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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Robust Scheduling of Microgrids Considering Unintentional Islanding Conditions

This paper proposes a robust scheduling model for microgrids considering the stochastic unintentional islanding conditions. The proposed model minimizes the total operating cost of the microgrid by efficiently coordinating the supply of power from local distributed energy resources and the main grid. To capture the prevailing uncertainties in renewable generation and demand as well as unintentional islanding conditions, a two-stage adaptive robust optimization model is formulated to minimize the total operating cost under the worst realization of the modeled uncertainties. The column and constraint generation (C&CG) method is used to solve the problem in an iterative manner. The solution of the proposed scheduling model ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, two micro-turbines, a diesel generator and a battery demonstrate the effectiveness of the proposed approach.

Liu, Guodong↗
DOE OSTI2020

Optimal Operation for Resilient and Economic Modes in an Islanded Alaskan Grid

Legacy energy management systems for distribution system or microgrids are typically driven by economics. During extreme events, resiliency can be defined as ability of the system to keep supplying critical loads. Resilient operation during extreme events (e.g. avalanche in Alaska) may require decision variables to be different and conflicting with economic operation. Operational objectives are driven by a complex consideration of the economic, reliable and resilient operation of the system: economic and reliable in normal operating state and resilient during extreme events. Challenge is to move between economic and resilient operation in optimal manner and setting up problem formulation and constraints specially with Distributed Energy Resources (DERs). In this paper, we focus on striking a balance between optimal economic and resilient operation using novel formulation and developed tool called Resiliency Enabled Energy System Operation Toolbox (RE-ESOT). Simulation results are provided for a real islanded grid in Alaska with battery energy systems.

50 EE - Wind and Water Power Program - Water (EE-4↗
DOE OSTI2025

Unified 0.25-degree gridded infrastructure-critical extreme weather for the United States from 1979 to 2100

Extreme weather events can severely disrupt critical infrastructure, triggering cascading effects on power, transportation, and essential services. However, standard weather and climate datasets often lack specialized variables necessary for hazard assessments. We present a unified dataset of infrastructure-critical weather and climate variables across the United States at 0.25° resolution, covering daily or sub-daily intervals from 1979 to 2100. The dataset includes temperature, dew point, wind gusts, precipitation partitioned by rain, snow, and freezing rain or ice pellets, lightning, and wildfire metrics. Historical conditions (1979-2023) are synthesized from observations and reanalysis products, while future projections are derived from 14 CMIP6 global climate models (historical, SSP245, and SSP585 experiments). Physically based and data-driven methods are used to estimate variables not directly provided by existing models. By integrating these variables into a single unified dataset, we enable consistent, high-resolution assessments of weather-related infrastructure risks across past and future periods, supporting wide-ranging applications in energy, transportation, water resources, emergency management, and beyond.

Climate and Earth system modelling↗
DOE OSTI2021

Dynamic-Phasor Model of Transformer Inrush Simulation for Unbalanced Distribution System

Abstract— Power system stability is a critical concern, with respect to the increasing distributed energy resources, microgrids, and emphasis on enhancing resiliency. For stability analysis and evaluation, modeling and simulation capabilities are important. In this paper, the authors’ previous work on modeling transformer inrush dynamics is improved with a new formulation of the key equation that converts the dynamic-phasor into real-time value. Simulation results in an electromechanical time-scale are compared with PSCAD results in an electromagnetic time-scale to demonstrate the improvement. The impact of simulation step size is discussed. A bug in PSCAD transformer setting that leads to incorrect magnetizing flux and current under Delta winding type is identified and reported. In addition, explanations and analysis are provided on two modeling techniques utilized since the previous work. This work is being incorporated into the next release of GridLAB-D. The improved transformer inrush calculation enables more accurate predictions of current amplitudes for analyzing the dynamics of distribution operation and control.

power transformers, power system simulation, satur↗
DOE OSTI2024

System and Component Development of Particle-Based Pumped Thermal Energy Storage

Reliable power supply from variable renewable resources requires energy storage at various scales to overcome resource intermittency. Long-duration energy storage (LDES, 10-100 hours) can improve dispatchability and grid reliability with increasing levels of renewable power supply. Thermal energy storage (TES) has siting flexibility and the ability to store a large capacity of energy, and thus has the potential to meet the LDES need and provide charging and discharging durations beyond the economic capacity of conventional batteries. TES technology has evolved from concentrating solar thermal power (CSP) generation and is recognized as an economic large-scale energy storage method. A standalone electric-thermal energy storage system supporting renewable integration of wind and solar power without CSP solar field can firm renewable generation and boost overall grid resilience and security.

concentrating solar thermal power↗
DOE OSTI2021

Renewable energy and biological conservation in a changing world

Without a shift to renewable energy sources, climate change will have adverse effects on many terrestrial and aquatic species. On the other hand, replacing fossil fuels with renewable energy will have different effects on wildlife, some negative. To protect biota, while navigating the complexities surrounding the transition to renewable energy, will require sharing knowledge between energy experts and wildlife experts, as well as policy makers and resource managers. This special issue is intended to advance the conversation. Interactions between renewable energy facilities and fauna are complex. Each source of renewable energy has its own conservation concerns, and each presents its own set of opportunities for mitigation; Other concerns and mitigations are shared. In this overview, we review climate risks to biota, clarify the complementarities among renewables for the conservation community, and summarize research on energy effects on biota for the energy community. The papers invited for this special issue address fossil (coal, oil, natural gas) and renewable energy (terrestrial biomass energy, hydropower, marine-hydrokinetic energy, wind energy, solar energy) and affected fauna. Finally, we hope that the results of these studies, and others like them, set us on a path toward low-carbon energy systems that provide society with energy while protecting future biodiversity.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential while Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗
DOE OSTI2024

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential While Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics: Preprint

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗
DOE OSTI2023

Renewable Energy Potential Model: Geothermal Supply Curves

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. The included paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results provided here should be considered with care due to the high uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for three scenarios: two hydrothermal (3.5km depth, USGS heat flow & SMU temperatures respectively) and one EGS (4.5km depth, SMU temperatures). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Impact of soiling on Si and CdTe PV modules: Case study in different Brazil climate zones

Soiling, particulate accumulation on photovoltaic (PV) module surfaces, reduces the available solar resource and the resulting generated device power. This case-study summarizes initial results of 5-year research on the contrasting soiling conditions in the tropical, subtropical, and semi-arid climates in Brazil. A major objective is to present a case study of the effects of soiling on PV module performance in different Brazil climate zones that represent the primary areas for the current and expanding Brazil solar installations. For this, the paper presents methodologies to quantify the soiling ratio (SRatio) and soiling rate (SRate) for two representative commercial technologies, polycrystalline or multicrystalline silicon (mc-Si) and thin-film cadmium telluride (CdTe) modules, through soiling monitoring stations deployed in the selected climate regions. An aim is to add to the growing soiling-research knowledge base through addressing these key factors and their relationships to critical electrical, solar resource, thermal, and local meteorological and environmental parameters. This paper presents, evaluates, and compares soiling rates and losses in Belo Horizonte, Minas Gerais (Equatorial Tropical: 19.92° S, 43.99° W), Porto Alegre, Rio Grande do Sul (Humid-Subtropical: 30.05° S, 51.17° W), and Brotas de Macaúbas, Bahia (Semi-Arid: 12.00° S, 42.63° W). The results show that soiling is moderate in all 3-regions, for example with 0.1%/day < SRate < 0.2%/day for Belo Horizonte. Precipitation dominates the cleaning of the modules in the summertime in this climate zone, while it is the major factor year-round in Rio Grande do Sul. Wind is the major issue mitigating the soiling accumulation for the Bahia installation. The methodology incorporates several key refinements, including the normalization and adjustment for the meteorological parameters (temperature, irradiance, wind, precipitation). The evaluations include the region-specific differing effects of non-uniform soiling, natural cleaning, and ambient temperatures.

14 SOLAR ENERGY↗
DOE OSTI2024

Energy Transitions: Local Water Concerns and Climate Impacts

This report summarizes important nuances in local water concerns and potential climate impacts that could influence the roll-out of technologies associated with energy transitions. Current investments in clean energy technologies are very high, which is driving a lot of investments in related manufacturing (i.e., hydrogen, solar, wind, and batteries) and mining (e.g., lithium, copper, and graphite) around the world. To understand how water and climate dynamics could be influencing these activities, we conducted a phased literature review for three countries: China, Germany, and France. China was selected due to its global dominance in manufacturing of solar panels, batteries, and electrolyzers as well as production of rare earth elements while Germany and France were selected due to their emerging leadership in energy transitions-related manufacturing within the European Union. For each of these three nations, we identified areas where manufacturing is occurring within the country and then evaluated relevant water resources and climate impacts. Multiple sources were consulted for this review, including BloombergNEF, international reports, industry sources, peer-reviewed literature, climate data, and media coverage.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Valuing EV Managed Charging for Bulk Power Systems

When and where electric vehicle (EV) charging occurs has significant implications for power systems supporting widespread EV adoption, especially with high shares of wind and solar generation. This study extends previous works by leveraging detailed simulation models for EV adoption, EV use, EV charging, and bulk power system operations, and by linking them with methods for describing charging flexibility at both the individual vehicle and aggregate levels. This technical potential study focuses on how the value of EV managed charging (EVMC) changes depending on charging flexibility type (within-charging session or within-week scheduling), dispatch mechanism (direct load control or one of several price-based mechanisms), and managed charging participation rate. We show that naively aggregating EV charging flexibility from individual vehicles into megawatt-scale resources grossly overestimates the flexibility of the fleet, because such aggregate models can unrealistically pair, e.g., one already-fully-charged vehicle's ability to increase load with another already-charging vehicle's ability to accept more charge, effectively requesting a charging rate that is infeasible for the latter vehicle. We find per-vehicle bulk system value is highest at low participation rates for all dispatch mechanisms. Factoring in production cost savings, avoided firm capacity savings, and combustion-related power sector emissions savings, we estimate the value of EVMC at low participation rates (5%) to be $33/vehicle-year to $69/vehicle-yr for within-session charging flexibility and $40/vehicle-yr to $120/vehicle-yr for within-week charging flexibility in an envisioned 2038 New England power system and monetary value reported in 2016 U.S. dollars. At 100% participation, per-vehicle value declines to $25/vehicle-yr to $31/vehicle-yr for within-session charging flexibility and to $29/vehicle-yr to $36/vehicle-yr for within-week charging flexibility; however, 100% participation yields the highest total system savings.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

WHONDRS River Corridor Sediment and Water Geochemistry and In Situ Sensor Data from Machine-Learning-Informed Sites across the Contiguous United States (v6)

This dataset supports a broader study examining hyporheic zone respiration rates to improve predictive models at a contiguous United States (CONUS) scale. The CONUS-Scale Model-Sample Study (CM) was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Sampling began in April 2022 and ended in October 2023. In addition to the widely distributed CONUS sites, a more spatially focused sampling occurred in the Yakima River Basin, WA in summer 2022. Data from this more spatially intensive sampling occurred under the label “Second Spatial Study (SSS)” and were also included in the machine learning models. Other data types collected from SSS that were not part of CM were published in a separate data package (https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566). This data package was originally published in February 2023. It was updated in June 2023 (v2; new and modified files); December 2023 (v3; new and modified files); June 2024 (v4; new and modified files); April 2024 (v5; new and modified files); and September 2025 (v6; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of two folders of field photos and videos, one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) international generic sample number (IGSN) mapping file; (6) field protocols; (7) a subfolder with sample data; and (8) a subfolder with sensor data. The sample data subfolder contains (1) surface water and sediment dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (2) surface water and sediment total nitrogen data and averages; (3) surface water major cations and anions and averages; (4) sediment grain size data; (5) sediment iron (II) data and averages; (6) wet sediment mass, dry sediment mass, water mass, and wet sediment volume in incubation and sediment ICR vials; (7) sediment incubation respiration rate data and averages; (8) normalized respiration rate data and averages; (9) methods codes; (10) sediment specific surface area; (11) sediment percent carbon and nitrogen; (12) sediment gravimetric moisture and averages; (15) sediment X-ray diffraction (XRD) data; (16) sediment adenosine triphosphate (ATP) and averages; (17) a subfolder with sediment incubation respiration data, scripts, and plots; (18) surface water and sediment FTICR methods; and (19) a subfolder of 9.4 Tesla (9.4T) FTICR-MS data. This folder contains five subfolders, one containing the sediment .xml data files, one containing the water .xml files, one containing the sediment CoreMS output files, one containing the water CoreMS output files, and the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS).The sensor data subfolder contains (1) a subfolder with miniDOT dissolved oxygen and temperature data and plots; (2) miniDOT dissolved oxygen and temperature summary data; and (3) miniDOT installation methods. All files are .csv, .pdf, .R, .xml, .d, .html, .Rmd, .py, .cal, .json, .jpg, .jpeg, .png, .mov, or .mp4. CORRECTION: Carbon and nitrogen content are reported as percentages. The current column headers "01395_C_percent_per_mg" and "01397_N_percent_per_mg" are incorrect. These should read "01395_C_percent" and "01397_N_percent" and will be corrected in the next version of this data package. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Machine learning model inputs, outputs, and scripts associated with “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “Artificial intelligence-guided iterations between observations and modeling significantly improve environmental predictions” (Malhotra et al., in prep). This effort was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the contiguous United States (CONUS). New machine learning models were created every month to guide sampling locations. Data from the resulting samples were used to test and rebuild the machine learning models for the next round of sampling guidance. Associated sediment and water geochemistry and in situ sensor data can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689, https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719, and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775. This data package is associated with two GitHub repositories found at https://github.com/parallelworks/dynamic-learning-rivers and https://github.com/WHONDRS-Hub/ICON-ModEx_Open_Manuscript. In addition to this readme, this data package also includes two file-level metadata (FLMD) files that describes each file and two data dictionaries (DD) that describe all column/row headers and variable definitions. This data package consists of two main folders (1) dynamic-learning-rivers and (2) ICON-ModEx_Open_Manuscript which contain snapshots of the associated GitHub repositories. The input data, output data, and machine learning models used to guide sampling locations are within dynamic-learning-rivers. The folder is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning (ML) models trained on the data in “input_data”; (3) “examples” contains files for direct experimentation with the machine learning model, including scripts for setting up “hindcast” run; (4) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; and (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please see the top-level README.md in the GitHub repository for more details on the automation. The scripts and data used to create figures in the manuscript are within ICON-ModEx_Open_Manuscript. The folder is organized into four folders which contain the scripts, data, and pdf for each figure. Within the “fig-model-score-evolution” folder, there is a folder called “intermediate_branch_data” which contains some intermediate files pulled from dynamic-learning-rivers and reorganized to easily integrate into the workflows. NOTE: THIS FOLDER INCLUDES THE FILES AT THE POINT OF PAPER SUBMISSION. IT WILL BE UPDATED ONCE THE PAPER IS ACCEPTED WITH ANY REVISIONS AND WILL INCLUDE A DD/FLMD AT THAT POINT. We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Washington State Parks and Recreation Commission (Scientific Research Permit #210901), and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the samples labeled “SSS” were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview. WHONDRS consortium members were asked to provide any acknowledgments for the collection of samples labeled “CM” and the following is a list of acknowledgments that were submitted with their corresponding Site IDs: (MART) Research activities were conducted in part on the Wind River Experimental Forest within the Gifford Pinchot National Forest; (MP- 100379) Philadelphia is part of Lenapehoking, the ancestral homelands of the Lenape peoples; (MP-102398) Land surveyed is the ancestral homelands of the Nookhose'iinenno (Arapaho), Tsis tsis'tas (Cheyenne), and Nuuchu (Ute); (MP-100749 and MP- 100747) Georgia Coastal Ecosystem LTER, OCE-1832178; (SP-70 and SP-72) Eastern Shoshone, Shoshone-Bannock; (MP- 102944) Funded by Oregon Watershed Enhancement Board. On the traditional lands of the Confederated Tribes of the Siletz, Confederated Tribes of the Grand Rhonde, and the Clatsop-Nehalem Confederated Tribe; (MP- 100607) Holiday Creek is located on the traditional territory of the Monacan Indian Nation; (SP-45) Lafayette Blue Springs State Park; (MP-102420) NSF DEB-2016749; (MP-100019) New Hampshire Agriculture Experiment Station; (SP-35) Rayonier (land owner; https://www.rayonier.com/); (MP- 101276) US Department of Energy, Office of Science, Biological and Environmental Research, Subsurface Biogeochemical Research, Watershed Dynamics and Evolution SFA at ORNL; (MP- 103224) Watershed Dynamics and Evolution SFA at ORNL; (MP- 101584) Traditional lands of the Oceti Sakowin (Dakota, Lakota, Nakoda) and Anishinaabe Peoples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

2021 Summer Resource Adequacy in the ERCOT Region

This report is an update to the NETL report on the same topic that examined the potential system performance of the Electric Reliability Council of Texas (ERCOT) system during the summer of 2020. Anticipated reserve margins in ERCOT have risen year-over-year from the 10.7 percent reported in ERCOT’s Final Seasonal Assessment of Resource Adequacy (SARA) for Summer 2020 to 15.7 percent for Summer 2021 in ERCOT’s Capacity, Demand and Reserves [CDR] report published in December 2020 and 2021 Summer Preliminary SARA published in March 2021. Using data from ERCOT’s 2021 Summer SARA, CDR, and historical performance data, six economic dispatch scenarios using PROMOD were developed to assess the risk to ERCOT of a load shedding event for Summer 2021. The results suggest that ERCOT could make it through the summer season without a loss of load event, as long as the weather remains normal. However, through a combination of scenarios, including increased demand, increased forced outages, and low wind output, ERCOT is likely to find itself operating in emergency conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Renewable Energy Systems in the Transition of North America to Net Zero Emissions

The North American transition to low-carbon energy sources depends on the technology decisions of its diverse communities and stakeholders. Each region and industry will have its own portfolio of renewable energy technologies to meet the common goal of net zero emissions. Our presentation will share integrated renewable energy solutions being explored at scale by major U.S cities, rural regions dependent on oil and gas, and industries redefining their infrastructure and their missions. Case examples will be presented on: the Los Angeles 100% renewable energy goals; challenges and opportunities of decarbonizing and reducing emissions from the oil and gas industry; repurposing oil and gas infrastructure such as using pipelines for hydrogen blends and renewable natural gas and idle wells for geothermal energy and energy storage; treatment of produced water using renewable energy for agriculture applications; and the preserving water and agricultural resources in the transition to renewable energy. Key challenges will also be discussed, including resilience to increasing disasters, stranded assets, unequal community impact, and meeting the last 5-20% of energy with renewable energy.

challenges↗
DOE OSTI2022

Water and Climate Impacts on ERCOT Long-Term Systems Assessment

Water and climate change pose many potential challenges to the electric power system. Substantial water is withdrawn every day to support thermoelectric power generating unit operations, and changes to water supply have the potential to affect generation dispatch. Climate change can accelerate growing demand for electricity, which can necessitate additional generating capacity, often in locations with limited water supply. Drought conditions also threaten thermoelectric power plant operations due to streamflow and reservoir levels dropping below intake structures, or water temperatures exceeding a power plants' permitted operating conditions. Here we explore how future climate change might influence decisions related to electricity capacity expansion planning in Electric Reliability Council of Texas (ERCOT) using a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on the future water supply for thermoelectric and hydropower generation for four future climate projections. Separately, temperature impacts on electricity load are evaluated for these scenarios. These climate impacts are applied to five alternative electricity futures in an electricity capacity expansion model that projects future generation and transmission capacity additions in ERCOT. Results indicate that climate has a measurable influence on future generation and transmission capacity needs, with temperature-driven increases in peak and average load resulting in 5-15 GW additional generating capacity and up to 1 GW additional transmission capacity. Additional capacity is a diverse mix of PV, natural gas, and wind, depending on the makeup of economic, policy and technology assumptions. Climate impacts increase total system costs 2-5%, while the marginal cost of energy and emissions are not affected substantially by climate change effects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗