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DOE OSTI2019

The Value of Resilience for Distributed Energy Resources: An Overview of Current Analytical Practices

Recent extreme weather events, natural disasters, and cyber incursions have brought the vulnerability of the electric system into sharp focus. These events have demonstrated that planning for long-duration power interruptions caused by high-impact, low-probability events will require new approaches to power system resilience above and beyond previous hardening efforts. At the same time, the rapid growth and declining costs of distributed energy resources (DERs) such as microgrids, solar photovoltaics, and batteries have introduced new technology options for energy resilience. Consequently, state policymakers across the country have established electricity resilience policies and programs, with several states focusing specifically on resilient DERs as part of clean energy programs and grid modernization efforts. Although it is clear that DERs offer resilience benefits, it is unclear how to determine the value of those benefits. Identifying appropriate methodologies to calculate the value of resilience will be an important step toward ensuring that resilient DERs are considered alongside alternatives and integrated into future energy infrastructure and investment planning efforts. This paper reviews current practices for calculating the value of resilience with a focus on valuing resilient DERs installed within the distribution system. It examines both regulatory decision-making and non-regulatory cost-benefit analyses in order to determine if, and how, a value of resilience was calculated and applied. The paper is designed to address questions that utility regulators have identified as being of interest: (1) Have regulators identified and utilized a value of resilience in regulatory decisions related to resilient DERs? (2) Is the value of resilience being used to analyze resilient DERs in venues other than regulatory proceedings? (3) What are the different methods to value energy resilience? (4) What are the pros and cons of different methods used to value resilience? (5) Can regulators adopt or improve value of resilience methods to support their decisions?

14 SOLAR ENERGY↗

DOE OSTI2021

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, SeyedMohammad↗

DOE OSTI2023

Data compression system using base values and methods thereof

Seyedzadehdelcheh, Seyed Mohammad↗

DOE OSTI2020

A review of coal heating value correlations with application to coal char, tar, and other fuels

Accurately measuring a fuel’s heating value is one of the first steps in the classification of a new fuel. Heating values are widely used in coal combustion research and are becoming more useful in other fuel types as well. Many different empirical correlations to predict heating values based on primary organic (CHONS) elemental composition are found in the literature, many of which were originally created to predict heating values of parent coal, and some of which have been extended for biomass. However, no correlations exist for heating values of coal chars and tars. Thirteen literature heating value correlations (10 model forms with 13 unique sets of coefficients) were evaluated for their predictive accuracy of a wide variety of fuel types, including 353 coals. This comparative analysis showed that the predictive capabilities of many of these literature models are very similar for coal-based fuels, with good correlation of coal char heating values. Correlation of coal tar heating values was hampered by the lack of heating value data. The choice of which model to use becomes more important for biomass fuels as well as for more unique fuel types like propellants and explosives, municipal solid waste, etc. Finally, the best model forms for each fuel type are suggested based on several statistical measures of fit.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

A new spatially distributed added value index for regional climate models: the EURO-CORDEX and the CORDEX-CORE highest resolution ensembles

The added value of using regional climate models (RCMs) to downscale data from general circulation models (GCMs) has often been questioned and researched. Although several studies have used different methods to identify (and in some cases quantify) the added value, there is still a need to find a general metric that quantifies the added value of any variable. This paper builds on past studies to propose a new metric of added value in the simulation of present-day climate which measures the difference in the probability density functions (PDFs) at each grid-cell between a model and an observation source, and then compares the results of the RCM and GCM in order to spatially compute the added value index. The same method is also adapted to quantify the climate change downscaling signal in a way that is consistent with the present-day metric. These new metrics are tested on the daily precipitation output from the EURO-CORDEX and CORDEX-CORE projection ensembles and reveal an overall positive added value of RCMs, especially at the tail-end of the distribution. Higher added value is obtained in areas of complex topography and coast-lines, as well as in tropical regions. Areas with large added value in present-day climate are consistent with areas of significant climate change downscaling signal in the RCP 8.5 far future simulations, and when the analysis is repeated at a low-resolution. The use of different resolution observations shows that the added value tends to decrease when models are compared to low-resolution observation datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Investigation of the potential ultralow Q-value β-decay candidates 89 Sr and 139 Ba using Penning trap mass spectrometry

Background: Ultralow Q-value β decays are interesting processes to study with potential applications to nuclear β-decay theory and neutrino physics. While a number of potential ultralow Q-value β-decay candidates exist, improved mass measurements are necessary to determine which of these are energetically allowed. Purpose: To perform precise atomic mass measurements of 89 Y and 139 La. Use these new measurements along with the precisely known atomic masses of 89 Sr and 139 Ba and nuclear energy level data for 89 Y and 139 La to determine if there could be an ultralow Q-value decay branch in the β decay of 89 Sr → 89 Y or 139 Ba → 139 La. Method: High-precision Penning trap mass spectrometry was used to determine the atomic mass of 89 Y and 139 La, from which β-decay Q values for 89 Sr and 139 Ba were obtained. Results: The 89 Sr → 89 Y and 139 Ba → 139 La β-decay Q values were measured to be Q Sr = 1502.20(0.35) keV and Q Ba = 2308.37(0.68) keV. These results were compared to energies of excited states in 89 Y at 1507.4(0.1) keV, and in 139La at 2310(19) keV and 2313(1) keV to determine Q values of -5.20(0.37) keV for the potential ultralow β-decay branch of 89 Sr and -1.6(19.0) keV and -4.6(1.2) keV for those of 139 Ba. Conclusion: The potential ultralow Q-value decay branch of 89 Sr to the 89 Y (3/2 - , 1507.4 keV) state is energetically forbidden and has been ruled out. The potential ultralow Q-value decay branch of 139 Ba to the 2313 keV state in 139 La with unknown J π has also been ruled out at the 4σ level, while more precise energy level data is needed for the 139 La (1/2 + , 2310 keV) state to determine if an ultralow Q-value β-decay branch to this state is energetically allowed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Empirical Indicators of Transmission Value in the Southeast United States

Concurrent differences in energy price between different parts of the electric grid are a key indicator of the value of additional transmission. In areas without a wholesale electricity market, such as the Southeast, an alternative indicator to price is the Federal Energy Regulatory Commission’s (FERC) system lambda data. This economic metric represents the minimized marginal production costs of thermal generators, including fuel and other variable operation and maintenance expenses. Balancing Authorities report a single system lambda for their entire balancing area. Most Southeastern lambdas exhibit sufficient price variation to support a transmission valuation analysis, although incomplete accounting of congestion costs or scarcity rents during peak load hours may underestimate the true value of transmission capacity. With transmission value defined as the annual average hourly absolute price difference between two regions and FERC’s system lambda data used as a price proxy, we find the following results in the Southeast region during 2012-2023 (reported in $\$2024$/MWh): Intra‐regional findings: Annual averages historically span $\$2$–$\$28$/MWh and average $\$12$/MWh in SERTP and span $\$4$–$\$19$/MWh and average $\$9$/MWh in FRCC, disregarding transmission value driven by anomalous data. The ranges of transmission value reported here are large, spanning an order of magnitude in some cases. Much of this variation is driven by year-to-year changes, with 2022 having a particularly high intra-regional transmission value due to elevated natural gas prices. Inter‐regional corridors: Annual average transmission values across three broader regions range from $\$6$ to $\$28$/MWh with a long-term average of $\$11$/MWh. Much of the transmission value is concentrated in a small portion of hours. Across all regions, severe weather—particularly polar vortex events in January 2018, February 2021, and December 2022—drives the largest price spreads. Seasonal patterns also emerge, with summer afternoons and fall mornings contributing consistently to transmission value, as for example between MISO and SOCO in 2023.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2015

Environment Modeling Using Runtime Values for JPF-Android

Software applications are developed to be executed in a specific environment. This environment includes external native libraries to add functionality to the application and drivers to fire the application execution. For testing and verification, the environment of an application is simplified abstracted using models or stubs. Empty stubs, returning default values, are simple to generate automatically, but they do not perform well when the application expects specific return values. Symbolic execution is used to find input parameters for drivers and return values for library stubs, but it struggles to detect the values of complex objects. In this work-in-progress paper, we explore an approach to generate drivers and stubs based on values collected during runtime instead of using default values. Entry-points and methods that need to be modeled are instrumented to log their parameters and return values. The instrumented applications are then executed using a driver and instrumented libraries. The values collected during runtime are used to generate driver and stub values on- the-fly that improve coverage during verification by enabling the execution of code that previously crashed or was missed. We are implementing this approach to improve the environment model of JPF-Android, our model checking and analysis tool for Android applications.

Verification↗

DOE OSTI2016

The Value of Regression Models in Determining Industrial Energy Savings

Determination of industrial facility energy savings is commonly undertaken to comply with legal requirements, meet sustainability goals, and quantify greenhouse gas emissions, cost savings, and as one way to demonstrate energy performance improvement per the requirements of an energy management system standard, such as ISO 50001. At their most basic level, energy savings are determined through a process of accounting for baseline and reporting period energy consumption followed by a calculation of energy savings as the difference in the levels of energy consumption between the two periods. The determination of industrial facility-wide energy savings can be undertaken in a wide variety of ways and in relation to energy performance improvement, energy savings can be expressed as any number of energy performance indicators (EnPIs). Energy savings values will vary depending upon if an absolute, intensity, or regression basis of calculation is selected for use. The use of one of the three basis of energy savings calculation will alter the relevance, meaning, and comparability of the resulting energy savings values To increase consistency in the determination of energy savings values, standards such as the International Performance Measurement and Verification Protocol (IPMVP), ISO 17747 – Determination of Energy Savings in Organization, and the U.S. Department of Energy (U.S. DOE) Superior Energy Performance (SEP) program Measurement and Verification Protocol (SEP M&V Protocol) have been published. Industrial facilities in the United States, Mexico, and Canada are certified to the U.S. DOE SEP program after becoming ISO 50001 certified and verification by a third party of calculated energy savings per the SEP M&V Protocol. This paper discusses some of the different meanings energy savings values will have when calculated by using the three different bases. Additionally, data from five SEP certified industrial facilities are used to calculate energy savings values using the three bases and compared as energy performance improvement percentage (EPI %) values. Discussion of why a large degree in variation in EPI % values are seen for any given set of data is provided. Ultimately, while each energy saving calculation basis has merit and can be used appropriately in its own context, the regression basis is shown to best translates energy savings values into contextualized energy performance improvement values by conveying comparable information regarding operational and behavioural improvements not otherwise observable with the absolute or intensity bases.

Therkelsen, Peter↗

DOE OSTI2017

Distributed metadata servers for cluster file systems using shared low latency persistent key-value metadata store

A cluster file system is provided having a plurality of distributed metadata servers with shared access to one or more shared low latency persistent key-value metadata stores. A metadata server comprises an abstract storage interface comprising a software interface module that communicates with at least one shared persistent key-value metadata store providing a key-value interface for persistent storage of key-value metadata. The software interface module provides the key-value metadata to the at least one shared persistent key-value metadata store in a key-value format. The shared persistent key-value metadata store is accessed by a plurality of metadata servers. A metadata request can be processed by a given metadata server independently of other metadata servers in the cluster file system. A distributed metadata storage environment is also disclosed that comprises a plurality of metadata servers having an abstract storage interface to at least one shared persistent key-value metadata store.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Residual value analysis of plug-in vehicles in the United States

Knowledge about the value of used plug-in electric vehicles (PEVs), known as residual value, is critical for potential PEV purchasers, corporations, and governments who wish to consider the lifetime cost effectiveness of PEVs. In this study, we investigate the residual values of PEVs and compare them with those of other vehicle powertrain technologies using the “true market value” data from Edmunds.com, and considering U.S. federal incentives. We define the adjusted retention rate as the ratio between resale value and the manufacturer’s suggested retail price, subtracting incentives. We develop statistical models to quantify the effects of powertrain technologies on residual value while controlling key factors, including vehicle size, brand, and market segments. We find that the long-range high-performance Tesla Model S holds value significantly better than any other vehicle type evaluated, including internal combustion engine vehicles. Hybrid electric vehicles and plugin hybrid electric vehicles are comparable to one another. They hold slightly less value than internal combustion engine vehicles, but significantly more than short-range (<125 miles) battery electric vehicles. However, short-range battery electric vehicles have a faster improvement in 3-year adjusted retention rate than any other powertrain technology from model year 2013–2014. Finally, the other factor that significantly influences adjusted retention rate is the home country of the vehicle manufacturer (e.g., United States, Japan, or Germany).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Direct determination of the La 138 β -decay Q value using Penning trap mass spectrometry

Background: The understanding and description of forbidden decays provides interesting challenges for nuclear theory. These calculations could help to test underlying nuclear models and interpret experimental data. Purpose: Compare a direct measurement of the 138 La $\beta$-decay $Q$ value with the beta-decay spectrum end-point energy measured by Quarati et al. using LaBr 3 detectors [Appl. Radiat. Isot. 108, 30 (2016)]. Use new precise measurements of the 138 La $\beta$-decay and electron capture (EC) $Q$ values to improve theoretical calculations of the $\beta$-decay spectrum and EC probabilities. Method: High-precision Penning trap mass spectrometry was used to measure cyclotron frequency ratios of 138 La, 138 Ce and 138 Ba ions from which beta-decay and EC Q values for 138 La were obtained.Results: The 138 La beta-decay and EC Q values were measured to be Q$_\beta$ = 1052.42(41) keV and $Q_{EC}$ = 1748.41(34) keV, improving the precision compared to the values obtained in the most recent atomic mass evaluation [Wang, et al., Chin. Phys. C 41, 030003 (2017)] by an order of magnitude. These results are used for improved calculations of the 138 La $\beta$-decay shape factor and EC probabilities. New determinations for the 138 Ce 2EC $Q$ value and the atomic masses of 138 La, 138 Ce, and 138 Ba are also reported. Conclusion: The 138 La $\beta$-decay Q value measured by Quarati et al. is in excellent agreement with our new result, which is an order of magnitude more precise. Uncertainties in the shape factor calculations for 138 La $\beta$-decay using our new $Q$ value are reduced by an order of magnitude. Uncertainties in the EC probability ratios are also reduced and show improved agreement with experimental data.

138La↗

DOE OSTI2021

Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils

The peroxide value of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine peroxide values using field portable and process instrumentation; those efforts presented “best-case scenarios” with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique or combination thereof is best for predicting peroxide values. Following peroxide value assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression calibration models to predict the peroxide value of unknown oil samples. A global peroxide value model based on near-infrared (8 mm optical path length) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm optical path length near infrared (5.1), Raman (6.9) and 50 µm optical path length mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global peroxide value models based on low-level fusion of the NIR (8 and 24 mm optical path length) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes. This work demonstrates the difficulty of developing a truly global method for determining peroxide value of oils.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Uplifting winds: The surprisingly positive community-wide impact of wind energy installations on property values

A primary concern of stakeholders when considering a new wind project is the potential negative effects wind turbines may have on home values. Yet, what has been surprisingly overlooked in the literature and general discourse around wind energy is that the well-researched positive economic development and fiscal and amenity benefits of wind energy (e.g., increased tax base, tax revenue, better public services and employment gains) might positively affect jurisdiction-wide housing values. With a focus on school districts in the United States, we compare home values in school districts with wind energy installations, before and after a wind energy installation becomes operational, to home values in other school districts located in the same county but without a wind energy installation to provide some of the first causal evidence on the relationship between wind energy projects and district-wide property values. We find that wind projects lead to economically meaningful increases in district-wide housing values of approximately 3 %, when those values are compared to similar homes located in school districts in same-county without wind energy. The effect is strongly correlated with wind project size. The mechanisms, our research suggests, are likely related to relatively large increases in school district per-pupil revenues and expenditures, which are also correlated with wind project size. We suggest other possible mechanisms for the increased values as well.

Attitudes↗

DOE OSTI2024

Seismic monitoring of EGS fracture stimulations at Utah FORGE (Part 1): Time-lapse variations of b-values and Shear-wave Splitting Rates of induced microearthquakes

Characterizing geologic formations near geothermal wells is crucial for efficient extraction of subsurface geothermal energy resources. To monitor effective fracture stimulation of enhanced geothermal systems (EGS), we analyze a catalog of induced microearthquakes to characterize seismicity induced by Phase 2C fracture stimulations of the Utah FORGE project and derive the b-values for 424 induced microearthquakes in three stages of fracture stimulation. Here, we find the time-lapse variations of these b-values. We apply eigenvector rotation and waveform correlation to 3C geophone data to separate fast S 1 and slow S 2 waves, determine their directions, measure their lag time, and calculate the S-wave splitting rate (SSR) for six induced microearthquakes in Stage 1 and 21 in Stage 3. Both SSR values and b-values increase with time. The increase in these values may be related to heavily fractured zones created by fracture stimulations during Stage 2 injections. Large and highly variable SSR values in Stage 3 may reflect heterogeneities in the fracture zones. Time-lapse variations of SSR values and b-values may be one of the most useful approaches to monitoring and characterizing the effectiveness and efficiency of various types of fracture stimulations.

58 GEOSCIENCES↗

DOE OSTI2017

Evaluation of the 235 U resonance parameters to fit the standard recommended values

A great deal of effort has been dedicated to the revision of the standard values in connection with the neutron interaction for some actinides. While standard data compilation are available for decades nuclear data evaluations included in existing nuclear data libraries (ENDF, JEFF, JENDL, etc.) do not follow the standard recommended values. Indeed, the majority of evaluations for major actinides do not conform to the standards whatsoever. In particular, for the n + 235 U interaction the only value in agreement with the standard is the thermal fission cross section. We performed a resonance re-evaluation of the n + 235 U interaction in order to address the issues regarding standard values in the energy range from 10-5 eV to 2250 eV. Recently, 235 U fission cross-section measurements have been performed at the CERN Neutron Time-o-Flight facility (TOF), known as n_TOF, in the energy range from 0.7 eV to 10 keV. The data were normalized according to the recommended standard of the fission integral in the energy range 7.8 eV to 11 eV. As a result, the n_TOF averaged fission cross sections above 100 eV are in good agreement with the standard recommended values. The n_TOF data were included in the 235 U resonance analysis that was performed with the code SAMMY. In addition to the average standard values related to the fission cross section, standard thermal values for fission, capture, and elastic cross sections were also included in the evaluation. Our paper presents the procedure used for re-evaluating the 235 U resonance parameters including the recommended standard values as well as new cross section measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Variations in pharmacokinetic-pharmacodynamic target values across MICs and their potential impact on determination of susceptibility test interpretive criteria

Abstract Background An antibacterial drug’s susceptibility test interpretive criteria (STIC) are determined by integrating clinical, microbiological and pharmacokinetic-pharmacodynamic (PK-PD) data. PTA analysis plays a pivotal or supportive role in STIC determination and is heavily dependent on the PK-PD target values determined from animal PK-PD studies. Therefore, variations in PK-PD target values may impact STIC determination. Factors contributing to variation in the PK-PD target values include the number of and MICs for bacterial isolates used in animal PK-PD studies. Objectives To analyse the relationship between PK-PD target values and MICs, describe the variations in PK-PD target values of isolates and evaluate whether the proposed/target STICs were within the ranges of the MICs for isolates used in animal PK-PD studies. Methods A database was compiled for this research by screening animal PK-PD study reports submitted to the FDA from 10 new drug applications (NDAs). Results A relationship evaluation between PK-PD target values and MICs for tested isolates for seven drugs (that used AUC/MIC ratio as the PK-PD index) showed that, generally, the AUC/MIC values decreased with an increase in MIC. These target values were highly variable, with the percentage coefficient of variation ranging between 1% and 132% for isolates having the same MIC. For 16/27 (59%) drug/bacteria combinations from all 10 drugs, the proposed/target STICs were higher than the highest MIC for bacteria isolates evaluated, while 6/27 (22.5%) were lower. Conclusions This research suggests that careful considerations related to selection of bacterial isolates for animal PK-PD studies could strengthen the STIC determination process.

Waack, Ursula↗

DOE OSTI2020

Effective Missing Value Imputation Methods for Building Monitoring Data

To understand behaviors of natural and man-made events, such as energy consumption of buildings, which accounts for 40% of energy uses in the US, we deploy automated monitoring devices to record periodic observations. However, such experimental and observation data often contains problems and irregularities that have to be cleaned up before analyses. Due to various conditions affecting sensor operations, the communication channels, recording steps, or the recording media, the recorded data might have missing values, errors, or anomalous values. An effective way to clean up these problems is to replace these missing values, errors and anomalous values with expected values, a process generally known as imputation. In this work, we survey commonly used missing value imputation techniques and compare their performance on a set of building monitoring data. To compare the different types of sensor measurements with widely varying characteristics, we use normalized root mean squared error (NRMSE) as the key metric for the effectiveness of the imputation methods. We additionally consider periodicity and run time when considering comparing methods. Through extensive testing, we find that for small gap sizes, up to 8 consecutive missing values, linear interpolation performs the best; for larger gaps stretching up to 48 consecutive missing values, K-nearest neighbors provides the most accurate imputations; for even larger gaps, more computational intensive methods, such as matrix factorization, achieve the smallest NRMSE. Additionally, we observe that these computationally intensive algorithms not only provide accurate imputations for large gaps, but are also more robust across all types of sensors.

Cho, B↗