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DOE OSTI2020

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

Abstract This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Land-use and atmospheric shifts jointly amplify U.S. drought-driven crop losses

Agricultural drought (AD) poses a major threat to food security, yet its future risk remains uncertain under co-evolving atmospheric conditions and land-use trajectories. Using an integrated, multi-sector modeling framework, we project AD risks for major crops across the contiguous United States (CONUS) through 2055 under a range of plausible futures that capture thermodynamic changes and land-use and land-cover change (LULCC) trajectories. Model projections reveal that drought-driven crop production losses increase sharply by nearly 60% for corn, 250% for wheat, and 135% for soybean relative to historical levels. The primary drivers of these increases, which include atmospheric shifts and LULCC, vary by region and crop type. LULCC acting as an important risk amplifier in regions experiencing cropland expansion into drought-prone regions, such as the Great Plains and northwestern U.S. Wheat exhibits the largest projected loss increases, a result that remains robust across scenarios. These findings highlight that interactions between atmospheric conditions and land-use trajectories shape future agricultural drought risk and should be jointly considered to support effective adaptation and food-system planning.

Yao, Lili↗

DOE OSTI2024

Model associated with: "Thermodynamic control on the decomposition of organic matter across different electron acceptors"

This model data package is associated with the publication “Thermodynamic control on the decomposition of organic matter across different electron acceptors” submitted to Soil Biology and Biochemistry (Zheng et al., 2023; https://doi.org/10.1016/j.soilbio.2024.109364).In this research, a thermodynamic modeling framework is built to flexibly incorporate both organic matter (OM) molecules and electron acceptors for estimating potential free energy release from various redox reactions and to further predict reaction rates based on Microbial Transition State Theory. The model package includes scripts for thermodynamic modeling and postprocessing. Input Fourier-transform ion cyclotron resonance (FTICR) data are from a previous experimental study (Boye et al., 2018), and model outputs are free energy predictions and stoichiometric coefficients associated with all possible redox reactions.This data package is associated with the project GitHub repository found at MM_bioenergetic_modeling.This data package contains four folders (Input_FTICR, Model, Output, and Output_processing), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Zheng_bioenergetic_modeling_flmd.csv for a list of all files contained in this data package and descriptions for each. The Zheng_bioenergetic_modeling_dd.csv file describes the csv column headers. The “Model” folder contains scripts to run energy balance calculations for each electron acceptor. The “Output” folder contains csv files with stoichiometric information from model simulations. And the "Output_processing" folder contains scripts for reaction rate calculations and to generate plots.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling

Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national security threats that sustained climate change poses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

A Multiphysics Thermoelastoviscoplastic Damage Internal State Variable Constitutive Model including Magnetism

We present a macroscale constitutive model that couples magnetism with thermal, elastic, plastic, and damage effects in an Internal State Variable (ISV) theory. Previous constitutive models did not include an interdependence between the internal magnetic (magnetostriction and magnetic flux) and mechanical fields. Although constitutive models explaining the mechanisms behind mechanical deformations caused by magnetization changes have been presented in the literature, they mainly focus on nanoscale structure–property relations. A fully coupled multiphysics macroscale ISV model presented herein admits lower length scale information from the nanoscale and microscale descriptions of the multiphysics behavior, thus capturing the effects of magnetic field forces with isotropic and anisotropic magnetization terms and moments under thermomechanical deformations. For the first time, this ISV modeling framework internally coheres to the kinematic, thermodynamic, and kinetic relationships of deformation using the evolving ISV histories. For the kinematics, a multiplicative decomposition of deformation gradient is employed including a magnetization term; hence, the Jacobian represents the conservation of mass and conservation of momentum including magnetism. The first and second laws of thermodynamics are used to constrain the appropriate constitutive relations through the Clausius–Duhem inequality. The kinetic framework employs a stress–strain relationship with a flow rule that couples the thermal, mechanical, and magnetic terms. Experimental data from the literature for three different materials (iron, nickel, and cobalt) are used to compare with the model’s results showing good correlations.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Phase-Field Modeling of Damage Evolution in Ceramic Matrix Composite (CMC) and Environmental Barrier Coating (EBC)

Ceramic matrix composites (CMCs) protected by environmental barrier coatings (EBCs) present a promising materials solution for next generation gas turbines. Developments of more robust and efficient EBCs and mechanically tougher CMCs are thus of significant technological importance. Here we develop a phase-field modeling framework that incorporates the thermally grown oxide (TGO), recognized as a critical factor for degradation and failure of EBCs. We simulate crack growth in the TGO and the potential extension into the bond coat / CMC substrate. The model efficiently takes account of the large inelastic deformation induced by the severe volume expansion of TGO, thanks to our recently developed, so-called incremental realization of inelastic deformation (IRID) algorithm. A phase-field model is built for damage evolution in CMCs including crack growth and interfacial sliding. The effects of fiber layout and interfacial sliding on the macroscopic toughness of CMCs are revealed by large-scale simulations and compared to experiments.

advanced energy systems and materials↗

DOE OSTI2024

A Multiphysics Thermoelastoviscoplastic Damage Internal State Variable Constitutive Model including Magnetism

36 MATERIALS SCIENCE↗

DOE OSTI2021

Stratigraphic Identification with Airborne Electromagnetic Methods at the Hanford Site, Washington

Stratigraphic units can influence the fate and transport of subsurface contaminants within groundwater. Units having coarse-grained sediments act as preferential flow pathways, and therefore can accelerate the transport of contaminants to reach human and ecological receptors. At legacy waste sites, detailed knowledge of subsurface stratigraphy can be used for effective monitoring and remediation planning to help minimize risk to human health and the environment. Airborne electromagnetic (AEM) methods can non-invasively provide information on kilometer-scale or larger subsurface stratigraphic features and fill informational gaps in directly sampled data from sparsely located boreholes. In this paper, we present inversion results of a 412 line-km frequency-domain AEM survey to delineate subsurface stratigraphic features at the Hanford Site, located in southeastern Washington State. The inversion was performed using a massively parallel 3D electromagnetic modeling and inversion code, where the modeling is based on solving frequency-domain Maxwell’s equations using an unstructured-mesh finite-element method and the inversion employs a Gauss-Newton optimization scheme. The results are compared to an underlying geologic framework model (GFM), built by interpolating contact depths of stratigraphic units interpreted from site borehole datasets. In areas with good borehole coverage, the inversion results show a good match with the GFM to a depth of about 60 m. Outside of these areas, the inversion results exhibit inconsistencies from the assumptions made to create the GFM, demonstrating that the AEM survey results can be used to improve the understanding of the geological conceptual model.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Entanglement and seniority

In quantum mechanics entanglement is the most striking phenomenon which has no counterpart in classical systems. Although, different approaches have already been developed to study correlations in the case of indistinguishable particles, the exploration of the so-called mode entanglement is still in its initial stage in nuclear physics. Study of mode-entanglement in the seniority model, derivation of analytic formulas for the one-body reduced density matrix of states with seniority $ν$ = 0, 1, 2, and $ν$ = 3, and also determination of the particle number dependence of the one-body reduced density matrix for arbitrary seniority. In addition, comparison of the predictions of the seniority model with the results of the shell model to gain insight into the structure and correlations of the ground and lowest yrast states. In the seniority model the analytic results are calculated using the quasi-spin formalism. The numerical shell model calculations are carried out in the standard shell model framework using an inert core. The applied realistic effective interactions are derived earlier using the $G$-matrix formalism. The density matrix renormalization group method is also applied in order to directly calculate the mode entropies. In the seniority model simple analytical expressions are given for the mode entropies. The peculiar behavior of the half-filled shells and the seniority-zero states are revealed. Numerical results are presented for the lightest stable calcium isotopes and for 94 Ru nucleus. For 94 Ru, the seniority model accounts for the 0$g$ 9/2 mode entropies, but seniority mixing is important for certain yrast states. Furthermore, interaction induced quantum fluctuations decrease the occupation of the 0$f$ 5/2 , 1$p$ 3/2 and 1$p$ 1/2 shells, and amount in non-negligible mode entropies on these shells, too, clearly outside the scope of the simple (0$g$ 9/2 ) 4 seniority model. The 0$f$ 7/2 shell-based seniority model is more accurate for the Ca isotopes, but seniority mixing is substantial for some 44 Ca yrast states, too. Mode and one-body entanglement entropies are useful tools to investigate the structure of quantum correlations in nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

From Cell to System: Accelerated hpc Simulations of BESS Aging under Frequency Regulation and Arbitrage use cases

Lithium-ion battery energy storage systems (BESS) packs have emerged as a leading solution for grid-scale energy storage, enhancing resiliency and balancing load fluctuations. Yet, experimental characterization of large-format LIB packs-particularly to assess performance and degradation over hundreds of cycles - demands substantial hardware investment and multi-year testing campaigns. In this work, we couple a hierarchical, physics-based modeling framework agnostic to electrode chemistries with high-performance computing to accelerate systems level evaluation by upto two orders of magnitude. Building on the open-source liionpack platform, we implement cell, module, and pack-scale electrochemical models enriched with mechanistic aging mechanisms and deploy them on an HPC cluster to simulate 150−200kWh systems over 500 - 1,000 cycles with in days. We subject these virtual B ESS to both constant-current cycling and realistic grid service profiles spanning frequency regulation, ramp-rate support, and energy arbitrage-and quantify the resulting degradation patterns. Our results reveal that localized cell aging can induce substantial nonuniformity at module and pack levels, with service-specific cycling protocols driving distinct aging modes. This rapid, multiscale modeling approach provides a powerful design-space exploration tool for optimizing electrical architecture, control strategies, and operational schedules to prolong pack lifetime and lower total cost of ownership.

Ayalasomayajula, Surya [ORNL] (ORCID:0009000860788↗

NASA NTRS1983

Model-based estimation and prediction of task-imposed mental workload

Mental workload has been an area of intensive research for better than a decade. One specific area of interest in aircrew related workload research is concerned with the development of quantitative indices of workload in aircraft piloting tasks. This paper presents a model-based approach for quantifying mental workload in operational terms. The suggested modeling framework is based on an interpreted Petri net characterization of a task in which 'places' are equated to specific task-related activities and 'transitions' are viewed as internal or external forcing events. It is shown that within this framework quantitative assessments can be made of both cumulative and instantaneous workload associated with the performance of a task and its individual component subtasks. It is suggested that insights gained from analyzing task-specific workload within this modeling paradigm can suggest plausible explanations for reconciling discrepancies between subjectively elicited workload estimates and behavioral/performance measures.

Madni, A. M.↗

NASA NTRS2024

Quantifying Food Security and Mitigation Risks Consequential to Climate Change Impacts on Crop Yields

Climate change is expected to impact crop yields globally, with some regions benefiting from favorable conditions and CO2 fertilization, while others face adverse effects from altered precipitation and higher temperatures. Changes in crop yields can destabilize the global food system and pose challenges to food security. Moreover, crop production is crucial, as biofuels are becoming increasingly important contributors to climate change mitigation measures aimed at limiting global warming. This study uses the Integrated Model to Assess the Global Environment integrated assessment model framework to analyze different indicators related to food security and climate change mitigation under varying climate change impacts on crop yields. Twelve spatially explicit crop productivity projections were taken from the full archive of the Global Gridded Crop Model Intercomparison of 120 climate-crop model combinations, forced by CMIP6-based climate scenarios. The selection includes two average-performing climate-crop model combinations, two pessimistic combinations that perform one standard deviation below the mean, and two optimistic model combinations that perform one standard deviation above the mean. To single out the effect of climate change on productivity changes, we drew samples from two representative concentration pathways (RCP2.6 and RCP8.5). These productivity projections were applied within an otherwise uniform scenario (SSP2) and analyzed for their effect on total calorie demand, crop prices, and number of people at risk of undernourishment to quantify food security. Risks to climate change mitigation targets were explored by modeling the total bioenergy supply, emissions, and global mean temperature. The results revealed significant differences in the risk of food security and mitigation potential between different regions and climate change scenarios. Across scenarios, the crop area extent can vary up to 2 million km2 due to changing crop yields. The projected change in global hunger ranges from 60 to 160 million undernourished people, indicating uncertainty between climate and crop model combinations. Low-income regions are especially impacted because of their high sensitivity to changes in food prices. Global climate change mitigation ambitions can also deviate by the latter part of the 21st century, as changes in yields will impact biofuel production as well as agriculture, forestry and other land use emissions. The quantitative insights generated by this study highlight the need for global policy efforts to make the agricultural system more adaptive to climate change to handle potential negative impacts.

crop yields↗

DOE OSTI2023

A forward modeling approach to analyzing galaxy clustering with S IM BIG

We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the SimBIG forward modeling framework. SimBIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply SimBIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, P ℓ (k), to k max = 0.5 h/Mpc. We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution Quijote N -body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of ΛCDM cosmological parameters: Ω m , Ω b , h, n s , σ 8 . We derive significant constraints on Ω m and σ 8 , which are consistent with previous works. Our constraint on σ 8 is 27% more precise than standard P ℓ analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, k > 0.25 h/Mpc. This improvement is equivalent to the statistical gain expected from a standard P ℓ analysis of galaxy sample ~ 60% larger than CMASS. While we focus on P ℓ in this work for validation and comparison to the literature, SimBIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent SimBIG analyses of summary statistics beyond P ℓ .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Development of experimental and computational frameworks to predict subcooled flow boiling in the LANL Isotope Production Facility

Cooling is crucial to maintain the integrity of target systems in isotope production facilities. At Los Alamos National Laboratory (LANL)’s Isotope Production Facility (IPF), multiple encapsulated targets are stacked and irradiated in tandem with a 100 MeV, ~250μA proton beam. To facilitate effective heat removal, these stacked targets are separated and cooled via a series of water channels. At these beam currents, this high-energy proton beam heats the target system, likely initiating subcooled flow boiling in the cooling channels. However, in-beam monitoring of the IPF target system is not possible due to the extreme radiation environment, and the necessarily significant shielding. To better understand high-power target performance, we developed ex-situ experimental and computational frameworks to predict the behavior of subcooled flow boiling at IPF. Subcooled flow boiling experiments on Inconel 625 samples under IPF conditions (2 bar pressure, 10 GPM flow rate (i.e., 2249 kg/m 2 /s), 85 K subcooling) revealed that IPF's average operating power is at the early stage of boiling with a heat transfer coefficient of 48,000 W/m 2 /s. The proposed modeling framework enables us to predict a complete boiling curve, i.e., single-phase heat transfer, onset of nucleate boiling, two-phase heat transfer, and critical heat flux (CHF), with specification of input boiling parameters up to intermediate heat flux levels. The estimated CHF under IPF conditions is 5.2 MW/m 2 . Experimental data under reduced conditions (2 bar pressure, 1.5 GPM flow rate (i.e., 337 kg/m 2 /s), 45 K subcooling) served as validation cases for the computational modeling. This computational model can be further extended to more complicated systems replicating the real IPF configuration, for instance, to study void distribution as a function of the incident proton beam profile and coolant velocity profile of multiple cooling channels. Finally, the proposed experimental and computational frameworks provide a means to better understand cooling systems in the isotope production facilities at different accelerators, where in-beam monitoring of the cooling process is not available.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2021

Investigation of the effects of turbulence modeling on the prediction of compression-ignition combustion unsteadiness

Adverse effects of global warming due to the greenhouse gas emissions is changing the actual paradigm for the use energy resources. In the absence of a mid-term solution for reducing these emissions in transportation, internal combustion (IC) engines are going to coexist in the social spheres in the foreseeable future. Therefore, the study of other IC engine-related problems remains relevant to ensuring the health of the society. In this investigation, a numerical methodology for comprehensive understanding of Noise, Vibration and Harshness in internal combustion engines is proposed. Due to its inherent complexity and lack of awareness, the main objective is to evaluate the impact of the turbulence modeling framework on the in-cylinder acoustic field recreation. Modal decomposition methods have been applied to isolate the coherent flow structures and to analyze how they change with the turbulence approach. Results demonstrate that the choice of the turbulence model is a critical aspect for noise modeling. Unsteady Reynolds-Averaged Navier-Stokes schemes predict a raw estimation of the internal acoustic field with the added value of being computationally less expensive. However, the use of more complex turbulence approaches such us large eddy simulation offers an accurate prediction of the acoustic structures and their cyclic dispersion.

Broatch, A.↗

DOE OSTI2023

Future Response of Coastal Wetlands to Environmental Stresses: Algorithm Comparison of Numerical Models

Coastal wetlands are a critical component of the earth system that strongly influence the global water and biogeochemical cycles. They are also likely important sentinel of climate change. Because eco-geomorphological processes have long been recognized to be important for coastal wetland survival under accelerated sea-level rise (SLR), many eco-geomorphology models have been developed to assess the impact of climate change on coastal wetlands. Although these models differ substantially in complexity and numerical methods, few studies have investigated the algorithm-level uncertainties in these models. In this study, we developed a multiple-algorithm model framework of coastal wetlands that represents coastal hydrodynamics (such as water level, significant wave height and bottom shear stress) and four eco-geomorphological processes: mineral accretion, organic matter accretion, storm surge erosion and landward migration. We validated the model at three representative coastal wetland sites (Venice Lagoon, Plum Island Estuary and Hunter Estuary) for hydrodynamics, mineral accretion and organic matter accretion. Through model-data comparison, we showed that the model can well capture the dynamics of hydrodynamical and eco-geomorphological conditions in the study sites. Importantly, analysis of the multiple-algorithm simulations suggests that differences in the process representation of mineral and organic matter accretion may contribute to the recent contradicting predictions of coastal wetland evolution under accelerated SLR.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Leveraging ARM Data to Improve Models for Predictive Understanding of Energy and Security Challenges

Extreme weather and natural hazards can disrupt the energy sector, affecting demand, generation, transmission, distribution, consumption and operational planning at regional and national scales. These disruptions stem from a broad range of atmospheric phenomena, including winter storms, freezing rain, wet snow loading, severe convection, flooding and landslides, wildfires, prolonged heat, and drought. Many of these same phenomena can also affect national security through impacts to transportation and infrastructure. To support the U.S. Department of Energy (DOE) focus on energy resilience and national security, the Atmospheric Radiation Measurement (ARM) User Facility is uniquely positioned to contribute measurement data, analyses, and modeling frameworks that can significantly improve predictive understanding of these hazards to mitigate their effects. To explore this opportunity, ARM convened a two-part virtual workshop in November 2025. The workshop engaged interdisciplinary experts in atmospheric science, energy systems, modeling, and operations. The goal of the meeting was to engage with these interdisciplinary experts to address three questions: • What are examples of atmospheric processes that represent significant risks to energy security or national security and where are those risks greatest? • What measurements or measurement strategies would improve ARM’s capacity to address these issues? • How can ARM and users of the ARM facility better work with the Energy Exascale Earth System Model (E3SM) and multi-sector modeling communities to apply ARM data to improving E3SM simulations of these phenomena? Participants were asked to submit white papers ahead of the meeting to initiate thinking on these themes and to help organize discussions. Workshop sessions were then organized around themes identified in the white papers. First from the white papers and then through subsequent discussions, workshop participants identified many examples that address the three questions listed above. Participants called out energy system vulnerabilities to weather phenomena such as the impact of freezing rain, strong winds, and excessive heat on power grids. They also noted the effects that weather phenomena could have on energy demand or supply (e.g., through effects of extreme temperatures). They called out security vulnerabilities such as impacts to crops from aerosol-borne pathogens and risks to industry due to melting permafrost in the Arctic. In all, over a dozen meteorological phenomena were linked to energy or security vulnerabilities. For many of the identified phenomena, participants pointed out where ARM was well poised to address issues (e.g., through measurements of cloud microphysics to inform studies of freezing rain) but also noted needs for additional measurements or modified measurement strategies. For example, adaptive scanning of severe weather would be valuable for probing winter storms or severe convection. Participants pointed out the value in integrating external observations with ARM measurements and with applying artificial intelligence (AI) to ARM observation analysis and they advocated for using model simulations to help optimize measurement strategies through Observing System Simulation Experiments (OSSEs). It was clear from the workshop that there are many ways that ARM observations can be used to mitigate energy and security concerns, but meeting participants were also asked to identify what they considered to be the greatest opportunities by ranking issues pertaining to the three workshop questions. This was accomplished through a survey administered to participants between the two virtual sessions. The highest-priority phenomena identified were winter storms, severe convection, and arctic processes. Discussion in the second session, therefore, focused primarily on these three areas, which were most fully developed in exploring ARM opportunities. Nevertheless, it was also clear that ARM has opportunities to contribute to all the identified topics. This report describes the workshop, including input from discussion and white papers (Sections 2 and 3) and a list of priority recommendations (section 4). Many other ideas for ARM contributions are discussed in individual white papers (Appendix D).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Projecting Future Colorado River Basin Water and Hydropower Operations

Drought, variable renewable energy (VRE) resources (primarily wind and photovoltaic technologies), and power markets can all impact hydropower plant operations. We analyze how these three factors collectively may potentially change the future operation and economics of federal dams and hydropower plants located in the Colorado River Basin (CRB). Federal CRB energy and capacity resources, including 4,200 MW of hydropower capacity, are marketed by Western Area Power Administration (WAPA) and sold through long-term Firm Electric Service (FES) contracts to entities located in 15 western and central States that serve millions of electricity consumers. The parameters of FES contracts allow a wide range of scheduling flexibility for FES customers. The FES customers will adjust the timing of energy delivery according to the changing water, power market, and VRE contribution level to the power grid. We simulate and project water and hydropower operations with two models: 1) reservoir operation and 2) power system operation (production cost) with various temporal granularity. We consider two sets of grid scenarios (one representing today's infrastructure, and one representing higher VRE contributions in 2036) and two sets of water scenarios (drought and normal hydrology). We modeled scenarios to understand the power market-energy price levels and patterns, FES customer scheduling, hydropower economics, WAPA finances and hydropower operation for various hydrologic conditions, and VRE contribution level to the power grid. The detailed modeling framework includes: (1) creating ensembles of CRB hydrologic and hydropower futures, (2) selecting representative futures, (3) simulation of FES customer contracts, (4) simulating western U.S. power grid economic scheduling and dispatch under two VRE contribution levels, utilizing information from modeling steps (1)-(4), (5) assessment of reservoir water and hydropower operation, and (6) comparing scenario operations and economics. Our study team comprising WAPA, and an interdisciplinary team of national labs will gain insight into how WAPA can prepare for and adapt its practices in response to an evolving U.S. power grid.

Colorado River↗