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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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468 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Developing a 20-year high-resolution wind data set for Puerto Rico

The purpose of this study is to develop a high-resolution wind resource data set for Puerto Rico as part of the wind resource assessment in the Puerto Rico Grid Resilience and Transition to 100 % Renewable Energy Study (PR100) project. The Weather and Research and Forecasting (WRF) is used to model 20-years (2001-2020) of wind resource data on a 3-km grid for the Puerto Rico region. Because accurately representing the planetary boundary layer (PBL)-physics in the WRF is key to accurately model low-level wind speed, 11 different PBL schemes are examined and evaluated to find a WRF configuration that can provide the most accurate wind data. The modeled wind speeds are validated with buoy observations, and the Shin-Hong PBL scheme is selected to generate the final wind data set. Here, the overall results demonstrate that the high-resolution data correctly represents the climate of Puerto Rico (e.g., a dominant easterly trade wind). At 160 m above ground, the southern regions of Puerto Rico show strong offshore wind speed (>8 m/s on average) throughout the daytime and nighttime. For land-based wind, average wind speed ranges from 5 m/s-7 m/s during daytime, whereas notable high wind speeds (>9 m/s) appear in the mountain regions during nighttime.

17 WIND ENERGY↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty [Brochure]

The four priority research directions outlined in this brochure represent a cohesive vision for advancing the science of inverse problems for complex systems under uncertainty. Together, they address the critical challenges of: discovering, exploiting, and preserving physical and problem structure; overcoming model limitations; integrating disparate, multimodal, and/or dynamic data; and tailoring the solution of inverse problems to downstream tasks. While each PRD focuses on a distinct aspect of inverse-problem research, their interconnected nature highlights the importance of a holistic approach that leverages progress across all areas to achieve transformative solutions. This agenda calls for research across mathematics, statistics, and computer science disciplines, which are guided and complemented by rapid advances in artificial intelligence, high-performance computing, and experimental facilities, to unlock new capabilities, maximize scientific impact, and meet the growing demands of inverse problems that arise across applications that are critical to DOE's mission.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Probabilistic Day-Ahead Forecasting Using an Analog Ensemble Approach for Wind Farm Grid Services

Wind resource assessment and wind power forecasting are used in research and industry to anticipate future power output at scales ranging from individual wind turbines to entire wind farms. Probabilistic day-ahead wind forecasting is useful for anticipating how a wind farm could potentially participate in the day-ahead market by providing upper and lower bounds for expected power generation, thus informing grid operators of its uncertainty. Understanding this uncertainty is part of a larger project focused on building a platform that combines efforts in weather forecasting, aerodynamic and economic modeling to create maximum value of a wind plant to better provide services to the grid. This effort is also known as the Atmosphere to Electrons to Grid (A2E2G) project. One method for producing a probabilistic forecast is through the analog ensemble approach (Delle Monache et al., 2011). This method leverages historical forecasts and their corresponding observations as a training data set from which future forecasts can be made. For some future forecast, the most similar historical forecasts (analogs) are identified on a regular time basis such as once per a 3-hour window. The most similar analogs, based on a metric such as root mean square error (RMSE), are recorded and their corresponding verifying observations are used as an ensemble member for this future forecast. Prior work in this area demonstrates improvements over raw Numerical Weather Prediction (NWP) forecasts and shows skill similar to techniques such as logistic regression and machine learning (Delle Monache et al., 2013; Alessandrini et al., 2015). Here, we take the High-Resolution Rapid Refresh model (HRRR) day-ahead forecast (0-36 hours) to create a probabilistic day-ahead forecast using an analog ensemble approach. The HRRR has an hourly temporal resolution, with a spatial resolution of 3 km. The 12 UTC HRRR model run is downloaded every day for one year from August 2019 - July 2020, with the first 11 months serving as a bank of analogs from which the forecasting algorithm can create a probabilistic forecast. Once downloaded, the original HRRR forecast is temporally interpolated to 5-minutes, aligning with both the temporal resolution of the observations as well as the timescale relevant for day-ahead power forecasts. The forecast is validated at the M2 tower at the Flatirons Campus of the National Renewable Energy Laboratory (NREL) at a typical wind turbine height of 80 m. Variables such as wind speed, wind direction, and turbulence intensity are incorporated into the probabilistic forecast model and weighted according to their relative importance to the forecast. Based on metrics such as mean bias error (MBE), mean absolute error (MAE), and root mean square error, the analog ensemble forecast outperforms the raw HRRR forecast during the testing period of July 2020. Figure 1 illustrates an example day-ahead forecast compared against the verifying observations. The general variability and ramps are captured throughout the day, with potential to further improve the analog ensemble model through machine learning techniques.

numerical weather prediction↗
DOE OSTI2023

Gas-Liquid Flow Modeling for Renewable Fuels Production

Aerobic/anaerobic and gas fermentation pathways have emerged as promising new technologies for the generation of renewable fuels/chemicals from biomass derived sugars, and mixtures of greenhouse/energy rich gas streams (CO2/CH4/H2/CO) via microbial action. Example pathways include sugars-to-ethanol conversion, biomethanation (CO2/H2 to CH4), biogas upgrading, CO fermentation and wet-waste conversion. Gas and liquid phase transport, mass-transfer, and mixing physics at large length scales can significantly affect microbial conversion rates, particularly when the microbial reaction requires a narrow set of conditions. These phenomena are difficult to study in small-scale bench-top reactors that are typically well-mixed. Predictive computational fluid dynamics (CFD) based simulations can therefore aid in the scale-up, design and optimization of these reactors. This work presents multiphase Euler-Euler CFD simulations of at-scale (~500 m3) bioreactors. Our mathematical model treats the gas and liquid as interpenetrating phases. This approach reduces the computational complexity of tracking individual gas bubbles that are several orders of magnitude smaller than reactor dimensions. We solve the Reynolds averaged Navier-Stokes (RANS) multiphase equations that account for phase and chemical species transport, interphase mass and momentum transfer and uses a phenomenological model for gas uptake by microbes. We use a customized solver derived from open-source CFD toolbox, OpenFOAM [1], to perform these simulations, which has been validated against small-scale reactors in our previous work [2]. There is currently a knowledge-gap regarding bubble-size distributions when using gas mixtures with vastly different properties, which can have a significant impact overall mass-transfer. For example, hydrogen bubbles are more buoyant compared to other relatively heavier gases (CO2/CH4/CO), resulting in a large distribution of residence times and bubble sizes. This work therefore develops a deeper understanding of bubble dynamics and interphase mass transfer in such heterogenous gas mixtures through well-resolved computational models. We use a population balance model (PBM) for bubble-size-distribution modeling that is validated against small-scale experiments in our solver with an uncertainty quantification study for bubble coalescence and break-up model parameters. Results pertaining to multiple simulations of gas-fermentation reactors are presented where gas mixtures with varying compositions of CO2/CH4/CO/H2 are imposed at the sparger boundaries. The spatio-temporal variations in bubble-size distribution and mass transfer coefficient are analyzed for varying superficial velocities and gas-compositions for varying sizes of bubble-column and airlift reactors. This work will also examine the performance of different reactor designs, viz. bubble column reactor, airlift reactor with an internal draft tube, and a stirred-tank reactor with Rushton impellers. Reactor mass-transfer coefficient, gas hold-up, and dissolved gas distribution are critically analyzed among reactors, and sensitivity studies pertaining to gas flow rates and reactor geometry will be presented. [1] Weller, H., Tabor, G., Jasak, H. and Fureby, C., A tensorial approach to computational continuum mechanics using object-oriented techniques, Computers in physics, 12, 6, 620--631, 1998. [2] Rahimi, M., Sitaraman, H., Humbird, D. and Stickel, J., Computational fluid dynamics study of full-scale aerobic bioreactors: Evaluation of gas-liquid mass transfer, oxygen uptake, and dynamic oxygen distribution, Chemical Engineering Research and Design, 139: 283-295.

BIOMASS FUELS↗
DOE OSTI2025

Unveiling Feedstock Variability: Insights into Corn Stover Conversion - Part I: Physicochemical Properties and Self-Degradation

Transforming agricultural waste into biofuels and bioproducts is crucial to advancing a low-carbon bioeconomy. However, the inherent variability in the composition and quality introduces uncertainties in the conversion efficiency and poses challenges in process development. Through integrating a high-throughput conversion system, material characterization techniques, and advanced data analysis tools, this study investigates the variability of corn stover and its subsequent impacts on carbohydrate conversion. The findings reveal that indoor storage substantially reduces the moisture and ash content and soil contamination, while other properties remain largely unchanged. Self-degradation due to microbial activity during storage decreases the carbohydrate content of corn stover but enhances glucose and xylose yields. A negative correlation is observed between sugar yields and lignin content across samples with varying ash and moisture content. The inhibitory effect of lignin diminishes in self-degraded samples likely due to the disrupted cell wall structure. Although self-degradation slightly increases cellulose crystallinity, no strong correlation was observed between the crystallinity and sugar yield. Hot water pretreatment under mild conditions effectively mitigates inherent variability, consistently improving the sugar yield from corn stover by up to 50%. By elucidating the feedstock variability and its impact on convertibility, these findings offer valuable insights into appropriate feedstock handling and management, highlighting potential strategies to address variability challenges.

09 BIOMASS FUELS↗
NASA NTRSDate not supplied

TPSAS-NF1676L-16988-DND

There has been renewed interest to uniformly recalibrate historical geostationary (GEO) data records to aid in climate monitoring. GEO sensors have annual repeatable angular sampling over a given location. The view angle is fixed and the imaging schedule is usually constant through its lifetime. Given the fact that colocated GEOs are always share the same sub-satellite point and maintain their imaging schedules provides repeatable angular sampling over decades. One of the biggest challenges in transferring a reference sensor calibration using invariant desert targets to another sensor is the accuracy of the bidirectional reflectance distribution function (BDRF). A well-calibrated GEO can be used to predict the daily exoatmospheric radiance model (DERM) over a desert target for a given GMT that is valid for any GEO sensor at the same location. The advantage of this method is that a BRDF is not needed. Another challenge of invariant target calibration is the unique spectra signature of the desert. However, since most GEOs are built in batches, the spectral response functions (SRF) are very similar for most historical GEOs, the spectral band adjustment factor (SBAF) between GEO sensors is much smaller than for MODIS and GEO sensors. Since the water vapor burden over the desert is seasonal, both the TOA and desert surface can be considered invariant for a given day of the year. The reference GEO can be inter-calibrated with MODIS or VIIRS, which have onboard visible calibration using solar diffusers, using other methods, such as ray-matching or deep convective clouds. Also the next generation GEOs will have onboard visible calibration, which will increase the accuracy of this method. Three Meteosats over the Libyan desert will be used to illustrate the DERM method. The reference Meteosat will be inter-calibrated against Aqua-MODIS. The reference GEO DERM will be constructed and used to calibrate the remaining Meteosats. The DERM calibration will be validated by comparing the calibration using Aqua-MODIS ray-matching. Similarly, two GOES sensors using the Sonoran desert will also be highlighted. An uncertainty analysis will also be performed with emphasis on the SBAF, derived over the desert targets using both SCIAMACHY and Hyperion hyper-spectral radiances.

David Doelling↗
DOE OSTI2021

Life cycle energy and environmental impacts of hydroprocessed renewable jet fuel production from pennycress

There is a growing interest in making the overall aviation industry more environmentally-friendly by developing advanced biofuels. In this context, the main driver is establishing feedstocks with lower environmental loads and higher energy use efficiency. The objective of this study was to evaluate the energy requirement and environmental impacts of hydroprocessed renewable jet fuel (HRJ) production from pennycress. The system boundary included pennycress production, logistics, oil extraction and conversion to HRJ, wastewater treatment, and electricity and steam generation. Energy allocation method was used for the analysis. The functional unit was considered to be one GJ of HRJ, and the biorefinery size was considered to be 18.9 million l/yr (5 million gal/yr) HRJ. Energy use efficiency of pennycress-based HRJ production was estimated to be 3.9–5.5 (90% central range), which was higher than that of HRJ from other oilseeds, such as canola. Global warming potential of pennycress-based HRJ (35–49 kgCO 2 eq/GJ of HRJ (90% central range)) was also lower than those of HRJ from similar oilseeds and the commercial jet fuel baseline. Pennycress production was the main contributor to the total energy use and environmental impacts of HRJ production, mainly due to the use of nitrogen fertilizer for production, and fuel combustion for farm operations. Pennycress yield was identified as the most sensitive parameter affecting the energy use efficiency and environmental impacts of HRJ production. Furthermore, the outcomes of this study are useful for identifying the performance targets towards improving energy use efficiency and environmental performance of pennycress-based HRJ production at the commercial scale.

09 BIOMASS FUELS↗
DOE OSTI2022

Multi-Fidelity Stochastic Economic Dispatch for Operating Low Carbon Power Grids

Grid operators can address the inherently stochastic nature of renewables by solving a two-stage stochastic programming model that minimizes the cost of dispatch decisions while accounting for the complex grid dynamics. It is common to use a sample average approximation to estimate the expectation of the second stage costs in this model. However, the large sample count needed for numerical accuracy makes effective modeling large-scale electric grids computationally intractable. We introduce a control variate multi-fidelity estimator for the second-stage recourse that enables high quality dispatch decisions in real-time with a reduced computational burden. We obtain a hierarchy of model fidelities by linearizing the AC power flow system representation to DC power flow, and by relaxing transmission and voltage network constraints. We evaluate the performance of our proposed method on a synthetic grid with 73 buses against a deterministic baseline with persistence forecast and a high-fidelity reference. Our analysis shows a computational speed-up of 7.62x with a minimal loss in accuracy. The multi-fidelity method is well suited to fidelity combinations that use a simplified network topology in their lower fidelity model and is an attractive option for applications where accurate grid modeling needed on a limited computational budget.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Updating the Building Science Advisor (BSA): A Tool to Assist in the Design of Durable Building Envelopes

Predicting the moisture durability of building envelope components remains challenging due to multiple influencing factors, including material selection, assembly positioning, local climate conditions, air tightness, interior environment, and construction quality. Building codes increasingly emphasize energy efficiency through enhanced insulation and tighter envelopes but offer limited guidance on moisture durability considerations. Consequently, builders face uncertainty, particularly as new materials and assemblies enter the market.The Building Science Advisor (BSA) is a free, web-based expert system developed to address these challenges by providing actionable insights into the moisture durability and energy efficiency of both new and retrofit wall designs. Recently updated, we are now providing version 3.0 of the tool. BSA features significant user interface improvements, enhancing navigation and user interaction through a refreshed, intuitive design. Additionally, the tool incorporates a newly developed database containing pre-simulated wall assembly cases, significantly reducing response times and improving the accuracy of moisture durability assessments. Furthermore, the updated BSA includes moisture content as a performance criterion, providing users with a more comprehensive understanding of moisture-related durability risks. These enhancements enable rapid, reliable assessments tailored to specific climate zones and local building practices. BSA continues to offer targeted guidance on wall retrofit scenarios and delivers access to an expanded library of location-specific building science resources.This paper describes these key updates, highlighting the enhanced features, expanded capabilities, and overall improvements to user experience and educational content. The paper includes a demonstration that illustrates how the revised BSA effectively supports practitioners in designing durable, energy-efficient building envelope assemblies.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2023

Assessing Low-Temperature Geothermal Play Types: Relevant Data and Play Fairway Analysis Methods

The U.S. Department of Energy (DOE) Geothermal Technologies Office (GTO) is supporting the Geothermal Heating and Cooling Geospatial Datasets and Analysis project conducted by the National Renewable Energy Laboratory (NREL) as part of a broader effort to demonstrate the multi-faceted value of integrating geothermal power and geothermal heating and cooling (GHC) technologies into national decarbonization plans and community energy plans. Currently, there is a need to establish baseline low-temperature geothermal resource datasets and evaluate methods for deploying these technologies to provide the basis for supporting private sector investment. This project is focused on collecting baseline datasets, updating conceptual models, and creating Play Fairway Analysis (PFA) workflows for low-temperature (<150 degrees Celsius) geothermal resources of different geothermal play types (i.e., sedimentary basin, orogenic belts, and radiogenic geothermal play types) that could be used for geothermal heating and cooling (GHC), combined heat and power (CHP), and other geothermal direct uses (GDU) applications. Low-temperature geothermal resources are defined as reservoirs - natural or engineered - with temperatures <150 degrees Celsius. While the focus in the NREL effort is on GHC, resources at the upper end of this temperature range can also be used for small-scale power generation. This project does not include Ground Source Heat Pumps (GSHPs) technologies because they can be effectively developed almost anywhere. Low-temperature geothermal resources have not been studied as extensively as higher- to medium-temperature geothermal resources, but there is recent interest in improving understanding of these types of resources with an uptick of interest in geothermal technologies for decarbonizing heating and cooling systems. In addition, Enhanced Geothermal Systems (EGS) and other emerging technologies for exploiting petrothermal resources have opened the possibility of utilizing deep sedimentary basin systems, where porous media provide permeability and high temperatures can be reached at great depths. This project takes the approach of classifying low- temperature geothermal resources by geothermal play type (GPT). We defined and characterized three major classes of low-temperature GPT: sedimentary basins, orogenic systems, and radiogenic systems. We develop methodologies for evaluating and analyzing the potential for these resources building off the PFA approach to de-risking geothermal exploration and characterization. The proposed PFA approach for low-temperature geothermal resources includes: 1) identifying relevant data (e.g., datasets such bottom-hole temperatures from oil and gas wells, heat flow data, Quaternary faults and stress field data, geophysical data, etc.); 2) grouping and weighting of relevant datasets into PFA criteria (e.g., geological, risk, and economic criteria); 3) uncertainty quantification; 4) developing favorability or common risk maps for low-temperature geothermal resources to identify potential locations for more focused data collection; and 5) estimating electric power generation and heating potential at those locations using the GeoRePORT Resource Size Assessment Tool (RSAT). This project should facilitate future deployment of GHC, CHP, and GDU by providing data, tools, and a workflow applicable to low-temperature geothermal resources. Increased deployment of GHC and GDU will help achieve national and local decarbonization goals.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Model Evaluation by Measurements from Collocated Remote Sensors in Complex Terrain

Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. Here the model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2–3 m s –1 . Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2–0.3 m s –1 . Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were ~0.1 m s –1 , but some reached 0.3 m s –1 . All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

New Norms or Old Habits: Evaluating Interlinked Trajectories of Online Shopping and Work Commute Post-Pandemic

The COVID-19 pandemic has significantly shifted travel behaviors, with major changes observed in online shopping and travel to work. Despite considerable research into pandemic-induced changes in travel behavior, it remains uncertain whether these new patterns have persisted or reverted to pre-pandemic norms. This study addresses this uncertainty by evaluating whether shifts in online shopping and work travel during the pandemic have become permanently ingrained in individuals' daily routine. Leveraging data from the 2022 National Household Travel Survey, a bivariate ordered probit model is employed to analyze changes in online shopping and work travel - whether they have increased, decreased, or remained stable compared to pre-pandemic levels across different population segments. The analysis finds that the pandemic did not significantly alter online shopping for home delivery and travel to work for the majority of society. However, a substantial portion of respondents reported increased online shopping for home delivery and reduced travel to work compared to pre-pandemic levels, with online shopping trends appearing more permanent. Segment-wise analysis and model results indicate heterogeneity in behavioral shifts with females engaging more in online shopping, while zero-vehicle households are traveling less to work, compared to pre-pandemic levels. Additionally, increase in online shopping frequency is significantly and negatively correlated with decrease in traveling to work. These findings highlight the need for improved digital infrastructure, flexible work policies, and integrated transportation solutions tailored to evolving demographic and socioeconomic needs in the post-pandemic era. Additionally, the study calls for integrating passenger and freight movement in a single framework rather than treating them in silos.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A Technical and Economic Assessment of LWR Flexible Operation for Generation and Demand Balancing to Optimize Plant Revenue

With increased penetration of subsidized variable renewable energy (VRE) resources and competition from low natural gas prices, existing light water reactor (LWR) nuclear power plants (NPPs) are struggling to remain economically competitive. This work examines the potential economic competitiveness of various thermal energy storage (TES) technologies when coupled directly or indirectly with a NPP. To highlight their relative economic competitiveness, we contrast several energy storage solutions in stochastic dispatch optimization. We leverage data from recent work analyzing a range of TES technologies with varying capital costs, performance, and technology readiness level (TRL) to establish our case. We explore inserting these technologies into an electricity market with existing nuclear generation and large projected variable renewable energy (VRE) penetration. Although these technologies' projected capital costs may make them unlikely candidates in their current state, this analysis demonstrates a high-fidelity techno-economic analysis of energy storage. Furthermore, as the projected cost of energy storage technologies evolves, this analysis sets a precedent for similar future investigations. One region with projected trends that may be unfavorable for existing nuclear capacity is the New York Independent System Operator (NYISO) market. New York state’s baseload generation has been historically provided by fossil-fired and nuclear assets. However, amid economic pressures from subsidized VREs and low natural gas prices, the state has recently deactivated Indian Point nuclear power plant units 2 and 3. Furthermore, the state plans to meet its zero-emission generation target by 2040 by replacing fossil-fired capacity with significant investments in VRE resources like wind and solar photovoltaic (PV) and battery storage. Increased intermittent resource penetration lowers the baseload power requirement, adding further economic pressure to the state’s three remaining NPPs still in operation. With three NPPs still in operation in New York, this work analyzes potential economic benefits to NPPs on the New York grid when directly or indirectly coupled with various TES technologies. This work requires two modeling steps to analyze the potential economic benefits of various system configurations of the TES directly or indirectly coupled with nuclear. First, this analysis leverages capacity expansion modeling by experts at the Electric Power Research Institute (EPRI). Using their deterministic capacity expansion model, U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN), EPRI analysts evaluated the capacity and generation evolution of the New York state energy market under four projection scenarios. These four projection scenarios were developed to represent the potential evolution of the capacity and generation in NYISO from 2015 to 2050 under various economic, technology, and policy constraints. The results from these capacity expansion models are then used as boundary conditions in the second modeling step. The second modeling step uses the Holistic Energy Resource Optimization Network (HERON) for a set of stochastic techno-economic analyses (STEAs) to investigate the potential increase in the economic viability of various configurations of the TES. With no current capacity expansion capabilities, HERON takes the data generated from US-REGEN for 2050 to generate synthetic load, solar, and wind data. Then HERON economically optimizes the capacity and dispatch of the various TES configurations. The potential economic benefit is the differential net present value (NPV) of the TES configurations from the no-TES baseline. As a stochastic techno-economic analysis package, HERON introduces uncertainty into the economic metrics, while US-REGEN trades resolution for reduced computational complexity. Using HERON also allows the modeling of direct thermal coupling, a feature not common in capacity and dispatch models. As expected, with high capital costs, the costs of introducing energy storage for all the technologies considered outweighed the potential economic benefit of this strategy for flexible plant operation. The benefit of this analysis is primarily in demonstrating a workflow that examines innovative solutions to increase NPP revenue via TES coupling. HERON’s stochastic capacity and dispatch optimization process used in this work has proven an effective tool in observing and evaluating the impact of introducing storage technologies in a grid energy system.

25 ENERGY STORAGE↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2026

Investigation of onshore wind farm wake recovery with in situ aircraft measurements during AWAKEN

The share of wind power for electricity supply is increasing worldwide. This highly variable resource requires the improved prediction of power output for network stability. The interaction between wind farm wakes and the atmospheric boundary layer (ABL) introduces uncertainties in power production that warrant detailed investigation. The flow downwind of wind farms is characterized by a reduction in wind speed and an increase in turbulence, which both vary with atmospheric conditions. During the American WAKE experimeNt (AWAKEN), the Technische Universität Braunschweig conducted measurement flights with a research aircraft upwind and downwind of onshore wind farms in the southern Great Plains in Oklahoma in the USA. This study utilizes data from 20 flights conducted at approximately hub height in September 2023 to investigate the wind field variability downwind of the wind farms and vertical profiles to observe atmospheric stratification. The flights were aligned perpendicular to the main wind direction downwind of the King Plains and Armadillo Flats wind farms. Additionally, lidar data from both upwind and downwind ground-based measurement sites and sonic anemometer data were used for comprehensive analysis. Results indicate that under stable ABL conditions, the wake persists at greater downwind distances with a higher velocity deficit in the wake relative to the undisturbed flow compared to unstable stratification. In homogeneous terrain under stable conditions, wake recovery to 95 % occurs between a distance of 4.5 and 9 km downwind of the wind farm. In the semi-complex terrain characterized by shallow hills, slopes, and valleys, the wake exhibits a higher velocity deficit compared to homogeneous terrain, while in some cases the wake was amplified by the terrain resulting in higher velocity deficit 10 km downwind of the wind farm compared to the measurements closer to the wind farm. The turbulent kinetic energy (TKE) and “TKE difference” was found to be a valuable measure in understanding wakes in a semi-complex terrain, showing a clear wake recovery and formation depending on the stratification of the ABL.

17 WIND ENERGY↗
DOE OSTI2025

Tonoplast Sucrose Transporter SUT4-Dependent Sugar Partitioning Modulates Phenological Transitions and Reproductive Success in Poplar

Climate uncertainty is intensifying the need for greater plasticity in carbohydrate reserve utilization to support winter survival and spring growth in woody perennials. In poplar, the single-copy SUT4, which encodes a tonoplast-localized sucrose transporter, and the SUT5/SUT6 genome duplicates, which encode plasma membrane-localized transporters, are expressed year-round, with SUT4 showing the highest expression during cool seasons. Given its role in vacuolar sucrose efflux and winter-predominant expression, SUT4 may play a key role in modulating seasonal carbohydrate dynamics. While SUT4-knockdown and knockout effects have been studied under greenhouse conditions, their impact under field conditions remains unexplored. Here, we report a field-based study comparing CRISPR knockout mutants of winter-expressed SUT4 and SUT5/SUT6 in Populus tremula x alba. We show that sut4, but not sut5/6, mutants exhibited earlier autumn leaf senescence, delayed spring bud flush, reduced stem growth, and altered sugar partitioning in winter xylem and bark relative to controls. After 2 years in the field, all genotypes flowered before leaf flush in early spring; however, sut4 mutants produced sterile ovules despite developing normal-looking catkins. Metabolic profiling revealed disrupted sucrose and raffinose dynamics in elongating sut4 catkins. This was accompanied by transcriptomic signatures of elevated stress and downregulation of proanthocyanidin biosynthesis and circadian clock genes. These findings highlight the critical role of SUT4 in coordinating sugar allocation, stress responses, and seasonal development in poplar.

09 BIOMASS FUELS↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2020

Subtask 1.3 – Integrated Carbon Capture and Storage for North Dakota Ethanol Production

The Energy & Environmental Research Center (EERC), in partnership with Red Trail Energy, LLC (RTE), a North Dakota ethanol producer; the North Dakota Industrial Commission (NDIC) Renewable Energy Program (REP); and the U.S. Department of Energy (DOE), conducted a feasibility and implementation study for a commercial carbon capture and storage (CCS) effort. This subtask provided technical support and developed recommended practices of how small-scale industrial CO2 emitters (<1,000,000 tonnes of CO2 emitted annually) may economically deploy CCS. The 64-million-gallon dry mill RTE ethanol facility, emitting an average 180,000 tonnes of CO2 annually, was the subject of the case study. The positive outcome of this research, which shows technical and economic potential for ethanol–CCS in North Dakota, has resulted in RTE acquiring an approved Permit to Drill on December 2, 2019, for a stratigraphic test well in early 2020, a necessary step toward a North Dakota CO2 Storage Facility Permit (SFP) for the RTE CCS effort. Outcomes include 1) validating the Broom Creek Formation as a regional target for CCS, 2) determining the full carbon life cycle of an industrial fuel production facility with CCS, 3) developing a field implementation plan (FIP) for small-scale CCS, and 4) determining the validity and pathway for using CCS to meet low-carbon fuel (LCF) standards. The technical team included the EERC, RTE, Trimeric Corporation, Schlumberger Carbon Services, and Computer Modelling Group (CMG). Findings from activities conducted November 2016 – May 2020 are summarized as follows. Several key steps have been accomplished toward validating the Broom Creek Formation as a CO2 injection and storage target at the RTE CCS site, including verification of the presence and structure of sandstone layers that may comprise the potential CO2 storage reservoir and the several thousand feet of overlying confining zone. Work included site characterization using existing data, interpretation of seismic data within the study area, and geologic modeling simulation of CO2 injection. Interpreted results estimate 3000 feet of confining zone between the Broom Creek Formation (storage target) and the lowermost underground source of drinking water (e.g., the Fox Hills Formation). The thickness of the Broom Creek injection target varies 230–420 ft within the survey area. Results were used to inform the location of a stratigraphic test well and associated characterization test program. No impediments were identified within the targeted CO2 storage complex that would prevent the project from moving forward. A stratigraphic test well is the next step to validate these results and to acquire remaining data necessary to develop a North Dakota CO2 SFP application. The EERC generated full carbon life cycle estimates for CCS integration with the RTE ethanol facility. Results indicated that an average 40% reduction in CO2 emissions is possible through CCS implementation. Approximately half of the carbon in the overall life cycle for a dry mill ethanol plant is generated through the fermentation process and emitted to the atmosphere; this is the CO2 stream targeted for CCS. The remaining carbon is attributed to corn feedstock farming (diesel, fertilizer), energy for fuel processing (natural gas, electricity), and transportation (diesel) of the fuel product. Life cycle carbon estimates are also affected by the anticipated energy consumption of a potential capture facility, which depend on the type of CO2 product generated. For example, a 30%–40% net CO2 emission reduction is estimated if a liquefied CO2 facility were incorporated compared to a 40%–50% net CO2 reduction if a supercritical “injection-grade” CO2 product is generated; i.e., more energy is required to further refine the CO2 stream, affecting the full carbon life cycle estimates. A CCS FIP was developed and initiated at the RTE CCS site, resulting in the development of several guidance documents: a CO2 Capture Process Design Package, a North Dakota CO2 geologic Storage Permits Template, and a Public Outreach Package for CCS in North Dakota. General FIP components include CO2 capture system, pipeline and well designs; monitoring, verification, and accounting (MVA) plans; geologic characterization and testing programs; and permitting and outreach plans. Vendor bids were also acquired for the CO2 liquefaction facility. Near-surface characterization (groundwater and soil gas sampling) and geologic characterization (seismic survey) were initiated to inform development of a UIC Class VI-compliant MVA plan compliant with a North Dakota CO2 SFP. Designs (well and geologic testing) were completed for a stratigraphic test well compliant with a North Dakota CO2 SFP. In addition, the outreach plan was executed, including community open houses, meetings with city/county/state officials, and development of public materials. Although other entities continued to mature incentive programs in 2019–2020, California and the Internal Revenue Service (IRS) currently provide the most advanced economic opportunities for CCS integrated with fuel production. The California Low-Carbon Fuel Standard (LCFS) adopted a CCS Protocol in January 2019, allowing submittal of a design-based pathway (DBP) application for an approved temporary (not certified) carbon intensity value for a fully engineered facility. An ethanol–CCS DBP application to the California LCFS Program (officially approved February 28, 2020) was developed to show that the RTE CCS effort meets LCFS requirements. The approved DBP provides confidence to advance the project and supports potential investments. Other entities continue to mature incentive programs. The IRS issued guidance in February 2020 that addresses the definition of beginning of construction and revenue procedure on partnerships for the Enhancement of Carbon Dioxide Sequestration Credit (a.k.a. Section 45Q) CCS tax credit program; the IRS anticipates issuing further guidance on issues such as secure geologic storage, utilization qualifications, and recapture of claimed credits. Maturing incentive programs coupled with workable permitting regulations provide confidence to advance CCS projects in North Dakota and support financial investment to proceed with designing, constructing, and implementing CCS projects at small-scale fuel production facilities. The largest hurdles for CCS implementation at small-scale industrial systems are often business/economic-related (i.e., not technical). Market uncertainty already exists for agriculture-based alternative fuels such as corn ethanol, for which production has increased by ~33%, and prices have correspondingly lowered since 2015. Passing the Section 45Q tax credit program improves economic feasibility for CCS but may require external investors for a small business to achieve maximum benefits. Public–private partnerships with NDIC and DOE have resulted in foundational technical and regulatory knowledge, growing stakeholder confidence, and a pathway to implementation that enables similar industrial CCS projects in the region to advance. This subtask was funded through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE-FE0024233. Nonfederal funding was provided by NDIC and RTE. The authors would also like to thank CMG, ESRI, IHS, Neuralog, and Schlumberger for allowing the use of their software packages in support of this work.

42 ENGINEERING↗