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DOE OSTI2021

Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Generating Sequential PV Deployment Scenarios for High Renewable Distribution Grid Planning

This paper introduces a novel approach for generating solar photovoltaic (PV) plant deployment scenarios for grid integration planning. The approach guarantees consistency among scenarios of the same deployment by ensuring that higher penetration scenarios contain PV units deployed in lower penetration scenarios. It also constrains the size and spatial distribution of the PV plants and considers three placement types. A case study on a real-world distribution system proves that the precepts of scenario consistency, deployment diversity, and placement are met. The study further investigates the impact of the resulting scenarios via a stochastic hosting capacity analysis. Results indicate that the ratio between PV and load sizes, referred to as the nodal PV penetration factor (NPPF), is a key driver of the grid integration impact. By reducing the NPPF from 5 to 2, the maximum hosting capacity increased by at least 112%. The study also reveals that scenarios under random placement can lead to higher hosting capacity values.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2022

Generating Sequential PV Deployment Scenarios for High Renewable Distribution Grid Planning: Preprint

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗

DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services

Flexible loads have great potential to improve the electric grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. This paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environmental impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services: Preprint

Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environment impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

control↗

DOE OSTI2024

Impact of volcanic eruptions on CMIP6 decadal predictions: a multi-model analysis

Abstract. In recent decades, three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal to decadal timescales that could have been potentially predicted with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of volcanic aerosols on the climate experienced during the years that followed those eruptions through the use of decadal predictions. The interest of conducting this exercise with climate predictions is that, thanks to the initialisation, they start from the observed climate conditions at the time of the eruptions, which helps to disentangle the climatic changes due to the initial conditions and internal variability from the volcanic forcing. The protocol consists of repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of their volcanic forcing, which are then compared with the baseline predictions to disentangle the simulated volcanic effects upon climate. We present the results from six Coupled Model Intercomparison Project Phase 6 (CMIP6) decadal prediction systems. These systems show strong agreement in predicting the well-known post-volcanic radiative effects following the three eruptions, which induce a long-lasting cooling in the ocean. Furthermore, the multi-model multi-eruption composite is consistent with previous work reporting an acceleration of the Northern Hemisphere polar vortex and the development of El Niño conditions the first year after the eruption, followed by a strengthening of the Atlantic Meridional Overturning Circulation the subsequent years. Our analysis reveals that all these dynamical responses are both model- and eruption-dependent. A novel aspect of this study is that we also assess whether the volcanic forcing improves the realism of the predictions. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that, overall, including the volcanic forcing results in better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed sea surface temperature (SST) variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo.

Bilbao, Roberto (ORCID:0000000307294980)↗

DOE OSTI2026

Ambient and Initial Temperature Effects on Energy Consumption Rate Modeled in FASTSim

Ambient and initial temperatures significantly impact the energy consumption rate (ECR) of battery electric vehicles (BEVs) due to auxiliary loads and the temperature dependence of battery efficiency. This study introduces a streamlined, physics-based thermal modeling approach within the FASTSim tool that bridges the gap between oversimplified constant-load models and computationally expensive high-fidelity simulations. By employing a lumped thermal mass framework, the model captures fundamental energy balances and critical non-linear energy penalties while maintaining the computational efficiency required for expansive sensitivity studies. The simulations evaluated a compact BEV hatchback with a resistive heater over city (UDDS) and highway (HWFET) test cycles. Compared to a 22 degrees Celsius initial and ambient temperature baseline, a -7 degrees Celsius initial/ambient temperature resulted in a 221% increase in the ECR for the city cycle and a 100% increase for the highway cycle. Conversely, a 45 degrees Celsius initial / 40 degrees Celsius ambient temperature resulted in a 40% increase for UDDS and an 18% increase for HWFET. These results demonstrate that while cold conditions impose the most severe energy penalties due to resistive heating, the impact is consistently more pronounced in city driving where auxiliary loads represent a larger proportion of total energy. This lightweight yet robust framework enables researchers to rapidly quantify BEV thermal sensitivity across diverse climates without the need for high-overhead simulation environments.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

FY25 Electric Grid Security Annual Report

Sandia’s Electric Grid Security program advances a national vision of energy dominance and accessibility, while applying our national security -emphasis on ensuring of a secure, resilient, and affordable electric system for all users. Our achievements reflect a strategic approach combining technology development; modeling, simulation, and data analytics; and partnered demonstrations and outreach to further the adoption of advanced grid and storage technologies. Our FY25 efforts leverage the strengths of our partnerships—spanning Sandia’s core science and technology competencies as well as external technology leaders—to develop the solutions today which enable the grid of tomorrow. Key accomplishments in this report that support our strategy span our technical program areas and include: • New open-source analytical tools for systems -level planning and optimization, including significant advances to the QuESt analytical environment; • Further advancement of artificial intelligence and machine learning to enhanced grid operations and planning as we rise to the challenge of new large loads; • Development of solid-state power conversion technologies and a new medium-voltage research lab; • New technologies to assess wildfire vulnerabilities and mitigate potential impacts; • Advanced applications of new cybersecurity technologies with industry partners; • Contributions to understanding the impacts of electromagnetic pulses and geomagnetic disturbances on grid components; and • Digital twin development for hybrid microgrids with multiple generators, storage, and loads. This report indicates key areas of research and engagement and summarizes the impact of Sandia’s contributions through notable accomplishments, journal publications, patents, and technical conferences and presentations. It is provided with the hope that readers discover ways we can further team to create our modern grid and apply the outcomes of our efforts. The bulk of work described herein is funded by several offices within the U.S. Department of Energy (USDOE), including the Office of Electricity (OE); Cybersecurity, Energy Security, and Emergency Response (CESER); former offices such as the Office of Energy Efficiency and Renewable Energy (EERE), the Grid Deployment Office (GDO), the Office of Clean Energy Demonstrations (OCED), and other key programs at USDOE. As we continue to state in these annual reports, the contributors to our successes are too numerous to name here, though our team wishes to express our deep gratitude to the numerous program and project sponsors at the US Department of Energy, who often function equally as technical collaborators; our many partners in industry, academia, utilities, and other national labs; and fellow researchers and business partners at Sandia whose leadership and creativity have enabled the accomplishments described herein.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Implementation of Advanced Grid Support Functionalities by Smart Operation of Residential Loads with low Cost Converter Interface

This paper investigates a grid-supportive load concept for small-scale residential appliances, focusing on a residential refrigerator. Power consumption is adjusted based on grid conditions to achieve IEEE-1547 grid support functions. Two key aspects are presented: a low-cost refrigerator converter with Lyapunov energy function-based local controllers for speed control, and the impact on a standard microgrid system, demonstrating advanced grid support from the load side. This method enhances grid resilience and reliability and can be extended to other residential loads. The study contributes to efficient and robust grid-supportive load management systems, showing promising performance. This approach has the potential to improve overall grid stability and can be adapted for various types of residential appliances. The modeling and simulations in MATLAB/Simulink and PLECS confirm the feasibility and effectiveness of the proposed solution. Future work will explore real-world implementation and scalability of this concept for broader applications.

grid supportive loads (GSL)↗

DOE OSTI2026

Spatially Resolved Domicile Charging Demands for Light-, Medium-, and Heavy-Duty Electric Vehicles in Virginia

The use of plug-in electric vehicles (PEVs) and resulting grid impacts are likely to grow rapidly, and evaluation of optimal smart charge management and grid integration strategies is warranted now. Evaluating distribution grid impacts requires fine-grained models of PEV operations to estimate charging loads across diverse vehicles at high spatial resolution. We propose such a model and consider a high-electrification scenario in Richmond and Newport News, Virginia. Our framework considers four categories of vehicle that are amenable to early aggressive electrification: light-duty passenger vehicles (LDV), trucks and vans with a focus on delivery or other local operations, school buses, and transit buses. These vehicles have a relatively consistent domicile, reducing the need for public charging infrastructure rollout to electrify. We apply a recent LDV model and propose new models for each vocation of medium- and heavy-duty vehicle, leveraging telematics data. We demonstrate our framework in Virginia and find energy demands in the region may total 15 GWh day, with most consumed by LDV. However, considering power demand at high spatial resolution reveals a different trend: LDVs have relatively small peak loads at specific sites (peak site demand around 800 kW) compared to average and high demand medium- and heavy-duty vehicle charging sites (peak site demand around 6,000 kW at a transit bus depot, 1,500 kW at a local freight hub, and 1,000 kW at a school). Our framework yields insights on the relative impacts of each vocation and enables future work to tailor grid integration strategies to each vehicle category.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Dynamic Strength of Copper at High Pressures Using Pressure Shear Plate Experiments

Measurement of strength at high pressures is critical to understanding the behavior of materials subjected to extreme loading conditions. Recent developments have extended the pressure shear plate impact (PSPI) technique to the high-pressure regime. Such experiments provide a unique opportunity to extract the complete stress-strain behavior of materials at relatively high pressures. The modified technique includes a new fiber-optic heterodyne transverse velocity interferometer system and new analysis methods to account for the inelastic response of the anvil plates. In this study, PSPI experiments are conducted on oxygen-free high conductivity (OFHC) copper at pressures ranging from 10 to 43 GPa and at strain rates of ~10 5 s -1 . Complete stress-strain curves of copper are obtained using a hybrid methodology that relies on numerical simulations and particle velocity records gathered in these experiments. Experiments are conducted at similar pressures and strain rates using two types of anvils that undergo different levels of plastic strains: tool steel and tungsten carbide, to check the robustness of the hybrid method. The behavior of copper at these high pressures and strain rates is compared with previous literature data, and the effect of pressure and strain rate on the strength behavior of copper are discussed. Here, it was observed that the rate of change in yield strength change in pressure was 2.5 times the rate of change of shear modulus with pressure. The strength models that predict the response of copper at these extreme conditions are also examined and validated.

36 MATERIALS SCIENCE↗

DOE OSTI2025

OR-AGENT framework – Architecting electrified heavy-duty drayage applications

The widespread adoption of zero-emission vehicles in heavy-duty (HD) commercial freight transportation faces considerable technoeconomic challenges. For heavy-duty trucks, ensuring high uptime, cost parity with diesel, and safety standards is especially critical as these vehicles operate over long distances with heavy loads, where any downtime or off-nominal behaviors significantly impacts logistics, productivity, and the total cost of ownership. Unlike traditional diesel refueling, BEV charging infrastructure must be co-optimized with vehicle deployment, operational demands, and grid capacity to ensure cost-effective and reliable freight operations. However, the lack of a standardized ownership and service model has led to a fragmented approach—where commercial vehicle operators may invest in, own, and maintain both vehicle/batteries and charging/energy infrastructure. This disconnect may exclude energy service providers from the equation, forcing fleet operators to explore ‘behind-the-fence’ energy solutions that increase capital investment, operational downtime, overhead costs, and, in some cases, net carbon emissions. To address these issues, this study introduces OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport), a comprehensive modeling framework that integrates powertrain architectures, charging infrastructures, and energy backbone systems into a cohesive strategy. In this paper, OR-AGENT is applied to develop an interconnected systems architecture for energy efficiency and resiliency enhancement of heavy-duty drayage vehicles at the Port of Savannah, GA. This framework showcases an interconnected systems approach to electrifying heavy-duty drayage vehicles at the Port of Savannah, GA. The study assessed BEVs with 400–1200 kWh battery capacities, accounting for seasonal variations in weather and freight routing. A diverse charging mix (150 kW–1250 kW) was evaluated alongside grid capacity constraints, cost, and carbon intensity analysis, leading to the development of a strategic microgrid/Distributed Energy Resources (DER) deployment architecture to ensure a reliable and sustainable transition. However, the findings also highlight the need for alternative zero-emission solutions for remaining trips, such as larger batteries, electrified roadways, hydrogen powertrains, or net-zero emission fuels. In conclusion, the findings are incorporated into a Total Cost of Ownership (TCO) model to identify optimal architectures for an interconnected electrified ecosystem.

Commercial vehicles↗

DOE OSTI2024

Epitopes recognition of SARS-CoV-2 nucleocapsid RNA binding domain by human monoclonal antibodies

Coronavirus nucleocapsid protein (NP) of SARS-CoV-2 plays a central role in many functions important for virus proliferation including packaging and protecting genomic RNA. The protein shares sequence, structure, and architecture with nucleocapsid proteins from betacoronaviruses. The N-terminal domain (NP RBD ) binds RNA and the C-terminal domain is responsible for dimerization. After infection, NP is highly expressed and triggers robust host immune response. The anti-NP antibodies are not protective and not neutralizing but can effectively detect viral proliferation soon after infection. Two structures of SARS-CoV-2 NP RBD were determined providing a continuous model from residue 48 to 173, including RNA binding region and key epitopes. Five structures of NP RBD complexes with human mAbs were isolated using an antigen-bait sorting. Complexes revealed a distinct complement-determining regions and unique sets of epitope recognition. This may assist in the early detection of pathogens and designing peptide-based vaccines. Mutations that significantly increase viral load were mapped on developed, full length NP model, likely impacting interactions with host proteins and viral RNA.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Effectiveness of street trees in reducing air temperature and outdoor heat exposure in Las Vegas

Urban greening and especially tree planting strategies are being widely planned and implemented to mitigate urban overheating and thermal stress in many urban areas. However, the effectiveness of these strategies depends on multiple factors, including urban morphology, environmental conditions, and tree characteristics. This study investigates the effects of street tree planting strategies on air temperature and outdoor heat exposure in Las Vegas by combining an urbanized mesoscale climate model coupled to a multi-layer street tree model (Weather Research and Forecasting-BEP-Tree; Δ x = 900 m) with a microscale pedestrian heat exposure model (Temperatures of Urban Facets for Pedestrian; Δ x = 1 m). A series of simulations are conducted for July and August 2022. Large city-wide increases of a drought tolerant tree species cool air temperature mainly during the nighttime (up to 1.5 °C), with daytime effects being limited due to leaves shedding sensible instead of latent heat as stomata close in response to high vapor pressure deficits. Increased evaporative cooling is achieved with a different tree species (double at night, and reaching 0.4 °C during the day), but water requirements increase threefold. Despite their relatively small effect on air temperature during the day, trees provide significant shade by intercepting solar radiation, reducing mean radiant temperature (up to 16 °C) and enhancing outdoor thermal comfort, a major benefit of street trees in hot arid climates. The nighttime cooling of trees and the daytime reduction of radiant loading show potential to reduce the heat-related health impacts. Our results highlight the need to evaluate the effects of street trees on a case-by-case basis.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

System-Level Efficiency Study of Modular DC/DC and Fuel Cell Stack Series–Parallel Configurations

This paper presents a system-level efficiency study of modular DC/DC converter and fuel cell stack configurations under series, parallel, and series–parallel connections. The investigation considers selected DC/DC converter topologies, including isolated and non-isolated architectures, boost, non-inverting buck-boost, and resonant converters, integrated with commercially available fuel cell stacks, including the Accelera FCE150, Ballard FCgen-HPS, and Toyota TFCM2. DC/DC converter modules are evaluated in modular configurations rated at 60 kW and 90 kW and two distinct output voltage ranges, specifically 580–730V and 780–930V, examining how different interconnection schemes impact overall system efficiency. The converters are evaluated under full load (100%), partial load (66%), and light load (33%) conditions, providing a comprehensive assessment of efficiency and operational characteristics across varying power demands. Approximately two-thousand efficiency data points are obtained from laboratory prototype–level component measurements and validated design evaluations across multiple converter topologies, modular configurations, voltage ranges, and load conditions, providing a robust dataset for comparative system-level efficiency analysis. The results highlight the effects of modularity and topology selection on system-level efficiency, offering a “playbook” framework for designers to select appropriate DC/DC converter arrangements and fuel cell stack connections for series, parallel, or hybrid configurations based on efficiency considerations.

DC/DC↗

DOE OSTI2020

U.S. Geothermal District Heating Systems and Funding Data

This dataset contains detailed information on geothermal district heating (GDH) systems across the United States, including technical specifications, costs, energy usage, and funding sources. Data includes system details such as state, site, location, operational years, temperature, flow rate, capacity, energy use, load factor, costs (original, updated, and operational), funding sources, employment impact, and project status. Additionally included are contributions to GDH systems, including federal, state, and local grants, as well as loans from agencies such as USDA and USDOE. This dataset serves as a resource for analyzing the economic and technical landscape of geothermal district heating in the United States.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Offshore Wind Project Study (Final Technical Report)

Within the Long Island electric service territory and throughout New York State there is increasing interest in and mandate for increasing clean energy technologies and renewable energy generation while reducing dependency on fossil fuels for electric production with goal of a carbon free system by 2040. This report discusses the implications of the reduction of forecasted load in the Long Island electric service territory and the impact of energy efficiency; increasing reliance of wind and other renewable resources on the transmission system; opportunities for interconnecting these resources; and reductions on the reliance of baseload fossil fueled power facilities over the coming years.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Current Practices in Distribution Utility Resilience Planning for Winter Storms

This report is part of a series of hazard-focused case studies examining common practices in electric utility resilience planning. We use standard terminology defining resilience as the ability to anticipate, withstand, absorb, and recover from hazards that cause long duration outages. We distinguish between reliability and resilience using Institute of Electrical and Electronics Engineers (IEEE) 1366-2022, which defines major events as an event that exceeds reasonable design and/or operational limits of the electric power system. Resilience planning is focused on major event days and reliability planning is focused on nonmajor event days. Utility resilience plans are assessed according to common resilience components identified in existing resilience frameworks. The focus of this report is on winter storms in which the primary hazards are heavy snowfall, freezing rain, ice, extreme cold, severe wind, and flooding. These hazards can also contribute to generation shortages, resulting in bulk power system impacts that have consequences for the distribution system, such as load shedding. Stand-alone reports focusing on wildfires and nonwinter storms have been published in parallel with this report. This report can be used as a starting point for understanding potential investment prioritization processes and investment options. This report is intended to improve utility resilience planning by supporting constructive dialogue among utilities, regulators, and other stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗