about harvesting operations on larger, commercial short–rotation woody crop systems. Another recent issue on commercial fields in northern New York is commercial growers conducting harvests during the growing season rather than the recommended dormant season when fields may be too wet to harvest. This study evaluated and modeled the in–field performance of a cut–and–chip harvester for almost 700 wagonloads of chips operating in commercial willow fields in a wider array of crop and field conditions than have been previously reported. Analysis indicated that the time of harvest (leaf–on or leaf–off) and whether site conditions were wet or dry affected the harvester's material capacity. Mean material capacity was greatest for leaf–off, dry conditions (71.8 Mg/hr) and lowest for leaf–on harvests, which were similar for wet (30.4 Mg/hr) and dry conditions (29.7 Mg/hr). Mean crop specific fuel consumption ranged between 1.3 and 3.3 L/Mg, but can get considerably higher for standing biomasses below 40 Mg/ha. Wet ground conditions and leaf–on harvests tend to decrease material capacity and increase fuel consumption as the harvester has to divert power to forward movement and material processing. Relationships for material capacity and fuel consumption based on standing biomass, time of harvest and ground conditions will be essential for evaluating and modeling the economic and environmental impacts of commercial–scale willow operations.

09 BIOMASS FUELS↗
DOE OSTI2024

Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale

Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence’s potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

An Introductory Guide to Generating, Using, and Storing Energy: Understanding electricity, energy storage, microgrids, and related technologies

This guide was created to empower Energy Storage for Social Equity (ES4SE) community members to learn more about power systems, energy distribution, renewable energy, and battery storage systems. It was developed in an accessible format for audiences of varied backgrounds and requires no previous experience in energy storage. It is intended to serve readers as both an educational and reference document as they consider, procure, and deploy power systems that include battery energy storage technologies and microgrids in their communities. By providing an introductory overview of the basics of electricity, power generation, and household energy consumption and loads, this guide prepares readers to learn even more about battery energy storage, microgrid systems, and the sizing of solar and battery systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗