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DOE OSTI2024

IDAES-PSE Software Tools for Optimizing Energy Systems and Market Interactions

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-eco-nomic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully generates multi-period price-taker models, facilitates model discrimination, and aids in analyzing various technologies for deployment in unique energy markets.

Laky, Daniel↗

DOE OSTI2025

Microreactor Optimization Using Simulation And Economics (mouse)

Microreactor Optimization Using Simulation and Economics (MOUSE) is a tool that integrates both nuclear microreactor design and reactor economics to provide comprehensive evaluations and optimizations. This tool enables stakeholders to explore the interplay between technical and economic variables, guiding them towards effective and competitive microreactor solutions. For the reactor core simulations, MOUSE leverages the OpenMC Monte Carlo Particle Transport Code to perform detailed core simulations for various microreactor designs. The included OpenMC models are 2D core designs of a Liquid Metal Thermal Microreactor (LMTR), a Gas-Cooled TRISO-Fueled Microreactor (GCMR), and a Heat Pipe Microreactor. Beyond core design, MOUSE includes simplified calculations for: - Calculating the masses of heat exchangers within the system. - Mechanical power of pumps. - Estimating the area occupied by various buildings within the nuclear plant. For the economic analysis, MOUSE provides detailed bottom-up cost estimates, encompassing a wide range of costs including preconstruction costs, direct costs, indirect costs, training costs, financial costs, operation & maintenance (O&M) costs, and fuel costs. These cost estimations are developed using data from the MARVEL project and additional literature sources, enabling the calculation of total capital costs and levelized cost of energy for both first-of-a-kind and nth-of-a-kind microreactors. MOUSE also enables analysis of the cost drivers and competitiveness in the electricity market. MOUSE allows users to modify a wide array of technical and economic parameters to evaluate different scenarios and their impacts. Examples of these parameters include: Fuels, coolants, or reflector materials Enrichment levels Control drum materials and geometry Fuel pin geometry and materials Moderator pin geometry and materials Reactor core and reflector dimensions Packing factor for the TRISO particles Nuclear reactor power and reactor burnup Number of sensors Shielding thickness Reactor vessel and guard vessel dimensions Operational staff requirements Number of emergency shutdowns Levelization period Interest rate Construction duration Since MOUSE is powered by the WATTS toolkit, it supports optimization studies, parametric analyses, and uncertainty calculations/propagation. The optimization techniques enable users to identify optimal design and economic configurations. The parametric analysis tools allow users to explore the sensitivity of various parameters, while uncertainty propagation helps quantify the impact of uncertainties on overall performance and cost. User Interface and Workflow: Currently, MOUSE is a command-line-based tool. Users can input various reactor design or economic parameters, modify the designs, run simulations, and visualize results through comprehensive data visualization and reporting capabilities. The typical workflow involves setting up the reactor model, defining economic parameters, running simulations, and analyzing the results to make informed decisions. By combining advanced design calculations with detailed economic modeling, MOUSE provides a robust framework for optimizing nuclear microreactor technologies, enhancing their competitiveness, and guiding stakeholders towards innovative and cost-effective solutions.

Hanna, Botros [Idaho National Laboratory (INL), Id↗

DOE OSTI2020

Synthetic Ancillary Service Generator (SynAS) v1

The balance of demand and supply in the electric power grid is a crucial element to grid reliability. While balancing is typically provided by conventional power plants, with spinning generators, distributed energy resources (DER) and especially electric vehicles (EVs) pose a great potential to provide such service. This project conducted a field demonstration at the Los Angeles Air Force Base (LA-AFB) where a fleet of mixed-use bi-directional EVs were actively participating in the California Independent System Operator (CAISO) market for frequency regulation. While participation in regulation markets is promising, a major drawback in designing control systems and assessing potential revenue is the lack of example frequency regulation signals. This project introduces an open-source dataset for 143 days of four-second regulation data, and the SynAS Python module to synthetically generate frequency regulation signals of arbitrary length.

Black, DouglasR↗

DOE OSTI2025

Seed coat transcriptomic profiling of 5-593, a genotype important for genetic studies of seed coat color and patterning in common bean ( Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) market classes have distinct seed coat colors, which are directly related to the diverse flavonoids found in the mature seed coat. To understand and elucidate the molecular mechanisms underlying the regulation of seed coat color, RNA-Seq data was collected from the black bean 5-593 and used for a differential gene expression and enrichment analysis from four different seed coat color development stages. 5-593 carries dominant alleles for 10 of the 11 major genes that control seed coat color and expression and has historically been used to develop introgression lines used for seed coat genetic analysis. Pairwise comparison among the four stages identified 6,294 differentially expressed genes (DEGs) varying from 508 to 5,780 DEGs depending on the compared stages. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that phenylpropanoid biosynthesis, flavonoid biosynthesis, and plant hormone signal transduction comprised the principal pathways expressed during bean seed coat pigment development. Transcriptome analysis suggested that most structural genes for flavonoid biosynthesis and some potential regulatory genes were significantly differentially expressed. Further studies detected 29 DEGs as important candidate genes governing the key enzymatic flavonoid biosynthetic pathways for common bean seed coat color development. Additionally, four gene models, Pv5-593.02G016100, 593.02G078700, Pv5-593.02G090900, and Pv5-593.06G121300, encode MYB-like transcription factor family protein were identified as strong candidate regulatory genes in anthocyanin biosynthesis which could regulate the expression levels of some important structural genes in flavonoid biosynthesis pathway. These findings provide a framework to draw new insights into the molecular networks underlying common bean seed coat pigment development.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Multicriteria-Based Selection of Microalgae Biorefineries: Biomass Composition Defines the Most Suitable Product Portfolios

The supply of microalgae-derived biofuels and bioproducts will be vital in a global-scale bioeconomy. As the diversity of microalgae strains and their compositional plasticity may yield a wide array of products of market interest, the design of effective biorefineries is a central aspect in the path to making microalgae-based products available in the market. In this way, the conversion of microalgae biomass should be planned to employ mature technologies, while maximizing economic and environmental benefits obtained from a diverse product portfolio. This study applied sequential, hybrid Multicriteria Decision Analyses (MCDA) to aid the decision-making process of outlining the most suitable biorefining pathways for specific compositional profiles. For this, the methodology simultaneously considered multiple technical, economic, and environmental criteria, such as market aspects for the main algae-derived products, potential reduction in greenhouse gas (GHG) emissions provided by the biobased alternatives, and technology readiness level of conversion routes, among others. The framework was tested using productivity and compositional data for 13 high-productivity summer strains cultivated under varying nutrient availability. The analysis pointed to compositional profile being a key driver in defining the core biorefining strategy of algae biomass, with lipid-to-carbohydrate ratios higher than roughly 1 warranting the preferential processing of lipids into hydrocarbon fuels with the remainder of the algae biomass compounds being sent to higher-value applications, such as carboxylic acids and renewable thermoplastic substitutes. An in-depth process simulation, techno-economic assessment (TEA), and life-cycle analysis (LCA) effort was carried out as a closing step to corroborate the results stemming from the proposed framework. This study validates the use of MCDAs as a screening method prior to implementing more time-consuming analysis techniques and makes a compelling case for this approach as a product selection tool on a wide range of algae species, thus helping establish species-agnostic (but composition-driven) biorefineries.

biofuels↗

DOE OSTI2026

Prime Time for Model-Predictive Control? Assessing the Technical and Market Readiness of Advanced Controls in Buildings

Despite three decades of extensive research and field testing that have consistently validated the benefits of Model Predictive Control (MPC) in building applications, the technology has seen limited market adoption. This paper evaluates the readiness of MPC for widespread deployment, showcases recent demonstrations and field tests across diverse building types, including residential, small commercial, large commercial, and campus settings. Our results demonstrate that MPC can optimize system operations to achieve load shifting, minimize curtailment of on-site generation, and reduce energy costs by up to 80 %, while maintaining or improving occupant comfort. We also show that MPC can effectively control large assets, such as MW-sized thermal storage systems, and respond to dynamic pricing signals. However, achieving scale remains difficult due to labor-intensive workflows, reliance on a “PhD-in-the-loop” for MPC design and maintenance, susceptibility to fragile data infrastructure, and persistent workforce education and acceptance barriers. To bridge this gap, we outline a transition from bespoke, labor intensive prototypes toward streamlined, segment-targeted deployment strategies that leverage model templates, semantic tools, and generative AI. By automating control configuration and reducing engineering effort, these recommendations provide a pathway for transforming successful research demonstrations into scalable, market ready solutions for MPC-based controls.

Pritoni, Marco↗

DOE OSTI2021

Domestic Sales Mix of Plug-In Electric Vehicles by Trim Variant and Vehicle Characteristics

As plug-in electric vehicles (PEVs) take up a larger share of the United States vehicle market, their variety increases as well. Since many electric vehicle manufacturers offer multiple PEV trim variants, the distribution of vehicle characteristics such as battery capacity, all-electric range, electricity consumption, and curb weight cannot be determined by sales data alone. Sales data and vehicle registration data were analyzed to quantify and map the trim variant distribution of PEV models from nine automakers. With this information, we quantify national sales-weighted characteristics of fourteen different models over ten years, including battery capacity, fuel economy, and vehicle weight. We find a positive correlation between share of all-wheel drive variants for Tesla vehicles and average annual snowfall, and general uniformity nationwide in other vehicle characteristics. The estimated trim variant distributions will be useful in informing decisions regarding materials recycling and EV impact on the energy grid, and this study found that the total installed battery capacity in PEV in the U.S. was 76 GWh through 2020.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2020

Enabling Power at Sea: Opportunities for Expanded Ocean Observations through Marine Renewable Energy Integration

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and in-person interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Powering the Blue Economy: Progress Exploring Marine Renewable Energy Integration With Ocean Observations

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and inperson interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

marine renewable energy, powering the blue economy↗

DOE OSTI2023

Potential Benefits in Remapping the Special Flood Hazard Area: Evidence from the U.S. Housing Market

A typical U.S. homebuyer's understanding of whether a property faces flood risk is based on whether the property is located inside the National Flood Insurance Program's (NFIP) Special Flood Hazard Area (SFHA). The SFHA boundary, however, may bias homebuyers’ perceptions of flood risk relative to unobserved true risk because the SFHA is an incomplete, and sometimes inaccurate, representation of flood hazards. Using a national dataset of property transactions, flood hazard data from the First Street Foundation, state-level measures of flood disclosure laws, and a spatially restricted triple-difference design, we distinguish capitalization effects of policy-driven (SFHA) versus quasi-objective (First Street) indicators of flood hazard. We identify these effects by assessing thousands of local spatial interactions between property SFHA designations, measures of quasi-objective flood hazard, and being in a state that mandates flood history disclosures. Being inside the SFHA generates a risk discount, but the signal is muted relative to underlying hazard exposure. Further, the SFHA signal can result in inefficient discounts for properties erroneously mapped in the SFHA. Disclosure requirements about flood history accentuate price effects for hazardous properties and result in more adequate risk internalization. In the absence of disclosures, however, we find either no or a weak risk signal for houses outside the SFHA that face flood hazard. Our results highlight potential benefits of updating SFHA boundaries to include all houses that may experience flooding, and argue in favor of requiring flood-related disclosures from sellers to improve the market's ability to internalize flood risk.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Monthly hydropower generation data for Western Canada to support Western-US interconnect power system studies

Hydroelectric power generation in Western Canada significantly contributes to power grid operations of the North American Western Interconnection through substantial generation, some of which is exported to the United States (U.S.). However, the lack of publicly available hydropower generation datasets poses challenges for future market projections and resource adequacy evaluations. We present a simulation-based monthly power system model-ready hydropower generation dataset for 110 facilities in British Columbia and Alberta from 1981 to 2019. These monthly hydropower generation estimates are developed from integrated hydrologic model simulations of runoff and reservoir-operated streamflow, followed by scaling that considers diversion inflow constraints based on hydropower water license information. To address the lack of comparable hydropower generation records, we conduct step-by-step evaluations for simulated runoff, regulated streamflow, and hydropower generation using available observations or estimates. The presented hydropower dataset aims to enhance the representation of hydropower resources in Western Canada, supporting power grid system studies for the Western Interconnection of the U.S. and Canada.

13 HYDRO ENERGY↗

DOE OSTI2024

Dataset of mechanically induced thermal runaway measurement and severity level on Li-ion batteries

The deployment of Li-ion batteries covers a wide range of energy storage applications, from mobile phones, e-bikes, electric vehicles (EV) and stationary energy storage systems. However, safety issue such as thermal runaway is always one of the most important concerns to prevent Li-ion batteries from further market penetration. A standardized single-side indentation test protocol was developed to mechanically induce an internal short-circuit. The cell voltage, compressive load, indenter stroke, and temperature at the indentation point are measured in time series. The test data of each cell, along with cell parameters such as dimensions, mass, chemistry, state of charge (SOC), capacity, are integrated together to calculate a thermal runaway severity score from 0 to100. Complete data collection process including the original measured record, test method, severity score calculation scheme is presented in this article. The thermal runaway severity analysis and the more than 100 tested Li-ion battery records provide a good data source for further comparison and ranking of thermal runaway risks.

25 ENERGY STORAGE↗

DOE OSTI2024

Cooperative Transmission Expansion Planning Experiment Data and Results

GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.

Capacity Expansion Model↗

DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Opportunities for bioenergy crops to support transitions from irrigated agriculture and conserve the U.S. High Plains Aquifer

This study investigates potential economic and groundwater driven transitions from irrigated maize production—the dominant irrigated cropping system in the High Plains Aquifer (HPA) region—to alternative crops such as sorghum and switchgrass, two common bioenergy feedstocks. Unsustainable groundwater extraction in the U.S. High Plains presents critical challenges including reduced irrigation capacities, diminished crop yields, lower land values, and escalating energy costs. Using a spatially explicit optimization framework combined with comprehensive economic and hydrogeological data, we evaluate optimal land-use strategies and irrigation system investments over a 30-year planning period. Results indicate significant regional variability in the future economic viability of irrigated agriculture, driven by differences in aquifer recharge rates, groundwater availability, and market conditions. Nebraska and parts of northern Texas can sustain irrigated maize profitability due to relatively favorable groundwater conditions and lower land rents, respectively. In contrast, many portions of Kansas and southern Texas are more likely to transition to dryland agriculture within two decades. Colorado and New Mexico show potential for significant adoption of switchgrass production as an alternative biomass-based energy crop. Overall, over the 30-year horizon, our model implies that approximately 23% of currently irrigated maize area across the HPA region may transition to dryland farming under business-as-usual conditions. This transition is complemented by a threefold increase in non-irrigated sorghum production, from 0.20 to 0.77 Mt yr−1, indicating that groundwater-driven shifts in agricultural production may support a larger regional base for bioenergy feedstocks. The study also reveals opportunities for producers to optimize economic returns and biomass production potential.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Field testing and validation of a low-cost MPC for demand flexibility for grid-interactive K-12 schools

K-12 school buildings account for the highest energy consumption within the public sector. Implementing advanced HVAC controls in grid-interactive K-12 schools could bring substantial economic advantages and grid flexibility. Our previous study demonstrated that a low-cost model predictive control (MPC) solution, which coordinates multiple packaged units, can enable demand flexibility without major hardware upgrades. However, a significant gap remains between academic pilots and market-ready scalable solutions. This paper extends the previous single-site pilot to a multi-site demonstration involving three school campuses (95 total units) through a commercial technology transfer process. Addressing the challenge of verifying performance with sparse field data, we present a new statistical approach using Bayesian methods to estimate the MPC’s effect on peak demand. Unlike traditional methods, this approach robustly quantifies uncertainty in non-normal, limited datasets. The results confirm the solution’s replicability, achieving a 21.6–38.9% reduction in HVAC peak demand (10.8–22.1% at the site-level) with > 98% probability across diverse locations. Finally, we document critical barriers to scaling software-as-a-service (SaaS) solutions–such as API instability and diverse legacy systems–and offer practical strategies to accelerate the commercial adoption of grid-interactive efficient buildings.

Ham, Sang Woo↗

DOE OSTI2022

WBS 1.2.3.405 - Life Cycle Assessment of Storage Technologies

Recent commitments by the Biden administration have established targets to achieve a net-zero energy system by 2050. Meeting these targets will spur a rapid transition to clean energy technologies and a commensurate need to develop and deploy energy storage technologies at scale. Pumped Storage Hydro (PSH) is expected to be part of this solution because its ability to provide grid flexibility and stability and enable the dispatching of disparate variable renewable energy technologies. Despite PSH being a mature technology with a history of deployment dating back several decades, there is very little information on the greenhouse gas (GHG) implications of PSH as compared to other storage technologies. The objective of this project is to perform a full lifecycle assessment (LCA) of new PSH projects in the U.S. This LCA includes all project phases (resource extraction, construction, operation, maintenance, end-of-life). The functional unit for this study is 1 kWh electricity delivered by system to grid substation connection point and the estimated lifetime for our base case is 80 years. Data used in this study are based on over 30 potential PSH projects that are in preliminary planning phases and are represent a wide range of potential closed-loop PSH systems in terms of location, technology, and capacity. The project approach, data sources, and modeling assumptions have been informed by a technical review committee of stakeholders that include experts from academia, national and international government, industry, and utilities. The GHGs and energy return on investment (EROI) from PSH will be compared to other storage technologies (e.g., stationary battery storage). Results from this project will improve the PSH community's understanding of the environmental impacts and sustainability of new PSH projects and how PSH compares to other storage technologies. The approach used in this project relies on open-source programming. The analysis framework (source code and data) and will be made publicly available at the end of the project. In addition to reporting results for the base case, we will perform rigorous sensitivity analysis to identify the major drivers, understand impacts of different configurations, and future energy markets. Results from this project will be published in a suitable journal.

ENERGY PLANNING, POLICY, AND ECONOMY,HYDRO ENERGY↗