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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

SECARB Regional Project Assessment

The SECARB Cranfield Early Test Project was designed to safely demonstrate large-scale, long-term CO 2 injection and storage in a CO 2 -enhanced oil recovery (EOR) operation and associated saline reservoir. Storage during EOR operations has the potential to offset commercial costs, holding a significant promise for future large-scale deployment in the southeastern United States. Denbury Onshore LLC began CO 2 -EOR operations in July 2008 at the Cranfield oilfield located east of Natchez, Mississippi (Figure 1). The location offered an ideal setting to investigate storage monitoring at a commercial CO 2 -EOR operation. The field team, led by the Gulf Coast Carbon Center of the Bureau of Economic Geology at the University of Texas at Austin, deployed the largest suite of monitoring, verification, accounting (MVA) technologies to collect data to assess long-term CO 2 utilization and storage. The CO 2 injection interval is within the 3,000-meter-deep fluvial lower Tuscaloosa Formation, a large and regionally extensive saline reservoir, at a broad four-way structural closure that defines the Cranfield oilfield. Tests were conducted in the oil-producing area as well as in the non-oil-producing brine aquifer. The CO 2 used during EOR operation was transported via pipeline from the Jackson Dome, a natural source of CO 2 . The study focused on four integrated research areas: (1) the High-Volume Injection Test area (HiVIT); (2) the Detailed Area of Study (DAS); (3) the Geomechanical area; and (4) the near-surface observatory, also called the “P-site.” CO 2 injection occurred at the HiVIT and the DAS (Figure 2 and Figure 3). The DAS was located down-dip of the oil-water interface of the Cranfield oilfield. The well layout included one injection well and two down-dip observation wells. The team was the first of the regional partnerships to monitor more than 1 million metric tons of CO 2 . At the end of the project, the team monitored more than 11 million metric tons of CO 2 injected and 5 million metric tons of CO 2 stored. The Cranfield Project was the fifth project worldwide, and the first such project in the United States, to reach this CO 2 injection volume while being monitored to demonstrate storage effectiveness.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

A Community Guide to Regulatory Barriers Affecting Microgrids (Reports 1-3)

In response to growing risks of power outages from extreme weather and aging infrastructure, communities are increasingly exploring the potential of microgrids to provide reliable energy access. Microgrids offer promising solutions to meet this challenge but face a complex landscape of non-technical barriers, particularly regulations concerning the provision and distribution of energy. Most existing legal and regulatory frameworks were designed for a centralized, one-way power system, and are often poorly suited to handle systems that independently balance distributed energy resources with local load. This three-part report series provides a strategic analysis of existing regulatory and legal factors affecting microgrid deployments to help non-technical community leaders and decision-makers better understand the feasibility of a microgrid in their community. -Report No. 1: Foundational Issues Facing Microgrids details the universal policy barriers all microgrids face, including utility interconnection processes, rate structures, and local permitting. -Report No. 2: Single Property Microgrids outlines how direct asset ownership and operating behind-the-meter can bypass some regulatory oversight, using the Blue Lake Rancheria microgrid as a case study. -Report No. 3: Multi-Property Microgrids tackles the complex challenges of crossing public rights-of-way and navigating utility franchise rights, highlighting the Coventry microgrid project. The series equips decision-makers with phased frameworks to navigate financial and regulatory complexities, engage effectively with local utilities and Authorities Having Jurisdiction (AHJs), and structure successful microgrid projects.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗
DOE OSTI2024

Voltage Calculations in Secondary Distribution Networks via Physics-Inspired Neural Network Using Smart Meter Data

The increasing penetration of distributed energy resources (DERs) leads to voltage issues across distribution networks, necessitating voltage calculations by utilities. Electric model-free voltage calculation offers an enticing solution. However, most researches mainly focus on primary distribution networks ignoring secondary distribution networks and commonly overlook extreme voltage case calculations, which require the model’s extrapolation abilities. Here, in addressing the gaps, this paper presents a customized physics-inspired neural network (PINN) model, the structure of which is inspired by the derived coupled power flow model of primary-secondary distribution networks. To ensure precision and rapid convergence, a crafted training framework for the PINN model is proposed. The PINN’s “structure-mimetic” design enables superior extrapolation for unseen scenarios and enhances physical information awareness. We demonstrate this through two applications: hosting capacity analysis and customer-transformer connectivity. The effectiveness and advantages of the proposed PINN model are validated on two public testing systems and one utility distribution feeder model.

Distribution network↗
DOE OSTI2022

Assessing the energy equity benefits of energy storage solutions

Safety, reliability, efficiency, and affordability are no longer the sole tenets of electric grid planning. The evolving social and policy climate have placed new explicit requirements on the electric grid, including resilience, decarbonization, and energy equity and justice. Integrating energy equity strategies into modern grid design is intended to achieve a fair and just distribution of benefits within the energy system. This study aims to characterize the energy equity and community benefits of energy storage systems (ESS) under the following three use-case models: utility ESS that are operated within the distribution system; community-owned ESS; and behind-the-meter ESS that are customer-owned to serve the household. The goal of this energy equity analysis is to characterize the environmental, economic, and social benefits of ESS through applied metrics such as energy burden, energy poverty, energy vulnerability, job creation, and more. A 13.8 kV, 265 node representative feeder corresponding to the hot-humid climate of Louisiana, chosen for its high energy burden, frequent hurricanes and outage events, was used to perform a storage adequacy analysis for six different outage scenarios to determine energy storage access for each use-case. The results of this analysis are then used to characterize both the grid and equity benefits of storage solutions to inform a prioritization framework matching community needs with system preferences for utility planning processes, market regulations, and the wider network of energy system stakeholders.

Energy Equity, energy justice, equity, reliability↗
DOE OSTI2020

Considerations for AMI-Based Operations for Distribution Feeders

More than $5 billion in investments in advanced metering infrastructure (AMI) technologies, AMI deployments, as pervasive secondary network voltage monitoring systems, provide opportunities for utility operations and controls. This paper focuses on the considerations for AMI-based tools and techniques as the industry moves toward operationalizing such large data sets. Phase identification is a first such tool. Numerous distribution network analysis, monitoring, and control applications - including volt/volt-ampere reactive control, state estimation, and distribution automation - require accurate phase connectivity information in the system models. The phase connectivity database maintained by utilities is inaccurate because of a significant amount of missing data, restoration activities, and network reconfiguration. Existing phase identification techniques that estimate phase connectivity work well in distribution feeders that have low or no photovoltaic (PV) generation; however, they fail to identify the phases accurately when considerable PV generation is present. This work addresses the phase identification problem in the presence of high PV generation using statistical analysis methods. Further, insights into the AMI data requirements for this application in terms of data window length and resolution are provided using sensitivity analysis performed on an actual distribution feeder model of San Diego Gas & Electric Company. The results of this study show that the phase connectivity, even in the presence of high PV generation, can be accurately identified using statistical analysis of AMI data of 1 day.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Generating realistic building electrical load profiles through the Generative Adversarial Network (GAN)

Building electrical load profiles can improve understanding of building energy efficiency, demand flexibility, and building-grid interactions. Current approaches to generating load profiles are time-consuming and not capable of reflecting the dynamic and stochastic behaviors of real buildings; some approaches also trigger data privacy concerns. In this study, we proposed a novel approach for generating realistic electrical load profiles of buildings through the Generative Adversarial Network (GAN), a machine learning technique that is capable of revealing an unknown probability distribution purely from data. The proposed approach has three main steps: (1) normalizing the daily 24-hour load profiles, (2) clustering the daily load profiles with the k-means algorithm, and (3) using GAN to generate daily load profiles for each cluster. The approach was tested with an open-source database – the Building Data Genome Project. We validated the proposed method by comparing the mean, standard deviation, and distribution of key parameters of the generated load profiles with those of the real ones. The KL divergence of the generated and real load profiles are within 0.3 for majority of parameters and clusters. Additionally, results showed the load profiles generated by GAN can capture not only the general trend but also the random variations of the actual electrical loads in buildings. We report the proposed GAN approach can be used to generate building electrical load profiles, verify other load profile generation models, detect changes to load profiles, and more importantly, anonymize smart meter data for sharing, to support research and applications of grid-interactive efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Building commissioning costs and savings across three decades and 1500 North American buildings

B.V. Building commissioning (Cx) is a process for assuring efficient building operations that can be applied to new construction and existing buildings, resulting in energy and non-energy benefits. Quantifying the benefits of commissioning is challenging, but a 2009 study of 643 commercial buildings provided a solid initial data set to which we added 839 additional buildings for a significantly expanded and updated meta-analysis representing 34.7 million square meters (373 million square feet) of floor area. Since 2009 the commissioning industry has continued to grow, driven by building codes, utility programs, and rising awareness of commissioning benefits. In parallel, building controls have become more sophisticated, and analytics software has emerged to assist with commissioning. We find that delivery mechanism and market segment are key determinants of outcomes, although significant and cost-effective savings are found across the spectrum. Median primary energy savings for Cx projects in existing buildings ranged from 5 percent for those conducted under utility programs, 9 percent for monitoring-based commissioning utility programs (i.e., augmented with submetering and diagnostics), and 14 percent for Cx projects outside of utility programs. Across all project types, median savings ranged from 3 percent for the lodging market segment to 16 percent for public order and safety facilities. Outcomes did not vary significantly by building size or by market segment. Energy savings are rarely estimated for new construction commissioning. We found that the median costs of Cx were lower for the 2018 sample than for the 2009 sample—$\$$2.85 per square meter ($\$$0.26 per square foot) for existing buildings (a 33 percent reduction) and $\$$8.78 per square meter ($\$$0.82 per square foot) for new construction (a reduction of almost 50 percent). The median simple payback time for existing buildings was 1.7 years, with a 25th–75th percentile range of 0.8–3.5 years. Overall, this article summarizes these and other key findings, and discusses how the 2018 data reflects shifts in commissioning practice and outcomes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic

In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Evaluating Synthetic Smart Meter Locations For Communication System Modeling

An important part of good communication models for smart grid applications, particularly when wireless protocols are used, is the location information of the nodes. We’ve developed metrics to assist in evaluating whether a given model with such information is representative of feeders found in the world. These metrics are applied to a set of feeder models with known good location information for the smart meter locations and compared to a set of feeder models with similar location information that is not expected to be representative of real-world feeders. The comparison reveals that the suspect models do not pass statistical tests utilizing the developed metrics, providing initial validation of the analysis technique.

smart meter, feeder model, GridLAB-D↗
DOE OSTI2024

Least-cost Optimal Distribution Grid Expansion (LODGE) v1

LODGE finds the least-cost portfolio of traditional distribution system upgrades to integrate new loads and behind-the-meter DERs in combination with advanced non-wire alternative (NWA) planning solutions, such as utility-owned storage and distributed PV. Working with a set of least-cost solutions per feeder/substation allows us to benchmark and compare techno-economic performance of different traditional and NWA planning solutions. An important aspect of the LODGE model is that it includes power flow operation constraints. This means that the least-cost planning solutions delivered by LODGE are technically feasible. Available projects in LODGE portfolio include: 1) feeder reconductoring; 2) transformers upgrades; 3) non-wire alternatives, such as strategic siting and sizing of storage and distributed PV; 4) voltage regulators.

Heleno, Miguel↗
DOE OSTI2020

Identification of hot water end-use process of electric water heaters from energy measurements

This paper presents an algorithm for the identification of parameters for a stochastic hot water end-use process that drives a homogeneous population of thermostatically controlled electric water heaters (EWH). Usually, only metered interval consumption data (kWh) is collected and the hot water end-use process is unobservable to utility and aggregators. However, the availability of EWHs for demand response (DR) is closely coupled with the hot water end-use process. In this context, the hot water end-use process is modeled as a two-state Markov chain (Use / No use), which causes the thermostatic ON-OFF switching process to behave as a Markov renewal process (MRP). A set of first passage-time problems is developed to obtain the moments of the transition probability densities of the MRP. These problems are addressed by establishing a system of coupled partial differential equations characterizing the temperature evolution of the EWH population. A key quantity in the methodology for estimating the parameters is the total time an EWH is ON within a period of interest. It is referred to as the total busy time. Total busy time in this approach is a random variable for which analytical expressions of the moments are developed as a function of the metered window length. The latter expressions become the basis of a hot water demand model identification algorithm which is validated using agent-based simulations of EWHs.

42 ENGINEERING↗
DOE OSTI2024

Maximizing the Benefits of On-Site Renewable Energy Generation Using On-Site Energy Storage

The commercial and industrial building sectors have a major role to play in a U.S. low-carbon future. Building owners can make a difference by deploying on-site renewable generation and storage technologies, which facilitates future-proofing their buildings as well as more effectively meeting the service requirements of the building. This resource provides an overview of common generation, storage, and load management technologies in behind-the-meter systems, and serves as a primer to demonstrate the impact on net building loads required from the grid for different sizes of renewable generation along with on-site electrical and thermal storage. These discussions can help commercial building owners communicate the value of these systems to project teams and develop innovative behind-the-meter solutions to achieve their energy goals.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2022

Analysis of Reactive Power Load Modeling Techniques for PV Impact Studies [Slides]

The increasing availability of advanced metering infrastructure (AMI) data has led to significant improvements in load modeling accuracy. However, since many AMI devices were installed to facilitate billing practices, few utilities record or store reactive power demand measurements from their AMI. When reactive power measurements are unavailable, simplifying assumptions are often applied for load modeling purposes, such as applying constant power factors to the loads. The objective of this work is to quantify the impact that reactive power load modeling practices can have on distribution system analysis, with a particular focus on evaluating the behaviors of distributed photovoltaic (PV) systems with advanced inverter capabilities. Quasi-static time-series simulations were conducted after applying a variety of reactive power load modeling approaches, and the results were compared to a baseline scenario in which real and reactive power measurements were available at all customer locations on the circuit. Overall, it was observed that applying constant power factors to loads can lead to significant errors when evaluating customer voltage profiles, but that performing per-phase time-series reactive power allocation can be utilized to reduce these errors by about 6x, on average, resulting in more accurate evaluations of advanced inverter functions.

14 SOLAR ENERGY↗
DOE OSTI2024

Static metrology of the meter-scale deformable heliostat

This paper describes a heliostat metrology system which is developed based on deflectometry, utilizing a static perforated panel instead of conventional dynamic monitor displays to provide incident rays. The developed method is named static screen deflectometry (SSD). This robust and scalable method is especially valuable for outdoor tests of large reflectors used in concentrating solar-thermal power (CSP) systems. The developed method has been successfully demonstrated on a 2.4 𝑚 × 3.3 𝑚 float glass deformable reflector bent to focus sunlight at 113 𝑚 distance throughout a day. From images obtained from a camera at 50𝑚 distance, the reflector surface was measured to an accuracy of less than 1 𝑚𝑟𝑎𝑑 rms slope error in the full test scope.

14 SOLAR ENERGY↗
DOE OSTI2020

Learning to Predict Crystal Plasticity at the Nanoscale: Deep Residual Networks and Size Effects in Uniaxial Compression Discrete Dislocation Simulations

The density and configurational changes of crystal dislocations during plastic deformation influence the mechanical properties of materials. These influences have become clearest in nanoscale experiments, in terms of strength, hardness and work hardening size effects in small volumes. The mechanical characterization of a model crystal may be cast as an inverse problem of deducing the defect population characteristics (density, correlations) in small volumes from the mechanical behavior. In this work, we demonstrate how a deep residual network can be used to deduce the dislocation characteristics of a sample of interest using only its surface strain profiles at small deformations, and then statistically predict the mechanical response of size-affected samples at larger deformations. As a testbed of our approach, we utilize high-throughput discrete dislocation simulations for systems of widths that range from nano- to micro- meters. We show that the proposed deep learning model significantly outperforms a traditional machine learning model, as well as accurately produces statistical predictions of the size effects in samples of various widths. By visualizing the filters in convolutional layers and saliency maps, we find that the proposed model is able to learn the significant features of sample strain profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Revising the Feasibility Study for Solar PV Deployment Conducted for Pueblo of Zia in 2013

The project team comprising ICAST (International Center for Appropriate and Sustainable Technology), a national 501 c3 nonprofit, and Pueblo of Zia worked to determine the feasibility of constructing two different types of solar projects. The first is a 5 MW solar farm on tribal land, adjacent to the Tri-State Generation and Transmission substation, the utility that has exclusive rights to serve the Pueblo of Zia. The second is behind-the-meter solar installations on or near Pueblo of Zia buildings, such as the Zia Enrichment Library, the T'siya Enrichment Center, the Health and Wellness Center, the Tribal Administration Building, and Zia Pueblo Senior Center.

14 SOLAR ENERGY↗